I. Robotics: From Industrial Automation to Humanoid Intelligence

The traditional robotics industry has developed a vertically integrated value chain, comprising four main layers: core components, control systems, complete machines, and system integration & applications.

• Core components (controllers, servos, reducers, sensors, batteries, etc.) have the highest technical barriers, defining both performance ceilings and cost floors.

• Control systems act as the robot’s “brain and cerebellum,” responsible for decision-making and motion planning.

• Complete machine manufacturing reflects the ability to integrate complex supply chains.

• System integration and application development determine the depth of commercialization and are becoming the key sources of value creation.

Globally, robotics is evolving along a clear trajectory — from industrial automation → scenario-specific intelligence → general-purpose intelligence — forming five major categories: industrial robots, mobile robots, service robots, special-purpose robots, and humanoid robots.

1. Industrial Robots: Currently the only fully mature segment, industrial robots are widely deployed in welding, assembly, painting, and handling processes across manufacturing lines. The industry features standardized supply chains, stable margins, and well-defined ROI. Within this category, collaborative robots (cobots)—designed for safe human–robot collaboration, lightweight operation, and rapid deployment.Representative companies: ABB, Fanuc, Yaskawa, KUKA, Universal Robots, JAKA, and AUBO

2. Mobile Robots: Including AGV (Automated Guided Vehicles) and AMR (Autonomous Mobile Robots), this category is widely adopted in logistics, e-commerce fulfillment, and factory transport. It is the most mature segment for B2B applications.Representative companies: Amazon Robotics, Geek+, Quicktron, Locus Robotics.

3. Service Robots: Targeting consumer and commercial sectors—such as cleaning,food service, and education—this is the fastest-growing category on the consumer side. Cleaning robots now follow a consumer electronics logic, while medical and delivery robots are rapidly commercializing. A new wave of more general manipulators (e.g., two-arm systems like Dyna) is emerging—more flexible than task-specific products, yet not as general as humanoids.

   Representative companies: Ecovacs, Roborock, Pudu Robotics,KEENON Robotics, iRobot, Dyna.

4. Special-Purpose Robots: Designed for high-risk or niche applications—healthcare, military, construction, marine, and aerospace—these robots serve small but profitable markets with strong entry barriers, typically relying on government or enterprise contracts.Representative companies: Intuitive Surgical, Boston Dynamics, ANYbotics, NASA Valkyrie, Honeybee Robotics

5. Humanoid Robots: Regarded as the future “universal labor platform,” humanoid robots are drawing the most attention at the frontier of embodied intelligence.Representative companies: Tesla (Optimus), Figure AI (Figure 01), Sanctuary AI (Phoenix), Agility Robotics (Digit), Apptronik (Apollo), 1X Robotics, Neura Robotics,  Unitree, UBTECH, Agibot

The core value of humanoid robots lies in their human-like morphology, allowing them to operate within existing social and physical environments without infrastructure modification. Unlike industrial robots that pursue peak efficiency, humanoids emphasize general adaptability and task transferability, enabling seamless deployment across factories, homes, and public spaces.

Most humanoid robots remain in the technical demonstration stage, focused on validating dynamic balance, locomotion, and manipulation capabilities. While limited deployments have begun to appear in highly controlled factory settings (e.g., Figure × BMW, Agility Digit), and additional vendors such as 1X are expected to enter early distribution starting in 2026, these are still narrow-scope, single-task applications—not true general-purpose labor integration. Meaningful large-scale commercialization is still years away.

The core bottlenecks span several layers:

• Multi-DOF coordination and real-time dynamic balance remain challenging;

• Energy and endurance are constrained by battery density and actuator efficiency;

• Perception–decision pipelines often destabilize in open environments and fail to generalize;

• A significant data gap limits the training of generalized policies;

• Cross-embodiment transfer is not yet solved;

• Hardware supply chains and cost curves—especially outside China—remain substantial barriers, making low-cost, large-scale deployment difficult.

The commercialization of humanoid robotics will advance in three stages: Demo-as-a-Service in the short term, driven by pilots and subsidies; Robotics-as-a-Service (RaaS) in the mid term, as task and skill ecosystems emerge; and a Labor Cloud model in the long term, where value shifts from hardware to software and networked services.  Overall, humanoid robotics is entering a pivotal transition from demonstration to self-learning. Whether the industry can overcome the intertwined barriers of control, cost, and intelligence will determine if embodied intelligence can truly become a scalable economic force.

II. AI × Robotics: The Dawn of the Embodied Intelligence Era

Traditional automation relies heavily on pre-programmed logic and pipeline-based control architectures—such as the DSOP paradigm (perception–planning–control)—which function reliably only in structured environments. The real world, however, is far more complex and unpredictable. The new generation of Embodied AI follows an entirely different paradigm: leveraging large models and unified representation learning to give robots cross-scene capabilities for understanding, prediction, and action. Embodied intelligence emphasizes the dynamic coupling of the body (hardware), the brain (models), and the environment (interaction). The robot is merely the vehicle—intelligence is the true core.

Generative AI represents intelligence in the symbolic and linguistic world—it excels at understanding language and semantics. Embodied AI, by contrast, represents intelligence in the physical world—it masters perception and action. The two correspond to the “brain” and “body” of AI evolution, forming two parallel but converging frontiers.

From an intelligence hierarchy perspective, Embodied AI is a higher-order capability than generative AI, but its maturity lags far behind. LLMs benefit from abundant internet-scale data and a well-defined “data → compute → deployment” loop. Robotic intelligence, however, requires egocentric, multimodal, action-grounded data—teleoperation trajectories, first-person video, spatial maps, manipulation sequences—which do not exist by default and must be generated through real-world interaction or high-fidelity simulation. This makes data far scarcer, costlier, and harder to scale. While simulated and synthetic data help, they cannot fully replace real sensorimotor experience. This is why companies like Tesla and Figure must operate teleoperation factories, and why data-collection farms have emerged in SEA. In short, LLMs learn from existing data; robots must create their own through physical interaction.

In the next 5–10 years, both will deeply converge through Vision–Language–Action (VLA) models and Embodied Agent architectures—LLMs will handle high-level cognition and planning, while robots will execute real-world actions, forming a bidirectional loop between data and embodiment, thus propelling AI from language intelligence toward true general intelligence (AGI).

The Core Technology Stack of Embodied Intelligence

Embodied AI can be conceptualized as a bottom-up intelligence stack, comprising:VLA (Perception Fusion), RL/IL/SSL (Learning), Sim2Real (Reality Transfer), World Model (Cognitive Modeling), and Swarm & Reasoning (Collective Intelligence and Memory).

Perception & Understanding: Vision–Language–Action (VLA)

The VLA model integrates Vision, Language, and Action into a unified multimodal system, enabling robots to understand human instructions and translate them into physical operations. The execution pipeline includes semantic parsing, object detection, path planning, and action execution, completing the full loop of “understand semantics → perceive world → complete task.”  Representative projects: Google RT-X, Meta Ego-Exo, and Figure Helix, showcasing breakthroughs in multimodal understanding, immersive perception, and language-conditioned control.

VLA systems are still in an early stage and face four fundamental bottlenecks:

1. Semantic ambiguity and weak task generalization: models struggle to interpret vague or open-ended instructions;

2. Unstable vision–action alignment: perception errors are amplified during planning and execution;

3. Sparse and non-standardized multimodal data: collection and annotation remain costly, making it difficult to build large-scale data flywheels;

4. Long-horizon challenges across temporal and spatial axes: long temporal horizons strain planning and memory, while large spatial horizons require reasoning about out-of-perception elements—something current VLAs lack due to limited world models and cross-space inference.

These issues collectively constrain VLA’s cross-scenario generalization and limit its readiness for large-scale real-world deployment.

Learning & Adaptation: SSL, IL, and RL

• Self-Supervised Learning (SSL): Enables robots to infer patterns and physical laws directly from perception data—teaching them to “understand the world.”

• Imitation Learning (IL): Allows robots to mimic human or expert demonstrations—helping them “act like humans.”

• Reinforcement Learning (RL): Uses reward-punishment feedback loops to optimize policies—helping them “learn through trial and error.”

In Embodied AI, these paradigms form a layered learning system: SSL provides representational grounding, IL provides human priors, and  RL drives policy optimization,jointly forming the core mechanism of learning from perception to action.

Sim2Real: Bridging Simulation and Reality

Simulation-to-Reality (Sim2Real) allows robots to train in virtual environments before deployment in the real world. Platforms like NVIDIA Isaac Sim, Omniverse, and DeepMind MuJoCo produce vast amounts of synthetic data—reducing cost and wear on hardware.

The goal is to minimize the “reality gap” through:

• Domain Randomization: Randomly altering lighting, friction, and noise to improve generalization.

• Physical Calibration: Using real sensor data to adjust simulation physics for realism.

• Adaptive Fine-tuning: Rapid on-site retraining for stability in real environments.

Sim2Real forms the central bridge for embodied AI deployment. Despite strong progress, challenges remain around reality gap, compute costs, and real-world safety. Nevertheless, Simulation-as-a-Service (SimaaS) is emerging as a lightweight yet strategic infrastructure for the Embodied AI era—via PaaS (Platform Subscription), DaaS (Data Generation), and VaaS (Validation) business models.

Cognitive Modeling: World Model — The Robot’s “Inner World”

A World Model serves as the inner brain of robots, allowing them to simulate environments and outcomes internally—predicting and reasoning before acting. By learning environmental dynamics, it enables predictive and proactive behavior. Representative projects: DeepMind Dreamer, Google Gemini + RT-2, Tesla FSD V12, NVIDIA WorldSim.

Core techniques include:

• Latent Dynamics Modeling: Compressing high-dimensional observations into latent states.

• Imagination-based Planning: Virtual trial-and-error for path prediction.

• Model-based Reinforcement Learning: Replacing real-world trials with internal simulations.

World Models mark the transition from reactive to predictive intelligence, though challenges persist in model complexity, long-horizon stability, and standardization.

Swarm Intelligence & Reasoning: From Individual to Collective Cognition

Multi-Agent Collaboration and Memory-Reasoning Systems represent the next frontier—extending intelligence from individual agents to cooperative and cognitive collectives.

• Multi-Agent Systems (MAS): Enable distributed cooperation among multiple robots via cooperative RL frameworks (e.g., OpenAI Hide-and-Seek, DeepMind QMIX / MADDPG). These have proven effective in logistics, inspection, and coordinated swarm control.

• Memory & Reasoning: Equip agents with long-term memory and causal understanding—crucial for cross-task generalization and self-planning. Research examples include DeepMind Gato, Dreamer, and Voyager, enabling continuous learning and “remembering the past, simulating the future.”

Together, these components lay the foundation for robots capable of collective learning, memory, and self-evolution.

Global Embodied AI Landscape: Collaboration and Competition

The global robotics industry is entering an era of cooperative competition.

• China leads in supply-chain efficiency, manufacturing, and vertical integration, with companies like Unitree and UBTECH already mass-producing humanoids. However, its algorithmic and simulation capabilities still trail the U.S. by several years.

• The U.S. dominates frontier AI models and software (DeepMind, OpenAI, NVIDIA), yet this advantage does not fully extend to robotics hardware—where Chinese players often iterate faster and demonstrate stronger real-world performance. This hardware gap partly explains U.S. industrial-reshoring efforts under the CHIPS Act and IRA.

• Japan remains the global leader in precision components and motion-control systems, though its progress in AI-native robotics remains conservative.

• Korea distinguishes itself through advanced consumer-robotics adoption, driven by LG, NAVER Labs, and a mature service-robot ecosystem.

• Europe maintains strong engineering culture, safety standards, and research depth; while much manufacturing has moved abroad, Europe continues to excel in collaboration frameworks and robotics standardization.

Together, these regional strengths are shaping the long-term equilibrium of the global embodied intelligence industry.

III. Robots × AI × Web3: Narrative Vision vs. Practical Pathways

In 2025, a new narrative emerged in Web3 around the fusion of robotics and AI. While Web3 is often framed as the base protocol for a decentralized machine economy, its real integration value and feasibility vary markedly by layer:

• Hardware manufacturing & service layer: Capital-intensive with weak data flywheels; Web3 can currently play only a supporting role in edge cases such as supply-chain finance or equipment leasing.

• Simulation & software ecosystem: Higher compatibility; simulation data and training jobs can be put on-chain for attribution, and agents/skill modules can be assetized via NFTs or Agent Tokens.

Platform layer: Decentralized labor and collaboration networks show the greatest potential—Web3 can unite identity, incentives, and governance to gradually build a credible “machine labor market,” laying the institutional groundwork for a future machine economy.

Long-term vision. The Orchestration and Platform layer is the most valuable direction for integrating Web3 with robotics and AI. As robots gain perception, language, and learning capabilities, they are evolving into intelligent actors that can autonomously decide, collaborate, and create economic value. For these “intelligent workers” to truly participate in the economy, four core hurdles must be cleared: identity, trust, incentives, and governance.

• Identity: Machines require attributable, traceable digital identities. With Machine DIDs, each robot, sensor, or UAV can mint a unique verifiable on-chain “ID card,” binding ownership, activity logs, and permission scopes to enable secure interaction and accountability.

• Trust: “Machine labor” must be verifiable, measurable, and priceable. Using smart contracts, oracles, and audits—combined with Proof of Physical Work (PoPW), Trusted Execution Environments (TEE), and Zero-Knowledge Proofs (ZKP)—task execution can be proven authentic and traceable, giving machine behavior accounting value.

• Incentives: Web3 enables automated settlement and value flow among machines via token incentives, account abstraction, and state channels. Robots can use micropayments for compute rental and data sharing, with staking/slashing to secure performance; smart contracts and oracles can coordinate a decentralized machine coordination marketplace with minimal human dispatch.

• Governance: As machines gain long-term autonomy, Web3 provides transparent, programmable governance: DAOs co-decide system parameters; multisigs and reputation maintain safety and order. Over time, this pushes toward algorithmic governance—humans set goals and bounds, while contracts mediate machine-to-machine incentives and checks.

The ultimate vision of Web3 × Robotics: a real-world evaluation network—distributed robot fleets acting as “physical-world inference engines” to continuously test and benchmark model performance across diverse, complex environments; and a robotic workforce—robots executing verifiable physical tasks worldwide, settling earnings on-chain, and reinvesting value into compute or hardware upgrades.

Pragmatic path today. The fusion of embodied intelligence and Web3 remains early; decentralized machine-intelligence economies are largely narrative- and community-driven. Viable near-term intersections concentrate in three areas:

1. Data crowdsourcing & attribution — on-chain incentives and traceability encourage contributors to upload real-world data.

2. Global long-tail participation — cross-border micropayments and micro-incentives reduce the cost of data collection and distribution.

3. Financialization & collaborative innovation — DAO structures can enable robot assetization, revenue tokenization, and machine-to-machine settlement.

Overall, the integration of robotics and Web3 will progress in phases: in the short term, the focus will be on data collection and incentive mechanisms; in the mid term, breakthroughs are expected in stablecoin-based payments, long-tail data aggregation, and the assetization and settlement of RaaS models; and in the long term, as humanoids scale, Web3 could evolve into the institutional foundation for machine ownership, revenue distribution, and governance, enabling a truly decentralized machine economy.

IV. Web3 Robotics Landscape & Curated Cases

Based on three criteria—verifiable progress, technical openness, and industrial relevance—this section maps representative projects at the intersection of Web3 × Robotics, organized into five layers: Model & Intelligence, Machine Economy, Data Collection, Perception & Simulation Infrastructure, and Robot Asset & Yield (RobotFi / RWAiFi). To remain objective, we have removed obvious hype-driven or insufficiently documented projects; please point out any omissions.

Model & Intelligence Layer

OpenMind — Building Android for Robots (https://openmind.org/)

OpenMind is an open-source Robot OS for Embodied AI & control, aiming to build the first decentralized runtime and development platform for robots. Two core components:

• OM1: A modular, open-source AI agent runtime layer built on top of ROS2, orchestrating perception, planning, and action pipelines for both digital and physical robots.

• FABRIC: A distributed coordination layer connecting cloud compute, models, and real robots so developers can control/train robots in a unified environment.

OpenMind acts as the intelligent middleware between LLMs and the robotic world—turning language intelligence into embodied intelligence and providing a scaffold from understanding (Language → Action) to alignment (Blockchain → Rules). Its multi-layered system forms a full collaboration loop: humans provide feedback/labels via the OpenMind App (RLHF data); the Fabric Network handles identity, task allocation, and settlement; OM1 robots execute tasks and conform to an on-chain “robot constitution” for behavior auditing and payments—completing a decentralized cycle of human feedback → task collaboration → on-chain settlement.

Progress & Assessment. OpenMind is in an early “technically working, commercially unproven” phase. OM1 Runtime is open-sourced on GitHub with multimodal inputs and an NL data bus for language-to-action parsing—original but experimental. Fabric and on-chain settlement are interface-level designs so far.  Ecosystem ties include Unitree, UBTECH, TurtleBot, and universities (Stanford, Oxford, Seoul Robotics) for education/research; no industrial rollouts yet. The App is in beta; incentives/tasks are early.

Business model: OM1 (open-source) + Fabric (settlement) + Skill Marketplace (incentives). No revenue yet; relies on ~$20M early financing (Pantera, Coinbase Ventures, DCG). Technically ambitious with long path and hardware dependence; if Fabric lands, it could become the “Android of Embodied AI.”

CodecFlow — The Execution Engine for Robotics (https://codecflow.ai)

CodecFlow is a decentralized Execution Layer for Robotics on Solana, providing on-demand runtime environments for AI agents and robotic systems—giving each agent an “Instant Machine.” Three modules:

• Fabric: Cross-cloud and DePIN compute aggregator (Weaver + Shuttle + Gauge) that spins up secure VMs, GPU containers, or robot control nodes in seconds.

• optr SDK: A Python framework that abstract hardware connectors, training algorithms and blockchain integration. To enable creating “Operators” that control desktops, sims, or real robots.

• Token Incentives: On-chain incentives for the open source contributors, buyback from revenue, and future economy for the marketplace

Goal: Unify the fragmented robotics ecosystem with a single execution layer that gives builders hardware abstraction, fine‑tuning tools, cloud simulation infrastructure, and onchain economics so they can launch and scale revenue generating operators for robots and desktop.

Progress & Assessment. Early Fabric (Go) and optr SDK (Python) are live; web/CLI can launch isolated compute instances, Integration with NRN, ChainLink, peaq. Operator Marketplace targets late-2025, serving AI devs, robotics labs, and automation operators.

Machine Economy Layer

BitRobot — The World’s Open Robotics Lab (https://bitrobot.ai)

A decentralized research & collaboration network for Embodied AI and robotics, co-initiated by FrodoBots Labs and Protocol Labs. Vision: an open architecture of Subnets + Incentives + Verifiable Robotic Work (VRW).

• VRW: Define & verify the real contribution of each robotic task.

• ENT (Embodied Node Token): On-chain robot identity & economic accountability.

• Subnets: Organize cross-region collaboration across research, compute, devices, and operators.

Senate + Gandalf AI: Human-AI co-governance for incentives and research allocation.

Since its 2025 whitepaper, BitRobot has run multiple subnets (e.g., SN/01 ET Fugi, SN/05 SeeSaw by Virtuals), enabling decentralized teleoperation and real-world data capture, and launched a $5M Grand Challenges fund to spur global research on model development.

peaq — The Machine Economy Computer (https://www.peaq.xyz/)

peaq is a Layer-1 chain built for the Machine Economy, providing machine identities, wallets, access control, and time-sync (Universal Machine Time) for millions of robots and devices. Its Robotics SDK lets builders make robots “Machine Economy–ready” with only a few lines of code, enabling vendor-neutral interoperability and peer-to-peer interaction.

The network already hosts the world’s first tokenized robotic farm and 60+ real-world machine applications. peaq’s tokenization framework allows robotics companies to raise liquidity for capital-intensive hardware and broaden participation beyond traditional B2B/B2C buyers. Its protocol-level incentive pools, funded by network fees, subsidize machine onboarding and support builders—creating a growth flywheel for robotics projects.

Data Layer

Purpose: unlock scarce, costly real-world data for embodied training via teleoperation (PrismaX, BitRobot Network), first-person & motion capture (Mecka, BitRobot Network, Sapien、Vader、NRN), and simulation/synthetic pipelines (BitRobot Network) to build scalable, generalizable training corpora.

Note: Web3 doesn’t produce data better than Web2 giants; its value lies in redistributing data economics. With stablecoin rails + crowdsourcing, permissionless incentives and on-chain attribution enable low-cost micro-settlement, provenance, and automatic revenue sharing. Open crowdsourcing still faces quality control and buyer demand gaps.

PrismaX (https://gateway.prismax.ai)

A decentralized teleoperation & data economy for Embodied AI—aiming to build a global robot labor market where human operators, robots, and AI models co-evolve via on-chain incentives.

• Teleoperation Stack: Browser/VR UI + SDK connects global arms/service robots for real-time control & data capture.

• Eval Engine: CLIP + DINOv2 + optical-flow semantic scoring to grade each trajectory and settle on-chain.

Completes the loop teleop → data capture → model training → on-chain settlement, turning human labor into data assets.

Progress & Assessment. Testnet live since Aug 2025 (gateway.prismax.ai). Users can teleop arms for grasping tasks and generate training data. Eval Engine running internally. Clear positioning and high technical completeness; strong candidate for a decentralized labor & data protocol for the embodied era, but near-term scale remains a challenge.

BitRobot Network  (https://bitrobot.ai/)

BitRobot Network subnets power data collection across video, teleoperation, and simulation. With SN/01 ET Fugi users remotely control robots to complete tasks, collecting navigation & perception data in a “real-world Pokemon Gogame”. The game led to the creation of FrodoBots-2K, one of the largest open human-robot navigation datasets, used by UC Berkeley RAIL and Google DeepMind. SN/05 SeeSaw crowdsources egocentric video data via iPhone from real-world environments at scale. Other announced subnets RoboCap and Rayvo focus on egocentric video data collection via low-cost embodiments.

Mecka (https://www.mecka.ai)

Mecka is a robotics data company that crowdsources egocentric video, motion, and task demonstrations—via gamified mobile capture and custom hardware rigs—to build large-scale multimodal datasets for embodied AI training.

Sapien (https://www.sapien.io/)

A crowdsourcing platform for human motion data to power robot intelligence. Via wearables and mobile apps, Sapien gathers human pose and interaction data to train embodied models—building a global motion data network.

Vader (https://www.vaderai.ai)

Vader crowdsources egocentric video and task demonstrations through EgoPlay, a real-world MMO where users record daily activities from a first-person view and earn $VADER. Its ORN pipeline converts raw POV footage into privacy-safe, structured datasets enriched with action labels and semantic narratives—optimized for humanoid policy training.

NRN Agents (https://www.nrnagents.ai/)

A gamified embodied-RL data platform that crowdsources human demonstrations through browser-based robot control and simulated competitions. NRN generates long-tail behavioral trajectories for imitation learning and continual RL, using sport-like tasks as scalable data primitives for sim-to-real policy training.

Embodied Data Collection — Project Comparison

Middleware & Simulation

The Middleware & Simulation layer forms the backbone between physical sensing and intelligent decision-making, covering localization, communication, spatial mapping, and large-scale simulation. The field is still early: projects are exploring high-precision positioning, shared spatial computing, protocol standardization, and distributed simulation, but no unified standard or interoperable ecosystem has yet emerged.

Middleware & Spatial Infrastructure

Core robotic capabilities—navigation, localization, connectivity, and spatial mapping—form the bridge between the physical world and intelligent decision-making. While broader DePIN projects (Silencio, WeatherXM, DIMO) now mention “robotics,” the projects below are the ones most directly relevant to embodied AI.

• RoboStack — Cloud-Native Robot Operating Stack (https://robostack.io)Cloud-native robot OS & control stack integrating ROS2, DDS, and edge computing. Its RCP (Robot Control Protocol) aims to make robots callable/orchestrable like cloud services.

  GEODNET — Decentralized GNSS Network (https://geodnet.com)A global decentralized satellite-positioning network offering cm-level RTK/GNSS. With distributed base stations and on-chain incentives, it supplies high-precision positioning for drones, autonomous driving, and robots—becoming the Geo-Infra Layer of the machine economy.

• Auki — Posemesh for Spatial Computing (https://www.auki.com)A decentralized Posemesh network that generates shared real-time 3D maps via crowdsourced sensors & compute, enabling AR, robot navigation, and multi-device collaboration—key infra fusing AR × Robotics.

• Tashi Network — Real-Time Mesh Coordination for Robots (https://tashi.network)A decentralized mesh network enabling sub-30ms consensus, low-latency sensor exchange, and multi-robot state synchronization. Its MeshNet SDK supports shared SLAM, swarm coordination, and robust map updates for real-time embodied AI.

• Staex — Decentralized Connectivity & Telemetry (https://www.staex.io)A decentralized connectivity and device-management layer from Deutsche Telekom R&D, providing secure communication, trusted telemetry, and device-to-cloud routing. Staex enables robot fleets to exchange data reliably and interoperate across operators.

Distributed Simulation & Learning Systems

Gradient – Towards Open Intelligence（https://gradient.network/）

Gradient is an AI R&D lab dedicated to building Open Intelligence, enabling distributed training, inference, verification, and simulation on a decentralized infrastructure. Its current technology stack includes Parallax (distributed inference), Echo (distributed reinforcement learning and multi-agent training), and Gradient Cloud (enterprise AI solutions).

In robotics, Gradient is developing Mirage — a distributed simulation and robotic learning platform designed to build generalizable world models and universal policies, supporting dynamic interactive environments and large-scale parallel training. Mirage is expected to release its framework and model soon, and the team has been in discussions with NVIDIA regarding potential collaboration.

Robot Asset & Yield (RobotFi / RWAiFi)

This layer converts robots from productive tools into financializable assets through tokenization, revenue distribution, and decentralized governance, forming the financial infrastructure of the machine economy.

XmaquinaDAO — Physical AI DAO (https://www.xmaquina.io)

XMAQUINA is a decentralized ecosystem providing global, liquid exposure to leading private humanoid-robotics and embodied-AI companies—bringing traditionally VC-only opportunities onchain. Its token DEUS functions as a liquid index and governance asset, coordinating treasury allocations and ecosystem growth. The DAO Portal and Machine Economy Launchpad enable the community to co-own and support emerging Physical AI ventures through tokenized machine assets and structured onchain participation.

GAIB — The Economic Layer for AI Infrastructure (https://gaib.ai/)

GAIB provides a unified Economic Layer for real-world AI infrastructure such as GPUs and robots, connecting decentralized capital to productive AI infra assets and making yields verifiable, composable, and on-chain.

For robotics, GAIB does not “sell robot tokens.” Instead, it financializes robot equipment and operating contracts (RaaS, data collection, teleop) on-chain—converting real cash flows → composable on-chain yield assets. This spans equipment financing (leasing/pledge), operational cash flows (RaaS/data services), and data-rights revenue (licensing/contracts), making robot assets and their income measurable, priceable, and tradable.

GAIB uses AID / sAID as settlement/yield carriers, backed by structured risk controls (over-collateralization, reserves, insurance). Over time it integrates with DeFi derivatives and liquidity markets to close the loop from “robot assets” to “composable yield assets.” The goal: become the economic backbone of intelligence in the AI era.

Web3 Robotics Stack Link: https://fairy-build-97286531.figma.site/

V. Conclusion: Present Challenges and Long-Term Opportunities

From a long-term perspective, the fusion of Robotics × AI × Web3 aims to build a decentralized machine economy (DeRobot Economy), moving embodied intelligence from “single-machine automation” to networked collaboration that is ownable, settleable, and governable. The core logic is a self-reinforcing loop—“Token → Deployment → Data → Value Redistribution”—through which robots, sensors, and compute nodes gain on-chain ownership, transact, and share proceeds.

That said, at today’s stage this paradigm remains early-stage exploration, still far from stable cash flows and a scaled commercial flywheel. Many projects are narrative-led with limited real deployment. Robotics manufacturing and operations are capital-intensive; token incentives alone cannot finance infrastructure expansion. While on-chain finance is composable, it has not yet solved real-asset risk pricing and cash-flow realization. In short, the “self-sustaining machine network” remains idealized, and its business model requires real-world validation.

• Model & Intelligence Layer. This is the most valuable long-term direction. Open-source robot operating systems represented by OpenMind seek to break closed ecosystems and unify multi-robot coordination with language-to-action interfaces. The technical vision is clear and systemically complete, but the engineering burden is massive, validation cycles are long, and industry-level positive feedback has yet to form.

• Machine Economy Layer. Still pre-market: the real-world robot base is small, and DID-based identity plus incentive networks struggle to form a self-consistent loop. We remain far from a true “machine labor economy.” Only after embodied systems are deployed at scale will the economic effects of on-chain identity, settlement, and collaboration networks become evident.

• Data Layer. Barriers are relatively lower—and this is closest to commercial viability today. Embodied data collection demands spatiotemporal continuity and high-precision action semantics, which determine quality and reusability. Balancing crowdscale with data reliability is the core challenge. PrismaX offers a partially replicable template by locking in B-side demand first and then distributing capture/validation tasks, but ecosystem scale and data markets will take time to mature.

• Middleware & Simulation Layer. Still in technical validation with no unified standards and limited interoperability. Simulation outputs are hard to standardize for real-world transfer; Sim2Real efficiency remains constrained.

• RobotFi / RWAiFi Layer. Web3’s role is primarily auxiliary—enhancing transparency, settlement, and financing efficiency in supply-chain finance, equipment leasing, and investment governance, rather than redefining robotics economics itself.

Even so, we believe the intersection of Robotics × AI × Web3 marks the starting point of the next intelligent economic system. It is not only a fusion of technical paradigms; it is also an opportunity to recast production relations. Once machines possess identity, incentives, and governance, human–machine collaboration can evolve from localized automation to networked autonomy. In the short term, this domain will remain driven by narratives and experimentation, but the emerging institutional and incentive frameworks are laying groundwork for the economic order of a future machine society. In the long run, combining embodied intelligence with Web3 will redraw the boundaries of value creation—elevating intelligent agents into ownable, collaborative, revenue-bearing economic actors.

Disclaimer: This article was assisted by AI tools (ChatGPT-5 and Deepseek). The author has endeavored to proofread and ensure accuracy, but errors may remain. Note that crypto asset markets often exhibit divergence between project fundamentals and secondary-market price action. This content is for information synthesis and academic/research exchange only and does not constitute investment advice or a recommendation to buy or sell any token.

一、机器人全景：从工业自动化到人形智能

传统机器人产业链已形成自下而上的完整分层体系，涵盖核心零部件—中间控制系统—整机制造—应用集成四大环节。核心零部件（控制器、伺服、减速器、传感器、电池等）技术壁垒最高，决定了整机性能与成本下限；控制系统是机器人的“大脑与小脑”，负责决策规划与运动控制；整机制造体现供应链整合能力。系统集成与应用决定商业化深度正成为新的价值核心。

按应用场景与形态，全球机器人正沿着“工业自动化 → 场景智能化 → 通用智能化”的路径演进，形成五大主要类型：工业机器人、移动机器人、服务机器人、特种机器人以及人形机器人

• 工业机器人（Industrial Robots）：当前唯一全面成熟的赛道，广泛应用于焊接、装配、喷涂与搬运等制造环节。行业已形成标准化供应链体系，毛利率稳定，ROI 明确。其中的子类协作机器人（Cobots）强调人机共作、轻量易部署，成长最快。代表企业：ABB、发那科(Fanuc)、安川电机（Yaskawa）、库卡(**KUKA)、Universal Robots、节卡、遨博。

• 移动机器人（Mobile Robots）：包括 AGV（自动导引车） 与 AMR（自主移动机器人），在物流仓储、电商配送与制造运输中大规模落地，已成为 B 端最成熟品类。代表企业：Amazon Robotics, 极智嘉(Geek+)、快仓（Quicktron）、Locus Robotics。

• 服务机器人（Service Robots）： 面向清洁、餐饮、酒店与教育等行业，是消费端增长最快的领域。清洁类产品已进入消费电子逻辑，医疗与商用配送加速商业化。此外一批更通用的操作型机器人正在兴起（如 Dyna 的双臂系统）——比 任务特定型产品更灵活，但又尚未达到人形机器人的通用性。代表企业：科沃斯、石头科技、普渡科技、擎朗智能、iRobot、 Dyna 等。

• 特种机器人 主要服务于医疗、军工、建筑、海洋与航天等场景，市场规模有限但利润率高、壁垒强，多依赖政府与企业订单，处于垂直细分成长阶段，典型项目包括 直觉外科、Boston Dynamics、ANYbotics、NASA Valkyrie等。

• 人形机器人（Humanoid Robots）：被视为未来“通用劳动力平台”。代表企业包括 Tesla（Optimus）、Figure AI（Figure 01）、Sanctuary AI (Phoenix)、Agility Robotics（Digit）、Apptronik (Apollo)、1X Robotics、Neura Robotics、宇树科技（Unitree）、优必选（UBTECH）、智元机器人 等。

人形机器人是当下最受关注的前沿方向，其核心价值在于以人形结构适配现有社会空间，被视为通往“通用劳动力平台”的关键形态。与追求极致效率的工业机器人不同，人形机器人强调通用适应性与任务迁移能力，可在不改造环境的前提下进入工厂、家庭与公共空间。

目前，大多数人形机器人仍停留在技术演示阶段，主要验证动态平衡、行走与操作能力。虽然已有部分项目在高度受控的工厂场景中开始小规模部署（如 Figure × BMW、Agility Digit），并预计自 2026 年起会有更多厂商（如 1X）进入早期分发，但这些仍是“窄场景、单任务”的受限应用，而非真正意义上的通用劳动力落地。整体来看，距离规模化商业化仍需数年时间。核心瓶颈包括：多自由度协调与实时动态平衡等控制难题；受限于电池能量密度与驱动效率的能耗与续航问题；在开放环境中容易失稳、难以泛化的感知—决策链路；显著的数据缺口（难以支撑通用策略训练）；跨形体迁移尚未攻克；以及硬件供应链与成本曲线（尤其在中国以外地区）仍构成现实门槛，使大规模、低成本部署的实现难度进一步提高。

未来商业化路径预计将经历三个阶段：短期以 Demo-as-a-Service 为主，依赖试点与补贴；中期演进为 Robotics-as-a-Service (RaaS)，构建任务与技能生态；长期以劳动力云与智能订阅服务为核心，推动价值重心从硬件制造转向软件与服务网络。总体而言，人形机器人正处于从演示到自学习的关键过渡期，未来能否跨越控制、成本与算法三重门槛，将决定其能否真正实现具身智能。

二、AI × 机器人：具身智能时代的黎明

传统自动化主要依赖预编程与流水线式控制（如感知–规划–控制的 DSOP 架构），只能在结构化环境中可靠运行。而现实世界更为复杂多变，新一代具身智能（Embodied AI）走的是另一条范式：通过大模型与统一表示学习，使机器人具备跨场景的“理解—预测—行动”能力。具身智能强调 身体（硬件）+ 大脑（模型）+ 环境（交互） 的动态耦合，机器人是载体，智能才是核心。

生成式 AI（Generative AI） 属于语言世界的智能，擅长理解符号与语义；具身智能（Embodied AI） 属于现实世界的智能，掌握感知与行动。二者分别对应“大脑”与“身体”，代表 AI 演化的两条平行主线。从智能层级上看，具身智能比生成式 AI 更高阶，但其成熟度仍明显落后。LLM 依赖互联网的海量语料，形成清晰的“数据 → 算力 → 部署”闭环；而机器人智能需要 第一视角、多模态、与动作强绑定的数据——包括远程操控轨迹、第一视角视频、空间地图、操作序列等，这些数据 天然不存在，必须通过真实交互或高保真仿真生成，因此更加稀缺且昂贵。虽然模拟与合成数据有所帮助，但仍无法替代真实的传感器—运动经验，这也是 Tesla、Figure 等必须自建遥操作数据工厂的原因，也是东南亚出现第三方数据标注工厂的原因。简而言之：LLM 从现成数据中学习，而机器人必须通过与物理世界互动来“创造”数据。未来 5–10 年，二者将在 Vision–Language–Action 模型与 Embodied Agent 架构上深度融合——LLM 负责高层认知与规划，机器人负责真实世界执行，形成数据与行动的双向闭环，共同推动 AI 从“语言智能”迈向真正的通用智能（AGI）。

具身智能的核心技术体系可视为一个自下而上的智能栈：VLA（感知融合）、RL/IL/SSL（智能学习）、Sim2Real（现实迁移）、World Model（认知建模）、以及多智能体协作与记忆推理（Swarm & Reasoning）。其中，VLA 与 RL/IL/SSL 是具身智能的“发动机”，决定其落地与商业化；Sim2Real 与 World Model 是连接虚拟训练与现实执行的关键技术；多智能体协作与记忆推理则代表更高层次的群体与元认知演化。

感知理解：视觉–语言–动作模型(Vision–Language–Action)

VLA 模型通过整合 视觉（Vision）—语言（Language）—动作（Action） 三个通道，使机器人能够从人类语言中理解意图并转化为具体操作行为。其执行流程包括语义解析、目标识别（从视觉输入中定位目标物体）以及路径规划与动作执行，从而实现“理解语义—感知世界—完成任务”的闭环，是具身智能的关键突破之一。当前代表项目有 Google RT-X、Meta Ego-Exo 与 Figure Helix，分别展示了跨模态理解、沉浸式感知与语言驱动控制等前沿方向。

目前，VLA 仍处于早期阶段，面临四类核心瓶颈：1）语义歧义与任务泛化弱：模型难以理解模糊、开放式指令；2）视觉与动作对齐不稳：感知误差在路径规划与执行中被放大；3）多模态数据稀缺且标准不统一：采集与标注成本高，难以形成规模化数据飞轮；4）长时任务的时间轴与空间轴挑战：任务跨度过长导致规划与记忆能力不足，而空间范围过大则要求模型推理“视野之外”的事物，当前 VLA 缺乏稳定世界模型与跨空间推理能力。

这些问题共同限制了 VLA 的跨场景泛化能力与规模化落地进程。

智能学习：自监督学习（SSL）、模仿学习 (IL)与强化学习 (RL)

• 自监督学习(Self-Supervised Learning)：从感知数据中自动提取语义特征，让机器人“理解世界”。 相当于让机器学会观察与表征。

• 模仿学习（Imitation Learning）：通过模仿人类演示或专家示例，快速掌握基础技能。相当于让机器学会像人一样做事。

• 强化学习（Reinforcement Learning）：通过“奖励-惩罚”机制，机器人在不断试错中优化动作策略。相当于让机器学会在试错中成长。

在 具身智能（Embodied AI） 中，自监督学习（SSL） 旨在让机器人通过感知数据预测状态变化与物理规律，从而理解世界的因果结构；强化学习（RL） 是智能形成的核心引擎，通过与环境交互和基于奖励信号的试错优化，驱动机器人掌握行走、抓取、避障等复杂行为；模仿学习（IL） 则通过人类示范加速这一过程，使机器人快速获得行动先验。当前主流方向是将三者结合，构建层次化学习框架：SSL 提供表征基础，IL 赋予人类先验，RL 驱动策略优化，以平衡效率与稳定性，共同构成具身智能从理解到行动的核心机制。

现实迁移：Sim2Real —— 从仿真到现实的跨越

Sim2Real（Simulation to Reality） 是让机器人在虚拟环境中完成训练、再迁移至真实世界。它通过高保真仿真环境（如 NVIDIA Isaac Sim & Omniverse、DeepMind MuJoCo）生成大规模交互数据，显著降低训练成本与硬件磨损。 其核心在于缩小“仿真现实鸿沟”，主要方法包括：

• 域随机化（Domain Randomization）：在仿真中随机调整光照、摩擦、噪声等参数，提高模型泛化能力；

• 物理一致性校准：利用真实传感器数据校正仿真引擎，增强物理逼真度；

• 自适应微调（Adaptive Fine-tuning）：在真实环境中进行快速再训练，实现稳定迁移。

Sim2Real 是具身智能落地的中枢环节，使 AI 模型能在安全、低成本的虚拟世界中学习“感知—决策—控制”的闭环。Sim2Real 在仿真训练上已成熟（如 NVIDIA Isaac Sim、MuJoCo），但现实迁移仍受限于 Reality Gap、高算力与标注成本，以及开放环境下泛化与安全性不足。尽管如此，Simulation-as-a-Service（SimaaS） 正成具身智能时代最轻、却最具战略价值的基础设施，其商业模式包括 平台订阅（PaaS）、数据生成（DaaS） 与 安全验证（VaaS）。

认知建模：World Model —— 机器人的“内在世界”

世界模型（World Model） 是具身智能的“内脑”，让机器人能在内部模拟环境与行动后果，实现预测与推理。它通过学习环境动态规律，构建可预测的内部表示，使智能体在执行前即可“预演”结果，从被动执行者进化为主动推理者，代表项目包括 DeepMind Dreamer、Google Gemini + RT-2、Tesla FSD V12、NVIDIA WorldSim 等。 典型技术路径包括：

• 潜变量建模（Latent Dynamics Modeling）：压缩高维感知至潜在状态空间；

• 时序预测想象训练（Imagination-based Planning）：在模型中虚拟试错与路径预测；

• 模型驱动强化学习（Model-based RL）：用世界模型取代真实环境，降低训练成本。

World Model 处于具身智能的理论前沿性，是让机器人从“反应式”迈向“预测式”智能的核心路径，但仍受限于建模复杂、长时预测不稳与缺乏统一标准等挑战。

群体智能与记忆推理：从个体行动到协同认知

多智能体协作（Multi-Agent Systems）与记忆推理（Memory & Reasoning）代表了具身智能从“个体智能”向“群体智能”和“认知智能”演进的两个重要方向。二者共同支撑智能系统的协作学习与长期适应能力。

多智能体协作（Swarm / Cooperative RL）：指多个智能体在共享环境中通过分布式或协作式强化学习实现协同决策与任务分配。该方向已有扎实研究基础，例如 OpenAI Hide-and-Seek 实验 展示了多智能体自发合作与策略涌现， DeepMind QMIX 和 MADDPG 算法 提供了集中训练、分散执行的协作框架。这类方法已在仓储机器人调度、巡检和集群控制等场景中得到应用验证。

记忆与推理（Memory & Reasoning）：聚焦让智能体具备长期记忆、情境理解与因果推理能力，是实现跨任务迁移和自我规划的关键方向。典型研究包括 DeepMind Gato （统一感知-语言-控制的多任务智能体）和 DeepMind Dreamer 系列 （基于世界模型的想象式规划），以及 Voyager 等开放式具身智能体，通过外部记忆与自我演化实现持续学习。这些系统为机器人具备“记得过去、推演未来”的能力奠定了基础。

全球具身智能产业格局：合作竞争并存

全球机器人产业正处于“合作主导、竞争深化”的时期。中国的供应链效率、美国的 AI 能力、日本的零部件精度、欧洲的工业标准共同塑造全球机器人产业的长期格局。

• 美国 在前沿 AI 模型与软件领域（DeepMind、OpenAI、NVIDIA）保持领先，但这一优势并未延伸至机器人硬件。中国厂商在迭代速度和真实场景表现上更具优势。美国通过《芯片法案》（CHIPS Act）和《通胀削减法案》（IRA）推动产业回流。

• 中国 凭借规模化制造、垂直整合与政策驱动，在零部件、自动化工厂与人形机器人领域形成领先优势，硬件与供应链能力突出，宇树与优必选等已实现量产，正向智能决策层延伸。但在 算法与仿真训练层与美国仍存较大差距。

• 日本 长期垄断高精度零部件与运动控制技术，工业体系稳健，但 AI 模型融合仍处早期阶段，创新节奏偏稳。

• 韩国在消费级机器人普及方面突出——由 LG、NAVER Labs 等企业引领，并拥有成熟强劲的服务机器人生态体系。

欧洲 工程体系与安全标准完善，1X Robotics 等在研发层保持活跃，但部分制造环节外迁，创新重心偏向协作与标准化方向。

三、机器人 × AI × Web3：叙事愿景与现实路径

2025 年，Web3 行业出现与机器人和 AI 融合的新叙事。尽管 Web3 被视为去中心化机器经济的底层协议，但其在不同层面的结合价值与可行性仍存在明显分化：

• 硬件制造与服务层资本密集、数据闭环弱，Web3 目前仅能在供应链金融或设备租赁等边缘环节发挥辅助作用；

• 仿真与软件生态层的契合度较高，仿真数据与训练任务可上链确权，智能体与技能模块也可通过NFT 或 Agent Token 实现资产化；

平台层，去中心化的劳动力与协作网络正展现出最大潜力——Web3 可通过身份、激励与治理一体化机制，逐步构建可信的“机器劳动力市场”，为未来机器经济奠定制度雏形。

从长期愿景来看，协作与平台层是 Web3 与机器人及 AI 融合中最具价值的方向。随着机器人逐步具备感知、语言与学习能力，它们正演化为能自主决策、协作与创造经济价值的智能个体。这些“智能劳动者”真正参与经济体系，仍需跨越四个身份、信任、激励与治理核心门槛。

• 在身份层，机器需具备可确权、可追溯的数字身份。通过Machine DID，每个机器人、传感器或无人机都能在链上生成唯一可验证的“身份证”，绑定其所有权、行为记录与权限范围，实现安全交互与责任界定。

• 在信任层，关键在于让“机器劳动”可验证、可计量、可定价。借助 智能合约、预言机与审计机制，结合 物理工作证明（PoPW）、可信执行环境（TEE） 与 零知识证明（ZKP），可确保任务执行过程的真实性与可追溯性，使机器行为具备经济核算价值。

• 在激励层，Web3 通过 Token 激励体系、账户抽象与状态通道 实现机器间的自动结算与价值流转。机器人可通过微支付完成算力租赁、数据共享，并以质押与惩罚机制保障任务履约；借助智能合约与预言机，还可形成无需人工调度的去中心化“机器协作市场”。

• 在治理层，当机器具备长期自治能力后，Web3 提供透明、可编程的治理框架：以 DAO 治理 共同决策系统参数，以 多签与信誉机制 维护安全与秩序。长期来看，这将推动机器社会迈向 “算法治理” 阶段——人类设定目标与边界，机器间以合约维系激励与平衡。

Web3 与机器人融合终极愿景：真实环境评测网络——由分布式机器人组成的“现实世界推理引擎”，在多样、复杂的物理场景中持续测试与基准模型能力；以及机器人劳动力市场——机器人在全球执行可验证的现实任务，通过链上结算获取收益，并将价值再投入算力或硬件升级。

从现实路径来看，具身智能与Web3的结合仍处于早期探索期， 去中心化机器智能经济体更多停留在叙事与社区驱动层面。现实中具备可行潜力的结合方向，主要体现在以下三方面：（1）数据众包与确权——Web3 通过链上激励与追溯机制，鼓励贡献者上传真实世界数据；（2）全球长尾参与——跨境小额支付与微激励机制有效降低数据采集与分发成本；（3）金融化与协作创新——DAO 模式可推动机器人资产化、收益凭证化及机器间结算机制。

总体来看，短期主要集中在数据采集与激励层；中期有望在“稳定币支付 + 长尾数据聚合”及 RaaS 资产化与结算层 实现突破；长期，若人形机器人规模化普及，Web3 或将成为机器所有权、收益分配与治理的制度底层，推动真正的去中心化机器经济形成。

四、Web3机器人生态图谱与精选案例

基于“可验证进展、技术公开度、产业相关度”三项标准，梳理当前 Web3 × Robotics 代表性项目，并按五层架构归类：模型智能层、机器经济层、数据采集层、感知与仿真基础层、机器人资产收益层。为保持客观，我们已剔除明显“蹭热点”或资料不足项目；如有疏漏，欢迎指正。

模型智能层（Model & Intelligence）

Openmind - Building Android for Robots  (https://openmind.org/)

OpenMind 是一个面向具身智能（Embodied AI）与机器人控制的开源操作系统（Robot OS），目标是构建全球首个去中心化机器人运行环境与开发平台。 项目核心包括两大组件：

• OM1：构建在 ROS2之上的模块化开源 AI 智能体运行时(AI Runtime Layer)，用于编排感知、规划与动作管线，服务于数字与实体机器人；

• FABRIC：分布式协调层（Fabric Coordination Layer），连接云端算力、模型与现实机器人，使开发者可在统一环境中控制和训练机器人。

OpenMind 的核心在于充当 LLM（大语言模型）与机器人世界之间的智能中间层，让语言智能真正转化为具身智能（Embodied Intelligence），构建起从 理解（Language → Action） 到 对齐（Blockchain → Rules） 的智能骨架。OpenMind 多层系统实现了完整的协作闭环：人类通过 OpenMind App 提供反馈与标注（RLHF 数据），Fabric Network 负责身份验证、任务分配与结算协调，OM1 Robots 执行任务并遵循区块链上的“机器人宪法”完成行为审计与支付，从而实现 人类反馈 → 任务协作 → 链上结算 的去中心化机器协作网络。

项目进展与现实评估

OpenMind 处于“技术可运行、商业未落地”的早期阶段。核心系统 OM1 Runtime 已在 GitHub 开源，可在多平台运行并支持多模态输入，通过自然语言数据总线（NLDB）实现语言到行动的任务理解，具备较高原创性但仍偏实验，Fabric 网络 与链上结算仅完成接口层设计。

生态上，项目已与 Unitree、Ubtech、TurtleBot 等开放硬件及 Stanford、Oxford、Seoul Robotics 等高校合作，主要用于教育与研究验证，尚无产业化落地。App 已上线测试版，但激励与任务功能仍处早期。

商业模式方面，OpenMind 构建了 OM1（开源系统）+ Fabric（结算协议）+ Skill Marketplace（激励层） 的三层生态，目前尚无营收，依赖约 2000 万美元早期融资（Pantera、Coinbase Ventures、DCG）。总体来看，技术领先但商业化与生态仍处起步阶段，若 Fabric 成功落地，有望成为“具身智能时代的 Android”，但周期长、风险高、对硬件依赖强。

CodecFlow - The Execution Engine for Robotics  (https://codecflow.ai)

CodecFlow 是一个基于 Solana 网络 的去中心化执行层协议（Fabric），旨在为 AI 智能体与机器人系统提供按需运行环境，让每一个智能体拥有“即时机器（Instant Machine）”。项目核心由三大模块构成：

• Fabric ：跨云算力聚合层（Weaver + Shuttle + Gauge），可在数秒内为AI任务生成安全的虚拟机、GPU容器或机器人控制节点；

• optr SDK：智能体执行框架（Python接口），用于创建可操作桌面、仿真或真实机器人的“Operator”；

• Token 激励：链上激励与支付层，连接计算提供者、智能体开发者与自动化任务用户，形成去中心化算力与任务市场。

CodecFlow 的核心目标是打造“AI与机器人操作员的去中心化执行底座”，让任何智能体可在任意环境（Windows / Linux / ROS / MuJoCo / 机器人控制器）中安全运行，实现从 算力调度（Fabric） → 系统环境（System Layer） → 感知与行动（VLA Operator） 的通用执行架构。

项目进展与现实评估

已发布早期版本的 Fabric 框架（Go） 与 optr SDK（Python），可在网页或命令行环境中启动隔离算力实例。Operator 市场 预计于 2025 年底上线，定位为 AI 算力的去中心化执行层，主要服务对象包括 AI 开发者、机器人研究团队与自动化运营公司。

机器经济层（Machine Economy Layer）

BitRobot - The World’s Open Robotics Lab  (https://bitrobot.ai)

BitRobot 是一个面向具身智能（Embodied AI）与机器人研发的去中心化科研与协作网络（Open Robotics Lab），由 FrodoBots Labs 与 Protocol Labs 联合发起。其核心愿景是：通过“子网（Subnets）+ 激励机制 + 可验证工作（VRW）”的开放架构， 核心作用包括：

• 通过 VRW (Verifiable Robotic Work) 标准定义并验证每一项机器人任务的真实贡献；

• 通过 ENT (Embodied Node Token) 为机器人赋予链上身份与经济责任；

• 通过 Subnets 组织科研、算力、设备与操作者的跨地域协作；

通过 Senate + Gandalf AI 实现“人机共治”的激励决策与科研治理。

自 2025 年发布白皮书以来，BitRobot 已运行多个子网（如 SN/01 ET Fugi、SN/05 SeeSaw by Virtuals Protocol），实现去中心化远程操控与真实场景数据采集，并推出 $5M Grand Challenges 基金 推动全球模型开发的科研竞赛。

peaq – The Economy of Things  (https://www.peaq.network)

peaq 是专为机器经济打造的 Layer-1 区块链，为数百万台机器人与设备提供机器身份、链上钱包、访问控制以及纳秒级时间同步（Universal Machine Time）等底层能力。其 Robotics SDK 使开发者能够以极少代码让机器人“机器经济就绪”，实现跨厂商、跨系统的互操作性与交互。

目前，peaq 已上线全球首个代币化机器人农场，并支持 60 余个真实世界的机器应用。其代币化框架帮助机器人公司为资本密集型硬件筹集资金，并将参与方式从传统 B2B/B2C 扩展至更广泛的社区层。凭借由网络费用注入的协议级激励池，peaq 可补贴新设备接入并支持开发者，从而形成推动机器人与物理 AI 项目加速扩张的经济飞轮。

数据采集层 （Data Layer）

旨在解决具身智能训练中稀缺且昂贵的高质量现实世界数据。通过多种路径采集和生成人机交互数据，包括远程操控（PrismaX, BitRobot Network）、第一视角与动作捕捉（Mecka、BitRobot Network、Sapien、Vader、NRN）以及仿真与合成数据（BitRobot Network），为机器人模型提供可扩展、可泛化的训练基础。

需要明确的是，Web3 并不擅长“生产数据”——在硬件、算法与采集效率上，Web2 巨头远超任何 DePIN 项目。其真正价值在于重塑数据的分配与激励机制。基于“稳定币支付网络 + 众包模型”，通过无许可的激励体系与链上确权机制，实现低成本的小额结算、贡献溯源与自动分润。但开放式众包仍面临质量与需求闭环难题——数据质量参差不齐，缺乏有效验证与稳定买方。

PrismaX  (https://gateway.prismax.ai)

PrismaX 是一个面向具身智能（Embodied AI）的去中心化远程操控与数据经济网络，旨在构建“全球机器人劳动力市场”，让人类操作者、机器人设备与AI模型通过链上激励系统协同进化。项目核心包括两大组件：

• Teleoperation Stack —— 远程操控系统（浏览器/VR界面 + SDK），连接全球机械臂与服务机器人，实现人类实时操控与数据采集；

• Eval Engine —— 数据评估与验证引擎（CLIP + DINOv2 + 光流语义评分），为每条操作轨迹生成质量评分并上链结算。

PrismaX 通过去中心化激励机制，将人类操作行为转化为机器学习数据，构建从 远程操控 → 数据采集 → 模型训练 → 链上结算 的完整闭环，实现“人类劳动即数据资产”的循环经济。

项目进展与现实评估： PrismaX 已在 2025 年 8 月上线测试版（gateway.prismax.ai），用户可远程操控机械臂执行抓取实验并生成训练数据。Eval Engine 已在内部运行， 整体来看，PrismaX 技术实现度较高，定位清晰，是连接“人类操作 × AI模型 × 区块链结算”的关键中间层。其长期潜力有望成为“具身智能时代的去中心化劳动与数据协议”，但短期仍面临规模化挑战。

BitRobot Network（https://bitrobot.ai/）

BitRobot Network 通过其子网实现视频、远程操控与仿真等多源数据采集。SN/01 ET Fugi 允许用户远程控制机器人完成任务，在“现实版 Pokémon Go 式”的交互中采集导航与感知数据。该玩法促成了 FrodoBots-2K 数据集的诞生，这是当前最大规模的人机导航开源数据集之一，被 UC Berkeley RAIL 和 Google DeepMind 等机构使用。SN/05 SeeSaw (Virtual Protocol)则通过 iPhone 在真实环境中大规模众包采集第一视角视频数据。其他已公布的子网，如 RoboCap 和 Rayvo，则专注于利用低成本实体设备采集第一视角视频数据。

Mecka  (https://www.mecka.ai)

Mecka 是一家机器人数据公司，通过游戏化的手机采集和定制硬件设备，众包获取第一视角视频、人体运动数据以及任务演示，用于构建大规模多模态数据集，支持具身智能模型的训练。

Sapien (https://www.sapien.io/)

Sapien 是一个以“人类运动数据驱动机器人智能”为核心的众包平台，通过可穿戴设备和移动端应用采集人体动作、姿态与交互数据，用于训练具身智能模型。项目致力于构建全球最大的人体运动数据网络，让人类的自然行为成为机器人学习与泛化的基础数据源。

Vader（https://www.vaderai.ai）

Vader 通过其现实世界 MMO 应用 EgoPlay 众包收集第一视角视频与任务示范：用户以第一人称视角记录日常活动并获得 $VADER 奖励。其 ORN 数据流水线 能将原始 POV 画面转换为经过隐私处理的结构化数据集，包含动作标签与语义叙述，可直接用于人形机器人策略训练。

NRN Agents（https://www.nrnagents.ai/）

一个游戏化的具身 RL 数据平台，通过浏览器端机器人控制与模拟竞赛来众包人类示范数据。NRN 通过“竞技化”任务生成长尾行为轨迹，用于模仿学习与持续强化学习，并作为可扩展的数据原语支撑 sim-to-real 策略训练。

具身智能数据采集层项目对比

感知与仿真（Middleware & Simulation）

感知与仿真层为机器人提供连接物理世界与智能决策的核心基础设施，包括定位、通信、空间建模、仿真训练等能力，是构建大规模具身智能系统的“中间层骨架”。当前该领域仍处于早期探索阶段，各项目分别在高精度定位、共享空间计算、协议标准化与分布式仿真等方向形成差异化布局，尚未出现统一标准或互通生态。

中间件与空间基建（Middleware & Spatial Infra）

机器人核心能力——导航、定位、连接性与空间建模——构成了连接物理世界与智能决策的关键桥梁。尽管更广泛的 DePIN 项目（Silencio、WeatherXM、DIMO）开始提及“机器人，但下列项目与具身智能最直接相关。

RoboStack – Cloud-Native Robot Operating Stack  (https://robostack.io)

RoboStack 是云原生机器人中间件，通过 RCP（Robot Context Protocol）实现机器人任务的实时调度、远程控制与跨平台互操作，并提供云端仿真、工作流编排与 Agent 接入能力。

GEODNET – Decentralized GNSS Network  (https://geodnet.com)

GEODNET 是全球去中心化 GNSS 网络，提供厘米级 RTK 高精度定位。通过分布式基站和链上激励，为无人机、自动驾驶与机器人提供实时“地理基准层”。

Auki – Posemesh for Spatial Computing (https://www.auki.com)

Auki 构建了去中心化的 Posemesh 空间计算网络，通过众包传感器与计算节点生成实时 3D 环境地图，为 AR、机器人导航和多设备协作提供共享空间基准。它是连接 虚拟空间与现实场景 的关键基础设施，推动 AR × Robotics 的融合。

Tashi Network — 机器人实时网格协作网络 (https://tashi.network)

去中心化实时网格网络，实现亚 30ms 共识、低延迟传感器交换与多机器人状态同步。其 MeshNet SDK 支持共享 SLAM、群体协作与鲁棒地图更新，为具身 AI 提供高性能实时协作层。

Staex — 去中心化连接与遥测网络 (https://www.staex.io)

源自德国电信研发部门的去中心化连接层，提供安全通信、可信遥测与设备到云的路由能力，使机器人车队能够可靠交换数据并跨不同运营方协作。

仿真与训练系统（Distributed Simulation & Learning）

Gradient - Towards Open Intelligence（https://gradient.network/）

Gradient 是建设“开放式智能（Open Intelligence）”的 AI 实验室，致力于基于去中心化基础设施实现分布式训练、推理、验证与仿真；其当前技术栈包括 Parallax（分布式推理）、Echo（分布式强化学习与多智能体训练） 以及 Gradient Cloud（面向企业的AI 解决方案）。在机器人方向，Mirage 平台面向具身智能训练提供 分布式仿真、动态交互环境与大规模并行学习 能力，用于加速世界模型与通用策略的训练落地。Mirage 正在与 NVIDIA 探讨与其 Newton 引擎的潜在协作方向。

机器人资产收益层（RobotFi / RWAiFi）

这一层聚焦于将机器人从“生产性工具”转化为“可金融化资产”的关键环节，通过 资产代币化、收益分配与去中心化治理，构建机器经济的金融基础设施。代表项目包括：

XmaquinaDAO – Physical AI DAO (https://www.xmaquina.io)

XMAQUINA 是一个去中心化生态系统，为全球用户提供对顶尖人形机器人与具身智能公司的高流动性参与渠道，将原本只属于风险投资机构的机会带上链。其代币 DEUS 既是流动化指数资产，也是治理载体，用于协调国库分配与生态发展。通过 DAO Portal 与 Machine Economy Launchpad，社区能够通过机器资产的代币化与结构化的链上参与，共同持有并支持新兴的 Physical AI 项目。

GAIB – The Economic Layer for AI Infrastructure  (https://gaib.ai/)

GAIB 致力于为 GPU 与机器人等实体 AI 基础设施提供统一的 经济层，将去中心化资本与真实AI基建资产连接起来，构建可验证、可组合、可收益的智能经济体系。

在机器人方向上，GAIB 并非“销售机器人代币”，而是通过将机器人设备与运营合同（RaaS、数据采集、遥操作等）金融化上链，实现“真实现金流 → 链上可组合收益资产”的转化。这一体系涵盖硬件融资（融资租赁 / 质押）、运营现金流（RaaS / 数据服务）与数据流收益（许可 / 合约）等环节，使机器人资产及其现金流变得 可度量、可定价、可交易。

GAIB 以 AID / sAID 作为结算与收益载体，通过结构化风控机制（超额抵押、准备金与保险）保障稳健回报，并长期接入 DeFi 衍生品与流动性市场，形成从“机器人资产”到“可组合收益资产”的金融闭环。目标是成为 AI 时代的经济主干（Economic Backbone of Intelligence）

五、总结与展望：现实挑战与长期机会

从长期愿景看，机器人 × AI × Web3 的融合旨在构建去中心化机器经济体系（DeRobot Economy），推动具身智能从“单机自动化”迈向“可确权、可结算、可治理”的网络化协作。其核心逻辑是通过“Token → 部署 → 数据 → 价值再分配”形成自循环机制，使机器人、传感器与算力节点实现确权、交易与分润。

然而，从现实阶段来看，该模式仍处早期探索期，距离形成稳定现金流与规模化商业闭环尚远。多数项目停留在叙事层面，实际部署有限。机器人制造与运维属资本密集型产业，单靠代币激励难以支撑基础设施扩张；链上金融设计虽具可组合性，但尚未解决真实资产的风险定价与收益兑现问题。因此，所谓“机器网络自循环”仍偏理想化，其商业模式有待现实验证。

• 模型智能层（Model & Intelligence Layer）是当前最具长期价值的方向。以 OpenMind 为代表的开源机器人操作系统，尝试打破封闭生态、统一多机器人协作与语言到动作接口。其技术愿景清晰、系统完整，但工程量巨大、验证周期长，尚未形成产业级正反馈。

• 机器经济层（Machine Economy Layer） 仍处于前置阶段，现实中机器人数量有限，DID 身份与激励网络尚难形成自洽循环。当前距离“机器劳动力经济”尚远。未来唯有具身智能实现规模化部署后，链上身份、结算与协作网络的经济效应才会真正显现。

• 数据采集层（Data Layer） 数据采集层门槛相对最低，但是目前最接近商业可行的方向。具身智能数据采集对时空连续性与动作语义精度要求极高，决定其质量与复用性。如何在“众包规模”与“数据可靠性”之间平衡，是行业核心挑战。PrismaX 先锁定 B 端需求，再分发任务采集验证一定程度上提供可复制模板，但生态规模与数据交易仍需时间积累。

• 感知与仿真层（Middleware & Simulation Layer） 仍在技术验证期，缺乏统一标准与接口尚未形成互通生态。仿真结果难以标准化迁移至真实环境，Sim2Real 效率受限。

• 资产收益层（RobotFi / RWAiFi）Web3 主要在供应链金融、设备租赁与投资治理等环节发挥辅助作用，提升透明度与结算效率，而非重塑产业逻辑。

当然，我们认为，机器人 × AI × Web3 的交汇点依然代表着下一代智能经济体系的原点。它不仅是技术范式的融合，更是生产关系的重构契机：当机器具备身份、激励与治理机制，人机协作将从局部自动化迈向网络化自治。短期内，这一方向仍以叙事与实验为主，但它所奠定的制度与激励框架，正为未来机器社会的经济秩序铺设基础。从长期视角看，具身智能与 Web3 的结合将重塑价值创造的边界——让智能体成为真正可确权、可协作、可收益的经济主体。

***免责声明：***本文在创作过程中借助了 ChatGPT-5 与Deepseek的 AI 工具辅助完成，作者已尽力校对并确保信息真实与准确，但仍难免存在疏漏，敬请谅解。需特别提示的是，加密资产市场普遍存在项目基本面与二级市场价格表现背离的情况。本文内容仅用于信息整合与学术/研究交流，不构成任何投资建议，亦不应视为任何代币的买卖推荐。

The evolution of ZK verifiable computing can be summarized as L2 zkRollup → zkVM → zkCoprocessor → L1 zkEVM.

• L2 zkRollups moved execution off-chain while posting validity proofs on-chain, achieving scalability and cost efficiency.

• zkVMs expanded into general-purpose verifiable computing, enabling cross-chain validation, AI inference, and cryptographic workloads.

• zkCoprocessors modularized this model into plug-and-play proof services for DeFi, RWA, and risk management.

• L1 zkEVMs brought this to Layer 1 Realtime Proving (RTP), integrating proofs directly into Ethereum’s execution pipeline.

Together, these advances mark blockchain’s shift from scalability to verifiability—ushering in an era of trustless computation.

I. Ethereum’s zkEVM Scaling Path: From L2 Rollups to L1 Realtime Proving

Ethereum’s zkEVM scalability journey can be divided into two phases:

• Phase 1 (2022–2024):  L2 zkRollups migrated execution to Layer 2 and posted validity proofs on Layer 1—achieving lower costs and higher throughput, but introducing liquidity and state fragmentation while L1 remained constrained by N-of-N re-execution.

• Phase 2 (2025– ):  L1 Realtime Proving (RTP) replaces full re-execution (N-of-N) with 1-of-N proof generation + lightweight network-wide verification, boosting throughput without compromising decentralization—an approach still under active development.

L2 zkRollups: Balancing Compatibility and Performance

In the flourishing Layer 2 ecosystem of 2022, Ethereum co-founder Vitalik Buterin classified ZK-EVMs into four types—Type 1–4—highlighting the structural trade-offs between compatibility and performance. This framework established the coordinates for zkRollup design:

• Type 1: Fully Ethereum-equivalent — replicates Ethereum exactly with no protocol changes, ensuring perfect compatibility but resulting in the slowest proving performance (e.g., Taiko).

• Type 2: Fully EVM-equivalent — identical to the EVM at the execution level but allows limited modifications to data structures for faster proof generation (e.g., Scroll, Linea).

• Type 2.5: EVM-equivalent except for gas costs — adjusts gas pricing for ZK-unfriendly operations to improve prover efficiency while maintaining broad compatibility (e.g., Polygon zkEVM, Kakarot).

• Type 3: Almost EVM-equivalent — simplifies or removes some hard-to-prove features such as precompiles, enabling faster proofs but requiring minor app-level adjustments (e.g., zkSync Era).

• Type 4: High-level-language equivalent — compiles Solidity or Vyper directly to ZK-friendly circuits, achieving the best performance but sacrificing bytecode compatibility and requiring ecosystem rebuilds (e.g., StarkNet / Cairo).

Today, the L2 zkRollup model is mature: execution runs off-chain, proofs are verified on-chain, maintaining Ethereum’s ecosystem and tooling while delivering high throughput and low cost. Yet, liquidity fragmentation and L1’s re-execution bottleneck remain persistent issues.

L1 zkEVM: Realtime Proving Redefines Ethereum’s Light-Verification Logic

In July 2025, the Ethereum Foundation published “Shipping an L1 zkEVM #1: Realtime Proving”, formally proposing the L1 zkEVM roadmap.

L1 zkEVM upgrades Ethereum from an N-of-N re-execution model to a 1-of-N proving + constant-time verification paradigm:  a small number of provers re-execute entire blocks to generate succinct proofs, and all other nodes verify them instantly. This enables Realtime Proving (RTP) at the L1 level—enhancing throughput, raising gas limits, and lowering hardware requirements—all while preserving decentralization. The rollout plan envisions zk clients running alongside traditional execution clients, eventually becoming the protocol default once performance, security, and incentive models stabilize.

L1 zkEVM Roadmap: Three Core Tracks

1. Realtime Proving (RTP): Achieving block-level proof generation within a 12-second slot via parallelization and hardware acceleration.

2. Client & Protocol Integration: Standardizing proof-verification interfaces—initially optional, later default.

3. Incentive & Security Design: Establishing a prover marketplace and fee model to reinforce censorship resistance and network liveness.

L1 zkEVM’s Realtime Proving (RTP) uses zkVMs to re-execute entire blocks off-chain and produce cryptographic proofs, allowing validators to verify results in under 10 seconds—replacing “re-execution” with “verify instead of execute” to drastically enhance Ethereum’s scalability and trustless validation efficiency.

According to the Ethereum Foundation’s zkEVM Tracker, the main teams participating in the L1 zkEVM RTP roadmap include:  SP1 Turbo (Succinct Labs), Pico (Brevis), Risc Zero, ZisK, Airbender (zkSync), OpenVM (Axiom), and Jolt (a16z).

II. Beyond Ethereum: General-Purpose zkVMs and zkCoprocessors

Beyond the Ethereum ecosystem, zero-knowledge proof (ZKP) technology has expanded into the broader field of Verifiable Computing, giving rise to two core technical systems: zkVMs and zkCoprocessors.

zkVM: General-Purpose Verifiable Computing Layer

A zkVM (zero-knowledge virtual machine) serves as a verifiable execution engine for arbitrary programs, typically built on instruction set architectures such as RISC-V, MIPS, or WASM.

Developers can compile business logic into the zkVM, where provers execute it off-chain and generate zero-knowledge proofs (ZKPs) that can be verified on-chain. This enables applications ranging from Ethereum L1 block proofs to cross-chain validation, AI inference, cryptographic computation, and complex algorithmic verification.

Its key advantages lie in generality and flexibility, supporting a wide range of use cases; however, it also entails high circuit complexity and proof generation costs, requiring multi-GPU parallelism and deep engineering optimization.Representative projects include Risc Zero, Succinct SP1, and Brevis Pico / Prism.

zkCoprocessor: Scenario-Specific Verifiable Module

A zkCoprocessor provides plug-and-play computation and proof services for specific business scenarios.These platforms predefine data access and circuit logic—such as historical on-chain data queries, TVL calculations, yield settlement, and identity verification—so that applications can simply call SDKs or APIs to receive both computation results and on-chain proofs.

This model offers fast integration, high performance, and low cost, though it sacrifices generality.Representative projects include Brevis zkCoprocessor, Axiom.

Comparative Logic and Core Differences

Overall, both zkVMs and zkCoprocessors follow the “off-chain computation + on-chain verification” paradigm of verifiable computing, where zero-knowledge proofs are used to validate off-chain results on-chain. Their economic logic rests on a simple premise: the cost of executing computations directly on-chain is significantly higher than the combined cost of off-chain proof generation and on-chain verification.

In terms of generality vs. engineering complexity:

• zkVM — a general-purpose computing infrastructure suitable for complex, cross-domain, or AI-driven tasks, offering maximum flexibility.

• zkCoprocessor — a modular verification service tailored for high-frequency, reusable scenarios such as DeFi, RWA, and risk management, offering low-cost, directly callable proof interfaces.

In terms of business models:

• zkVM follows a Proving-as-a-Service model, charging per proof (ZKP). It mainly serves L2 Rollups and infrastructure providers, characterized by large contracts, long cycles, and stable gross margins.

• zkCoprocessor operates under a Proof-API-as-a-Service model, charging per task via API or SDK integration—similar to SaaS—targeting DeFi and application-layer protocols with fast integration and high scalability.

Overall, zkVMs are the foundational engines of verifiable computation, while zkCoprocessors are the application-layer verification modules. The former builds the technical moat, and the latter drives commercial adoption—together forming a universal trustless computing network.

III. Brevis: Product Landscape and Technical Roadmap

Starting from Ethereum’s L1 Realtime Proving (RTP), zero-knowledge (ZK) technology is evolving toward an era of Verifiable Computing built upon the architectures of general-purpose zkVMs and zkCoprocessors.

Brevis Network represents a fusion of these two paradigms — a universal verifiable computing infrastructure that combines high performance, programmability, and zero-knowledge verification — an Infinite Compute Layer for Everything.

3.1 Pico zkVM: Modular Proof Architecture for General-Purpose Verifiable Computing

In 2024, Vitalik Buterin proposed the concept of “Glue and Coprocessor Architectures”, envisioning a structure that separates general-purpose execution layers from specialized coprocessor acceleration layers.  Complex computations can thus be divided into flexible business logic (e.g., EVM, Python, RISC-V) and performance-focused structured operations (e.g., GPU, ASIC, hash modules).

This “general + specialized” dual-layer model is now converging across blockchain, AI, and cryptographic computing: EVM accelerates via precompiles; AI leverages GPU parallelism; ZK proofs combine general-purpose VMs with specialized circuits. The future lies in optimizing the “glue layer” for security and developer experience, while letting the “coprocessor layer” focus on efficient execution—achieving a balance among performance, security, and openness.

Pico zkVM, developed by Brevis, is a representative realization of this idea.It integrates a general-purpose zkVM with hardware-accelerated coprocessors, merging programmability with high-performance ZK computation.

• Its modular architecture supports multiple proof backends (KoalaBear, BabyBear, Mersenne31), freely combining execution, recursion, and compression modules into a ProverChain.

• Developers can write business logic in Rust, automatically generating cryptographic proofs without prior ZK knowledge—significantly lowering the entry barrier.

• The architecture supports continuous evolution by introducing new proof systems and application-level coprocessors (for on-chain data, zkML, or cross-chain verification).

Compared to Succinct’s SP1 (a relatively monolithic RISC-V zkVM) and Risc Zero R0VM (a universal RISC-V execution model), Pico’s Modular zkVM + Coprocessor System decouples execution, recursion, and compression phases, supports backend switching, and enables coprocessor integration—yielding superior performance and extensibility.

3.2 Pico Prism: Multi-GPU Cluster Breakthrough

Pico Prism marks a major leap for Brevis in multi-server GPU architecture, setting new records under the Ethereum Foundation’s RTP (Realtime Proving) framework.It achieves 6.9-second average proof time and 96.8% RTP coverage on a 64×RTX 5090 GPU cluster, leading the zkVM performance benchmarks.

This demonstrates the transition of zkVMs from research prototypes to production-grade infrastructure through optimizations at the architectural, engineering, hardware, and system levels.

• Architecture: Traditional zkVMs (SP1, R0VM) focus on single-machine GPU optimization. Pico Prism pioneers cluster-level zkProving—multi-server, multi-GPU parallel proving—scaling ZK computation through multithreading and sharding orchestration.

• Engineering: Implements an asynchronous multi-stage pipeline (Execution / Recursion / Compression), cross-layer data reuse (proof chunk caching, embedding reuse), and multi-backend flexibility—boosting throughput dramatically.

• Hardware: On a 64×RTX 5090 ($128K) setup, achieves 6.0–6.9s average proving time and 96.8% RTP coverage, delivering a 3.4× performance-to-cost improvement over SP1 Hypercube (160×4090, 10.3s).

• System Evolution: As the first zkVM to meet EF RTP benchmarks (>96% sub-10s proofs, <$100K hardware), Pico Prism establishes zk proving as mainnet-ready infrastructure for Rollups, DeFi, AI, and cross-chain verification scenarios.

3.3 ZK Data Coprocessor: Intelligent ZK Layer for Blockchain Data

Traditional smart contracts “lack memory”—they cannot access historical states, recognize user behavior over time, or analyze cross-chain data.  Brevis addresses this with a high-performance ZK Data Coprocessor, enabling contracts to query, compute, and verify historical blockchain data in a trustless way. This empowers data-driven DeFi, active liquidity management, reward distribution, and cross-chain identity verification.

Brevis workflow:

1. Data Access: Contracts call APIs to retrieve historical data trustlessly.

2. Computation Execution: Developers define logic via SDK; Brevis performs off-chain computation and generates ZK proofs.

Result Verification: Proofs are verified on-chain, triggering subsequent contract logic.

Brevis supports both Pure-ZK and coChain (Optimistic) models:

• The former achieves full trustlessness at higher cost.

• The latter introduces PoS verification with ZK challenge-response, lowering costs while maintaining verifiability.

Validators stake on Ethereum and are slashed if ZK challenges succeed—striking a balance between security and efficiency. Through the integration of ZK + PoS + SDK, Brevis builds a scalable and verifiable data computation layer. Currently, Brevis powers PancakeSwap, Euler, Usual, Linea, and other protocols. All zkCoprocessor partnerships operate under the Pure-ZK model, providing trusted data support for DeFi incentives, reward distribution, and on-chain identity systems, enabling smart contracts to truly gain “memory and intelligence.”

3.4 Incentra: ZK-Powered Verifiable Incentive Distribution Layer

Incentra, built on the Brevis zkCoprocessor, is a verifiable incentive platform that uses ZK proofs for secure, transparent, and on-chain reward distribution. It enables trustless, low-cost, cross-chain automation, allowing anyone to verify rewards directly while supporting compliant, access-controlled execution.

Supported incentive models:

• Token Holding: Rewards based on ERC-20 time-weighted average balances (TWA).

• Concentrated Liquidity: Rewards tied to AMM DEX fee ratios; compatible with Gamma, Beefy, and other ALM protocols.

• Lending & Borrowing: Rewards derived from average balances and debt ratios.

Already integrated by PancakeSwap, Euler, Usual, and Linea, Incentra enables a fully verifiable on-chain incentive loop—a foundational ZK-level infrastructure for DeFi rewards.

3.5 Brevis: Complete Product and Technology Stack Overview

IV. Brevis zkVM: Technical Benchmarks and Performance Breakthroughs

The Ethereum Foundation (EF)’s L1 zkEVM Realtime Proving (RTP) standard has become the de facto benchmark and entry threshold for zkVMs seeking mainnet integration. Its core evaluation criteria include:

• Latency: <= 10s for P99 of mainnet blocks

• On-prem CAPEX: <= 100k USD

• On-prem power: <= 10kW

• Code: Fully open source

• Security: >= 128 bits

Proof size: <= 300KiB with no trusted setups

In October 2025, Brevis released the report “Pico Prism — 99.6% Real-Time Proving for 45M Gas Ethereum Blocks on Consumer Hardware,” announcing that Pico Prism became the first zkVM to fully meet the Ethereum Foundation’s RTP standard for block-level proving.

Running on a 64×RTX 5090 GPU cluster (~$128K), Pico Prism achieved:

• Average latency: 6.9 seconds

• 96.8% <10s coverage, 99.6% <12s coverage for 45M gas blocks, significantly outperforming Succinct SP1 Hypercube (36M gas, 10.3s average, 40.9% <10s coverage) With 71% lower latency and half the hardware cost, Pico Prism demonstrated a 3.4× improvement in performance-per-dollar efficiency.

Public recognition from Ethereum Foundation, Vitalik Buterin, and Justin Drake.

V. Brevis Ecosystem Expansion and Application Deployment

The Brevis zkCoprocessor handles complex computations that dApps cannot efficiently perform—such as analyzing historical user behavior, aggregating cross-chain data, or performing large-scale analytics—and outputs zero-knowledge proofs (ZKPs) that can be verified on-chain. This allows on-chain applications to trustlessly consume results by verifying a small proof, dramatically reducing gas, latency, and trust costs. Unlike traditional oracles that merely deliver data, Brevis provides mathematical assurance that the data is correct.  Its application scenarios can be broadly categorized as follows:

• Intelligent DeFi: Data-driven incentives and personalized user experiences based on behavioral and market history (e.g., PancakeSwap, Uniswap, MetaMask).

• RWA & Stable Token Growth: Automated distribution of real-world yield and stablecoin income via ZK verification (e.g., OpenEden, Usual Money, MetaMask USD).

• Privacy-Preserving DEX (Dark Pools): Off-chain matching with on-chain verification—upcoming deployment.

• Cross-Chain Interoperability: Cross-chain restaking and Rollup–L1 verification, building a shared security layer (e.g., Kernel, Celer, 0G).

• Blockchain Bootstrap: ZK-based incentive mechanisms accelerating new chain ecosystems (e.g., Linea, TAC).

• High-Performance Blockchains (100× Faster L1s): Leveraging Realtime Proving (RTP) to enhance mainnet throughput (e.g., Ethereum, BNB Chain).

Verifiable AI: Privacy-preserving and verifiable inference for the AgentFi and data-intelligence economy (e.g., Kaito, Trusta).

Network Scale and Metrics, According to Brevis Explorer (as of October 2025):

• Over 125 million ZK proofs generated

• Covering ~95,000 on-chain addresses and ~96,000 application requests

• Cumulative incentive distribution: $223 million+

• TVL supported: >$2.8 billion

• Total verified transaction volume: >$1 billion

Brevis’s ecosystem currently focuses on DeFi incentive distribution and liquidity optimization, with computing power mainly consumed by Usual Money, PancakeSwap, Linea Ignition, and Incentra, which together account for over 85% of network load.

• Usual Money (46.6M proofs): Demonstrates long-term stability in large-scale incentive distribution.

• PancakeSwap (20.6M): Highlights Brevis’s performance in real-time fee and discount computation.

• Linea Ignition (20.4M): Validates Brevis’s high-concurrency capacity for L2 ecosystem campaigns.

Incentra (15.2% share): Marks Brevis’s transition from SDK toolkit to standardized incentive platform.

DeFi Incentive Layer: Through Incentra, Brevis supports multiple protocols with transparent and continuous reward allocation:

• Usual Money — Annual incentives exceeding $300M, sustaining stablecoin and LP yields.

• OpenEden & Bedrock — CPI-based models for automated U.S. Treasury and Restaking yield distribution.

• Euler, Aave, BeraBorrow — ZK-verified lending positions and reward calculations.

Liquidity Optimization: Protocols such as PancakeSwap, QuickSwap, THENA, and Beefy employ Brevis’s dynamic fee and ALM incentive plugins for trade discounts and cross-chain yield aggregation.  Jojo Exchange and the Uniswap Foundation use ZK verification to build safer, auditable trading incentive systems.

Cross-Chain & Infrastructure Layer: Brevis has expanded from Ethereum to BNB Chain, Linea, Kernel DAO, TAC, and 0G, offering verifiable computation and cross-chain proof capabilities across multiple ecosystems.  Projects like Trusta AI, Kaito AI, and MetaMask are integrating Brevis’s ZK Data Coprocessor to power privacy-preserving loyalty programs, reputation scoring, and reward systems, advancing data intelligence within Web3.

At the infrastructure level, Brevis leverages the EigenLayer AVS network for restaking security, and integrates NEBRA’s Universal Proof Aggregation (UPA) to compress multiple ZK proofs into single submissions—reducing on-chain verification cost and latency.

Overall, Brevis now spans the full application cycle—from long-term incentive programs and event-based rewards to transaction verification and platform-level services. Its high-frequency verification tasks and reusable circuit templates provide Pico/Prism with real-world performance pressure and optimization feedback, which in turn can reinforce the L1 zkVM Realtime Proving (RTP) system at both the engineering and ecosystem levels—forming a two-way flywheel between technology and application

VI. Team Background and Project Funding

Mo Dong | Co-founder, Brevis NetworkDr. Mo Dong is the co-founder of Brevis Network. He holds a Ph.D. in Computer Science from the University of Illinois at Urbana–Champaign (UIUC). His research has been published in top international conferences, adopted by major technology companies such as Google, and cited thousands of times.

As an expert in algorithmic game theory and protocol mechanism design, Dr. Dong focuses on integrating zero-knowledge computation (ZK) with decentralized incentive mechanisms, aiming to build a trustless Verifiable Compute Economy. He also serves as a Venture Partner at IOSG Ventures, where he actively supports early-stage investments in Web3 infrastructure.

The Brevis team was founded by cryptography and computer science Ph.D. holders from UIUC, MIT, and UC Berkeley. The core members have years of research experience in zero-knowledge proof systems (ZKP) and distributed systems, with multiple peer-reviewed publications in the field.  Brevis has received technical recognition from the Ethereum Foundation, with its core modules regarded as foundational components for on-chain scalability infrastructure.

In November 2024, Brevis completed a $7.5 million seed round, co-led by Polychain Capital and Binance Labs, with participation from IOSG Ventures, Nomad Capital, HashKey, Bankless Ventures, and strategic angel investors from Kyber, Babylon, Uniswap, Arbitrum, and AltLayer.

VII. Competitive Landscape: zkVM and zkCoprocessor Markets

The Ethereum Foundation–backed ETHProofs.org has become the primary public platform tracking the L1 zkEVM Realtime Proving (RTP) roadmap, providing open data on zkVM performance, security, and mainnet readiness.

RTP Track: Four Core Competitive Dimensions

1. Maturity: Succinct SP1 leads in production deployment; Brevis Pico demonstrates the strongest performance, nearing mainnet readiness; RISC Zero is stable but has not yet disclosed RTP benchmarks.

2. Performance: Pico’s proof size (~990 kB) is about 33% smaller than SP1’s (1.48 MB), reducing cost and latency.

3. Security & Audit: RISC Zero and SP1 have both undergone independent audits; Pico is currently completing its formal audit process.

4. **Developer Ecosystem:**Most zkVMs use the RISC-V instruction set; SP1 leverages its Succinct Rollup SDK for broad ecosystem integration; Pico supports Rust-based auto proof generation, with a rapidly maturing SDK.

Market Structure: Two Leading Tiers

• Tier 1 — Brevis Pico (+ Prism) & Succinct SP1 Hypercube  Both target the EF RTP P99 ≤ 10 s benchmark.

  • Pico innovates through a distributed multi-GPU architecture, delivering superior performance and cost efficiency.

  • SP1 maintains robustness with a monolithic system and ecosystem maturity.→ Pico represents architectural innovation and performance leadership, while SP1 represents *production readiness and ecosystem dominance.

• Tier 2 — RISC Zero, ZisK, ZKM  These projects focus on lightweight and compatibility-first designs but have not published complete RTP metrics (latency, power, CAPEX, security bits, proof size, reproducibility).  Scroll (Ceno) and Matter Labs (Airbender) are extending Rollup proof systems to the L1 verification layer, signaling a shift from L2 scaling toward L1 verifiable computing.

2025 zkVM field has converged on RISC-V standardization, modular evolution, recursive proof standardization, and parallel hardware acceleration.  The Verifiable Compute Layer can be categorized into three main archetypes:

• Performance-oriented: Brevis Pico, SP1, Jolt, ZisK — focus on low-latency, realtime proving via recursive STARKs and GPU acceleration.

• Modular / Extensible: OpenVM, Pico, SP1 — emphasize plug-and-play modularity and coprocessor integration.

Ecosystem / Developer-friendly: RISC Zero, SP1, ZisK — prioritize SDK completeness and language compatibility for mass adoption.

zkVM Project Comparison (as of Oct 2025)

zkCoprocessor Landscape

The zk-Coprocessor market is now led by Brevis, Axiom, Herodotus, and Lagrange.

• Brevis stands out with a hybrid architecture combining a ZK Data Coprocessor + General-Purpose zkVM, enabling historical data access, programmable computation, and L1 Realtime Proving (RTP) capability.

• Axiom specializes in verifiable queries and circuit callbacks.

• Herodotus focuses on provable access to historical blockchain states.

• Lagrange adopts a ZK + Optimistic hybrid design to improve cross-chain computation efficiency.

Overall, zk-Coprocessors are emerging as “Verifiable Service Layers” that bridge DeFi, RWA, AI, and digital identity through trustless computational APIs.

VIII. Conclusion: Business Logic, Engineering Implementation, and Potential Risks

Business Logic: Performance-Driven Flywheel at Dual Layers

Brevis builds a multi-chain verifiable computing layer by integrating its general-purpose zkVM (Pico/Prism) with a data coprocessor (zkCoprocessor).

• zkVM addresses verifiability of arbitrary computation,

• zkCoprocessor enables business deployment for historical and cross-chain data.

This creates a “Performance → Ecosystem → Cost” positive feedback loop:as Pico Prism’s RTP performance attracts leading protocol integrations, proof volume scales up and per-proof cost declines, forming a self-reinforcing dual flywheel.

Brevis’s core competitive advantages can be summarized as:

• Reproducible performance — verified within the Ethereum Foundation’s ETHProofs RTP framework;

• Architectural moat — modular design with multi-GPU parallel scalability;

• Commercial validation — large-scale deployment across incentive distribution, dynamic fee modeling, and cross-chain verification.

Engineering Implementation: Verification-as-Execution

Through its Pico zkVM and Prism parallel proving framework, Brevis achieves 6.9-second average latency and P99 < 10 seconds for 45M gas blocks (on a 64×5090 GPU setup, <$130K CAPEX) — maintaining top-tier performance and cost efficiency. The zkCoprocessor module supports historical data access, circuit generation, and on-chain proof verification, flexibly switching between Pure-ZK and Hybrid (Optimistic + ZK) modes.  Overall, its performance now aligns closely with the Ethereum RTP hardware and latency benchmarks.

Potential Risks and Key Considerations

• Technical & Compliance: Brevis must validate power use, security level, proof size, and trusted setup via third-party audits. Performance tuning and potential EIP changes remain key challenges.

• Competition: Succinct (SP1/Hypercube) leads in ecosystem maturity, while RISC Zero, Axiom, OpenVM, Scroll, and zkSync continue to compete strongly.

• Revenue Concentration: Proof volume is ~80% concentrated in four apps; diversification across chains and sectors is needed. GPU price volatility may also affect margins.

Overall, Brevis has established an initial moat across both technical reproducibility and commercial deployment: Pico/Prism firmly leads the L1 RTP track, while the zkCoprocessor unlocks high-frequency, reusable business applications. Going forward, Brevis should aim to fully meet the Ethereum Foundation’s RTP benchmarks, continue to standardize coprocessor products and expand ecosystem integration, and advance third-party reproducibility, security audits, and cost transparency. By balancing infrastructure and SaaS-based revenues, Brevis can build a sustainable commercial growth loop.

**Disclaimer:**This report was prepared with assistance from the AI tool ChatGPT-5. The author has made every effort to ensure factual accuracy and reliability; however, minor errors may remain.  Please note that crypto asset markets often show a disconnect between project fundamentals and secondary-market token performance.  All content herein is intended for informational and academic/research purposes only, and does not constitute investment advice or a recommendation to buy or sell any token.

ZK 可信计算的演进可概括为 L2 zkRollup → zkVM → zkCoprocessor → L1 zkEVM。早期 L2 zkRollup 将执行迁至二层并在一层提交有效性证明（Validity Proof），以最小改动实现高吞吐与低成本扩容。 zkVM 随后扩展为通用可验证计算层，支持跨链验证、AI 推理与加密计算（代表项目：Risc Zero、Succinct、Brevis Pico）。 zkCoprocessor 与之并行发展，作为场景化验证模块，为 DeFi、RWA、风控等提供即插即用的计算与证明服务（代表项目：Brevis、Axiom）。2025 年，zkEVM 概念延伸至 L1 实时证明（Realtime Proving, RTP），在 EVM 指令级构建可验证电路，使零知识证明直接融入以太坊主网执行与验证流程，成为原生可验证的执行机制。这一脉络体现出区块链从“可扩展”迈向“可验证”的技术跃迁，开启可信计算的新阶段。

一、以太坊zkEVM扩容之路：从 L2 Rollup 到 L1实时证明

以太坊的 zkEVM 扩容路径经历两个阶段：

• 阶段一（2022–2024）：L2 zkRollup将执行搬至二层，在一层提交有效性证明；显著降低成本并提升吞吐，但带来流动性与状态碎片化，L1 仍受制于 N-of-N 重执行。

• 阶段二（2025–）：L1 实时证明（Realtime Proving, RTP） 以 “1-of-N 证明 + 全网轻量验证” 取代重执行，在不牺牲去中心化的前提下提升吞吐，仍在演进发展中。

L2 zkRollup 阶段：兼容与扩容性能间平衡

在 2022 年 在Layer2生态百花齐放的阶段，以太坊创始人 Vitalik Buterin 提出了 ZK-EVM 四类分类（Type 1–4），系统性揭示了 兼容性（compatibility）与性能（performance）之间的结构性权衡。这一框架为后续 zkRollup 技术路线确立了清晰的坐标：

• Type 1 完全等价：与以太坊字节码一致，迁移成本最低、证明最慢。Taiko。

• Type 2 完全兼容：极少底层优化，兼容性最强。Scroll、Linea。

• Type 2.5 准兼容：小幅改动（gas/预编译等）换性能。Polygon zkEVM、Kakarot。

• Type 3 部分兼容：改动更大，能跑多数应用但难完全复用 L1 基建。zkSync Era。

• Type 4 语言级：放弃字节码兼容，直接由高级语言编译为电路，性能最优但需重建生态（代表：Starknet / Cairo）。

当前 L2 zkRollup 模式已趋成熟：通过将执行迁移至二层、在一层提交有效性证明（Validity Proof），以最小改动沿用以太坊生态与工具链，成为主流的扩容与降费方案。其证明对象为 L2 区块与状态转移，而结算与安全仍锚定于 L1。该架构显著提升吞吐与效率，并保持对开发者的高度兼容，但也带来 流动性与状态碎片化，且 L1 仍受限于 N-of-N 重执行瓶颈。

L1 zkEVM：实时证明重塑以太坊轻验证逻辑

2025 年 7 月，以太坊基金会发表文章《Shipping an L1 zkEVM #1: Realtime Proving》 正式提出 L1 zkEVM 路线。L1 zkEVM 把以太坊从 N-of-N 重执行 升级为 1-of-N 证明 + 全网快速验证：由少数 prover 对整块 EVM 状态转移生成短证明，所有验证者仅做常数时间验证。该方案在不牺牲去中心化的前提下，实现 L1 级实时证明（Realtime Proving），安全提升主网 Gas 上限与吞吐，并显著降低节点硬件门槛。其落地计划是以 zk 客户端 替代传统执行客户端，先行并行运行，待性能、安全与激励机制成熟后，逐步成为协议层的新常态。

• N of N 旧范式：所有验证者重复执行整块交易来校验，安全但吞吐受限、峰值费高。

• 1 of N 新范式：由少数 prover 执行整块并产出短证明；全网只做常数时间验证。验证成本远低于重执行，可安全提高 L1 gas 上限，并减少硬件要求。

L1 zkEVM 路线图三大主线

1. 实时证明（Realtime Proving）：在 12 秒槽时间内完成整块证明，通过并行化与硬件加速压缩延迟；

2. 客户端与协议集成：标准化证明验证接口，先可选、后默认；

3. 激励与安全：建立 Prover 市场与费用模型，强化抗审查与网络活性。

以太坊 L1 实时证明（RTP） 是用 zkVM 在链下重执行整块交易并生成加密证明，让验证者无需重算、只需在 10 秒内验证一个小型证明，从而实现“以验代执”，大幅提升以太坊的可扩展性与去信任验证效率。根据以太坊基金会官方 zkEVM Tracker 页面，目前参与 L1 zkEVM 实时证明路线的主要团队包括 SP1 Turbo（Succinct Labs）、Pico（Brevis）、Risc Zero、ZisK、Airbender（zkSync）、OpenVM(Axiom）和Jolt(a16z)。

二、超越以太坊：通用zkVM和zkCoprocessor

而在以太坊生态之外，零知识证明（ZKP）技术也延伸至更广泛的 通用可验证计算（Verifiable Computing） 领域，形成以 zkVM 与 zkCoprocessor 为核心的两类技术体系。

zkVM：通用可验证计算层

面向任意程序的可验证执行引擎，常见指令集架构包括 RISC-V、MIPS 与 WASM。开发者可将业务逻辑编译至 zkVM，由 prover 在链下执行并生成可在链上验证的零知识证明（ZKP），既可用于 以太坊 L1 的区块证明，也适用于 跨链验证、AI 推理、加密计算与复杂算法 等场景。其优势是通用性与适配范围广，但电路复杂、证明成本高，需依赖多 GPU 并行与强工程优化。代表项目包括 Risc Zero、Succinct SP1、Brevis Pico / Prism。

zkCoprocessor：场景化可验证模块

面向具体业务场景提供“即插即用”的计算与证明服务。平台预置数据访问与电路逻辑（如历史链上数据读取、TVL、收益结算、身份验证等），应用方通过 SDK / API 调用即可获得计算结果与证明上链消费。该模式上手快、性能优、成本低，但通用性有限。典型项目包括 Brevis zkCoprocessor、Axiom等。

总体而言，zkVM 与 zkCoprocessor 均遵循“链下计算 + 链上验证”的可信计算范式，通过零知识证明在链上验证链下结果。其经济逻辑建立在这样一个前提之上：链上直接执行的成本远高于链下证明生成与链上验证的综合成本。

在通用性与工程复杂度上，二者的关键差异在于 ：

• zkVM 是 通用计算基础设施，适合复杂、跨域或 AI 场景，具备最高灵活度；

• zkCoprocessor 是 模块化验证服务，为高频可复用场景（DeFi、RWA、风控等）提供低成本、可直接调用的验证接口。

在商业路径上，zkVM 与 zkCoprocessor 二者的差异在于：

• zkVM 采用 Proving-as-a-Service 模式，按每次证明（ZKP）计费，主要面向 L2 Rollup 等基础设施客户，特点是合同规模大、周期长、毛利率稳定；

• zkCoprocessor 则以 Proof API-as-a-Service 为主，通过 API 调用或 SDK 集成按任务计费，更接近 SaaS 模式，面向 DeFi等应用层协议，集成快、扩张性强。

总体而言，zkVM 是可验证计算的底层引擎，zkCoprocessor 是应用层验证模块：前者构筑技术护城河，后者驱动商业化落地，共同构成通用可信计算网络。

三、Brevis的产品版图与技术路径

从以太坊的 L1 实时证明（Realtime Proving） 出发，ZK 技术正逐步迈向以 通用 zkVM 与 zkCoprocessor 架构为核心的 可验证计算时代。而Brevis Network 是 zkVM 与 zkCoprocessor 的融合体，构建了一个以零知识计算为核心、兼具高性能与可编程性的 通用可验证计算基础设施 —— 通向万物的无限计算层(The Infinite Compute Layer for Everything.)

3.1 Pico zkVM：通用可验证计算的模块化证明架构

2024年Vitalik 在《Glue and Coprocessor Architectures》中提出“**通用执行层 + 协处理器加速层”（glue & coprocessor）**架构。复杂计算可拆分为通用的业务逻辑与结构化的密集计算——前者追求灵活性（如 EVM、Python、RISC-V），后者追求效率（如 GPU、ASIC、哈希模块）。这一架构正成为区块链、AI 与加密计算的共同趋势：EVM 通过 precompile 提速，AI 借助 GPU 并行，ZK 证明则结合通用 VM 与专用电路。未来的关键，是让“胶水层”优化安全与开发体验，而“协处理层”聚焦高效执行，在性能、安全与开放性之间取得平衡。

Pico zkVM 由 Brevis开发，正是这一理念的代表性实现。通过 “通用 zkVM + 协处理器加速” 架构，将灵活的可编程性与专用电路的高性能计算结合。其模块化设计支持多种证明后端（KoalaBear、BabyBear、Mersenne31），并可自由组合执行、递归、压缩等组件形成 ProverChain。

Pico 的模块化体系不仅可自由重组核心组件，还能引入新的证明后端与应用级协处理器（如链上数据、zkML、跨链验证），实现持续演进的可扩展性。开发者可直接使用 Rust 工具链编写业务逻辑，无需零知识背景即可自动生成加密证明，大幅降低开发门槛。

相较于 Succinct SP1 的相对单体化 RISC-V zkVM 架构和 RISC Zero R0VM 的通用 RISC-V 执行模型，Pico 通过 Modular zkVM + Coprocessor System 实现执行、递归与压缩阶段的解耦与扩展，支持多后端切换及协处理器集成，在性能与可扩展性上形成差异化优势。

3.2 Pico Prism：多 GPU 集群的性能突破

Pico Prism 是 Brevis 在多服务器 GPU 架构上的重要突破，并在以太坊基金会的“实时证明（Real-Time Proving, RTP）”框架下创下新纪录。在 64×5090 GPU 集群上实现 6.9 秒平均证明时间 与 96.8% RTP 覆盖率，性能位居同类 zkVM 之首。该系统在架构、工程、硬件与系统层面均实现优化，标志着 zkVM 正从研究原型迈向生产级基础设施。

1. 架构设计：传统 zkVM（如 SP1、R0VM）主要依赖单机 GPU 优化。Pico Prism 首次实现多服务器、多 GPU 集群并行证明（Cluster-Level zkProving），通过多线程与分片调度，将 zk 证明扩展为分布式计算体系，大幅提升并行度与可扩展性。

2. 工程实现：构建多阶段异步流水线（Execution / Recursion / Compression）与跨层数据复用机制（proof chunk 缓存与 embedding 重用），并支持多后端切换（KoalaBear、BabyBear、M31），显著提升吞吐效率。

3. 硬件策略： 在 64×RTX 5090 GPU（约 $128K）配置下，Pico Prism 实现 6.0–6.9 秒平均证明时间、96.8% RTP 覆盖率，性能/成本比提升约 3.4 倍，较 SP1 Hypercube（160×4090 GPU，10.3 秒）表现更优。

4. 系统演进： 作为首个满足以太坊基金会 RTP 指标（>96% sub-10s、<$100K 成本）的 zkVM， Pico Prism 标志着 zk 证明系统从研究原型迈向主网级生产基础设施，为 Rollup、DeFi、AI 与跨链验证等场景提供更具经济性的零知识计算方案。

3.3 ZK Data Coprocessor：区块链数据智能零知识协处理层

智能合约原生设计中“缺乏记忆”——无法访问历史数据、识别长期行为或跨链分析。Brevis 提供的高性能的零知识协处理器（ZK Coprocessor），为智能合约提供跨链历史数据访问与可信计算能力，对区块链的全部历史状态、交易与事件进行验证与计算，应用于数据驱动型 DeFi、主动流动性管理、用户激励及跨链身份识别 等场景。

Brevis 的工作流程包括三步：

1. 数据访问：智能合约通过 API 无信任地读取历史数据；

2. 计算执行：开发者使用 SDK 定义业务逻辑，由 Brevis 链下计算并生成 ZK 证明；

结果验证：证明结果回传链上，由合约验证并调用后续逻辑。

Brevis 同时支持 Pure-ZK 与 CoChain（OP）模型：前者实现完全信任最小化，但成本较高；后者通过 PoS 验证与 ZK 挑战机制，允许以更低成本实现可验证计算。验证者在以太坊上质押，若结果被 ZK 证明挑战成功将被罚没，从而在安全与效率间取得平衡。通过 ZK + PoS + SDK 的架构融合，Brevis 在安全性与效率之间取得平衡，构建出一个可扩展的可信数据计算层。目前，Brevis 已服务于 PancakeSwap、Euler、Usual、Linea 等协议，所有 zkCoprocessor 合作 均基于 **Pure-ZK 模式，**为 DeFi、奖励分配与链上身份系统提供可信数据支撑，使智能合约真正具备“记忆与智能”。

3.4 Incentra：基于 ZK 的“可验证激励分发层

Incentra 是由 Brevis zkCoprocessor 驱动的可信激励分发平台，为 DeFi 协议提供安全、透明、可验证的奖励计算与发放机制。它通过零知识证明在链上直接验证激励结果，实现了 无信任、低成本、跨链化 的激励执行。系统在 ZK 电路中完成奖励计算与验证，确保任何用户都可独立验证结果；同时支持跨链操作与访问控制，实现合规、安全的自动化激励分发。

Incentra 主要支持三类激励模型：

• Token Holding：基于 ERC-20 时间加权余额（TWA）计算长期持有奖励；

• Concentrated Liquidity：根据 AMM DEX 手续费比例分配流动性奖励，兼容 Gamma、Beefy 等 ALM 协议；

• Lend & Borrow：基于余额与债务均值计算借贷奖励。

该系统已应用于 PancakeSwap、Euler、Usual、Linea 等项目，实现从激励计算到分发的全链可信闭环，为 DeFi 协议提供了 ZK 级的可验证激励基础设施。

3.5 Brevis 产品技术栈总览

四、Brevis zkVM 技术指标与性能突破

以太坊基金会（EF）提出的 L1 zkEVM 实时证明标准（Realtime Proving, RTP），已成为 zkVM 能否进入以太坊主网验证路线的行业共识与准入门槛，其核心评估指标包括：

• 延迟要求： P99 ≤ 10 秒（匹配以太坊 12 秒出块周期）；

• 硬件约束： CAPEX ≤ $100K、功耗 ≤ 10kW（适配家用/小型机房）；

• 安全等级： ≥128-bit（过渡期 ≥100-bit）；

• 证明尺寸： ≤300 KiB；

系统要求： 不得依赖可信设置、核心代码需完全开源。

2025 年 10 月，Brevis发布《Pico Prism — 99.6% Real-Time Proving for 45M Gas Ethereum Blocks on Consumer Hardware》报告，宣布其 Pico Prism 成为首个全面通过以太坊基金会（EF）实时块证明（RTP）标准的 zkVM。

在 64×RTX 5090 GPU（约 $128K） 配置下，Pico Prism 在 45M gas 区块中实现 平均延迟 6.9 秒、96.8% <10s、99.6% <12s 的性能表现，显著优于 Succinct SP1 Hypercube（36M gas，均时 10.3s，40.9% <10s）。在延迟降低 71%、硬件成本减半的条件下，整体性能/成本效率提升约 3.4×。该成果已获以太坊基金会、Vitalik Buterin 与 Justin Drake 的公开认可。

五、Brevis生态扩张与应用落地

Brevis的ZK 数据协处理器(zkCoprocessor)，负责处理 dApp 无法高效完成的复杂计算（如历史行为、跨链数据、聚合分析），并生成可验证的 零知识证明（ZKP）。链上仅需验证这份小证明即可安全调用结果，大幅降低 Gas、延迟与信任成本。相较传统预言机，Brevis 提供的不只是“结果”，更是“结果正确的数学保证”，其主要应用场景可以分为如下几类

• 智能 DeFi（Intelligent DeFi）：基于历史行为与市场状态，实现智能激励与差异化体验（PancakeSwap、Uniswap、MetaMask等）

• RWA 与稳定币增长（RWA & Stable Token Growth）：通过 ZK 验证实现稳定币与 RWA 收益的自动化分配（OpenEden、Usual Money、MetaMask USD）

• 隐私去中心化交易（DEX with Dark Pools）：采用链下撮合与链上验证的隐私交易模型，即将上线

• 跨链互操作（Cross-chain Interoperability）：支持跨链再质押与 Rollup–L1 互操作，构建共享安全层（Kernel、Celer、0G）

• 公链冷启动（Blockchain Bootstrap）：以 ZK 激励机制助力新公链生态冷启动与增长（Linea、TAC）

• 高性能公链（100× Faster L1s）：通过实时证明（RTP）技术推动以太坊等公链性能提升（Ethereum、BNB Chain）

可验证 AI（Verifiable AI）：融合隐私保护与可验证推理，为 AgentFi 与数据经济提供可信算力（Kaito、Trusta）

根据 Brevis Explorer 数据，截至 2025 年 10 月，Brevis 网络 已累计生成超 1.25 亿条 ZK 证明，覆盖 近 9.5 万个地址、9.6 万次应用请求，广泛服务于奖励分发、交易验证与质押证明等场景。生态层面，平台累计分发激励约 2.23 亿美元，支撑的 TVL 超 28 亿美元，相关交易量累计突破 10 亿美元。

当前 Brevis 的生态业务主要聚焦 DeFi 激励分发 与 流动性优化 两大方向，算力核心消耗由 Usual Money、PancakeSwap、Linea Ignition、Incentra 四个项目贡献，合计占比超 85%。其中

• Usual Money（46.6M proofs）：展现其在大规模激励分发中的长期稳定性；

• PancakeSwap（20.6M）：体现 Brevis 在实时费率与折扣计算中的高性能；

• Linea Ignition（20.4M）：验证其在 L2 生态活动中的高并发处理能力；

• Incentra（15.2%）：标志着 Brevis 从 SDK 工具向标准化激励平台的演进。

在 DeFi 激励领域，Brevis 依托 Incentra 平台支撑多个协议实现透明、持续的奖励分配：

• Usual Money 年激励规模超 $300M，为稳定币用户与 LP 提供持续收益；

• OpenEden 与 Bedrock 基于 CPI 模型实现美债与 Restaking 收益分配；

• Euler、Aave、BeraBorrow 等协议通过 ZK 验证借贷仓位与奖励计算。

在 流动性优化 方面，PancakeSwap、QuickSwap、THENA、Beefy 等采用 Brevis 的动态费率与 ALM 激励插件，实现交易折扣与跨链收益聚合；Jojo Exchange 与 Uniswap Foundation 则利用 ZK 验证机制构建更安全的交易激励体系。

在 跨链与基础设施层，Brevis 已从以太坊扩展至 BNB Chain、Linea、Kernel DAO、TAC 与 0G，为多链生态提供可信计算与跨链验证能力。与此同时，Trusta AI、Kaito AI、MetaMask 等项目正利用 ZK Data Coprocessor 构建隐私保护型积分、影响力评分与奖励系统，推动 Web3 数据智能化发展。在系统底层，Brevis 依托 EigenLayer AVS 网络 提供再质押安全保障，并结合 NEBRA 聚合证明（UPA） 技术，将多份 ZK 证明压缩为单次提交，显著降低链上验证成本与时延。

整体来看，Brevis 已覆盖从 长期激励、活动奖励、交易验证到平台化服务 的全周期应用场景。其高频验证任务与可复用电路模板为 Pico/Prism 提供了真实的性能压力与优化反馈，有望在工程与生态层面反哺 L1 zkVM 实时证明体系，形成技术与应用的双向飞轮。

六、团队背景及项目融资

Mo Dong｜联合创始人（Co-founder, Brevis Network）

Dr. Mo Dong 是 Brevis Network 的联合创始人，拥有伊利诺伊大学香槟分校（UIUC）计算机科学博士学位，他的研究成果发表于国际顶级学术会议，被谷歌等科技公司采纳，并获得数千次学术引用。他是算法博弈论与协议机制设计领域的专家，专注推动 零知识计算（ZK） 与 去中心化激励机制 的结合，致力于构建可信的 Verifiable Compute Economy。作为 IOSG Ventures 的风险合伙人，亦长期关注 Web3 基础设施的早期投资。

Brevis团队由来自 UIUC、MIT、UC Berkeley 的密码学与计算机科学博士创立，核心成员在零知识证明系统（ZKP）与分布式系统领域具有多年研究经验，并发表多篇经过同行评审的论文。Brevis 曾获 以太坊基金会（Ethereum Foundation） 的技术认可，其核心模块被视为关键的链上可扩展性基础设施。

Brevis 于 2024 年 11 月完成 750 万美元种子轮融资，由 Polychain Capital 与 Binance Labs 共同领投，参投方包括 IOSG Ventures、Nomad Capital、HashKey、Bankless Ventures 及来自 Kyber、Babylon、Uniswap、Arbitrum、AltLayer 的战略天使投资人。

七、ZKVM与ZK Coprocessor市场竞品分析

目前，以太坊基金会支持的 ETHProofs.org 已成为 L1 zkEVM 实时证明（Realtime Proving, RTP）路线的核心追踪平台，用于公开展示各 zkVM 的性能、安全与主网适配进展。

综合来看，RTP 赛道竞争正聚焦四个核心维度：

• 成熟度：SP1 生产化部署最成熟；Pico 性能领先且接近主网标准；RISC Zero 稳定但 RTP 数据未公开。

• 性能表现：Pico 证明体积约 990 kB，较 SP1（1.48 MB）缩小约 33%，成本更低；

• 安全与审计：RISC Zero 与 SP1 均已通过独立安全审计；Pico 正在审计流程中；

• 开发生态：主流 zkVM 均采用 RISC-V 指令集，SP1 依托 Succinct Rollup SDK 形成广泛集成生态；Pico 支持 Rust 自动生成证明，SDK 完善度快速提升。

从最新数据看，目前RTP 赛道已形成“两强格局

• 第一梯队Brevis Pico（含 Prism） 与 Succinct SP1 Hypercube 均直指 EF 设定的 P99 ≤ 10s 标准。前者以分布式多 GPU 架构实现性能与成本突破；后者以单体化系统保持工程成熟与生态稳健。Pico 代表性能与架构创新，SP1 代表实用化与生态领先。

• 第二梯队RISC Zero、ZisK、ZKM 在生态兼容与轻量化方面持续探索，但尚未公开完整 RTP 指标（延迟、功耗、CAPEX、安全位、证明体积、可复现性）。Scroll（Ceno） 与 Matter Labs（Airbender） 则尝试将 Rollup 技术延伸至 L1 验证层，体现出从 L2 扩容向 L1 可验证计算的演进趋势。

2025 年，zkVM 赛道已形成以 RISC-V 统一、模块化演进、递归标准化、硬件加速并行 的技术格局。zkVM的通用可验证计算层（Verifiable Compute Layer）可分为三个类别：

• 性能导向型：Brevis Pico、SP1、Jolt、ZisK 聚焦低延迟与实时证明，通过递归 STARK 与 GPU 加速提升计算吞吐。

• 模块化与可扩展型：OpenVM、Pico、SP1强调模块化可插拔，支持协处理器接入。

• 生态与通用开发型：RISC Zero、SP1、ZisK 聚焦 SDK 与语言兼容，推动普适化。

zkVM 竞品项目对比（截至 2025 年 10 月）

当前 zk-Coprocessor 赛道已形成以 Brevis、Axiom、Herodotus、Lagrange 为代表的格局。 其中 Brevis 以「ZK 数据协处理器 + 通用 zkVM」融合架构领先，兼具历史数据读取、可编程计算与 L1 RTP 能力；Axiom 聚焦可验证查询与电路回调；Herodotus 专注历史状态访问；Lagrange 以 ZK+Optimistic 混合架构优化跨链计算性能。 整体来看，zk-Coprocessor 正以“可验证服务层”的方式成为连接 DeFi、RWA、AI、身份 等应用的可信计算接口。

八、总结：商业逻辑、工程实现及潜在风险

商业逻辑：性能驱动与双层飞轮Brevis 以「通用 zkVM（Pico/Prism）」与「数据协处理器（zkCoprocessor）」构建多链可信计算层：前者解决任意计算可验证问题，后者实现历史与跨链数据的业务落地。其增长逻辑形成“性能—生态—成本”正循环：Pico Prism 的 RTP 性能吸引头部协议集成，带来证明规模增长与单次成本下降，形成持续强化的双层飞轮。竞争优势主要在三点：

1. 性能可复现 —— 已纳入以太坊基金会 ETHProofs RTP 体系；

2. 架构壁垒 —— 模块化设计与多 GPU 并行实现高扩展性；

3. 商业验证 —— 已在激励分发、动态费率与跨链验证中规模化落地。

工程实现：从“重执行”到“以验代执”

Brevis 通过 Pico zkVM 与 Prism 并行框架，在 45M gas 区块中实现平均 6.9 秒、P99 < 10 秒（64×5090 GPU，<$130 K CAPEX），性能与成本均处领先。 zkCoprocessor 模块支持历史数据读取、电路生成与回链验证，并可在 Pure-ZK 与 Hybrid 模式间灵活切换，整体性能已基本对齐以太坊 RTP 硬标准。

潜在风险与关注要点

• 技术与合规门槛：Brevis 仍需完成功耗、安全位、证明大小及可信设置依赖等硬指标的公开与第三方验证。长尾性能优化仍为关键，EIP 调整可能改变性能瓶颈。

• 竞争与替代风险： Succinct（SP1/Hypercube）在工具链与生态整合上依然领先，Risc Zero、Axiom、OpenVM、Scroll、zkSync 等团队竞争力依然不容忽视。

• 收入集中与业务结构： 当前证明量高度集中（前四大应用占比约 80%），需通过多行业、多公链、多用例拓展降低依赖。GPU 成本或将影响单位毛利。

综合来看，Brevis 已在“性能可复现”与“业务可落地”两端构筑了初步护城河：Pico/Prism 已稳居 L1 RTP 赛道第一梯队，zkCoprocessor 则打开高频、可复用的商业化场景。未来建议以达成以太坊基金会 RTP 全量硬指标为阶段性目标，持续强化协处理器产品标准化与生态拓展，同时推进第三方复现、安全审计与成本透明。通过在基础设施与 SaaS 收入间实现结构平衡，形成可持续的商业增长闭环。

免责声明：本文在创作过程中借助了 ChatGPT-5 的 AI 工具辅助完成，作者已尽力校对并确保信息真实与准确，但仍难免存在疏漏，敬请谅解。需特别提示的是，加密资产市场普遍存在项目基本面与二级市场价格表现背离的情况。本文内容仅用于信息整合与学术/研究交流，不构成任何投资建议，亦不应视为任何代币的买卖推荐。

I. The Industry Landscape of ZK Hardware Acceleration

GPU, FPGA, and ASIC represent the three mainstream paths of hardware acceleration:

• GPU (Graphics Processing Unit): A general-purpose parallel processor, originally designed for graphics rendering but now widely used in AI, ZK, and scientific computing.

• FPGA (Field Programmable Gate Array): A reconfigurable hardware circuit that can be repeatedly configured at the logic-gate level “like LEGO blocks,” bridging between general-purpose processors and specialized circuits.

• ASIC (Application-Specific Integrated Circuit): A dedicated chip customized for a specific task. Once fabricated, its function is fixed — offering the highest performance and efficiency but the least flexibility.

GPU Dominance:

GPUs have become the backbone of both AI and ZK computation. In AI, GPUs’ parallel architecture and mature software ecosystem (CUDA, PyTorch, TensorFlow) make them nearly irreplaceable — the long-term mainstream choice for both training and inference.In ZK, GPUs currently offer the best trade-off between cost and availability, but their performance in big integer modular arithmetic, MSM, and FFT/NTT operations is limited by memory and bandwidth constraints. Their energy efficiency and scalability economics remain insufficient, suggesting the eventual need for more specialized hardware.

FPGA Flexibility:

Paradigm’s 2022 investment thesis highlighted FPGA as the “sweet spot” balancing flexibility, efficiency, and cost. Indeed, FPGAs are programmable, reusable, and quick to prototype, suitable for rapid algorithm iteration, low-latency environments (e.g., high-frequency trading, 5G base stations), edge computing under power constraints, and secure cryptographic tasks.However, FPGAs lag behind GPUs and ASICs in raw performance and scale economics. Strategically, they are best suited as development and iteration platforms before algorithm standardization, or for niche verticals requiring long-term customization.

ASIC as the Endgame:

ASICs are already dominant in crypto mining (e.g., Bitcoin’s SHA-256, Litecoin/Dogecoin’s Scrypt). By hardwiring algorithms directly into silicon, ASICs achieve orders of magnitude better performance and energy efficiency — becoming the exclusive infrastructure for mining.In ZK proving (e.g., Cysic) and AI inference (e.g., Google TPU, Cambricon), ASICs show similar potential. Yet, in ZK, algorithmic diversity and operator variability have delayed standardization and large-scale demand. Once standards solidify, ASICs could redefine ZK compute infrastructure — delivering 10–100× improvements in performance and efficiency with minimal marginal cost post-production.In AI, where training workloads evolve rapidly and rely on dynamic matrix operations, GPUs will remain the mainstream for training. Still, ASICs will hold irreplaceable value in fixed-task, large-scale inference scenarios.

Dimension Comparison: GPU vs FPGA vs ASIC

In the evolution of ZK hardware acceleration, GPUs are currently the optimal solution — balancing cost, accessibility, and development efficiency, making them ideal for rapid deployment and iteration. FPGAs serve more as specialized tools, valuable in ultra-low-latency, small-scale interconnect, and prototyping scenarios, but unable to compete with GPUs in economic efficiency.In the long term, as ZK standards stabilize, ASICs will emerge as the industry’s core infrastructure, leveraging unmatched performance-per-cost and energy efficiency.

Overall trajectory:Short term – rely on GPUs to capture market share and generate revenue;Mid term – use FPGAs for verification and interconnect optimization;Long term – bet on ASICs to build a sustainable compute moat.

II. Hardware Perspective: The Underlying Technical Barriers of ZK Acceleration

Cysic’s core strength lies in hardware acceleration for zero-knowledge proofs (ZK). In the representative paper “ZK Hardware Acceleration: The Past, the Present and the Future,” the team highlights that GPUs offer flexibility and cost efficiency, while ASICs outperform in energy efficiency and peak performance—but require trade-offs between development cost and programmability. Cysic adopts a dual-track strategy — combining ASIC innovation with GPU acceleration — driving ZK from “verifiable” to “real-time usable” through a full-stack approach from custom chips to general SDKs.

1. The ASIC Path: Cysic C1 Chip and Dedicated Devices Cysic’s self-developed C1 chip is built on a zkVM-based architecture, featuring high bandwidth and flexible programmability. Based on this, Cysic plans to launch two hardware products: ZK Air: a portable accelerator roughly the size of an iPad charger, plug-and-play, designed for lightweight verification and developer use; ZK Pro: a high-performance system integrating the C1 chip with front-end acceleration modules, targeting large-scale zkRollup and zkML workloads. Cysic’s research directly supports its ASIC roadmap. The team introduced Hypercube IR, a ZK-specific intermediate representation that abstracts proof circuits into standardized parallel patterns—reducing the difficulty of cross-hardware migration. It explicitly preserves modular arithmetic and memory access patterns in circuit logic, enabling better hardware recognition and optimization. In Million Keccak/s experiments, a single C1 chip achieved ~1.31M Keccak proofs per second (~13× acceleration), demonstrating the throughput and energy-efficiency potential of specialized hardware. In HyperPlonk hardware analysis, the team showed that MSM/MLE operations parallelize well, while Sumcheck remains a bottleneck. Overall, Cysic is developing a holistic methodology across compiler abstraction, hardware verification, and protocol adaptation, laying a strong foundation for productization.

2. The GPU Path: General SDK + ZKPoG End-to-End Stack On the GPU side, Cysic is advancing both a general-purpose acceleration SDK and a full ZKPoG (Zero-Knowledge Proof on GPU) stack: General GPU SDK: built on Cysic’s custom CUDA framework, compatible with Plonky2, Halo2, Gnark, Rapidsnark, and other backends. It surpasses existing open-source frameworks in performance, supports multiple GPU models, and emphasizes compatibility and ease of use. ZKPoG: developed in collaboration with Tsinghua University, it is the first end-to-end GPU stack covering the entire proof flow—from witness generation to polynomial computation. On consumer-grade GPUs, it achieves up to 52× speedup (average 22.8×) and expands circuit scale by 1.6×, verified across SHA256, ECDSA, and MVM applications.

Cysic’s key differentiator lies in its hardware–software co-design philosophy.Its in-house ZK ASICs, GPU clusters, and portable mining devices together form a full-stack compute infrastructure, enabling deep integration from the chip layer to the protocol layer. By leveraging the complementarity between ASICs’ extreme energy efficiency and scalability and GPUs’ flexibility and rapid iteration, Cysic has positioned itself as a leading ZKP hardware provider for high-intensity proof workloads — and is now extending this foundation toward the financialization of ZK hardware (ComputeFi) as its next industrial phase.

III. Protocol Perspective: Cysic Network — A Universal Proof Layer under PoC Consensus

On September 24, 2025, the Cysic team released the Cysic Network Whitepaper.The project centers on ComputeFi, financializing GPU, ASIC, and mining hardware into programmable, verifiable, and tradable computational assets. Built with Cosmos CDK, Proof-of-Compute (PoC) consensus, and an EVM execution layer, Cysic Network establishes a decentralized “task-matching + multi-verification” marketplace supporting ZK proving, AI inference, mining, and HPC workloads.

By vertically integrating self-developed ZK ASICs, GPU clusters, and portable miners, and powered by a dual-token model ($CYS / $CGT), Cysic aims to unlock real-world compute liquidity — filling a key gap in Web3 infrastructure: verifiable compute power.

Modular Architecture: Four Layers of ComputeFi Infrastructure

Cysic Network adopts a bottom-up four-layer modular architecture, enabling cross-domain expansion and verifiable collaboration:

1. **Hardware Layer:**Comprising CPUs, GPUs, FPGAs, ASIC miners, and portable devices — forming the network’s computational foundation.

2. **Consensus Layer:**Built on Cosmos CDK, using a modified CometBFT + Proof-of-Compute (PoC) mechanism that integrates token staking and compute staking into validator weighting, ensuring both computational and economic security.

3. **Execution Layer:**Handles task scheduling, workload routing, bridging, and voting, with EVM-compatible smart contracts enabling programmable, multi-domain computation.

4. **Product Layer:**Serves as the application interface — integrating ZK proof markets, AI inference frameworks, crypto mining, and HPC modules, while supporting new task types and verification methods.

ZK Proof Layer: Decentralization Meets Hardware Acceleration

Zero-knowledge proofs allow computation to be verified without revealing underlying data — but generating these proofs is time- and cost-intensive.  Cysic Network enhances efficiency through decentralized Provers + GPU/ASIC acceleration, while off-chain verification and on-chain aggregation reduce latency and verification costs on Ethereum.

Workflow:  ZK projects publish proof tasks via smart contracts → decentralized Provers compete to generate proofs → Verifiers perform multi-party validation → results are settled via on-chain contracts.

By combining hardware acceleration with decentralized orchestration, Cysic builds a scalable Proof Layer that underpins ZK Rollups, zkML, and cross-chain applications.

Node Roles: Cysic Prover Mechanism

Within the network, Prover nodes are responsible for heavy-duty computation.Users can contribute their own compute resources or purchase Digital Harvester devices to perform proof tasks and earn $CYS / $CGT rewards.  A Multiplier factor boosts task acquisition speed. Each node must stake 10 CYS as collateral, which may be slashed for misconduct.

Currently, the main task is ETHProof Prover — generating ZK proofs for Ethereum mainnet blocks, advancing the base layer’s ZK scalability.Provers thus form the computational and security backbone of the Cysic Network, also providing trusted compute power for future AI inference and AgentFi applications.

Node Roles: Cysic Verifier Mechanism

Complementing Provers, Verifier nodes handle lightweight proof verification to enhance network security and scalability.Users can run Verifiers on a PC, server, or official Android app, with the Multiplier also boosting task efficiency and rewards.

The participation barrier is much lower — requiring only 0.5 CYS as collateral. Verifiers can join or exit freely, making participation accessible and flexible.This low-cost, light-participation model expands Cysic’s reach to mobile and general users, strengthening decentralization and trustworthy verification across the network.

Network Status and Outlook

As of October 15, 2025, the Cysic Network has reached a significant early milestone:

• ≈42,000 Prover nodes and 100,000+ Verifier nodes

• ≈91,000 total tasks completed

• ≈700,000 $CYS/$CGT distributed as rewards

However, despite the impressive node count, activity and compute contribution remain uneven due to entry and hardware differences.  Currently, the network is integrated with three external projects, marking the beginning of its ecosystem. Whether Cysic can evolve into a stable compute marketplace and core ComputeFi infrastructure will depend on further real-world integrations and partnerships in the coming phases.

IV. AI Perspective: Cysic AI — Cloud Services, AgentFi, and Verifiable Inference

Cysic AI’s business framework follows a three-tier structure — Product, Application, and Strategy: At the base, Serverless Inference offers standardized APIs to lower the barrier for AI model access; At the middle, the Agent Marketplace explores on-chain applications of AI Agents and autonomous collaboration; At the top, Verifiable AI integrates ZKP + GPU acceleration to enable trusted inference, representing the long-term vision of ComputeFi.

1. Standard Product Layer: Cloud Inference Service (Serverless Inference)

Cysic AI provides instant-access, pay-as-you-go inference services, allowing users to call large language models via APIs without managing or maintaining compute clusters.This serverless design achieves low-cost and flexible intelligent integration for both developers and enterprises.

Currently supported models include:

• Meta-Llama-3-8B-Instruct (task & dialogue optimization)

• QwQ-32B (reasoning-enhanced)

• Phi-4 (lightweight instruction model)

• Llama-Guard-3-8B (content safety review)

These cover diverse needs — from general conversation and logical reasoning to compliance auditing and edge deployment.The service balances cost and efficiency, supporting both rapid prototyping for developers and large-scale inference for enterprises, forming a foundational layer in Cysic’s trusted AI infrastructure.

2. Application Layer: Decentralized Intelligent Agent Marketplace (Agent Marketplace)

The Cysic Agent Marketplace functions as a decentralized platform for AI Agent applications.  Users can simply connect their Phantom wallet, complete verification, and interact with various Agents — payments are handled automatically through Solana USDC.

Currently, the platform integrates three core agents:

• X Trends Agent — analyzes real-time X (Twitter) trends and generates creative MEME coin concepts.

• Logo Generator Agent — instantly creates custom project logos from user descriptions.

Publisher Agent — deploys MEME coins on the Solana network (e.g., via Pump.fun) with one click.

Technically, the marketplace leverages the Agent Swarm Framework to coordinate multiple autonomous agents into collaborative task groups (Swarms), enabling division of labor, parallelism, and fault tolerance.Economically, it employs the Agent-to-Agent Protocol, achieving on-chain payments and automated incentives where users pay only for successful actions.

Together, these features form a complete on-chain loop — trend analysis → content generation → deployment, demonstrating how AI Agents can be financialized and integrated within the ComputeFi ecosystem.

3. Strategic Layer: Hardware-Accelerated Verifiable Inference (Verifiable AI)

A core challenge in AI inference is trust — how to mathematically guarantee that an inference result is correct without exposing inputs or model weights.Verifiable AI addresses this through zero-knowledge proofs (ZKPs), ensuring cryptographic assurance over model outputs.However, traditional ZKML proof generation is too slow for real-time use.Cysic solves this via GPU hardware acceleration, introducing three key technical innovations:

1. **Parallelized Sumcheck Protocol:**Breaks large polynomial computations into tens of thousands of CUDA threads running in parallel, achieving near-linear speedup relative to GPU core count.

2. **Custom Finite Field Arithmetic Kernels:**Deeply optimized across register allocation, shared memory, and warp-level parallelism to overcome modular arithmetic memory bottlenecks — keeping GPUs consistently saturated and efficient.

3. **End-to-End ZKPoG Acceleration Stack:**Covers the full chain — from witness generation to proof creation and verification, compatible with Plonky2 and Halo2 backends.Benchmarking shows up to 52× speedup over CPUs and ~10× acceleration on CNN-4M models.

Through this optimization suite, Cysic advances verifiable inference from being “theoretically possible but impractically slow” to **“real-time deployable.”**This dramatically reduces latency and cost, making Verifiable AI viable for the first time in real-world, latency-sensitive applications.

The platform supports PyTorch and TensorFlow — developers can simply wrap their model in a VerifiableModule to receive both inference results and corresponding cryptographic proofs without changing existing code.On its roadmap, Cysic plans to extend support to CNN, Transformer, Llama, and DeepSeek models, release real-time demos for facial recognition and object detection, and open-source code, documentation, and case studies to foster community collaboration.

Cysic AI’s three-layer roadmap forms a bottom-up evolution logic:

• Serverless Inference solves “can it be used”,

• Agent Marketplace answers “can it be applied”,

• Verifiable AI ensures “can it be trusted.”

The first two serve as transitional and experimental stages, while the true strategic differentiation lies in Verifiable AI — where Cysic integrates ZK hardware acceleration and decentralized compute networks to establish its long-term competitive edge within the ComputeFi ecosystem.

V. Financialization Perspective: NFT-Based Compute Access and ComputeFi Nodes

Cysic Network introduces the “Digital Compute Cube” Node NFT, which tokenizes high-performance compute assets such as GPUs and ASICs, creating a ComputeFi gateway accessible to mainstream users.  Each NFT functions as a verifiable node license, simultaneously representing yield rights, governance rights, and participation rights.Users can delegate or proxy participation in ZK proving, AI inference, and mining tasks — without owning physical hardware — and earn $CYS rewards directly.

The total NFT supply is 29,000 units, with approximately 16.45 million CYS distributed (1.65% of total supply, within the community allocation cap of 9%).Vesting: 50% unlocked at TGE + 50% linearly over six months.Beyond fixed token allocations, holders enjoy Multiplier boosts (up to 1.2×), priority access to compute tasks, and governance weight.Public sales have ended, and the NFTs are now tradable on OKX NFT Marketplace.

Unlike traditional cloud-compute rentals, the Compute Cube model represents on-chain ownership of physical compute infrastructure, combining:

• Fixed token yield: Each NFT secures a guaranteed allocation of $CYS.

• Real-time compute rewards: Node-connected workloads (ZK proving, AI inference, crypto mining) distribute earnings directly to holders’ wallets.

• Governance and priority rights: Holders gain voting power in compute scheduling and protocol upgrades, along with early access privileges.

• Positive feedback loop: More workloads → more rewards → greater staking → stronger governance influence.

In essence, Node NFTs convert fragmented GPU/ASIC resources into liquid on-chain assets, opening a new investment market for compute power in the era of surging AI and ZK demand.  This ComputeFi flywheel — more tasks → more rewards → stronger governance — serves as a key bridge for expanding Cysic’s compute network to retail participants.

VI. Consumer Use Case: Home ASIC Miners (Dogecoin & Cysic)

Dogecoin, launched in 2013, uses Scrypt PoW and has been merge-mined with Litecoin (AuxPoW) since 2014, sharing hashpower for stronger network security.  Its tokenomics feature infinite supply with a fixed annual issuance of 5 billion DOGE, emphasizing community and payment utility.  Among all ASIC-based PoW coins, Dogecoin remains the most popular after Bitcoin — its meme culture and loyal community sustain long-term ecosystem stickiness.

On the hardware side, Scrypt ASICs have fully replaced GPU/CPU mining, with industrial miners like Bitmain Antminer L7/L9 dominating. However, unlike Bitcoin’s industrial-scale mining, Dogecoin still supports home mining, with devices such as Goldshell MiniDoge, Fluminer L1, and ElphaPex DG Home 1 catering to retail miners, combining cash flow and community engagement.

For Cysic, entering the Dogecoin ASIC sector holds three strategic advantages:

1. Lower technical threshold: Scrypt ASICs are simpler than ZK ASICs, allowing faster validation of mass production and delivery capabilities.

2. Mature cash flow: Mining generates immediate and stable revenue streams.

3. Supply chain & brand building: Dogecoin ASIC production strengthens Cysic’s manufacturing and market expertise, paving the way for future ZK/AI ASICs.

Thus, home ASIC miners represent a pragmatic revenue base and a strategic stepping stone for Cysic’s long-term ZK/AI hardware roadmap.

Cysic Portable Dogecoin Miner: A Home-Scale Innovation

During Token2049, Cysic unveiled the DogeBox 1, a portable Scrypt ASIC miner for home and community users — designed as a verifiable consumer-grade compute terminal:

• Portable & energy-efficient: pocket-sized, 55 W power, suitable for households and small setups.

• Plug-and-play: managed via mobile app, built for global retail users.

• Dual functionality: mines DOGE and verifies DogeOS ZK proofs, achieving L1 + L2 security.

• Circular incentive: integrates DOGE mining + CYS rewards, forming a DOGE → CYS → DogeOS economic loop.

This product synergizes with DogeOS (a ZK-based Layer-2 Rollup developed by the MyDoge team, backed by Polychain Capital) and MyDoge Wallet, enabling DogeBox users to mine DOGE and participate in ZK validation — combining DOGE rewards + CYS subsidies to reinforce engagement and integrate directly into the DogeOS ecosystem.

The Cysic Dogecoin home miner thus serves as both a practical cashflow device and a strategic bridge to ZK/AI ASIC deployment.By merging mining + ZK verification, Cysic gains hands-on experience in market distribution and hardware scaling — while bringing a scalable, verifiable, community-driven L1 + L2 narrative to the Dogecoin ecosystem.

VII. Ecosystem Expansion and Core Progress

1. Collaboration with Succinct & Boundless Prover Networks: Cysic operates as a multi-node Prover within Succinct Network, leveraging its GPU clusters to handle SP1 zkVM real-time proofs and co-develop GPU optimization layers. It has also joined the Boundless Mainnet Beta, providing hardware acceleration for its Proof Marketplace.

2. Early Partnership with Scroll: In early stages, Cysic provided high-performance ZK computation for Scroll, executing large-scale proving tasks on GPU clusters with low latency and cost, generating over 10 million proofs. This validated Cysic’s engineering capability and laid the foundation for its future computer-network development.

3. Home Miner Debut at Token2049: Cysic’s DogeBox 1 portable ASIC miner officially entered the Dogecoin/Scrypt compute market. Specs: 55 W power, 125 MH/s hashrate, 100 × 100 × 35 mm, Wi-Fi + Bluetooth support, noise < 35 dB — ideal for home or community use. Beyond DOGE/LTC mining, it supports DogeOS ZK verification, achieving dual-layer (L1 + L2) security and forming a DOGE → CYS → DogeOS incentive loop.

4. Testnet Completion & Mainnet Readiness: On Sept 18, 2025, Cysic completed Phase III: Ignition, marking the end of its testnet and transition toward mainnet launch.

The testnet onboarded Succinct, Aleo, Scroll, and Boundless, attracting 55,000+ wallets, 8 million transactions, and 100,000+ reserved high-end GPU devices. 1.36 million registered users, 13 million transactions, ~223 k Verifiers + 41.8 k Provers = 260 k+ total nodes.  1.46 million total tokens distributed (733 k $CYS + 733 k $CGT + 4.6 million FIRE) and 48,000+ users staked, validating both incentive sustainability and network scalability.

1. Ecosystem Integration Overview:  According to Cysic’s official ecosystem map, the network is now interconnected with leading ZK and AI projects, underscoring its hardware-compatibility and openness across the decentralized compute stack.These integrations strengthen Cysic’s position as a foundational compute and hardware acceleration provider, supporting future expansion across ZK, AI, and ComputeFi ecosystems. Partner Categories:

   • zkEVM / L2: zkSync, Scroll, Manta, Nil, Kakarot

   • zkVM / Prover Networks: Succinct, Risc0, Nexus, Axiom

   • zk Coprocessors: Herodotus, Axiom

   • Infra / Cross-chain: zkCloud, ZKM, Polyhedra, Brevis

   • Identity & Privacy: zkPass, Human.tech

   • Oracles: Chainlink, Blocksense

AI Ecosystem: Talus, Modulus Labs, Gensyn, Aspecta, Inference Labs

VIII. Token Economics Design

Cysic Network adopts a dual-token system: the network token $CYS and the governance token $CGT.

**$CYS (Network Token):**A native, transferable asset used for paying transaction fees, node staking, block rewards, and network incentives—ensuring network activity and economic security. $CYS is also the primary incentive for compute providers and verifiers. Users can stake $CYS to obtain governance weight and participate in resource allocation and governance decisions of the Computing Pool.

**$CGT (Governance Token):**A non-transferable asset minted 1:1 by locking $CYS, with a longer unbonding period to participate in Computing Governance (CG). $CGT reflects compute contribution and long-term participation. Compute providers must maintain a reserve of $CGT as an admission bond to deter malicious behavior.

During network operation, compute providers connect their resources to Cysic Network to serve ZK, AI, and crypto-mining workloads. Revenue sources include block rewards, external project incentives, and compute governance distributions. Scheduling and reward allocation are dynamically adjusted by multiple factors, with external project incentives (e.g., ZK, AI, Mining rewards) as a key weight.

IX. Team Background & Fundraising

**Co-founder & CEO: Xiong (Leo) Fan.**Previously an Assistant Professor of Computer Science at Rutgers University (USA); former researcher at Algorand and Postdoctoral Researcher at the University of Maryland; Ph.D. from Cornell University. Leo’s research focuses on cryptography and its intersections with formal verification and hardware acceleration, with publications at top venues such as IEEE S&P, ACM CCS, POPL, Eurocrypt, and Asiacrypt, spanning homomorphic encryption, lattice cryptography, functional encryption, and protocol verification. He has contributed to multiple academic and industry projects, combining theoretical depth with systems implementation, and has served on program committees of international cryptography conferences.

According to public information on LinkedIn, the Cysic team blends backgrounds in hardware acceleration, cryptographic research, and blockchain applications. Core members have industry experience in chip design and systems optimization and academic training from leading institutions across the US, Europe, and Asia. The team’s strengths are complementary across hardware R&D, ZK optimization, and business operations.

**Fundraising:**In May 2024, Cysic announced a $12M Pre-A round co-led by HashKey Capital and OKX Ventures, with participation from Polychain, IDG, Matrix Partners, SNZ, ABCDE, Bit Digital, Coinswitch, Web3.com Ventures, as well as notable angels including George Lambeth (early investor in Celestia/Arbitrum/Avax) and Ken Li (Co-founder of Eternis).

X. Competitive Landscape in ZK Hardware Acceleration

1) Direct Competitors (Hardware-Accelerated)

In the hardware-accelerated prover and ComputeFi track, Cysic’s core peers include Ingonyama, Irreducible (formerly Ulvetanna), Fabric Cryptography, and Supernational—all focusing on “hardware + networks that accelerate ZK proving.”

• Cysic: Full-stack (GPU + ASIC + network) with a ComputeFi narrative. Strengths lie in the tokenization/financialization of compute; challenges include market education and hardware mass-production.

• Irreducible: Strong theory + engineering; exploring new algebraic structures (Binius) and zkASIC. High theoretical innovation; commercialization pace may be constrained by FPGA economics.

• Ingonyama: Open-source friendly; ICICLE SDK is a de-facto GPU ZK acceleration standard with high ecosystem adoption, but no in-house hardware.

Fabric: “Hardware–software co-design” path; building a VPU (Verifiable Processing Unit) general crypto-compute chip—business model akin to “CUDA + NVIDIA,” targeting a broader cryptographic compute market.

2) Indirect Competitors (ZK Marketplace / Prover Network / zk Coprocessor)

In ZK Marketplaces, Prover Networks, and zk Coprocessors, Cysic currently acts more as an upstream compute supplier, while Succinct, Boundless, Risc0, Axiom target the same end customers (L2s, zkRollups, zkML) via zkVMs, task routing, and open markets.

• Short term: Cooperation dominates. Succinct routes tasks; Cysic supplies high-performance provers. zk Coprocessors may offload tasks to Cysic.

Long term: If Boundless and Succinct scale their marketplace models (auction vs. routing) while Cysic also builds a marketplace, direct competition at the customer access layer is likely. Similarly, a mature zk Coprocessor loop could disintermediate direct hardware access, risking Cysic’s marginalization as an “upstream contractor.”

XI. Conclusion: Business Logic, Engineering Execution, and Potential Risks

Business LogicCysic centers on the ComputeFi narrative—connecting compute from hardware production and network scheduling to financialized assets.

• Short term: Leverage GPU clusters to meet current ZK prover demand and generate revenue.

• Mid term: Enter a mature cash-flow market with Dogecoin home ASIC miners to validate mass production and tap community-driven retail hardware.

• Long term: Develop dedicated ZK/AI ASICs, combined with Node NFTs / Compute Cubes to assetize and marketize compute—building an infrastructure-level moat.

Engineering Execution

• Hardware: Completed GPU-accelerated prover/verifier optimizations (MSM/FFT parallelization); disclosed ASIC R&D (1.3M Keccak/s prototype).

• Network: Built a Cosmos SDK-based validation chain for prover accounting and task distribution; tokenized compute via Compute Cube / Node NFTs.

• AI: Released the Verifiable AI framework; accelerated Sumcheck and finite-field arithmetic via GPU parallelism for trusted inference—though differentiation from peers remains limited.

Potential Risks

• Market education & demand uncertainty: ComputeFi is new; it’s unclear whether customers will invest in compute via NFTs/tokens.

• Insufficient ZK demand: The prover market is early; current GPU capacity may satisfy most needs, limiting ASIC shipment scale and revenue.

• ASIC engineering & mass-production risk: Proving systems aren’t fully standardized; ASIC R&D takes 12–18 months with high tape-out costs and uncertain yields—impacting commercialization timelines.

• Home-miner capacity constraints: The household market is limited; electricity costs and community-driven behavior skew toward “enthusiast consumption,” hindering stable scale revenue.

• Limited AI differentiation: Despite GPU parallel optimizations, cloud inference services are commoditized and the Agent Marketplace has low barriers—overall defensibility remains modest.

• Competitive dynamics: Long-term clashes at the customer access layer with Succinct/Boundless (marketplaces) or mature zk Coprocessors could push Cysic into an upstream “contract manufacturer” role.

**Disclaimer:**This article was produced with assistance from ChatGPT-5 as an AI tool. The author has endeavored to proofread and ensure the accuracy of all information, yet errors may remain. Note that in crypto markets, a project’s fundamentals often diverge from secondary-market price performance. The content herein is for information aggregation and academic/research exchange only; it does not constitute investment advice nor a recommendation to buy or sell any token.

一、ZK 硬件加速的行业格局

GPU、FPGA 和 ASIC 构成了硬件加速的三大主流方案：GPU 以通用并行架构和成熟生态在 AI、ZK 等领域广泛应用；FPGA 依靠可重构特性适合算法快速迭代和低延迟场景；ASIC 则通过专用电路实现极致性能与能效，是规模化和长期基础设施的最终形态。

• GPU (Graphics Processing Unit)： 通用并行处理器，最初为图形渲染优化，现在广泛用于 AI、ZK与科学计算。

• FPGA (Field Programmable Gate Array)： 可编程硬件电路，逻辑门级别“像乐高一样”可以反复配置，介于通用处理和专用电路之间。

• ASIC (Application-Specific Integrated Circuit)： 为特定任务定制的专用芯片，一次烧录，固定功能，性能和能效最高，但灵活性最差。

GPU市场主流：GPU 已成为 AI 与 ZK 的核心算力资源。在 AI 领域，GPU 依托并行架构与成熟生态（CUDA、PyTorch、TensorFlow），几乎不可替代，是训练与推理的长期主流。在 ZK 领域，GPU 凭借成本与可得性优势成为现阶段最佳方案，但其在大整数模运算、MSM 与 FFT/NTT 等任务上受限于存储与带宽，能效与规模化经济性不足，长期仍需更专用的硬件方案。

FPGA灵活方案：Paradigm 在 2022 年曾押注 FPGA，认为其在灵活性、效率与成本之间处于“甜蜜点”。FPGA 的确具备灵活可编程、开发周期短、硬件可复用等优势，适用于 ZK 证明算法迭代、原型验证、低延迟场景（高频交易、5G 基站）、功耗受限的边缘计算与高安全加密等任务。但在性能和规模化经济性上，FPGA 难以与 GPU、ASIC 竞争。其战略定位更接近“算法未定型时的验证与迭代平台”，以及少数细分行业中的长期刚需。

ASIC终局形态：ASIC 在加密货币挖矿中已高度成熟（比特币SHA-256、莱特币/狗狗币Scryp），通过将算法固化到电路中，ASIC 实现数量级的性能与能效优势成为矿业唯一主导。ASIC在 ZK 证明（如Cysic）与 AI 推理（如 Google TPU、寒武纪）中同样展现巨大潜力。但在 ZK 证明中，由于算法和算子尚未完全标准化，大规模需求仍在酝酿。未来一旦标准固化，ASIC 有望凭借 10–100 倍的性能与能效优势，以及量产后的低边际成本，像矿业 ASIC 一样重塑 ZK 的算力基建。在 AI 领域，由于算法迭代频繁、训练高度依赖矩阵并行，GPU 将继续占据训练主流，但 ASIC 在固定任务和规模化推理中将具备不可替代的价值。

在 ZK 硬件加速的演进路径中，GPU 目前是最优解，兼顾成本、可得性与开发效率，适合快速上线与迭代；FPGA 更像“专项工具”，在超低时延、小批量互联和原型验证中具备价值，但难与 GPU 的经济性抗衡；长期来看，随着 ZK标准趋于稳定，ASIC 将凭借极致的性能/成本与能效优势成为行业主力。整体路径为：短期依赖 GPU 抢占市场与营收，中期以 FPGA 做验证和互联优化，长期押注 ASIC 构筑算力护城河。

二、硬件视角：ZK 加速的底层技术壁垒 Cysic 的核心优势在于 零知识证明（ZK）的硬件加速。在代表性论文 《ZK Hardware Acceleration: The Past, the Present and the Future》 中，团队指出 GPU 具备灵活性和成本效率，而 ASIC 在能效和极致性能上更胜一筹，但需权衡开发成本与可编程性。Cysic 走 ASIC 创新 + GPU 加速 双线并进的路线，从定制芯片到通用 SDK，推动 ZK 从“可验证”走向“实时可用”。

1. ASIC 路线：Cysic C1 芯片与专用设备 Cysic 自研的 C1 芯片 基于 zkVM 架构，具备高带宽与灵活可编程性。基于此Cysic 规划推出ZK Air（便携式）与ZK Pro（高性能）两款硬件产品 ZK Air：便携式加速器，体积类似 iPad 充电器，即插即用，面向轻量级验证与开发； ZK Pro：高性能系统，结合 C1 芯片与前端加速模块，定位于大规模 zkRollup、zkML 等场景。 Cysic 的研究成果直接支撑其 ASIC 路线。团队提出 Hypercube IR 作为 ZK 专用中间表示，将证明电路抽象为规则化并行模式，降低跨硬件迁移门槛，并在电路逻辑中显式保留模运算与访存模式，便于硬件识别与优化；在 Million Keccak/s 实验中，自研 C1 芯片单片实现约 1.31M 次 Keccak 证明/秒（约 13× 加速），展示了专用硬件在能效与吞吐上的潜力；在 Hyperplonk 硬件分析 中，则指出 MSM/MLE 更易并行化，而 Sumcheck 仍是瓶颈。整体来看，Cysic 正在编译抽象、硬件验证和协议适配三方面形成完整方法论，为产品化奠定基础。

2. GPU 路线：通用 SDK + ZKPoG 端到端栈 在 GPU 方向，Cysic 同时推进 通用加速 SDK 与 ZKPoG 全流程优化栈： 通用 GPU SDK：基于自研 CUDA 框架，兼容 Plonky2、Halo2、Gnark、Rapidsnark 等后端，性能超越开源方案，支持多型号 GPU，强调 兼容性与易用性。 ZKPoG（Zero-Knowledge Proof on GPU）：与清华大学合作研发的端到端 GPU 栈，首次实现从 witness 生成到多项式计算的全流程优化。在消费级 GPU 上最高提速 52×（平均 22.8×），并扩展电路规模 1.6 倍，已在 SHA256、ECDSA、MVM 等应用中验证。

Cysic 的核心竞争力在于 软硬件一体化设计（Hardware–Software Co-Design）。团队自研的 ZK ASIC、GPU 集群与便携矿机 共同构成算力供给的全栈体系，实现从芯片层到协议层的深度协同。Cysic 通过 “ASIC 的极致能效与规模化” 与 “GPU 的灵活性与快速迭代” 的互补格局，在高强度零知识证明场景中确立了领先的 ZKP 硬件供应商地位，并以此为基础，持续推进 ZK 硬件金融化（ComputeFi） 的产业路径。

三、协议视角Cysic Network：PoC 共识下的通用 Proof Layer

Cysic 团队于 2025 年 9 月 24 日发布《Cysic Network Whitepaper》。项目以 ComputeFi 为核心，将 GPU、ASIC 与矿机金融化为可编程、可验证、可交易的算力资产，基于 Cosmos CDK + Proof-of-Compute (PoC) 与 EVM 执行层构建去中心化“任务撮合 + 多重验证”市场，统一支持 ZK 证明、AI 推理、挖矿与 HPC。依托自研 ZK ASIC、GPU 集群与便携矿机 的垂直整合能力，以及 CYS/CGT 双代币机制，Cysic 旨在释放真实算力流动性，补齐 Web3 基础设施中“算力”这一关键支柱。

Cysic Network 采用 自底向上的四层模块化架构，实现跨领域的灵活扩展与可验证协作：

• 硬件层（Hardware Layer）：由 CPU、GPU、FPGA、ASIC 矿机及便携式设备组成，构成网络算力基础。

• 共识层（Consensus Layer）：基于 Cosmos CDK 构建，并采用改良版 CometBFT + Proof-of-Compute (PoC) 共识机制，将代币质押与算力质押同时纳入验证权重，确保计算与经济安全性统一。

• 执行层（Execution Layer）：负责任务调度、负载路由、桥接与投票等核心逻辑，通过 EVM 兼容智能合约 实现多域可编程计算。

• 产品层（Product Layer）：面向最终应用场景，集成 ZK 证明市场、AI 推理框架、加密挖矿与 HPC 模块，可灵活接入新型任务类型与验证方法。

作为面向全行业的 ZK Proof Layer，Cysic 提供高性能、低成本的证明生成与验证服务。网络通过 去中心化 Prover 网络 与 离链验证 + 聚合上链机制 提升效率，并以 PoC 模型 将算力贡献与质押权重结合，构建兼具安全性与激励性的计算治理体系。

ZK Proof Layer：去中心化与硬件加速

零知识证明虽能在不泄露信息的前提下验证计算，但生成过程高耗时高成本。Cysic Network 通过 Prover 去中心化 + GPU/ASIC 加速 提升效率，并以 离链验证 + 聚合上链 模式降低以太坊验证的延迟与成本。其流程为：ZK 项目通过合约发布任务 → Prover 去中心化竞争生成证明 → Verifier 多方验证 → 链上合约结算。整体上，Cysic 将硬件加速与去中心化调度结合，打造可扩展的 Proof Layer，为 ZK Rollup、ZKML 与跨链应用提供底层支撑。

节点角色：Cysic Prover 机制

Cysic 在其 ZK 网络中引入 Prover 节点，用户可直接贡献算力或购买 Digital Harvester 执行证明任务，并以 CYS 与 CGT 获取奖励。通过提升 Multiplier 倍速因子可加快任务获取速度。节点需抵押 10 CYS 作为保证金，违规将被扣留。

当前 Prover 的核心任务为 ETHProof Prover，聚焦以太坊主网的区块证明，旨在推动底层的 ZK 化与扩展性建设。整体上，Prover 承担高强度计算任务，是 Cysic 网络性能与安全的核心执行层，并为后续可信推理与 AgentFi 应用提供算力保障。

节点角色：Cysic Verifier 机制

与 Prover 相对应，Verifier 节点负责对证明结果进行轻量级验证，提升网络安全与可扩展性。用户可在 PC、服务器或 官方 Android 应用运行 Verifier，并通过 Multiplier 倍速因子提高任务处理与奖励效率。

Verifier 的参与门槛更低，仅需抵押 0.5 CYS 作为保证金，运行方式简单，可随时加入或退出。整体上，Verifier 以 低成本、轻参与的模式吸引更多用户加入，扩展了 Cysic 在移动端和大众层面的覆盖，增强网络的去中心化与可信验证能力。

截至 2025 年 10月15日，Cysic 网络已初具规模：共运行约 4.2 万 Prover 节点 与 10 万+ Verifier 节点，累计处理任务 9.1 万余个，已分配奖励约 70 万枚 $CYS/$CGT。需注意的是，节点虽数量庞大，但因准入与硬件差异，活跃度与算力贡献分布不均。目前网络已对接 3 个项目，生态仍处早期阶段，其能否进一步演化为 稳定的算力网络与 ComputeFi 基础设施，仍取决于更多实际应用与合作落地。

四、AI 视角Cysic AI：云服务、AgentFi 与可信推理

Cysic AI 的业务布局呈现“产品—应用—战略”三层：底层 Serverless Inference 提供标准化推理 API，降低模型调用门槛；中层 Agent Marketplace 探索 AI Agent 的链上闭环应用；顶层 Verifiable AI 以 ZKP+GPU 加速支撑可信推理，承载 ComputeFi 的长期愿景。

标准产品层：云端推理服务（Serverless Inference）

Cysic AI推出即开即用、按需计费的标准推理服务，用户无需自建或维护算力集群，即可通过 API 快速调用多种主流大模型，实现低门槛的智能化接入。当前支持的模型包括 Meta-Llama-3-8B-Instruct（任务与对话优化）、QwQ-32B（推理增强型）、Phi-4（轻量化指令模型）、以及 Llama-Guard-3-8B（内容安全审查），覆盖通用对话、逻辑推理、轻量部署与合规审查等多元需求。该服务在成本与效率之间取得平衡，既满足开发者快速原型搭建，也能支撑企业级应用的规模化推理，是 Cysic 构建可信 AI 基础设施的重要一环。

应用实验层：去中心化智能体市场(Agent Marketplace)

Cysic AI推出的 Agent Marketplace 提供一个去中心化的智能体应用平台，用户只需连接 Phantom 钱包并完成认证，即可调用不同的 AI Agent 并通过 Solana USDC 实现自动支付。平台目前已集成三类核心智能体：

• X Trends Agent：实时解析 X 平台趋势，生成可转化为 MEME Coin 的创意概念；

• Logo Generator Agent：根据描述快速生成专属项目标识；

Publisher Agent：一键将 MEME Coin 部署到 Solana 网络（如 Pump.fun）。

Agent Marketplace 在应用上依托 Agent Swarm Framework 提升协作效率，将多个自治智能体组合为任务协作群体（Swarm），实现分工、并行与容错；在经济上通过 Agent-to-Agent Protocol 实现链上支付与自动激励，确保安全、透明的链上结算，用户仅为成功操作付费。通过这一组合，Cysic 打造了一个涵盖 趋势分析 → 内容生成 → 链上发布 的完整闭环，展示了 AI Agent 在 链上金融化与 ComputeFi 生态 中的落地路径。

战略支柱层：可信推理的硬件加速(Verifiable AI)

“推理结果是否可信”是 AI 推理领域的核心挑战。Verifiable AI 以零知识证明（ZKP）对推理结果提供数学级担保、无需泄露输入与模型；传统 ZKML 证明生成过慢难以满足实时需求，Cysic以 GPU 硬件加速突破这一瓶颈， 针对 Verifiable AI 提出了三方面的硬件加速创新：

• 首先，在 Sumcheck 协议并行化 上，将庞大的多项式计算任务拆分为数万个 CUDA 线程同时执行，使证明生成速度能够随 GPU 核心数实现近乎线性提升。

• 其次，通过 定制有限域算术内核，在寄存器、共享内存及 warp-level 并行设计上进行深度优化，大幅缓解传统 GPU 在模运算中的内存瓶颈，使 GPU始终保持高效运转。

• 最后，Cysic 在 端到端加速栈 ZKPoG 中，覆盖 witness 生成—证明生成—验证的全链路优化，兼容 Plonky2、Halo2 等主流后端，实测最高达 CPU 的 52× 性能，并在 CNN-4M 模型上实现约 10 倍加速。

通过这一整套优化，Cysic 将可验证推理从“理论可行但过慢”真正推向“可实时落地”的阶段，显著降低了延迟与成本，使 Verifiable AI 首次具备进入实时应用场景的可能性。

Cysic 平台兼容 PyTorch 与 TensorFlow，开发者只需将模型封装进 VerifiableModule，即可在不改写代码的前提下，获得推理结果及对应加密证明。在路线图上，将逐步扩展对 CNN、Transformer、Llama、DeepSeek 等模型的支持，并发布人脸识别、目标检测等实时 Demo 验证可用性；同时于未来数月开放代码、文档与案例，推动社区共建。

整体来看，Cysic AI 的三层路径形成了一条自下而上的演进逻辑：Serverless Inference 解决“能用”，Agent Marketplace 展示“能应用”，Verifiable AI 则承担“可信性与护城河”。前两者更多是过渡与试验，真正的价值和差异化将在 Verifiable AI 的落地中体现，其与 ZK 硬件及去中心化算力网络结合，才是 Cysic 未来在 ComputeFi 生态中建立长期优势的关键。

五、金融化视角：NFT 化算力入口与ComputeFi 节点

Cysic Network 通过 “Digital Compute Cube” Node NFT 将 GPU、ASIC 等高性能算力资产代币化，打造面向大众用户的 ComputeFi 入口。每枚 NFT 即是网络节点许可（verifiable license），同时承载 收益权 + 治理权 + 参与权：用户无需自建硬件，即可代理或委托参与 ZK 证明、AI 推理与挖矿任务，并直接获得 $CYS 激励。

NFT 总量为 29,000 枚，累计分配约 1,645 万 CYS（占总供应 1.65%，在社区分配上限 9% 内）。解锁方式为 50% TGE 即时解锁 + 50% 六个月线性释放。除固定分配外，NFT 持有者还享有 Multiplier 火力加速（最高 1.2x）、优先算力任务权、治理权重等额外权益。目前公开销售已经结束，用户可在 OKX NFT Marketplace 进行交易。

与传统云算力租赁不同，Compute Cube 本质上是对底层硬件基础设施的 链上所有权确权：

• 固定 Token 收益：每枚 NFT 锁定一定比例 $CYS 分配；

• 实时算力收益：节点接入实际工作负载（ZK 证明、AI 推理、加密挖矿），收益直接分发至持有者钱包；

• 治理与优先权：持有者在算力调度、协议升级中拥有治理权重与优先使用权；

• 正向循环效应：更多任务 → 更多奖励 → 更多质押 → 更强治理影响力。

整体上，Node NFT首次将零散 GPU/ASIC 转化为可流通的链上资产，在 AI 与 ZK 需求并行爆发的背景下，开辟了全新的 算力投资市场。ComputeFi 的循环效应（更多任务 → 更多奖励 → 更强治理权）是成为 Cysic 扩展算力网络至大众用户的重要桥梁。

六、消费场景：家庭 ASIC 矿机 （Doge & Cysic）

Dogecoin 诞生于 2013 年，采用 Scrypt PoW，并自 2014 年起与 Litecoin 合并挖矿（AuxPoW），通过共享算力提升网络安全。其代币机制为无限供应 + 每年固定增发 50 亿 DOGE，更偏向社区文化与支付属性。在完全 ASIC 化的 PoW 矿币中，Dogecoin 是除比特币外热度最高的代表，其 Meme 文化与社群效应形成了长期生态粘性。

硬件层面，Scrypt ASIC 已全面取代 GPU/CPU，Bitmain Antminer L7/L9 等工业级矿机占据主流。但不同于比特币已彻底矿场化，Dogecoin 仍保留家庭矿机空间，Goldshell MiniDoge、Fluminer L1、ElphaPex DG Home 1 等轻量产品使其兼具现金流与社群驱动特征。

对 Cysic 而言，切入 Dogecoin ASIC 具备三重意义：其一，Scrypt ASIC 难度低于 ZK ASIC，可快速验证量产与交付能力；其二，挖矿市场现金流成熟，可提供稳定营收；其三，Doge ASIC 有助于积累供应链与品牌经验，为未来 ZK/AI 专用芯片奠定基础。总体来看，家庭 ASIC 矿机是 Cysic 的务实落点，同时为长期布局 ZK/AI ASIC 提供过渡支撑。

Cysic Portable Dogecoin Miner：家庭级创新路径

Cysic 于 Token2049 期间正式发布 DogeBox 1，这是一款面向家庭与社区用户的 便携式 Scrypt ASIC 矿机，定位为“可验证的家庭级算力终端”：

• 便携节能：口袋大小，适合家庭与社区用户，降低参与门槛；

• 即插即用：手机 App 管理，面向全球零售市场；

• 双重功能：既可挖矿 DOGE，又能验证 DogeOS 的 ZK 证明，实现 L1+L2 安全；

• 激励循环：DOGE 挖矿 + CYS 补贴，形成 DOGE→CYS→DogeOS 的经济闭环。

该产品与 DogeOS（MyDoge 团队开发的基于零知识证明的 Layer-2 Rollup， Polychain Capital 领投）和 MyDoge 钱包 的协同，使 Cysic 矿机不仅能挖矿 DOGE，还能参与 ZK 验证，并通过 DOGE 奖励 + CYS 补贴 建立激励循环，增强用户黏性并融入 DogeOS 生态。

Cysic 的 Dogecoin 家庭矿机既是 务实的现金流落点，也是 长期 ZK/AI ASIC 的战略铺垫；通过“挖矿+ZK 验证”的混合模式，不仅积累市场与供应链经验，还为 Dogecoin 引入 可扩展、可验证、社区驱动的 L1+L2 新叙事。

七、Cysic生态布局与核心进展

1. 与 Succinct / Boundless Prover Network的合作 Cysic 已作为多节点 Prover 接入 Succinct Network，依托高性能 GPU 集群承接 SP1 zkVM 的实时证明任务，并在优化 GPU 代码层面与团队深度协作。与此同时，Cysic 也已加入 Boundless Mainnet Beta，为其 Proof Marketplace 提供硬件加速能力。

2. 早期合作项目（Scroll） 在早期阶段，Cysic 曾为 Scroll 提供高性能 ZK 计算，依托 GPU 集群为其承接大规模 Proving 任务，确保低延迟与低成本运行，累计生成超千万个证明。这一合作不仅验证了 Cysic 的工程实力，也为其后续在硬件加速和算力网络方向的探索奠定了基础。

3. 家庭矿机亮相 Token2049 Cysic 在 Token2049 发布其首款便携式家庭 ASIC 矿机 DogeBox 1，正式切入 Dogecoin/Scrypt 算力市场。该设备定位为“掌上级算力终端”。DogeBox 1 具备 轻量、低功耗、即插即用 特征，仅 55 W 功耗、125 MH/s 算力，机身仅 100×100×35 mm，支持 Wi-Fi 与蓝牙连接，噪音低于 35 dB，适合家庭与社区用户使用。 除 DOGE/LTC 挖矿外，设备还支持 DogeOS ZK 验证，实现 L1+L2 双层安全，并通过 DOGE 挖矿 + CYS 补贴 构建「DOGE → CYS → DogeOS」的三重激励循环。

4. 测试网收官，主网在即 Cysic 于 2025 年 9 月 18 日完成 Phase III: Ignition，标志测试网阶段正式结束并进入主网筹备期。继 Phase I 验证硬件与代币模型、Phase II 扩展 Genesis Node 规模后，本阶段全面验证了算力网络的用户参与度、激励机制与资产化逻辑。 Cysic 已在测试网阶段接入 Succinct、Aleo、Scroll 与 Boundless 等零知识项目，官网数据显示，测试网期间共汇聚 55,000+ 钱包地址、800万笔交易 与 100,000+ 预留高端 GPU 设备。Phase III：Ignition 测试网共吸引 136 万注册用户，累计处理 约 1,300 万笔交易，形成由 约 22.3 万 Verifiers 与 4.18 万 Provers 构成的 26 万+ 节点网络。激励层面，累计分发 约 146 万枚代币（73.3 万 $CYS + 73.3 万 $CGT） 与 460 万 FIRE，共有 48,000+ 用户参与质押，验证了其激励机制与算力网络的可持续性。 此外，从官网的生态地图来看，Cysic 已经与 ZK 与 AI 领域的核心项目形成了广泛连接，展现出其作为底层算力和硬件加速提供方的广泛兼容性和开放性。这些生态链接为未来在 ZK、AI 与 ComputeFi 路线的拓展提供了良好的外部接口与合作基础。 zkEVM 与 L2：zkSync、Scroll、Manta、Nil、Kakarot zkVM / Prover Network：Succinct、Risc0、Nexus、Axiom zk Coprocessor：Herodotus、Axiom 基础设施 / 跨链：zkCloud、ZKM、Polyhedra、Brevis 身份与隐私：zkPass、Human.tech 预言机：Chainlink、Blocksense AI 生态：Talus、Modulus Labs、Gensyn、Aspecta、Inference Labs

八、Cysic代币经济模型设计

Cysic Network 采用 双代币体系：网络代币 $CYS 与治理代币 $CGT。

• $CYS（网络代币）：为原生可转让资产，用于支付交易费用、节点抵押、区块奖励及网络激励，确保网络活跃度与经济安全。$CYS 也是计算提供者与验证者的主要激励来源。用户可通过质押 $CYS 获取治理权重，并参与算力池（Computing Pool）的资源分配与治理决策。

• $CGT（治理代币）：为不可转让资产，仅能通过抵押 $CYS 以 1:1 比例获得，并在解押周期更长的机制下参与 Computing Governance (CG)。$CGT 反映算力贡献与长期参与度，计算提供者需预留一定数量的 $CGT 作为准入保证金，以防止恶意行为。

在网络运行中，计算提供者将算力接入 Cysic Network，为 ZK、AI 与加密挖矿等任务提供服务。其收益来源包括区块奖励、外部项目激励及算力治理分配。算力的调度与奖励分布将根据多维因素动态调整，其中 外部项目激励（如 ZK、AI、Mining 奖励） 是关键权重。

九、团队背景及项目融资

Cysic 联合创始人兼首席执行官为Xiong (Leo) Fan，他曾任美国罗格斯大学计算机科学系助理教授。在此之前，他先后担任 Algorand 研究员、马里兰大学博士后研究员，并在康奈尔大学获得博士学位。Leo Fan 的研究长期聚焦于密码学及其在形式化验证与硬件加速中的交叉方向，已在 IEEE S&P、ACM CCS、POPL、Eurocrypt、Asiacrypt 等国际顶级会议和期刊发表多篇论文，涵盖同态加密、格密码、功能加密、协议验证等领域。他曾参与多个学术与行业项目，兼具理论研究与系统实现经验，并在国际密码学学术会议中担任程序委员会成员。

根据LinkedIn的公开信息，Cysic 团队由硬件加速、加密研究与区块链应用背景的成员组成，核心成员具备芯片设计与系统优化的产业经验，同时拥有欧美及亚洲顶尖高校的学术训练。团队在 硬件研发、零知识证明优化及运营拓展 等方向形成互补。

在融资方面，2024 年 5 月，Cysic 宣布完成 1200 万美元 Pre-A 轮融资，由 HashKey Capital 与 OKX Ventures 联合领投，参投方包括 Polychain、IDG、Matrix Partners、SNZ、ABCDE、Bit Digital、Coinswitch、Web3.com Ventures，以及 Celestia/Arbitrum/Avax 早期投资人 George Lambeth 与 Eternis 联合创始人 Ken Li 等知名天使。

十、ZK硬件加速市场竞品分析

1. 直接竞品（硬件加速型）

在硬件加速型 Prover 与 ComputeFi 赛道，Cysic 的核心对手包括 Ingonyama、Irreducible（前 Ulvetanna）、Fabric Cryptography、Supernational，均围绕“加速 ZK Proving 的硬件与网络”展开。

• Cysic：全栈化（GPU+ASIC+网络），主打 ComputeFi 叙事，优势在算力资产化与金融化，但ComputeFi 模式尚需市场教育，同时硬件量产也具备一定挑战。

• Irreducible：学术与工程结合，探索新代数结构（Binius）与 zkASIC，理论创新强，但其商业化落地节奏可能受制于 FPGA 规模化经济性。

• Ingonyama：开源友好，ICICLE SDK 已成为 GPU ZK 加速事实标准，生态采用率高，但缺乏自研硬件。

Fabric：定位为“软硬一体”路径，试图打造通用加密计算芯片（VPU），商业模式类似“CUDA + NVIDIA”，谋求更广泛的加密计算市场。

2. 间接竞品（ZK Marketplace / Prover Network / zk Coprocessor）

在 ZK Marketplace、Prover Network 与 zk Coprocessor 赛道，Cysic 当前更多扮演 上游算力供应商 的角色，而 Succinct、Boundless、Risc0、Axiom 等项目则通过 zkVM、任务调度和开放市场撮合切入同一客户群（L2、zkRollup、ZKML）。

短期来看，Cysic 与这些项目以协作为主：Succinct 负责任务路由，Cysic 提供高性能 Prover 节点；zk Coprocessor 则可能分流部分任务至 Cysic。 但长期若 Boundless 与 Succinct 的 Marketplace 模式（竞拍 vs 路由）继续壮大，而 Cysic 自建 Marketplace，则三方将在 客户入口层 不可避免地产生直接冲突。类似地，zk Coprocessor 若形成闭环，可能成为客户入口替代硬件直连，Cysic 有被边缘化为“代工厂”的风险。

十一、总结：商业逻辑、工程实现及潜在风险

商业逻辑

Cysic 以 **ComputeFi **为核心叙事，试图将算力从硬件生产、网络调度到金融化资产打通。短期依托 GPU 集群满足现有 ZK Prover 需求并形成营收；中期通过 Dogecoin 家庭 ASIC 矿机进入现金流成熟市场，验证量产能力并借助社群文化打开消费级硬件入口；长期目标是自研 ZK/AI 专用 ASIC，叠加 Node NFT 与 Compute Cube，实现算力资产化与市场化，构筑基础设施型护城河。

工程实现在硬件层面，Cysic 已完成 GPU 加速 Prover/Verifier 优化（MSM、FFT 并行化），并公布 ASIC 研发成果（1.3M Keccak/s 原型实验）。在网络层面，构建基于 Cosmos SDK 的验证链，支持 Prover 节点记账与任务分发，并以 Compute Cube/Node NFT 实现算力代币化。AI 方向上，推出 Verifiable AI 框架，通过 GPU 并行优化 Sumcheck 与有限域运算，实现可信推理，但与行业同类产品相比差异化有限。

潜在风险

1. 市场教育与需求不确定性：ComputeFi 模式尚属新概念，客户是否愿意通过 NFT/代币形式投资算力尚需市场验证。

2. ZK 业务需求不足：ZK Prover 行业仍处早期，现阶段 GPU 已能满足大部分需求，难以支撑 ASIC 的大规模出货，营收贡献有限。

3. ASIC 工程与量产风险：证明系统尚未完全标准化，ASIC 研发需 12–18 个月，叠加高额流片成本与量产良率不确定性，可能冲击商业化进度。

4. Doge 家庭矿机产能瓶颈：家庭场景整体市场容量有限，电价与社群驱动导致更多是“兴趣型”消费，难以形成稳定规模化收入。

5. AI 业务差异性不足：Cysic 的 Verifiable AI 虽展示 GPU 并行优化，但其云端推理服务差异化有限，Agent Marketplace 门槛较低，整体壁垒仍不突出。

6. 竞争格局动态：长期则可能与 Succinct、Boundless 等 zkMarketplace 或 zkCoprocessor 项目在客户入口层发生冲突，被动退居“上游代工”角色。

免责声明：***本文在创作过程中借助了 ChatGPT-5 的 AI 工具辅助完成，作者已尽力校对并确保信息真实与准确，但仍难免存在疏漏，敬请谅解。需特别提示的是，加密资产市场普遍存在项目基本面与二级市场价格表现背离的情况。本文内容仅用于信息整合与学术/研究交流，不构成任何投资建议，亦不应视为任何代币的买卖推荐。

I. Outlook for AI Asset RWAization

In discussions around RWA (Real-World Asset) tokenization, the market generally believes that standard assets such as U.S. Treasuries, U.S. equities, and gold will remain at the core in the long term. These assets are highly liquid, have transparent valuations, and follow well-defined compliance pathways — making them the natural carriers of the on-chain “risk-free rate.”

By contrast, the RWAization of non-standard assets faces greater uncertainty. Segments such as carbon credits, private credit, supply chain finance, real estate, and infrastructure all represent massive markets. However, they often suffer from opaque valuation, high execution complexity, long cycles, and strong policy dependence. The real challenge lies not in tokenization itself, but in enforcing off-chain asset execution — especially post-default recovery and liquidation, which still depend on due diligence, post-loan management, and traditional legal processes.

Despite these challenges, RWAization remains significant for several reasons:

1. On-chain transparency — contracts and asset pool data are publicly visible, avoiding the “black box” problem of traditional funds.

2. Diversified yield structures — beyond interest income, investors can earn additional returns through mechanisms like Pendle PT/YT, token incentives, and secondary market liquidity.

3. Bankruptcy protection — investors usually hold securitized shares via SPC structures rather than direct claims, providing a degree of insolvency isolation.

Within AI assets, GPU hardware is widely regarded as the first entry point for RWAization due to its clear residual value, high degree of standardization, and strong demand. Beyond hardware, compute lease contracts offer an additional layer — their contractual and predictable cash flow models make them particularly suitable for securitization.

Looking further, robotics hardware and service contracts also carry RWA potential. Humanoid and specialized robots, as high-value equipment, could be mapped on-chain via financing lease agreements. However, robotics is far more operationally intensive, making execution significantly harder than GPU-backed assets.

In addition, data centers and energy contracts are worth attention. Data centers — including rack leasing, electricity, and bandwidth agreements — represent relatively stable infrastructure cash flows. Energy contracts, exemplified by green energy PPAs, provide not only long-term revenue but also ESG attributes, aligning well with institutional investor mandates.

Overall, AI asset RWAization can be understood across different horizons:

• Short term: centered on GPU and related compute lease contracts.

• Mid term: expansion to data center and energy agreements.

• Long term: breakthrough opportunities in robotics hardware and service contracts.

The common logic across all layers is high-value hardware + predictable cash flow, though the execution pathways vary.

II. The Priority Value of GPU Asset RWAization

Among the many non-standard AI assets, GPUs may represent one of the most practical directions for exploration:

• Standardization & Clear Residual Value: Mainstream GPU models have transparent market pricing and well-defined residual value.

• Active Secondary Market: Strong resale liquidity ensures partial recovery in case of default.

• Real Productivity Attributes: GPU demand is directly tied to AI industry growth, providing real cash flow generation capacity.

• High Narrative Fit: Positioned at the intersection of AI and DeFi — two of the hottest narratives — GPUs naturally attract investor attention.

As AI compute data centers remain a highly nascent industry, traditional banks often struggle to understand their operating models and are therefore unable to provide loan support. Only large enterprises such as CoreWeave and Crusoe can secure financing from major private credit institutions like Apollo, while small and mid-sized operators are largely excluded — highlighting the urgent need for financing channels that serve the mid-to-small enterprise segment.

It should be noted, however, that GPU RWAization does not eliminate credit risk. Enterprises with strong credit profiles can typically obtain cheaper financing from banks, and may have little need for on-chain financing. Tokenized financing often appeals more to small and medium-sized enterprises, which inherently face higher default risk. This creates a structural paradox in RWA: high-quality borrowers do not need tokenization, while higher-risk borrowers are more inclined to adopt it.

Nevertheless, compared to traditional equipment leasing, GPUs’ high demand, recoverability, and clear residual value make their risk-return profile more attractive. The significance of RWAization lies not in eliminating risk, but in making risk more transparent, priceable, and tradable. As the flagship of non-standard asset RWAs, GPUs embody both industrial value and experimental potential — though their success ultimately depends on off-chain due diligence and enforcement, rather than purely on-chain design.

III. Frontier Exploration of Robotics Asset RWAization

Beyond computing hardware, the robotics industry is also entering the RWAization landscape, with the market projected to exceed $185 billion by 2030, signaling immense potential. The rise of Industry 4.0 is ushering in an era of intelligent automation and human–machine collaboration. In the coming years, robots will become ubiquitous—across factories, logistics, retail, and even homes. By enabling the adoption and deployment of intelligent robots through structured, on-chain financing, while creating an investable product that allows users to participate in this global shift. Feasible pathways include:

1. Robotics Hardware Financing

   • Provides capital for production and deployment.

   • Returns come from leasing, direct sales, or Robot-as-a-Service (RaaS) models.

   • Cash flows can be mapped on-chain through SPC structures with insurance coverage, reducing default and disposal risks.

2. Data Stream Financialization

   • Embodied AI requires large-scale real-world data.

   • Financing can support sensor deployment and distributed data collection networks.

   • Data usage rights or licensing revenues can be tokenized, giving investors exposure to the future value of data.

3. Production & Supply Chain Financing

   • Robotics involves long value chains, including components, manufacturing capacity, and logistics.

   • Unlock working capital through trade finance, and mapping future shipments and cash flows on-chain.

Compared with GPU assets, robotics assets are far more dependent on operations and real-world deployment. Cash flows are more vulnerable to fluctuations in utilization, maintenance costs, and regulatory constraints. Therefore, it is recommended to adopt a shorter-term structure with higher overcollateralization and reserve ratios to ensure stable returns and liquidity safety.

IV. GAIB Protocol: An Economic Layer Linking Off-Chain AI Assets and On-Chain DeFi

The RWAization of AI assets is moving from concept to implementation. GPUs have emerged as the most practical on-chain asset class, while robotics financing represents a longer-term growth frontier. To give these assets true financial attributes, it is essential to build an economic layer that can bridge off-chain financing, generate yield-bearing instruments, and connect seamlessly with DeFi liquidity.

GAIB was born in this context. Rather than directly tokenizing AI hardware, it brings on-chain the financing contracts collateralized by enterprise-grade GPUs or robots, thereby building an economic bridge between off-chain cash flows and on-chain capital markets.

Off-chain, enterprise-grade GPU clusters or robotic assets purchased and used by cloud service providers and data centers serve as collateral; On-chain, AID is used for price stability and liquidity management (non-yield-bearing, fully backed by T-Bills), while sAID provides yield exposure and automatic compounding (underpinned by a financing portfolio plus T-Bills).

GAIB’s Off-Chain Financing Model

GAIB partners with global cloud providers and data centers, using GPU clusters as collateral to design three types of financing agreements:

• Debt Model: Fixed interest payments (annualized ~10–20%).

• Equity Model: Revenue-sharing from GPU & Robotics income (annualized ~60–80%+).

• Hybrid Model: Combination of fixed interest and revenue-sharing.

Risk management relies on over-collateralization of physical GPUs and bankruptcy-isolated legal structures, ensuring that in case of default, assets can be liquidated or reassigned to partnered data centers to continue generating cash flow. With enterprise-grade GPUs featuring short payback cycles, financing tenors are significantly shorter than traditional debt products, typically 3–36 months.

To enhance security, GAIB works with third-party underwriters, auditors, and custodians to enforce strict due diligence and post-loan management. In addition, Treasury reserves serve as supplementary liquidity protection.

On-Chain Mechanisms

• Minting & Redemption: Qualified users (Whitelist + KYC) can mint AID with stablecoins or redeem AID back into stablecoins via smart contracts.In addition, non-KYC users can also obtain it through secondary market trading.

• Staking & Yield: Users can stake AID to obtain sAID, which automatically accrues yield and appreciates over time.

• Liquidity Pools: GAIB will deploy AID liquidity pools on mainstream AMMs, enabling users to swap between AID and stablecoins.

DeFi Use Cases

• Lending: AID can be integrated into lending protocols to improve capital efficiency.

• Yield Trading: sAID can be split into PT/YT (Principal/ Yield Tokens), supporting diverse risk-return strategies.

• Derivatives: AID and sAID can serve as yield-bearing primitives for derivatives such as options and futures.

• Custom Strategies: Vaults and yield optimizers can incorporate AID/sAID, allowing for personalized portfolio allocation.

In essence, GAIB’s core logic is to convert off-chain real cash flows — backed by GPUs, Robotic Assets, and Treasuries — into on-chain composable assets. Through the design of AID/sAID and integration with DeFi protocols, GAIB enables the creation of markets for yield, liquidity, and derivatives. This dual foundation of real-world collateral + on-chain financial innovation builds a scalable bridge between the AI economy and crypto finance.

V. Off-Chain: GPU Asset Tokenization Standards and Risk Management Mechanisms

GAIB uses an SPC (Segregated Portfolio Company) structure to convert off-chain GPU financing into on-chain yield certificates. Investors deposit stablecoins to mint AI Synthetic Dollars (AID), which can be staked for sAID to earn returns from GAIB’s GPU and robotics financing. As repayments flow into the protocol, sAID appreciates in value, and holders can burn it to redeem principal and yield — creating a one-to-one link between on-chain assets and real cash flows.

Tokenization Standards and Operational Workflow

GAIB requires assets to be backed by robust collateral and guarantee mechanisms. Financing agreements must include monthly monitoring, delinquency thresholds, over-collateralization compliance, and require underwriters to have at least 2+ years of lending experience with full data disclosure.

Process flow:  Investor deposits stablecoins → Smart contract mints AID (non-yield-bearing, backed by T-Bills) → Holder stakes and receives sAID (yield-bearing) → the staked funds are used for GPU/robotics financing agreements → SPC repayments flow back into GAIB → the value of sAID appreciates over time → investors burn sAID to redeem their principal and yield.

Risk Management Mechanisms

• Over-Collateralization — Financing pools maintain ~30% over-collateralization.

• Cash Reserves — ~5–7% of funds are allocated to independent reserve accounts for interest payments and default buffering.

• Credit Insurance — Cooperation with regulated insurers to partially transfer GPU provider default risk.

• Default Handling — In case of default, GAIB and underwriters may liquidate GPUs, transfer them to alternative operators, or place them under custodial management to continue generating cash flows. SPC’s bankruptcy isolation ensures each asset pool remains independent and unaffected by others.

In addition, the GAIB Credit Committee is responsible for setting tokenization standards, credit evaluation frameworks, and underwriter admission criteria. Using a structured risk analysis framework — covering borrower fundamentals, external environment, transaction structure, and recovery rates — it enforces due diligence and post-loan monitoring to ensure security, transparency, and sustainability of transactions.

Structured Risk Evaluation Framework (Illustrative reference only)

VI. On-Chain: AID Synthetic Dollar , sAID Yield Mechanism, and the Early Deposit Program

GAIB Dual-Token Model: AID Synthetic Stablecoin and sAID Yield-Bearing Certificate

GAIB introduces AID (AI Synthetic Dollar) — a synthetic asset backed by U.S. Treasury reserves. Its supply is dynamically linked to protocol capital:

• AID is minted when funds flow into the protocol.

• AID is burned when profits are distributed or redeemed.

This ensures that AID’s scale always reflects the underlying asset value. AID itself only serves as a stable unit of account and medium of exchange, without directly generating yield.

To capture yield, users stake AID to receive sAID. As a yield-bearing, transferable certificate, sAID appreciates over time in line with protocol revenues (GPU/robotics financing repayments, U.S. Treasury interest, etc.). Returns are reflected through the exchange ratio between sAID and AID. Holders automatically accumulate yield without any additional actions. At redemption, users can withdraw their initial principal and accrued rewards after a short cooldown period.

• AID provides stability and composability, making it suitable for trading, lending, and liquidity provision.

• sAID carries the yield property, both appreciating in value directly and supporting further composability in DeFi (e.g., splitting into PT/YT for risk-return customization).

In summary, AID + sAID form GAIB’s dual-token economic layer: AID ensures stable circulation and sAID captures real yield tied to AI infrastructure. This design preserves the usability of a synthetic asset while giving users a yield gateway linked to the AI infrastructure economy.

GAIB AID / sAID vs. Ethena USDe / sUSDe vs. Lido stETH

The relationship between AID and sAID is comparable to Ethena’s USDe / sUSDe and Lido’s ETH / stETH:

• The base asset (USDe, AID, ETH) itself is non-yield-bearing.

• Only after conversion to the yield-bearing version (sUSDe, sAID, stETH) does it automatically accrue yield.

The key difference lies in the yield source: sAID derives yield from GPU financing agreement + US Treasuries.  sUSDe yields come from derivatives hedging/arbitrage. and stETH yield comes from ETH staking.

AID Alpha: GAIB’s Liquidity Bootstrapping and Incentive Program (Pre-Mainnet)

Launched on May 12, 2025, AID Alpha serves as GAIB’s early deposit program ahead of the AID mainnet, designed to bootstrap liquidity while rewarding early participants through extra incentives and gamified mechanics. Initial deposits are allocated to U.S. Treasuries for safety, then gradually shifted into GPU financing transactions, creating a transition from low-risk → high-yield.

On the technical side, AID Alpha contracts follow the ERC-4626 standard, issuing AIDα receipt tokens (e.g., AIDaUSDC, AIDaUSDT) to represent deposits and ensure cross-chain composability.

During the Final Spice stage, GAIB expanded deposit options to multiple stablecoins (USDC, USDT, USR, CUSDO, USD1). Each deposit generates a corresponding AIDα token, which serves as proof of deposit, automatically tracks yield and counts toward the Spice points system, which enhances rewards and governance allocation.

Current AIDα Pools (TVL capped at $80M):

All AIDα deposits have a lock-up period of up to two months. After the campaign ends, users can choose to either convert their AIDα into mainnet AID and stake it as sAID to earn ongoing yields, or redeem their original assets while retaining the accumulated Spice points.

Spice is GAIB’s incentive point system launched during the AID Alpha phase, designed to measure early participation and allocate future governance rights. The rule is “1 USD = 1 Spice per day”, with additional multipliers from various channels (e.g., 10× for deposits, 20× for Pendle YT, 30× for Resolv USR), up to a maximum of 30×, creating a dual incentive model of “yield + points.”

In addition, a referral mechanism further amplifies rewards (Level 1: 20%, Level 2: 10%). After the Final Spice event concludes, all points will be locked and used for governance and reward distribution upon mainnet launch.

Fremen Essence NFT:  GAIB also issued 3,000 limited Fremen Essence NFTs as early supporter badges: Top 200 depositors automatically qualify.Remaining NFTs distributed via whitelist and minimum $1,500 deposit requirement. Minting is free (gas only).NFT holders gain exclusive mainnet rewards, priority product testing rights, and core community status. Currently, the NFTs are trading at around 0.1 ETH on secondary markets, with a total trading volume of 98 ETH.

VII. GAIB Transparency: On-Chain Funds and Off-Chain Assets

GAIB maintains a high standard of transparency across both assets and protocols.

• On-chain, users can track asset categories (USDC, USDT, USR, CUSDO, USD1), cross-chain distribution (Ethereum, Sei, Arbitrum, Base, etc.), TVL trends, and detailed breakdowns in real time via the official website, DefiLlama, and Dune dashboards.

• Off-chain, the official site discloses portfolio allocation ratios, active deal amounts, expected returns, and selected pipeline projects.

• GAIB Official Transparency Portal: https://aid.gaib.ai/transparency

• DefiLlama: https://defillama.com/protocol/tvl/gaib

• Dune: https://dune.com/gaibofficial

Asset Allocation Snapshot

As of October 7, 2025, GAIB manages a total of $175.29 million in assets. This “dual-layer allocation” balances stability with excess returns from AI infrastructure financing.

• Reserves account for 71% ($124.9M), mainly U.S. Treasuries, around 4% APY

Deployed assets account for 29% ($50.4M), allocated to off-chain GPU and robotics financing with an average 15% APY.

On-chain fund distribution: According to the latest Dune Analytics data, Ethereum holds 83.2% of TVL, Sei 13.0%, while Base and Arbitrum together make up less than 4%. By asset type, deposits are dominated by USDC (52.4%) and USDT (47.4%), with smaller allocations to USD1 (~2%), USR (0.1%), and CUSDO (0.09%).

Off-chain asset deployment: GAIB’s active deals are aligned with its capital allocation, including:

• Siam.AI (Thailand): $30M, 15% APY

• Two Robotics Financing deals: $15M combined, 15% APY

• US Neocloud Provider: $5.4M, 30% APY

In addition, GAIB has also established approximately $725M in projects pipeline reserves, with a broader total pipeline outlook of over $2.5B within 1–2 years:

• GMI Cloud and Nvidia Cloud Partners across Asia ($200M and $300M), Europe ($60M), and the UAE ($80M).

• North America Neocloud Providers ($15M and $30M).

• Robotics asset providers ($20M).

This pipeline lays a solid foundation for future expansion and scaling.

VIII. Ecosystem: Compute, Robotics, and DeFi

GAIB’s ecosystem consists of three pillars — GPU computing resources, robotics innovation enterprises, and DeFi protocol integrations — designed to form a closed-loop cycle of: Real Compute Assets → Financialization → DeFi Optimization.

GPU Compute Ecosystem: On-Chain Tokenization of Compute Assets

Within the on-chain financing ecosystem for AI infrastructure, GAIB partners with a diverse set of compute providers, spanning both sovereign/enterprise-level clouds (GMI, Siam.AI) and decentralized networks (Aethir, PaleBlueDot.AI). This ensures both operational stability and an expanded RWA narrative.

• GMI Cloud: One of NVIDIA’s six Global Reference Platform Partners, operating seven data centers across five countries, with ~$95M already financed. Known for low-latency, AI-native environments. With GAIB’s financing model, GMI’s GPU expansion gains enhanced cross-regional flexibility.

• Siam.AI: Thailand’s first sovereign-level NVIDIA Cloud Partner. Achieves up to 35x performance improvement and 80% cost reduction in AI/ML and rendering workloads. Completed a $30M GPU tokenization deal with GAIB, marking GAIB’s first GPU RWA case and securing first-mover advantage in Southeast Asia.

• Aethir: A leading decentralized GPUaaS network with 40,000+ GPUs (incl. 3,000+ H100s). In early 2025, GAIB and Aethir jointly conducted the first GPU tokenization pilot on BNB Chain — raising $100K in 10 minutes. Future integrations aim to connect AID/sAID with Aethir staking, creating dual-yield opportunities.

• PaleBlueDot.AI: An emerging decentralized GPU cloud provider, adding further strength to GAIB’s DePIN narrative.

Robotics Ecosystem: On-Chain Financing of Embodied Intelligence

GAIB has formally entered the Embodied AI (robotics) sector, extending the GPU tokenization model into robotics. The aim is to create a dual-engine ecosystem of Compute + Robotics, using SPV collateral structures and cash flow distribution. By packaging robotics and GPU returns into AID/sAID, GAIB enables the financialization of both hardware and operations.

To date, GAIB has allocated $15M on robotics financing deals aiming at generating ~15% APY, together with partners including OpenMind, PrismaX, CAMP, Kite, and SiamAI Robotics, spanning hardware, data streams, and supply chain innovations.

• PrismaX: Branded as “Robots as Miners”, PrismaX connects operators, robots, and data buyers through a teleoperation platform. It produces high-value motion and vision data priced at $30–50/hour, and has validated early commercialization with a $99-per-session paid model. GAIB provides financing to scale robot fleets, while data sales revenues are funneled back to investors via AID/sAID — creating a data-centric financialization pathway.

• OpenMind: With its FABRIC network and OM1 operating system, OpenMind offers identity verification, trusted data sharing, and multimodal integration — effectively acting as the “TCP/IP” of robotics. GAIB tokenizes task and data contracts to provide capital support. Together, the two achieve a complementary model of technical trustworthiness + financial assetization, enabling robotics assets to move from lab experiments to scalable, financeable, and verifiable growth.

Overall, through PrismaX’s data networks, OpenMind’s control systems, and CAMP’s infrastructure deployment, GAIB is building a full-stack ecosystem covering robotics hardware, operations, and data value chains — accelerating both the industrialization and financialization of embodied intelligence.

DeFi Ecosystem: Protocol Integrations and Yield Optimization

During the AID Alpha stage, GAIB deeply integrated AID/aAID assets into a broad range of DeFi protocols. By leveraging yield splitting, liquidity mining, collateralized lending, and yield boosting, GAIB created a cross-chain, multi-layered yield optimization system, unified under the Spice points incentive framework.

• Pendle: Users split AIDaUSDC/USDT into PT (Principal Tokens) and YT (Yield Tokens). PTs deliver ~15% fixed yield; YTs capture future yield and carry a 30x Spice bonus. LP providers also earn 20x Spice.

• Equilibria & Penpie: Pendle yield enhancers. Equilibria adds ~5% extra yield, while Penpie boosts up to 88% APR. Both carry 20x Spice multipliers.

• Morpho: Enables PT-AIDa to be used as collateral for borrowing USDC, giving users liquidity while retaining positions, and extending GAIB into Ethereum’s major lending markets.

• Curve: AIDaUSDC/USDC liquidity pool provides trading fee income plus a 20x Spice boost, ideal for conservative strategies.

• CIAN & Takara (Sei chain): Users collateralize enzoBTC with Takara to borrow stablecoins, which CIAN auto-deploys into GAIB strategies. This combines BTCfi with AI yield, with a 5x Spice multiplier.

• Wand (Story Protocol): On Story chain, Wand provides a Pendle-like PT/YT split for AIDa assets, with YTs earning 20x Spice, further enhancing cross-chain composability of AI yield.

In summary, GAIB’s DeFi integration strategy spans Ethereum, Arbitrum, Base, Sei, Story Protocol, BNB Chain, and Plume Network. Through Pendle and its ecosystem enhancers (Equilibria, Penpie), lending markets (Morpho), stablecoin DEXs (Curve), BTCfi vaults (CIAN + Takara), and native AI-narrative protocols (Wand), GAIB delivers comprehensive yield opportunities — from fixed income to leveraged yield, and from cross-chain liquidity to AI-native strategies.

IX. Team Background and Project Financing

The GAIB team unites experts from AI, cloud computing, and DeFi, with backgrounds spanning L2IV, Huobi, Goldman Sachs, Ava Labs, and Binance Labs. Core members hail from top institutions such as Cornell, UPenn, NTU, and UCLA, bringing deep experience in finance, engineering, and blockchain infrastructure. Together, they form a strong foundation for bridging real-world AI assets with on-chain financial innovation.

Kony Kwong — Co-Founder & CEOKony brings cross-disciplinary expertise in traditional finance and crypto venture capital. He previously worked as an investor at L2 Iterative Ventures and managed funds and M&A at Huobi. Earlier in his career, he held roles at CMB International, Goldman Sachs, and CITIC Securities. He holds a First-Class Honors degree in International Business & Finance from the University of Hong Kong and a Master’s in Computer Science from the University of Pennsylvania. Observing the lack of financialization (“-fi”) in AI infrastructure, Kony co-founded GAIB to transform real compute assets such as GPUs and robotics into investable on-chain products.

Jun Liu — Co-Founder & CTOJun has a dual background in academic research and industry practice, focusing on blockchain security, crypto-economics, and DeFi infrastructure. He previously served as VP at Sora Ventures, Technical Manager at Ava Labs (supporting BD and smart contract auditing), and led technical due diligence for Blizzard Fund. He holds dual bachelor’s degrees in Computer Science and Electrical Engineering from National Taiwan University and pursued a PhD in Computer Science at Cornell University, contributing to IC3 blockchain research. His expertise lies in building secure and scalable decentralized financial architectures.

Alex Yeh — Co-Founder & AdvisorAlex is also the founder and CEO of GMI Cloud, one of the world’s leading AI-native cloud service providers and one of NVIDIA’s six Reference Platform Partners. Alex has a background in semiconductors and AI cloud, manages the Realtek family office, and previously held positions at CDIB and IVC.  At GAIB, Alex spearheads industry partnerships, bringing GMI’s GPU infrastructure and client networks into the protocol to drive the financialization of AI infra assets.

Financing

• In December 2024, GAIB closed a $5M Pre-Seed round led by Hack VC, Faction, and Hashed, with participation from The Spartan Group, L2IV, CMCC Global, Animoca Brands, IVC, MH Ventures, Presto Labs, J17, IDG Blockchain, 280 Capital, Aethir, NEAR Foundation, and other notable institutions, along with several industry and crypto angel investors.

In July 2025, GAIB raised an additional $10M in strategic investment, led by Amber Group with participation from multiple Asian investors. The funds will be used to accelerate GPU asset tokenization, expand infrastructure and financial products, and deepen strategic collaborations across the AI and crypto ecosystems, strengthening institutional participation in on-chain AI infrastructure.

X. Conclusion: Business Logic and Potential Risks

Business LogicGAIB’s core positioning is RWAiFi — transforming AI infrastructure assets (GPUs, robotics, etc.) into composable financial products through tokenization. The business logic is built on three layers:

• Asset Layer: GPUs and robotics have the combined characteristics of high-value hardware + predictable cash flows, aligning with RWA requirements. GPUs, with standardization, clear residual value, and strong demand, are the most practical entry point. Robotics represent a longer-term direction, with monetization via teleoperation, data collection, and RaaS models.

• Capital Layer: Through a dual-token structure of AID (for stable settlement, non-yield-bearing, backed by T-Bills) and sAID (a yield-bearing fund token underpinned by a financing portfolio plus T-Bills), GAIB separates stable circulation from yield capture. It further unlocks yield and liquidity through DeFi integrations such as PT/YT (Principal/ Yield Tokens), lending, and LP liquidity.

• Ecosystem Layer: Partnerships with GMI, Siam.AI (sovereign-level GPU clouds), Aethir(decentralized GPU networks), and PrismaX, OpenMind (robotics innovators) build a cross-industry network spanning hardware, data, and services, advancing the Compute + Robotics dual-engine model.

Core Mechanisms

• Financing Models: Debt (10–20% APY), revenue share (60–80%+), or hybrid, with short tenors (3–36 months) and rapid payback cycles.

• Credit & Risk Management: Over-collateralization (~30%), cash reserves (5–7%), credit insurance, and default handling (GPU liquidation/custodial operations), alongside third-party underwriting and due diligence, supported by internal credit rating systems.

• On-Chain Mechanisms: AID minting/redemption and sAID yield accrual, integrated with Pendle, Morpho, Curve, CIAN, Wand, and other protocols for cross-chain, multi-dimensional yield optimization.

• Transparency: Real-time asset and cash flow tracking provided via the official site, DefiLlama, and Dune ensures clear correspondence between off-chain financing and on-chain assets.

Potential RisksDespite GAIB’s transparent design (AID, sAID, AID Alpha, GPU Tokenization, etc.), underlying risks remain, and investors must carefully assess their own risk tolerance:

• Market & Liquidity Risks: GPU financing returns and digital asset prices are subject to volatility, with no guaranteed returns. Lockups may create liquidity challenges or discounted exits under adverse market conditions.

• Credit & Execution Risks: Financing often involves SMEs, which face higher default risk. Recovery depends heavily on off-chain enforcement — weak execution may directly affect investor repayments.

• Technical & Security Risks: Smart contract vulnerabilities, hacking, oracle manipulation, or key loss could cause asset losses. Deep integration with external DeFi protocols (e.g., Pendle, Curve) boosts TVL growth but also introduces external security and liquidity risks.

• Asset-Specific & Operational Risks: GPUs benefit from standardization and residual markets, but robotics assets are non-standard, highly operationally dependent, and vulnerable to regulatory differences across jurisdictions.

• Compliance & Regulatory Risks: The computing power assets invested in by GAIB belong to a new market and asset class that does not fall under the scope of traditional financial licensing. This could lead to regional regulatory challenges, including potential restrictions on business operations, asset issuance, and usage.

DisclaimerThis report was produced with the assistance of ChatGPT-5 AI tools. The author has carefully proofread and ensured accuracy, but errors or omissions may remain. Importantly, crypto assets often exhibit divergence between project fundamentals and secondary market token performance. This content is provided for informational and academic/research purposes only, and does not constitute investment advice or a recommendation to buy or sell any token.

一、AI 资产RWA化的展望

在 RWA 化的讨论中，市场普遍认为 美债、美股、黄金等标准资产 将长期占据核心地位。这类资产流动性深、估值透明、合规路径明确，是链上“无风险利率”的天然载体。

相比之下，非标资产 RWA 化 面临更大不确定性。碳信用、私募信贷、供应链金融、房地产及基础设施虽具备庞大市场规模，但普遍存在估值不透明、执行难度大、周期过长和政策依赖性强等问题。其真正挑战不在于代币化本身，而在于如何有效约束链下资产的执行力，尤其是违约后的处置与回收，仍需依赖尽调、贷后管理和清算环节。

尽管如此，RWA 化依然具有积极意义：（1）链上合约与资产池数据公开透明，避免“资金池黑箱”；（2）收益结构更为多元，除利息外，还可通过 Pendle PT/YT、代币激励及二级市场流动性实现叠加收益；（3）投资人通常通过 SPC 结构持有证券化份额，而非直接债权，从而具备一定破产隔离效果。

在 AI 算力资产中，GPU等算力硬件 因具备残值明确、标准化程度高以及需求旺盛，被普遍视为 RWA 化的首要切入点。围绕算力层，还可以进一步延伸至 算力租赁合同（Compute Lease），其现金流模式具备合同化与可预测性，适合证券化。

在算力资产之后，机器人硬件与服务合同 同样具备 RWA 化潜力。人形或专用机器人作为高价值设备，可通过融资租赁合同映射至链上；但机器人资产高度依赖运营与维护，其落地难度显著比GPU更高。

此外，数据中心与能源合同 也是值得关注的方向。前者包括机柜租赁、电力与带宽合同，属于相对稳定的基础设施现金流；后者则以绿色能源 PPA 为代表，不仅提供长期收益，还兼具 ESG 属性，符合机构投资者需求。

总体而言，AI 资产的 RWA 化可以分为几个层次：短期以内以 GPU 等算力硬件与算力合同为核心；中期则扩展至数据中心与能源合同；而长期来看，机器人硬件与服务合同有望在特定场景中实现突破。其共同逻辑均围绕 高价值硬件 + 可预测现金流，但落地路径存在差异。

AI 资产 RWA 化的潜在方向

二、GPU资产RWA化的优先价值

在众多非标AI资产当中，GPU 或许是相对更具探索价值的方向之一：

• 标准化与残值明确：主流 GPU 型号具备清晰的市场定价，且残值较为明确。

• 二手市场活跃：具备再流通性，违约时仍可实现部分回收；

• 真实生产力属性：GPU 与AI产业需求直接挂钩，具有现金流生成能力。

• 叙事契合度高：结合 AI 与 DeFi 的双重市场热点，易于获得投资者关注。

由于 AI 算力数据中心属于极为新兴的行业，传统银行往往难以理解其运营模式，因此无法提供贷款支持。只有像 CoreWeave、Crusoe 这类大型企业，才能从 Apollo 等大型私募信贷机构获得融资，而中小型企业则被排除在外，服务于中小企业的融资通道迫在眉睫。

需要指出的是，GPU RWA 并不能消除信用风险。资质优良的企业通常可通过银行以更低成本融资，不一定需要上链；而选择代币化融资的多为中小企业，违约风险更高。这也导致了 RWA 的结构性悖论：优质资产方不需要上链，而风险更高的借款人更倾向参与。

尽管如此，相较传统融资租赁，GPU 的 高需求、可回收性和残值明确 使其风险收益特征更具优势。RWA 化的意义并非消灭风险，而是让风险更加透明、可定价与可流动化。GPU 作为非标资产 RWA 的代表，具备产业价值与探索潜力，但其成败最终仍取决于链下资质审查与执行能力，而非单纯的链上设计。

三、机器人资产RWA化的前沿探索

在 AI 硬件之外，机器人产业也正逐步进入 RWA 化的视野。预计到 2030 年，市场规模将突破 1,850 亿美元，发展潜力巨大。随着 工业 4.0 的到来，智能自动化与人机协作的新时代正加速到来，未来几年内，机器人将在工厂、物流、零售乃至家庭等场景中广泛落地。通过结构化的链上融资机制，加速智能机器人的部署与普及，同时为普通用户创造可参与这一产业变革的投资入口。其可行路径主要包括：

• 机器人硬件融资：为生产与部署提供资金，回报来自租赁、销售或 Robot-as-a-Service（RaaS） 模式下的运营收入；现金流通过 SPC 结构与保险覆盖映射到链上，降低违约与处置风险。

• 数据流金融化：Embodied AI 模型需要大规模真实世界数据，可为传感器部署和分布式采集网络提供资金，并将数据使用权或授权收入 Token 化，赋予投资人分享未来数据价值的渠道。

• 生产与供应链融资：机器人产业链长，涉及零部件、产能与物流。通过贸易融资释放营运资金，并将未来的货物流与现金流映射到链上。

相较于 GPU 资产，机器人资产 更依赖运营与场景落地，现金流波动也更受利用率、维护成本和法规约束的影响。因此，建议采取 期限更短、超额抵押与储备金更高的交易结构确保稳定收益与流动性安全。

四、GAIB 协议：链下AI资产与链上DeFi 的经济层

AI 资产的 RWA 化正从概念走向落地。GPU 已成为最具可行性的链上化资产，而机器人融资代表更长期的增长方向。要让这些资产真正具备金融属性，关键在于构建一个能承接链下融资、生成收益凭证并连接 DeFi 流动性的经济层。

GAIB 正是在此背景下诞生，它并非将AI硬件直接代币化，而是将企业级GPU或机器人作为抵押的融资合同上链，构建起连接链下现金流与链上资本市场的经济桥梁。在链下，由云服务商与数据中心购置并使用的企业级 GPU 集群或机器人资产作为抵押物；在链上，AID 用于稳定计价与流动性管理（非生息，T-Bills 全额储备）；sAID 用于收益敞口与自动累计（底层为融资组合 + T-Bills）。

GAIB的链下融资模式GAIB 与全球云服务商及数据中心合作，以 GPU 集群为抵押，设计三类融资协议：

• 债务模式：支付固定利息（年化 ~10–20%）；

• 股权模式：分享 GPU或机器人收入（年化 ~60–80%+）；

• 混合模式：利息 + 收入分成。

GAIB 的风险管理机制建立在 实体 GPU 的超额抵押与破产隔离法律结构 之上，确保在违约情况下能够通过清算 GPU 或托管至合作数据中心继续产生现金流。由于企业级 GPU 回本周期短，整体期限显著低于传统债务产品，融资期限通常为 3–36 个月。GAIB 与第三方信用承销机构、审计方和托管方合作，严格执行尽调与贷后管理，并以国债储备作为补充流动性保障。

链上机制

• 铸造与赎回：通过合约，合格用户（Whitelist + KYC）可用稳定币铸造 AID，或以 AID 赎回稳定币。此外对于非KYC用户亦可通过二级市场交易获得。

• 质押与收益：用户可将 AID 质押为 sAID，后者自动累积收益，价值随时间升值。

• 流动性池：GAIB 将在主流 AMM 部署 AID 流动性池，用户可用稳定币兑换 AID。

• DeFi 场景：

  • 借贷：AID 可接入借贷协议，提升资本效率；

  • 收益交易：sAID 可拆分为 PT/YT，支持多元风险收益策略；

  • 衍生品：AID 与 sAID 作为底层收益资产，支持期权、期货等衍生品创新；

  • 定制化策略：接入 Vault 与收益优化器，实现个性化资产配置。

总之， GAIB 的核心逻辑是通过 GPU+机器人资产+国债资产的融资与代币化，将链下真实现金流转化为链上可组合资产；再通过 AID/sAID 与 DeFi 协议 形成收益、流动性与衍生品市场。这一设计兼具实体资产支撑与链上金融创新，为 AI 经济与加密金融之间搭建了可扩展的桥梁。

五、链下：GPU资产代币化标准及风险管理机制

GAIB 通过 SPC（Segregated Portfolio Company） 结构，将链下 GPU 融资协议转化为链上可流通的收益凭证。投资者投入稳定币后，将获得等值的 AI 合成美元（AID），可用于参与 GAIB 生态。当投资者质押并获得质押资产 sAID 后，即可分享来自 GAIB GPU 与机器人融资项目的收益。随着底层还款流入协议，sAID 的价值持续增长，投资者最终可通过销毁代币赎回本金与收益，从而实现链上资产与真实现金流的一对一映射。

代币化标准与运作流程：

GAIB 要求资产具备完善的抵押与担保机制，融资协议需包含 月度监控、逾期阈值、超额抵押合规 等条款，并限定承销方需有 ≥2 年放贷经验及完整数据披露。流程上，投资者存入稳定币 → 智能合约铸造 AID（非生息，T-Bills 储备） → 持有人质押并获得 sAID（收益型） → 质押资金用于 GPU/机器人融资协议 → SPC 还款流入 GAIB → sAID 价值随时间增长 → 投资者销毁 sAID 赎回本金与收益。

风险管理机制：

1. 超额抵押 —— 融资池资产通常保持约 30% 的超额抵押率。

2. 现金储备 —— 约 5–7% 的资金被划入独立储备账户，用于利息支付与违约缓冲。

3. 信用保险 —— 通过与合规保险机构合作，部分转移 GPU Provider 的违约风险。

4. 违约处置 —— 若违约发生，GAIB 与承销方可选择清算 GPU、转移至其他运营商或托管继续产生现金流。SPC 的破产隔离结构确保各资产池之间独立，不受连带影响。

此外，GAIB 信用委员会负责制定 代币化标准、信用评估框架与承销准入门槛，并基于结构化风险分析框架（涵盖借款人基本面、外部环境、交易结构与回收率）实施尽调和贷后监控，确保交易的 安全性、透明度与可持续性。

结构化风险评估框架（仅供参考示例）

六、链上：AID合成美金、sAID 收益机制及Alpha存款计划

GAIB 双币模型：AID 合成美金与 sAID 流动性收益凭证

GAIB 推出的 AID（AI Synthetic Dollar） 是一种以美债储备为支撑的合成美金。其供应与协议资本动态挂钩：资金流入协议时铸造 AID，收益分配或赎回时销毁 AID，从而确保其规模与底层资产价值保持一致。AID 本身仅承担稳定计价与流通职能，并不直接产生收益。

为了获取收益，用户需要将 AID 质押转换为 sAID。sAID 作为一种可流通的收益凭证，其价值会随协议层的真实收益（GPU/机器人融资回款、美债利息等）逐步升值。收益通过 sAID/AID 的兑换比率 体现，用户无需额外操作，只需持有 sAID 即可自动累积收益。在赎回时，用户可经过冷却期取回初始本金与累计奖励。

从功能上看，AID 提供 稳定性与可组合性，可被用于交易、借贷、流动性提供；而 sAID 承载 收益属性，既可直接增值，也可进一步进入 DeFi 协议拆分为 本金与收益代币（PT/YT），满足不同风险偏好的投资者需求。

总体而言，AID 与 sAID 构成了 GAIB 经济层的核心双币结构：AID 保障稳定流通，sAID 捕捉真实收益。这种设计既保持了合成稳定币的可用性，又为用户提供了与 AI 基础设施经济挂钩的收益入口。

GAIB AID / sAID vs Ethena USDe / sUSDe vs Lido stETH 收益模式对比

AID 与 sAID 的关系，可类比 Ethena 的 USDe / sUSDe 以及 Lido 的 ETH / stETH：前者作为合成美元本身不产生收益，只有在转换为 sToken 后才能自动累积收益。不同点在于，sAID 的收益来源于 GPU 融资合同与美债，sUSDe 的收益来自 衍生品对冲，而 stETH 则依托于 ETH Staking。

AID Alpha：GAIB 主网前的流动性启动与积分激励机制

AID Alpha 于 2025 年 5 月 12 日正式上线，作为 AID 主网前的流动性启动阶段（Early Deposit Program），旨在通过早期存款引导协议资金，同时给予参与者额外奖励与游戏化激励。所有存款初期将进入美债（T-Bills）以确保安全性，随后逐步配置至 GPU 融资交易，形成从“低风险—高收益”的过渡路径。

技术层面，AID Alpha 智能合约遵循 ERC-4626 标准，用户每存入一美元稳定币或合成稳定币，都会获得对应链上的 AIDα 收据 Token（如 AIDaUSDC、AIDaUSDT），保证跨链一致性与可组合性。

在 Final Spice 阶段，GAIB 通过 AIDα 机制开放了多元化的稳定币入口，包括 USDC、USDT、USR、CUSDO 以及 USD1。用户存入稳定币后，会获得对应的 AIDα 收据 Token（如 AIDaUSDC、AIDaUSD1），该 Token 即代表存款凭证，并自动计入 Spice 积分体系，可进一步参与 Pendle、Curve 等 DeFi 组合玩法。

截至目前，AIDα 总存款规模已触及 $80M 上限，AIDα 资产池明细如下：

所有 AIDα 存款均设有不超过两个月的锁定期，活动结束后，用户可选择将 AIDα 兑换为主网 AID 并质押成 sAID享受持续收益，也可直接赎回原始资产，同时保留累积的 Spice 积分。Spice 是 GAIB 在 AID Alpha 阶段推出的积分体系，用于衡量早期参与度与分配未来治理权。其规则为“1 USD = 1 Spice/天”，并叠加多渠道倍数（如存款 10x、Pendle YT 20x、Resolv USR 30x），最高可达 30 倍，形成“收益 + 积分”的双重激励。此外，推荐机制进一步放大收益（一级 20%、二级 10%），Final Spice 结束后积分将被锁定，用于主网上线时的治理与奖励分配。

此外，GAIB 发行了 3,000 枚限量版 Fremen Essence NFT，作为早期支持者的专属凭证。前 200 名大额存款者享有保留名额，其余名额则通过白名单及 $1,500+ 存款资格分配。NFT 可 免费铸造（仅需支付 Gas 费），持有者将获得主网上线时的专属奖励、产品优先测试权及核心社区身份。目前，该 NFT 在二级市场的价格约为 0.1 ETH，累计交易量已达 98 ETH。

七、GAIB 链上资金与链下资产透明度

GAIB 在资产与协议透明度方面保持高标准，用户可通过官网、DefiLlama 与 Dune 实时追踪其链上资产类别（USDC、USDT、USR、CUSDO、USD1）、跨链分布（Ethereum、Sei、Arbitrum、Base等）、TVL趋势及明细；同时，官网还披露了链下底层资产的配置比例、在投项目(Active Deals)金额、预期收益及管道项目(Selected Pipeline)情况。

• GAIB官方网站：https://aid.gaib.ai/transparency

• Defillama：https://defillama.com/protocol/tvl/gaib

• Dune：https://dune.com/gaibofficial

截至 2025 年 10 月，GAIB 管理资产总规模约 $175.29M，“双层配置”既兼顾稳健性，又带来 AI Infra 融资的超额回报。

• 储备资产（Reserves）占 71%，约 $124.9M，主要为美债，预期年化收益约 4%；

• 已部署资产（Deployed）占 29%，约 $50.4M，用于链下 GPU 与机器人融资项目，平均年化收益约 15%。

链上资金分布方面，根据 Dune 最新数据，跨链分布上，Ethereum 占比 83.2%，Sei 占 13.0%，Base 与 Arbitrum 合计不足 4%。按资产结构计算，资金主要来自 USDC（52.4%）与USDT（47.4%），其余为 USD1（~2%）、USR（0.1%）、CUSDO（0.09%）。

链下资产分布方面，GAIB 在投项目与资金部署保持一致，已包括泰国 Siam.AI（$30M，15% APY）、两笔 Robotics Financing（合计 $15M，15% APY）以及美国 US Neocloud Provider（$5.4M，30% APY）。与此同时，GAIB 还建立了约 $725M 的项目储备，更广义的总项目储备展望为 $2.5B+ / 1–2 年，覆盖 GMI Cloud 及多地区的 Nvidia Cloud Partners（亚洲 $200M 与 $300M、欧洲 $60M、阿联酋 $80M）、北美 Neocloud Providers（$15M 与 $30M），以及机器人资产提供方（$20M），为后续扩张与放量奠定坚实基础。

八、生态体系：算力、机器人与 DeFi

GAIB 的生态体系由 GPU 计算资源、机器人创新企业以及 DeFi 协议集成三大部分构成，旨在形成“真实算力资产 → 金融化 → DeFi 优化”的完整闭环。

GPU 计算生态资源：算力资产上链

在 AI 基础设施的链上融资生态中，GAIB 已与多类算力服务商合作，覆盖主权级/企业级云（GMI、Siam.AI） 与 去中心化网络（Aethir、PaleBlueDot.AI），既保证算力稳定性，也拓展了 RWA 的叙事空间。

• GMI Cloud：NVIDIA 全球 6 家 Reference Platform Partner 之一，运营 7 个数据中心、5 个国家，已融资约 $95M。以低延迟、AI 原生环境见长。通过 GAIB 的融资模式，其 GPU 扩张具备更强的跨区域弹性。

• Siam.AI：泰国首家主权级 NVIDIA Cloud Partner，在 AI/ML 与渲染场景中性能最高提升 35x、成本下降 80%。已与 GAIB 完成 $30M GPU Tokenization，为 GAIB 首单 GPU RWA 案例，奠定其在东南亚市场的先发优势。

• Aethir：领先的去中心化 GPUaaS 网络，规模 40,000+ GPU（含 3,000+ H100）。2025 年初与 GAIB 在 BNB Chain 联合完成 首批 GPU Tokenization 试点，10 分钟完成 $100K 融资。未来将探索 AID/sAID 与 Aethir staking 打通，形成双重收益。

• PaleBlueDot.AI：新兴去中心化 GPU 云，其参与强化了 GAIB 的 DePIN 叙事。

机器人生态：具身智能的链上融资

GAIB 已正式切入具身智能（Embodied AI）赛道，正将 GPU Tokenization 模式延伸至机器人产业，构建“Compute + Robotics”双引擎生态，以 SPV 抵押结构和现金流分配为核心，并通过 AID/sAID 将机器人与 GPU 收益打包，实现硬件和运营的链上金融化。目前已部署合计 1,500 万美元的机器人融资，预期年化收益率约 15%，合作伙伴包括 OpenMind、PrismaX、CAMP、Kite 及 SiamAI Robotics，覆盖硬件、数据流和供应链的多维创新。

• PrismaX：PrismaX 的定位是“机器人即矿机”，通过遥操作平台连接操作员、机器人与数据需求方，生成高价值的动作与视觉数据，单价约 30–50 美元/小时，并已通过 $99/次的付费模式验证早期商业化。GAIB 为其提供融资以扩展机器人规模，数据出售收益则通过 AID/sAID 回流投资人，形成以数据采集为核心的金融化路径。

• OpenMind：OpenMind 则以 FABRIC 网络与 OM1 操作系统提供身份认证、可信数据共享和多模态集成，相当于行业“TCP/IP”。GAIB 将这些任务与数据合同资产化上链，为其提供资本支持。双方结合实现“技术可信性 + 金融资产化”的互补，使机器人资产从实验室阶段走向可融资、可迭代、可验证的规模化发展。

整体而言，GAIB 通过与 PrismaX 的数据网络、OpenMind 的控制系统及 CAMP 的基础设施部署协作，逐步构建覆盖机器人硬件、运营与数据价值链的完整生态，加速具身智能的产业化与金融化。

DeFi 生态：协议集成与收益优化

在 AID Alpha 阶段，GAIB 将 AID/aAID 资产与多类 DeFi 协议深度集成，通过 收益拆分、流动性挖掘、抵押借贷与收益增强 等方式，形成了跨链、多元的收益优化体系，并以 Spice 积分 作为统一激励。

• Pendle：用户可将 AIDaUSDC/USDT 分拆为 PT（本金 Token）与 YT（收益 Token）。PT 提供约 15% 固定收益，YT 则承载未来收益并享有 30 倍积分加成，LP 流动性提供者可获得 20 倍积分。

• Equilibria 与 Penpie：作为 Pendle 的收益增强器，前者可在原有收益上额外提升 ~5%，后者最高可达 88% APR，两者均叠加 20 倍积分放大。

• Morpho：支持将 PT-AIDa 作为抵押物借出 USDC，赋予用户在保持仓位的同时获取流动性的能力，并拓展至以太坊主流借贷市场。

• Curve：AIDaUSDC/USDC 流动性池可获取交易费收益，同时获得 20 倍积分，适合偏好稳健策略的参与者。

• CIAN & Takara（Sei 链）：用户可将 enzoBTC 抵押于 Takara 借出稳定币，再经 CIAN 智能金库自动注入 GAIB 策略，实现 BTCfi 与 AI Yield 的结合，并享有 5 倍积分加成。

• Wand（Story Protocol）：在 Story 链上，Wand 为 AIDa 资产提供类似 Pendle 的 PT/YT 拆分结构，YT Token 可获得 20 倍积分，进一步强化了 AI Yield 的跨链组合性。

整体来看，GAIB 的 DeFi 集成策略涵盖 Ethereum、Arbitrum、 Base、Sei 与 Story Protocol、 BNB Chain和Plume Network等公链，通过 Pendle 及其生态增强器（Equilibria、Penpie）、借贷市场（Morpho）、稳定币 DEX（Curve）、BTCfi 金库（CIAN + Takara）、以及原生 AI 叙事的 Wand 协议，实现了从固定收益、杠杆收益到跨链流动性的全方位覆盖。

九、团队背景及项目融资

GAIB 团队汇聚了来自 AI、云计算与 DeFi 领域的专家，核心成员曾任职于 L2IV、火币、 高盛、Ava Labs 与 Binance Labs 等机构。团队成员毕业于康奈尔大学、宾夕法尼亚大学、南洋理工大学与加州大学洛杉矶分校，具备深厚的金融、工程与区块链基础设施经验，共同构建起连接真实世界 AI 资产与链上金融创新的坚实基础。

Kony Kwong 为 GAIB 联合创始人兼 CEO，具备传统金融与加密风投的跨界经验。曾任 L2 Iterative Ventures 投资人，并在 Huobi M&A 负责基金管理与并购，早年就职于招银国际、高盛、中信证券等机构。毕业于香港大学国际商务与金融学（一等荣誉），并获宾夕法尼亚大学计算机科学硕士学位。他认为 AI 基础设施缺乏金融化（“-fi”）环节，因此创立 GAIB，将 GPU 与机器人等真实算力资产转化为链上可投资产品。

Jun Liu 为 GAIB 联合创始人兼 CTO，兼具学术研究与产业实践背景，专注于区块链安全、加密经济学与 DeFi 基础设施。曾任 Sora Ventures 副总裁，亦在 Ava Labs 担任技术经理，支持 BD 团队并负责智能合约审计，同时在 Blizzard Fund 主导技术尽调工作。本科毕业于台湾大学计算机科学与电机工程双学位，后于康奈尔大学攻读计算机科学博士并参与 IC3 区块链研究。他的专长在于构建安全可扩展的去中心化金融架构。

Alex Yeh 为 GAIB 联合创始人及顾问，同时担任 GMI Cloud 创始人兼 CEO。GMI Cloud 是全球领先的 AI 原生云计算服务商之一，并获选为 6 家 NVIDIA Reference Platform Partner 之一。Alex 拥有半导体与 AI Cloud 背景，管理Realtek 家族办公室，并曾在 CDIB与IVC 任职。在 GAIB，他主要负责产业合作，将 GMI 的 GPU 基础设施与客户网络引入协议，推动 AI Infra 资产的金融化落地。

2024 年 12 月，GAIB 完成 500 万美元 Pre-Seed 融资，由 Hack VC、Faction、Hashed 领投，参投方包括 The Spartan Group、L2IV、CMCC Global、Animoca Brands、IVC、MH Ventures、Presto Labs、J17、IDG Blockchain、280 Capital、Aethir、NEAR Foundation 等知名机构，以及多位产业与加密领域的天使投资人。随后在 2025 年 7 月，GAIB 又获得 1,000 万美元战略投资，由 Amber Group 领投，多家亚洲投资者跟投。此次资金将重点用于 GPU 资产 Token 化，进一步推动 GAIB 基础设施完善、GPU 金融化产品扩展，并深化与 AI 和加密生态的战略合作，强化机构在链上 AI 基础设施中的参与度。

十、总结：商业逻辑及潜在风险

商业逻辑：GAIB 的核心定位是 RWAiFi，即将 AI 基础设施资产（GPU、机器人等）通过链上化的方式转化为可组合的金融产品，形成 “真实资产 → 现金流证券化 → DeFi 优化” 的闭环。其商业逻辑建立在三点：

1. 资产端：GPU 与机器人具备“高价值硬件 + 可预测现金流”的特性，符合 RWA 化的基本要求。GPU 因标准化、残值明确与需求旺盛，成为当前最现实的切入点；机器人则代表更长期的探索方向，依托遥操作、数据采集与 RaaS 模式逐步实现现金流上链。

2. 资金端：通过 AID（稳定结算、非生息、T-Bills 储备） 与 sAID（收益型基金代币，底层为融资组合 + T-Bills） 的双层结构，GAIB 实现稳定流通与收益捕获分离。并通过 PT/YT、借贷、LP 流动性等 DeFi 集成释放收益与流动性。

3. 生态端：与 GMI、Siam.AI 等主权级 GPU 云，Aethir等去中心化网络，以及 PrismaX、OpenMind 等机器人公司合作，建立跨硬件、数据与服务的产业网络，推动“Compute + Robotics”双引擎发展。

此外GAIB 采用 SPC（Segregated Portfolio Company）结构 将链下融资协议转化为链上收益凭证。核心机制包括：

• 融资模式：债务（10–20% APY）、收益分成（60–80%+）、混合结构，期限短（3–36 个月），回本周期快。

• 信用与风控：通过超额抵押（约 30%）、现金储备（5–7%）、信用保险与违约处置（GPU 清算/托管运营）保障安全性；并配合第三方承销与尽调，建立内部信用评级体系。

• 链上机制：AID 铸造/赎回与 sAID 收益累积，结合 Pendle、Morpho、Curve、CIAN、Wand 等协议，实现跨链、多维度的收益优化。

• 透明度：官网、DefiLlama 与 Dune 提供实时资产与资金流追踪，确保链下融资与链上资产对应关系清晰。

潜在风险：GAIB 及其相关产品（AID、sAID、AID Alpha、GPU Tokenization 等）在设计上通过链上透明化提升了收益可见性，但其底层风险依然存在，投资者需充分评估自身风险承受能力谨慎参与：

• 市场与流动性风险：GPU 融资收益和数字资产价格均受市场波动影响，回报并无保证；产品存在锁定期，若市场环境恶化投资者可能面临流动性不足或折价退出的风险。

• 信用与执行风险：GPU 与机器人融资多涉及中小企业，违约概率相对更高；资产回收高度依赖链下执行力，若处置不畅，将直接影响投资人回款。

• 技术与安全风险：智能合约漏洞、黑客攻击、预言机操纵或私钥遗失，均可能造成资产损失；与第三方 DeFi 协议（如 Pendle、Curve 等）的深度绑定，虽能提升 TVL 增长，但也引入了外部协议的安全与流动性风险。

• 资产特性与运营风险：GPU 具备标准化和残值市场，而机器人资产非标准化程度高，运营依赖利用率与维护；跨区域扩张中，机器人资产尤其容易受到法规差异和政策不确定性影响。

• 合规与监管风险：GAIB 投资的算力资产属于新的市场与资产类别，而并不非传统金融牌照的覆盖范围内。这可能会引发地区性监管问题，包括对其业务运营、资产发行及使用的限制。

免责声明：本文在创作过程中借助了 ChatGPT-5 的 AI 工具辅助完成，作者已尽力校对并确保信息真实与准确，但仍难免存在疏漏，敬请谅解。需特别提示的是，加密资产市场普遍存在项目基本面与二级市场价格表现背离的情况。本文内容仅用于信息整合与学术/研究交流，不构成任何投资建议，亦不应视为任何代币的买卖推荐。

At the same time, our past research has consistently highlighted the rise of Agent Networks. Their value lies in enabling complex tasks to be completed through autonomous cooperation and division of labor across multiple agents, accelerating the shift from “large monolithic models” toward “multi-agent ecosystems.”

Federated Learning, with its foundations of local data retention, contribution-based incentives, distributed design, transparent rewards, privacy protection, and regulatory compliance, has laid important groundwork for multi-party collaboration. These same principles can be directly adapted to the development of Agent Networks. The FedML team has been following this trajectory: evolving from open-source roots to TensorOpera (an AI infrastructure layer for the industry), and further advancing to ChainOpera (a decentralized Agent Network).

That said, Agent Networks are not simply an inevitable extension of Federated Learning. Their essence lies in autonomous collaboration and task specialization among agents, and they can also be built directly on top of Multi-Agent Systems (MAS), Reinforcement Learning (RL), or blockchain-based incentive mechanisms.

I. Federated Learning and the AI Agent Technology Stack

Federated Learning (FL) is a framework for collaborative training without centralizing data. Its core principle is that each participant trains a model locally and uploads only parameters or gradients to a coordinating server for aggregation, thereby ensuring “data stays within its domain” and meeting privacy and compliance requirements.

Having been tested in sectors such as healthcare, finance, and mobile applications, FL has entered a relatively mature stage of commercialization. However, it still faces challenges such as high communication overhead, incomplete privacy guarantees, and efficiency bottlenecks caused by heterogeneous devices.

Compared with other training paradigms:

• Distributed training emphasizes centralized compute clusters to maximize efficiency and scale.

• Decentralized training achieves fully distributed collaboration via open compute networks.

Federated learning lies in between, functioning as a form of “controlled decentralization”: it satisfies industrial requirements for privacy and compliance while enabling cross-institution collaboration, making it more suitable as a transitional deployment architecture.

AI Agent Protocol Stack In our previous research, we categorized the AI Agent protocol stack into three major layers:

1. Infrastructure Layer (Agent Infrastructure Layer) The foundational runtime support for agents, serving as the technical base of all Agent systems. Core Modules: Agent Framework – development and runtime environment for agents. Agent OS – deeper-level multitask scheduling and modular runtime, providing lifecycle management for agents.

Supporting Modules: Agent DID (decentralized identity) Agent Wallet & Abstraction (account abstraction & transaction execution) Agent Payment/Settlement (payment and settlement capabilities)

1. Coordination & Execution Layer Focuses on agent collaboration, task scheduling, and incentive systems—key to building collective intelligence among agents. Agent Orchestration: Centralized orchestration and lifecycle management, task allocation, and workflow execution—suited for controlled environments. Agent Swarm: Distributed collaboration structure emphasizing autonomy, division of labor, and resilient coordination—suited for complex, dynamic environments. Agent Incentive Layer: Economic layer of the agent network that incentivizes developers, executors, and validators, ensuring sustainable ecosystem growth.

2. Application & Distribution Layer Covers distribution channels, end-user applications, and consumer-facing products. Distribution Sub-layer: Agent Launchpads, Agent Marketplaces, Agent Plugin Networks Application Sub-layer: AgentFi, Agent-native DApps, Agent-as-a-Service Consumer Sub-layer: Social/consumer agents, focused on lightweight end-user scenarios Meme Sub-layer: Hype-driven “Agent” projects with little actual technology or application—primarily marketing-driven.

II. Federated Learning Benchmark: FedML and the TensorOpera Full-Stack Platform

FedML is one of the earliest open-source frameworks for Federated Learning (FL) and distributed training. Originating from an academic team at USC, it gradually evolved into the core product of TensorOpera AI through commercialization.

For researchers and developers, FedML provides cross-institution and cross-device tools for collaborative data training. In academia, FedML has become a widely adopted experimental platform for FL research, frequently appearing at top conferences such as NeurIPS, ICML, and AAAI. In industry, it has earned a strong reputation in privacy-sensitive fields such as healthcare, finance, edge AI, and Web3 AI—positioning itself as the benchmark toolchain for federated learning.

TensorOpera represents the commercialized evolution of FedML, upgraded into a full-stack AI infrastructure platform for enterprises and developers. While retaining its federated learning capabilities, it extends into GPU marketplaces, model services, and MLOps, thereby expanding into the broader market of the LLM and Agent era.

Its overall architecture is structured into three layers: Compute Layer (foundation), Scheduler Layer (coordination), and MLOps Layer (application).

1. **Compute Layer (Foundation)**The Compute layer forms the technical backbone of TensorOpera, continuing the open-source DNA of FedML.

   • Core Functions: Parameter Server, Distributed Training, Inference Endpoint, and Aggregation Server.

   • Value Proposition: Provides distributed training, privacy-preserving federated learning, and a scalable inference engine. Together, these support the three core capabilities of Train / Deploy / Federate, covering the full pipeline from model training to deployment and cross-institution collaboration.

   **Scheduler Layer (Coordination)**The Scheduler layer acts as the compute marketplace and scheduling hub, composed of GPU Marketplace, Provision, Master Agent, and Schedule & Orchestrate modules.

   • Capabilities: Enables resource allocation across public clouds, GPU providers, and independent contributors.

   • Significance: This marks the pivotal step from FedML to TensorOpera—supporting large-scale AI training and inference through intelligent scheduling and orchestration, covering LLM and generative AI workloads.

   • Tokenization Potential: The “Share & Earn” model leaves an incentive mechanism interface open, showing compatibility with DePIN or broader Web3 models.

   **MLOps Layer (Application)**The MLOps layer provides direct-facing services for developers and enterprises, including Model Serving, AI Agents, and Studio modules.

   • Applications: LLM chatbots, multimodal generative AI, and developer copilot tools.

   • Value Proposition: Abstracts low-level compute and training capabilities into high-level APIs and products, lowering the barrier to use. It offers ready-to-use agents, low-code environments, and scalable deployment solutions.

Positioning: Comparable to new-generation AI infrastructure platforms such as Anyscale, Together, and Modal—serving as the bridge from infrastructure to applications.

In March 2025, TensorOpera upgraded into a full-stack platform oriented toward AI Agents, with its core products covering AgentOpera AI App, Framework, and Platform:

• Application Layer: Provides ChatGPT-like multi-agent entry points.

• Framework Layer: Evolves into an “Agentic OS” through graph-structured multi-agent systems and Orchestrator/Router modules.

• Platform Layer: Deeply integrates with the TensorOpera model platform and FedML, enabling distributed model services, RAG optimization, and hybrid edge–cloud deployment.

The overarching vision is to build “one operating system, one agent network”, allowing developers, enterprises, and users to co-create the next-generation Agentic AI ecosystem in an open and privacy-preserving environment.

III. The ChainOpera AI Ecosystem: From Co-Creators and Co-Owners to the Technical Foundation

If FedML represents the technical core, providing the open-source foundations of federated learning and distributed training; and TensorOpera abstracts FedML’s research outcomes into a commercialized, full-stack AI infrastructure—then ChainOpera takes this platform capability on-chain.

By combining AI Terminals + Agent Social Networks + DePIN-based compute/data layers + AI-Native blockchains, ChainOpera seeks to build a decentralized Agent Network ecosystem.

The fundamental shift is this: while TensorOpera remains primarily enterprise- and developer-oriented, ChainOpera leverages Web3-style governance and incentive mechanisms to include users, developers, GPU providers, and data contributors as co-creators and co-owners. In this way, AI Agents are not only “used” but also “co-created and co-owned.”

Co-Creator Ecosystem

Through its Model & GPU Platform and Agent Platform, ChainOpera provides toolchains, infrastructure, and coordination layers for collaborative creation. This enables model training, agent development, deployment, and cooperative scaling.

The ecosystem’s co-creators include:

• AI Agent Developers – design and operate agents.

• Tool & Service Providers – templates, MCPs, databases, APIs.

• Model Developers – train and publish model cards.

• GPU Providers – contribute compute power via DePIN or Web2 cloud partnerships.

• Data Contributors & Annotators – upload and label multimodal datasets.

Together, these three pillars—development, compute, and data—drive the continuous growth of the agent network.

Co-Owner Ecosystem

ChainOpera also introduces a co-ownership mechanism through shared participation in building the network.

• AI Agent Creators (individuals or teams) design and deploy new agents via the Agent Platform, launching and maintaining them while pushing functional and application-level innovation.

• AI Agent Participants (from the community) join agent lifecycles by acquiring and holding Access Units, thereby supporting agent growth and activity through usage and promotion.

These two roles represent the supply side and demand side, together forming a value-sharing and co-development model within the ecosystem.

Ecosystem Partners: Platforms and Frameworks

ChainOpera collaborates widely to enhance usability, security, and Web3 integration:

• AI Terminal App combines wallets, algorithms, and aggregation platforms to deliver intelligent service recommendations.

• Agent Platform integrates multi-framework and low-code tools to lower the development barrier.

• TensorOpera AI powers model training and inference.

• FedML serves as an exclusive partner, enabling cross-institution, cross-device, privacy-preserving training.

The result is an open ecosystem balancing enterprise-grade applications with Web3-native user experiences.

Hardware Entry Points: AI Hardware & Partners

Through DeAI Phones, wearables, and robotic AI partners, ChainOpera integrates blockchain and AI into smart terminals. These devices enable dApp interaction, edge-side training, and privacy protection, gradually forming a decentralized AI hardware ecosystem.

Central Platforms and Technical Foundation

• TensorOpera GenAI Platform – provides full-stack services across MLOps, Scheduler, and Compute; supports large-scale model training and deployment.

• TensorOpera FedML Platform – enterprise-grade federated/distributed learning platform, enabling cross-organization/device privacy-preserving training and serving as a bridge between academia and industry.

• FedML Open Source – the globally leading federated/distributed ML library, serving as the technical base of the ecosystem with a trusted, scalable open-source framework.

ChainOpera AI Ecosystem Structure

IV. ChainOpera Core Products and Full-Stack AI Agent Infrastructure

In June 2025, ChainOpera officially launched its AI Terminal App and decentralized tech stack, positioning itself as a “Decentralized OpenAI.” Its core products span four modules:

1. Application Layer – AI Terminal & Agent Network

2. Developer Layer – Agent Creator Center

3. Model & GPU Layer – Model & Compute Network

4. CoAI Protocol & Dedicated Chain

Together, these modules cover the full loop from user entry points to underlying compute and on-chain incentives.

AI Terminal App

Already integrated with BNB Chain, the AI Terminal supports on-chain transactions and DeFi-native agents. The Agent Creator Center is open to developers, providing MCP/HUB, knowledge base, and RAG capabilities, with continuous onboarding of community-built agents. Meanwhile, ChainOpera launched the CO-AI Alliance, partnering with io.net, Render, TensorOpera, FedML, and MindNetwork.

According to BNB DApp Bay on-chain data (past 30 days): 158.87K unique users, 2.6M transactions and Ranked #2 in the entire “AI Agent” category on BSC, This demonstrates strong and growing on-chain activity.

Super AI Agent App – AI Terminal 👉 chat.chainopera.ai

Positioned as a decentralized ChatGPT + AI Social Hub, the AI Terminal provides: Multimodal collaboration, Data contribution incentives, DeFi tool integration, Cross-platform assistance, Privacy-preserving agent collaboration (Your Data, Your Agent). Users can directly call the open-source DeepSeek-R1 model and community-built agents from mobile. During interactions, both language tokens and crypto tokens circulate transparently on-chain.

Core Value: transforms users from “content consumers” into “intelligent co-creators.” Applicable across DeFi, RWA, PayFi, e-commerce, and other domains via personalized agent networks.

AI Agent Social Network  👉 chat.chainopera.ai/agent-social-network

Envisioned as **LinkedIn + Messenger for AI Agents.**Provides virtual workspaces and Agent-to-Agent collaboration mechanisms (MetaGPT, ChatDEV, AutoGEN, Camel).Evolves single agents into multi-agent cooperative networks spanning finance, gaming, e-commerce, and research.Gradually enhances memory and autonomy.

AI Agent Developer Platform👉 agent.chainopera.ai

Designed as a “LEGO-style” creation experience for developers.Supports no-code and modular extensions, Blockchain smart contracts ensure ownership rights, DePIN + cloud infrastructure lower entry barriers and Marketplace enables discovery and distribution

Core Value: empowers developers to rapidly reach users, with contributions transparently recorded and rewarded.

AI Model & GPU Platform 👉 platform.chainopera.ai

Serving as the infrastructure layer, it combines DePIN and federated learning to address Web3 AI’s reliance on centralized compute. Capabilities include:Distributed GPU network, Privacy-preserving data training, Model and data marketplace, End-to-end MLOpsVision: shift from “big tech monopoly” to “community-driven infrastructure”—enabling multi-agent collaboration and personalized AI.

ChainOpera Full-Stack Architecture Overview

V. ChainOpera AI Roadmap

Beyond the already launched full-stack AI Agent platform, ChainOpera AI holds a firm belief that Artificial General Intelligence (AGI) will emerge from multimodal, multi-agent collaborative networks. Its long-term roadmap is structured into four phases:

Phase I (Compute → Capital):

• Build decentralized infrastructure: GPU DePIN networks, federated learning, distributed training/inference platforms.

• Introduce a Model Router to coordinate multi-end inference.

• Incentivize compute, model, and data providers with usage-based revenue sharing.

Phase II (Agentic Apps → Collaborative AI Economy):

• Launch AI Terminal, Agent Marketplace, and Agent Social Network, forming a multi-agent application ecosystem.

• Deploy the CoAI Protocol to connect users, developers, and resource providers.

• Introduce user–developer matching and a credit system, enabling high-frequency interactions and sustainable economic activity.

Phase III (Collaborative AI → Crypto-Native AI):

• Expand into DeFi, RWA, payments, and e-commerce scenarios.

• Extend to KOL-driven and personal data exchange use cases.

• Develop finance/crypto-specialized LLMs and launch Agent-to-Agent payments and wallet systems, unlocking “Crypto AGI” applications.

Phase IV (Ecosystems → Autonomous AI Economies):

• Evolve into autonomous subnet economies, each subnet specializing in applications, infrastructure, compute, models, or data.

• Enable subnet governance and tokenized operations, while cross-subnet protocols support interoperability and cooperation.

• Extend from Agentic AI into Physical AI (robotics, autonomous driving, aerospace).

Disclaimer: This roadmap is for reference only. Timelines and functionalities may adjust dynamically with market conditions and do not constitute a delivery guarantee.

VI. Token Incentives and Protocol Governance

ChainOpera has not yet released a full token incentive plan, but its CoAI Protocol centers on “co-creation and co-ownership.” Contributions are transparently recorded and verifiable via blockchain and a Proof-of-Intelligence (PoI) mechanism. Developers, compute providers, data contributors, and service providers are compensated based on standardized contribution metrics. Users consume services.Resource providers sustain operations.Developers build applications. All participants share in ecosystem growth dividends. The platform sustains itself via a 1% service fee, allocation rewards, and liquidity support—building an open, fair, and collaborative decentralized AI ecosystem.

Proof-of-Intelligence (PoI) Framework

PoI is ChainOpera’s core consensus mechanism under the CoAI Protocol, designed to establish a transparent, fair, and verifiable incentive and governance system for decentralized AI.  It extends Proof-of-Contribution into a blockchain-enabled collaborative machine learning framework, addressing federated learning’s persistent issues: insufficient incentives, privacy risks, and lack of verifiability.

Core Design:

• Anchored in smart contracts, integrated with decentralized storage (IPFS), aggregation nodes, and zero-knowledge proofs (zkSNARKs).

• Achieves five key objectives:

  1. Fair rewards based on contribution, ensuring trainers are incentivized for real model improvements.

  2. Data remains local, guaranteeing privacy protection.

  3. Robustness mechanisms against malicious participants (poisoning, aggregation attacks).

  4. ZKP verification for critical processes: model aggregation, anomaly detection, contribution evaluation.

Efficiency and generality across heterogeneous data and diverse learning tasks.

Token Value Flows in Full-Stack AI

ChainOpera’s token design is anchored in utility and contribution recognition, not speculation. It revolves around five core value streams:

• LaunchPad – for agent/application initiation.

• Agent API – service access and integration.

• Model Serving – inference and deployment fees.

• Contribution – data annotation, compute sharing, or service input.

• Model Training – distributed training tasks.

Stakeholders:

• AI Users – spend tokens to access services or subscribe to apps; contribute by providing/labeling/staking data.

• Agent & App Developers – use compute/data for development; rewarded for contributing agents, apps, or datasets.

• Resource Providers – contribute compute, data, or models; rewarded transparently.

• Governance Participants (Community & DAO) – use tokens to vote, shape mechanisms, and coordinate the ecosystem.

• Protocol Layer (CoAI) – sustains development through service fees and automated balancing of supply/demand.

• Nodes & Validators – secure the network by providing validation, compute, and security services.

Protocol Governance

ChainOpera adopts DAO-based governance, where token staking enables participation in proposals and voting, ensuring transparency and fairness.

Governance mechanisms include:

• Reputation System – validates and quantifies contributions.

• Community Collaboration – proposals and voting drive ecosystem evolution.

• Parameter Adjustments – covering data usage, security, and validator accountability.

The overarching goal: prevent concentration of power, ensure system stability, and sustain community co-creation.

VIII. Team Background and Project Financing

The ChainOpera project was co-founded by Professor Salman Avestimehr, a leading scholar in federated learning, and Dr. Aiden Chaoyang He. The core team spans academic and industry backgrounds from institutions such as UC Berkeley, Stanford, USC, MIT, Tsinghua University, and tech leaders including Google, Amazon, Tencent, Meta, and Apple. The team combines deep research expertise with extensive industry execution capabilities and has grown to over 40 members to date.

Co-Founder: Professor Salman Avestimehr

• Title & Roles: Dean’s Professor of Electrical & Computer Engineering at University of Southern California (USC), Founding Director of the USC-Amazon Center on Trusted AI, and head of the vITAL (Information Theory & Machine Learning) Lab at USC.

• Entrepreneurship: Co-Founder & CEO of FedML, and in 2022 co-founded TensorOpera/ChainOpera AI.

• Education & Honors: Ph.D. in EECS from UC Berkeley (Best Dissertation Award). IEEE Fellow with 300+ publications in information theory, distributed computing, and federated learning, cited over 30,000 times. Recipient of PECASE, NSF CAREER Award, and the IEEE Massey Award, among others.

• Contributions: Creator of the FedML open-source framework, widely adopted in healthcare, finance, and privacy-preserving AI, which became a core foundation for TensorOpera/ChainOpera AI.

Co-Founder: Dr. Aiden Chaoyang He

• Title & Roles: Co-Founder & President of TensorOpera/ChainOpera AI; Ph.D. in Computer Science from USC; original creator of FedML.

• Research Focus: Distributed & federated learning, large-scale model training, blockchain, and privacy-preserving computation.

• Industry Experience: Previously held R&D roles at Meta, Amazon, Google, Tencent; served in core engineering and management positions at Tencent, Baidu, and Huawei, leading the deployment of multiple internet-scale products and AI platforms.

• Academic Impact: Published 30+ papers with 13,000+ citations on Google Scholar. Recipient of the Amazon Ph.D. Fellowship, Qualcomm Innovation Fellowship, and Best Paper Awards at NeurIPS and AAAI.

Technical Contributions: Led the development of FedML, one of the most widely used open-source frameworks in federated learning, supporting 27 billion daily requests. Core contributor to FedNLP and hybrid model parallel training methods, applied in decentralized AI projects such as Sahara AI.

In December 2024, ChainOpera AI announced the completion of a $3.5M seed round, bringing its total funding (combined with TensorOpera) to $17M. Funds will be directed toward building a blockchain Layer 1 and AI operating system for decentralized AI Agents.

• Lead Investors: Finality Capital, Road Capital, IDG Capital

• Other Participants: Camford VC, ABCDE Capital, Amber Group, Modular Capital

• Strategic Backers: Sparkle Ventures, Plug and Play, USC

• Notable Individual Investors:Sreeram Kannan, Founder of EigenLayer and David Tse, Co-Founder of BabylonChain

The team stated that this round will accelerate its vision of creating a decentralized AI ecosystem where resource providers, developers, and users co-own and co-create.

IX. Market Landscape Analysis: Federated Learning and AI Agent Networks

Federated Learning Landscape

The federated learning (FL) field is shaped by four main frameworks. FedML is the most comprehensive, combining FL, distributed large-model training, and MLOps, making it enterprise-ready. Flower is lightweight and widely used in teaching and small-scale experiments. TFF (TensorFlow Federated) is academically valuable but weak in industrialization. OpenFL targets healthcare and finance, with strong compliance features but a closed ecosystem. In short: FedML is the industrial-grade all-rounder, Flower emphasizes ease of use, TFF remains academic, and OpenFL excels in vertical compliance.

Industry Platforms & Infrastructure

TensorOpera, the commercialized evolution of FedML, integrates cross-cloud GPU scheduling, distributed training, federated learning, and MLOps in a unified stack. Positioned as a bridge between research and industry, it serves developers, SMEs, and Web3/DePIN ecosystems. Effectively, TensorOpera is like “Hugging Face + W&B” for federated and distributed learning, offering a more complete and general-purpose platform than tool- or sector-specific alternatives.

Innovation Layer: ChainOpera vs. Flock

ChainOpera and Flock both merge FL with Web3 but diverge in focus. ChainOpera builds a full-stack AI Agent platform, turning users into co-creators through the AI Terminal and Agent Social Network. Flock centers on Blockchain-Augmented FL (BAFL), stressing privacy and incentives at the compute and data layer. Put simply: ChainOpera emphasizes applications and agent networks, while Flock focuses on low-level training and privacy-preserving computation.

Federated Learning & AI Infrastructure Landscape

Agent Network Layer: ChainOpera vs. Olas

At the agent-network level, the most representative projects are ChainOpera and Olas Network.

• ChainOpera: rooted in federated learning, builds a full-stack loop across models, compute, and agents. Its Agent Social Network acts as a testbed for multi-agent interaction and social collaboration.

Olas Network (Autonolas / Pearl): originated from DAO collaboration and the DeFi ecosystem, positioned as a decentralized autonomous service network. Through Pearl, it delivers direct-to-market DeFi agent applications—showing a very different trajectory from ChainOpera.

X. Investment Thesis and Risk Analysis

Investment Thesis

• Technical Moat: ChainOpera’s strength lies in its unique evolutionary path: from FedML (the benchmark open-source framework for federated learning) → TensorOpera (enterprise-grade full-stack AI infrastructure) → ChainOpera (Web3-enabled agent networks + DePIN + tokenomics). This trajectory integrates academic foundations, industrial deployment, and crypto-native narratives, creating a differentiated moat.

• Applications & User Scale: The AI Terminal has already reached hundreds of thousands of daily active users and a thriving ecosystem of 1,000+ agent applications. It ranks #1 in the AI category on BNBChain DApp Bay, showing clear on-chain user growth and verifiable transaction activity. Its multimodal scenarios, initially rooted in crypto-native use cases, have the potential to expand gradually into the broader Web2 user base.

• Ecosystem Partnerships: ChainOpera launched the CO-AI Alliance, partnering with io.net, Render, TensorOpera, FedML, and MindNetwork to build multi-sided network effects across GPUs, models, data, and privacy computing. In parallel, its collaboration with Samsung Electronics to validate mobile multimodal GenAI demonstrates expansion potential into hardware and edge AI.

• Token & Economic Model: ChainOpera’s tokenomics are based on the Proof-of-Intelligence consensus, with incentives distributed across five value streams: LaunchPad, Agent API, Model Serving, Contribution, and Model Training. A 1% platform service fee, reward allocation, and liquidity support form a positive feedback loop, avoiding reliance on pure “token speculation” and enhancing sustainability.

Potential Risks

1. Technical execution risks: ChainOpera’s proposed five-layer decentralized architecture spans a wide scope. Cross-layer coordination—especially in distributed inference for large models and privacy-preserving training—still faces performance and stability challenges and has not yet been validated at scale.

2. User and ecosystem stickiness: While early user growth is notable, it remains to be seen whether the Agent Marketplace and developer toolchain can sustain long-term activity and high-quality contributions. The current Agent Social Network is mainly LLM-driven text dialogue; user experience and retention still need refinement. Without carefully designed incentives, the ecosystem risks short-term hype without long-term value.

3. Sustainability of the business model: At present, revenue primarily depends on platform service fees and token circulation; stable cash flows are not yet established. Compared with AgentFi or Payment-focused applications that carry stronger financial or productivity attributes, ChainOpera’s current model still requires further validation of its commercial value. In addition, the mobile and hardware ecosystem remains exploratory, leaving its market prospects uncertain.

Disclaimer: This report was prepared with assistance from AI tools (ChatGPT-5). The author has made every effort to proofread and ensure accuracy, but some errors or omissions may remain. Readers should note that crypto asset markets often exhibit divergence between project fundamentals and secondary-market token performance. This report is intended solely for information consolidation and academic/research discussion. It does not constitute investment advice, nor should it be interpreted as a recommendation to buy or sell any token.

联邦学习以“数据不出本地、按贡献激励”奠定了多方协作的基础，其分布式基因、透明激励、隐私保障与合规实践为 Agent Network 提供了可直接复用的经验。FedML 团队正是沿着这一路径，将开源基因升级为 TensorOpera（AI产业基础设施层），再演进至 ChainOpera（去中心化 Agent 网络）。当然，Agent Network 并非联邦学习的必然延伸，其核心在于多智能体的自治协作与任务分工，也可直接基于多智能体系统（MAS）、强化学习（RL）或区块链激励机制构建。

一、联邦学习与AI Agent技术栈架构

联邦学习（Federated Learning, FL） 是一种在不集中数据的前提下进行协同训练的框架，其基本原理是由各参与方在本地训练模型，仅上传参数或梯度至协调端进行聚合，从而实现“数据不出域”的隐私合规。经过医疗、金融和移动端等典型场景的实践，联邦学习 已进入较为成熟的商用阶段，但仍面临通信开销大、隐私保护不彻底、设备异构导致收敛效率低等瓶颈。与其他训练模式相比，分布式训练强调算力集中以追求效率与规模，去中心化训练则通过开放算力网络实现完全分布式协作，而联邦学习则处于二者之间，体现为一种 “受控去中心化” 方案：既能满足产业在隐私与合规方面的需求，又提供了跨机构协作的可行路径，更适合工业界过渡性部署架构。

而在整个AI Agent协议栈中，我们在之前的研报中将其划分为三个主要层级，即

• 基础设施层（Agent Infrastructure Layer）:该层为智能体提供最底层的运行支持，是所有 Agent 系统构建的技术根基。

• 核心模块：包括 Agent Framework（智能体开发与运行框架）和 Agent OS（更底层的多任务调度与模块化运行时），为 Agent 的生命周期管理提供核心能力。

• 支持模块：如 Agent DID（去中心身份）、Agent Wallet & Abstraction（账户抽象与交易执行）、Agent Payment/Settlement（支付与结算能力）。

• 协调与调度层（Coordination & Execution Layer）关注多智能体之间的协同、任务调度与系统激励机制，是构建智能体系统“群体智能”的关键。

• Agent Orchestration：是指挥机制，用于统一调度和管理 Agent 生命周期、任务分配和执行流程，适用于有中心控制的工作流场景。

• Agent Swarm：是协同结构，强调分布式智能体协作，具备高度自治性、分工能力和弹性协同，适合应对动态环境中的复杂任务。

• Agent Incentive Layer：构建 Agent 网络的经济激励系统，激发开发者、执行者与验证者的积极性，为智能体生态提供可持续动力。

• 应用层（Application & Distribution Layer）

  • 分发子类：包括Agent Launchpad、Agent Marketplace 和Agent Plugin Network

  • 应用子类：涵盖AgentFi、Agent Native DApp、Agent-as-a-Service等

  • 消费子类：Agent Social / Consumer Agent为主，面向消费者社交等轻量场景

  • Meme：借 Agent 概念炒作，缺乏实际的技术实现和应用落地，仅营销驱动。

二、联邦学习标杆 FedML 与 TensorOpera 全栈平台

FedML 是最早面向联邦学习（Federated Learning）与分布式训练的开源框架之一，起源于学术团队（USC）并逐步公司化成为 TensorOpera AI 的核心产品。它为研究者和开发者提供跨机构、跨设备的数据协作训练工具，在学术界，FedML 因频繁出现在 NeurIPS、ICML、AAAI 等顶会上，已成为联邦学习研究的通用实验平台；在产业界，FedML在医疗、金融、边缘 AI 及 Web3 AI 等隐私敏感场景中具备较高口碑，被视为 联邦学习领域的标杆性工具链。

TensorOpera是 FedML基于商业化路径升级为面向企业与开发者的全栈 AI 基础设施平台：在保持联邦学习能力的同时，扩展至 GPU Marketplace、模型服务与 MLOps，从而切入大模型与 Agent 时代的更大市场。TensorOpera的整体架构可分为Compute Layer（基础层）、Scheduler Layer（调度层）和MLOps Layer（应用层）三个层级：

1. Compute Layer（底层） Compute 层是 TensorOpera 的技术基底，延续 FedML 的开源基因，核心功能包括 Parameter Server、Distributed Training、Inference Endpoint 与 Aggregation Server。其价值定位在于提供分布式训练、隐私保护的联邦学习以及可扩展的推理引擎，支撑 “Train / Deploy / Federate” 三大核心能力，覆盖从模型训练、部署到跨机构协作的完整链路，是整个平台的基础层。

2. Scheduler Layer（中层） Scheduler 层相当于算力交易与调度中枢，由 GPU Marketplace、Provision、Master Agent 与 Schedule & Orchestrate 构成，支持跨公有云、GPU 提供商和独立贡献者的资源调用。这一层是 FedML 升级为 TensorOpera 的关键转折，能够通过智能算力调度与任务编排实现更大规模的 AI 训练和推理，涵盖 LLM 与生成式 AI 的典型场景。同时，该层的 Share & Earn 模式预留了激励机制接口，具备与 DePIN 或 Web3 模式兼容的潜力。

3. MLOps Layer（上层） MLOps 层是平台直接面向开发者与企业的服务接口，包括 Model Serving、AI Agent 与 Studio 等模块。典型应用涵盖 LLM Chatbot、多模态生成式 AI 和开发者 Copilot 工具。其价值在于将底层算力与训练能力抽象为高层 API 与产品，降低使用门槛，提供即用型 Agent、低代码开发环境与可扩展部署能力，定位上对标 Anyscale、Together、Modal 等新一代 AI Infra 平台，充当从基础设施走向应用的桥梁。

2025年3月，TensorOpera 升级为面向 AI Agent 的全栈平台，核心产品涵盖 AgentOpera AI App、Framework 与 Platform。应用层提供类 ChatGPT 的多智能体入口，框架层以图结构多智能体系统和 Orchestrator/Router 演进为“Agentic OS”，平台层则与 TensorOpera 模型平台和 FedML 深度融合，实现分布式模型服务、RAG 优化和混合端云部署。整体目标是打造 “一个操作系统，一个智能体网络”，让开发者、企业与用户在开放、隐私保护的环境下共建新一代 Agentic AI 生态。

三、ChainOpera AI生态全景：从共创共有者到技术基座

如果说 FedML 是技术内核，提供了联邦学习与分布式训练的开源基因；TensorOpera 将 FedML 的科研成果抽象为可商用的全栈 AI 基础设施，那么 ChainOpera 则是将TensorOpera 的平台能力“上链”，通过 AI Terminal + Agent Social Network + DePIN 模型与算力层 + AI-Native 区块链 打造一个去中心化的 Agent 网络生态。其核心转变在于，TensorOpera 仍主要面向企业与开发者，而 ChainOpera 借助 Web3 化的治理与激励机制，把用户、开发者、GPU/数据提供者纳入共建共治，让 AI Agent 不只是“被使用”，而是“被共创与共同拥有”。

共创者生态（Co-creators）

ChainOpera AI 通过 Model & GPU Platform 与 Agent Platform 为生态共创提供工具链、基础设施与协调层，支持模型训练、智能体开发、部署与扩展协作。

ChainOpera 生态的共创者涵盖 AI Agent 开发者（设计与运营智能体）、工具与服务提供方（模板、MCP、数据库与 API）、模型开发者（训练与发布模型卡）、GPU 提供方（通过 DePIN 与 Web2 云伙伴贡献算力）、数据贡献者与标注方（上传与标注多模态数据）。三类核心供给——开发、算力与数据——共同驱动智能体网络的持续成长。

共有人生态（Co-owners）

ChainOpera 生态还引入 共有人机制，通过合作与参与共同建设网络。AI Agent 创作者是个人或团队，通过 Agent Platform 设计与部署新型智能体，负责构建、上线并持续维护，从而推动功能与应用的创新。AI Agent 参与者则来自社区，他们通过获取和持有访问单元（Access Units）参与智能体的生命周期，在使用与推广过程中支持智能体的成长与活跃度。两类角色分别代表 供给端与需求端，共同形成生态内的价值共享与协同发展模式。

生态合作伙伴：平台与框架

ChainOpera AI 与多方合作，强化平台的可用性与安全性，并注重 Web3 场景融合：通过 AI Terminal App 联合钱包、算法与聚合平台实现智能服务推荐；在 Agent Platform 引入多元框架与零代码工具，降低开发门槛；依托 TensorOpera AI 进行模型训练与推理；并与 FedML 建立独家合作，支持跨机构、跨设备的隐私保护训练。整体上，形成兼顾 企业级应用 与 Web3 用户体验 的开放生态体系。

**硬件入口：AI 硬件与合作伙伴（AI Hardware & Partners）**通过 DeAI Phone、可穿戴与 Robot AI 等合作伙伴，ChainOpera 将区块链与 AI 融合进智能终端，实现 dApp 交互、端侧训练与隐私保护，逐步形成去中心化 AI 硬件生态。

中枢平台与技术基座：TensorOpera GenAI & FedMLTensorOpera 提供覆盖 MLOps、Scheduler、Compute 的全栈 GenAI 平台；其子平台 FedML 从学术开源成长为产业化框架，强化了 AI “随处运行、任意扩展” 的能力。

ChainOpera AI 生态体系

四、ChainOpera核心产品及全栈式 AI Agent 基础设施

2025年6月，ChainOpera正式上线 AI Terminal App 与去中心化技术栈，定位为“去中心化版 OpenAI”，其核心产品涵盖四大模块：应用层（AI Terminal & Agent Network）、开发者层（Agent Creator Center）、模型与 GPU 层（Model & Compute Network）、以及 CoAI 协议与专用链，覆盖了从用户入口到底层算力与链上激励的完整闭环。

AI Terminal App 已集成 BNBChain ，支持链上交易与 DeFi 场景的 Agent。Agent Creator Center 面向开发者开放，提供 MCP/HUB、知识库与 RAG 等能力，社区智能体持续入驻；同时发起 CO-AI Alliance，联动 io.net、Render、TensorOpera、FedML、MindNetwork 等伙伴。

根据BNB DApp Bay 近 30 日的链上数据显示，其独立用户 158.87K，近30日交易量260万，在在 BSC「AI Agent」分类中排名全站第二，显示出强劲的链上活跃度。

**Super AI Agent App – AI Terminal (https://chat.chainopera.ai/)**作为去中心化 ChatGPT 与 AI 社交入口，AI Terminal 提供多模态协作、数据贡献激励、DeFi 工具整合、跨平台助手，并支持 AI Agent 协作与隐私保护（Your Data, Your Agent）。用户可在移动端直接调用开源大模型 DeepSeek-R1 与社区智能体，交互过程中语言 Token 与加密 Token 在链上透明流转。其价值在于让用户从“内容消费者”转变为“智能共创者”，并能在 DeFi、RWA、PayFi、电商等场景中使用专属智能体网络。

**AI Agent Social Network (https://chat.chainopera.ai/agent-social-network)**定位类似 LinkedIn + Messenger，但面向 AI Agent 群体。通过虚拟工作空间与 Agent-to-Agent 协作机制（MetaGPT、ChatDEV、AutoGEN、Camel），推动单一 Agent 演化为多智能体协作网络，覆盖金融、游戏、电商、研究等应用，并逐步增强记忆与自主性。

**AI Agent Developer Platform (https://agent.chainopera.ai/)**为开发者提供“乐高式”创作体验。支持零代码与模块化扩展，区块链合约确保所有权，DePIN + 云基础设施降低门槛，Marketplace 提供分发与发现渠道。其核心在于让开发者快速触达用户，生态贡献可透明记录并获得激励。

**AI Model & GPU Platform (https://platform.chainopera.ai/)**作为基础设施层，结合 DePIN 与联邦学习，解决 Web3 AI 依赖中心化算力的痛点。通过分布式 GPU、隐私保护的数据训练、模型与数据市场，以及端到端 MLOps，支持多智能体协作与个性化 AI。其愿景是推动从“大厂垄断”到“社区共建”的基建范式转移。

五、ChainOpera AI 的路线图规划

除去已正式上线全栈 AI Agent平台外， ChainOpera AI 坚信通用人工智能（AGI）来自 多模态、多智能体的协作网络。因此其远期路线图规划分为四个阶段：

• 阶段一（Compute → Capital）：构建去中心化基础设施，包括 GPU DePIN 网络、联邦学习与分布式训练/推理平台，并引入 模型路由器（Model Router）协调多端推理；通过激励机制让算力、模型与数据提供方获得按使用量分配的收益。

• 阶段二（Agentic Apps → Collaborative AI Economy）：推出 AI Terminal、Agent Marketplace 与 Agent Social Network，形成多智能体应用生态；通过 CoAI 协议 连接用户、开发者与资源提供者，并引入 用户需求–开发者匹配系统 与信用体系，推动高频交互与持续经济活动。

• 阶段三（Collaborative AI → Crypto-Native AI）：在 DeFi、RWA、支付、电商等领域落地，同时拓展至 KOL 场景与个人数据交换；开发面向金融/加密的专用 LLM，并推出 Agent-to-Agent 支付与钱包系统，推动“Crypto AGI”场景化应用。

• 阶段四（Ecosystems → Autonomous AI Economies）：逐步演进为自治子网经济，各子网围绕 应用、基础设施、算力、模型与数据 独立治理、代币化运作，并通过跨子网协议协作，形成多子网协同生态；同时从 Agentic AI 迈向 Physical AI（机器人、自动驾驶、航天）。

免责声明：本路线图仅供参考，时间表与功能可能因市场环境动态调整，不构成交付保证承诺。

七、代币激励与协议治理

目前 ChainOpera 尚未公布完整的代币激励计划，但其 CoAI 协议以“**共创与共拥有”**为核心，通过区块链与 Proof-of-Intelligence 机制实现透明可验证的贡献记录：开发者、算力、数据与服务提供者的投入按标准化方式计量并获得回报，用户使用服务、资源方支撑运行、开发者构建应用，所有参与方共享增长红利；平台则以 1% 服务费、奖励分配和流动性支持维持循环，推动开放、公平、协作的去中心化 AI 生态。

Proof-of-Intelligence 学习框架

Proof-of-Intelligence (PoI) 是 ChainOpera 在 CoAI 协议下提出的核心共识机制，旨在为去中心化 AI 构建提供透明、公平且可验证的激励与治理体系。其基于Proof-of-Contribution（贡献证明） 的区块链协作机器学习框架，旨在解决联邦学习（FL）在实际应用中存在的激励不足、隐私风险与可验证性缺失问题。该设计以智能合约为核心，结合去中心化存储（IPFS）、聚合节点和零知识证明（zkSNARKs），实现了五大目标：① 按贡献度进行公平奖励分配，确保训练者基于实际模型改进获得激励；② 保持数据本地化存储，保障隐私不外泄；③ 引入鲁棒性机制，对抗恶意训练者的投毒或聚合攻击；④ 通过 ZKP 确保模型聚合、异常检测与贡献评估等关键计算的可验证性；⑤ 在效率与通用性上适用于异构数据和不同学习任务。

全栈式 AI 中代币价值ChainOpera 的代币机制围绕五大价值流（LaunchPad、Agent API、Model Serving、Contribution、Model Training）运作，核心是 服务费、贡献确认与资源分配，而非投机回报。

• AI 用户：用代币访问服务或订阅应用，并通过提供/标注/质押数据贡献生态。

• Agent/应用开发者：使用平台算力与数据进行开发，并因其贡献的 Agent、应用或数据集获得协议认可。

• 资源提供者：贡献算力、数据或模型，获得透明记录与激励。

• 治理参与者（社区 & DAO）：通过代币参与投票、机制设计与生态协调。

• 协议层（COAI）：通过服务费维持可持续发展，利用自动化分配机制平衡供需。

• 节点与验证者：提供验证、算力与安全服务，确保网络可靠性。

协议治理

ChainOpera 采用 DAO 治理，通过质押代币参与提案与投票，确保决策透明与公平。治理机制包括：声誉系统（验证并量化贡献）、社区协作（提案与投票推动生态发展）、参数调整（数据使用、安全与验证者问责）。整体目标是避免权力集中，保持系统稳定与社区共创。

八、团队背景及项目融资

ChainOpera项目由在联邦学习领域具有深厚造诣的 Salman Avestimehr 教授 与 何朝阳（Aiden）博士 共同创立。其他核心团队成员背景横跨 UC Berkeley、Stanford、USC、MIT、清华大学 以及 Google、Amazon、Tencent、Meta、Apple 等顶尖学术与科技机构，兼具学术研究与产业实战能力。截止目前，ChainOpera AI 团队规模已超过 40 人。

联合创始人：Salman Avestimehr

Salman Avestimehr 教授是 南加州大学（USC）电气与计算机工程系的 Dean’s Professor，并担任 USC-Amazon Trusted AI 中心创始主任，同时领导 USC 信息论与机器学习实验室（vITAL）。他是 FedML 联合创始人兼 CEO，并在 2022 年共同创立了 TensorOpera/ChainOpera AI。

Salman Avestimehr 教授毕业于 UC Berkeley EECS 博士（最佳论文奖）。作为IEEE Fellow，在信息论、分布式计算与联邦学习领域发表高水平论文 300+ 篇，引用数超 30,000，并获 PECASE、NSF CAREER、IEEE Massey Award 等多项国际荣誉。其主导创建 FedML 开源框架，广泛应用于医疗、金融和隐私计算，并成为 TensorOpera/ChainOpera AI 的核心技术基石。

联合创始人：Dr. Aiden Chaoyang He

Dr. Aiden Chaoyang He 是 TensorOpera/ChainOpera AI 联合创始人兼总裁，南加州大学（USC）计算机科学博士、FedML 原始创建者。其研究方向涵盖分布式与联邦学习、大规模模型训练、区块链与隐私计算。在创业之前，他曾在 Meta、Amazon、Google、Tencent 从事研发，并在腾讯、百度、华为担任核心工程与管理岗位，主导多个互联网级产品与 AI 平台的落地。

学术与产业方面，Aiden 已发表 30 余篇论文，Google Scholar 引用超过 13,000，并获 Amazon Ph.D. Fellowship、Qualcomm Innovation Fellowship 及 NeurIPS、AAAI 最佳论文奖。他主导开发的 FedML 框架是联邦学习领域最广泛使用的开源项目之一，支撑 日均 270 亿次请求；并作为核心作者提出 FedNLP 框架、混合模型并行训练方法，被广泛应用于Sahara AI等去中心化AI项目。

2024 年 12 月，ChainOpera AI 宣布完成 350 万美元种子轮融资，累计与 TensorOpera 共计融资 1700 万美元，资金将用于构建面向去中心化 AI Agent 的区块链 L1 与 AI 操作系统。本轮融资由 Finality Capital、Road Capital、IDG Capital 领投，跟投方包括 Camford VC、ABCDE Capital、Amber Group、Modular Capital 等，亦获得 Sparkle Ventures、Plug and Play、USC 以及 EigenLayer 创始人 Sreeram Kannan、BabylonChain 联合创始人 David Tse 等知名机构和个人投资人支持。团队表示，此轮融资将加速实现 “AI 资源贡献者、开发者与用户共同 co-own 和 co-create 的去中心化 AI 生态” 愿景。

九、联邦学习与AI Agent市场格局分析

联邦学习框架主要有四个代表：FedML、Flower、TFF、OpenFL。其中，FedML 最全栈，兼具联邦学习、分布式大模型训练与 MLOps，适合产业落地；Flower 轻量易用，社区活跃，偏教学与小规模实验；TFF 深度依赖 TensorFlow，学术研究价值高，但产业化弱；OpenFL 聚焦医疗/金融，强调隐私合规，生态较封闭。总体而言，FedML 代表工业级全能路径，Flower 注重易用性与教育，TFF 偏学术实验，OpenFL 则在垂直行业合规性上具优势。

在产业化与基础设施层，TensorOpera（FedML 商业化）的特点在于继承开源 FedML 的技术积累，提供跨云 GPU 调度、分布式训练、联邦学习与 MLOps 的一体化能力，目标是桥接学术研究与产业应用，服务开发者、中小企业及 Web3/DePIN 生态。总体来看，TensorOpera 相当于 “开源 FedML 的 Hugging Face + W&B 合体”，在全栈分布式训练和联邦学习能力上更完整、通用，区别于以社区、工具或单一行业为核心的其他平台。

在创新层代表中，ChainOpera 与 Flock 都尝试将联邦学习与 Web3 结合，但方向存在明显差异。ChainOpera 构建的是 全栈 AI Agent 平台，涵盖入口、社交、开发和基础设施四层架构，核心价值在于推动用户从“消费者”转变为“共创者”，并通过 AI Terminal 与 Agent Social Network 实现协作式 AGI 与社区共建生态；而 Flock 则更聚焦于 区块链增强型联邦学习（BAFL），强调在去中心化环境下的隐私保护与激励机制，主要面向算力和数据层的协作验证。ChainOpera 更偏向 应用与 Agent 网络层 的落地，Flock 则偏向 底层训练与隐私计算 的强化。

在Agent网络层面，业内最有代表性的项目是Olas Network。ChainOpera 前者源自联邦学习，构建模型—算力—智能体的全栈闭环，并以 Agent Social Network 为实验场探索多智能体的交互与社交协作；Olas Network源于 DAO 协作与 DeFi 生态，定位为去中心化自主服务网络，通过 Pearl推出可直接落地的Defi收益场景，与ChainOpera展现出截然不同的路径。

十、投资逻辑与潜在风险分析

投资逻辑

ChainOpera 的优势首先在于其 技术护城河：从 FedML（联邦学习标杆性开源框架）到 TensorOpera（企业级全栈 AI Infra），再到 ChainOpera（Web3 化 Agent 网络 + DePIN + Tokenomics），形成了独特的连续演进路径，兼具学术积累、产业落地与加密叙事。

在 应用与用户规模 上，AI Terminal 已形成数十万日活用户与千级 Agent 应用生态，并在 BNBChain DApp Bay AI 类目排名第一，具备明确的链上用户增长与真实交易量。其多模态场景覆盖的加密原生领域有望逐步外溢至更广泛的 Web2 用户。

生态合作 方面，ChainOpera 发起 CO-AI Alliance，联合 io.net、Render、TensorOpera、FedML、MindNetwork 等伙伴，构建 GPU、模型、数据、隐私计算等多边网络效应；同时与三星电子合作验证移动端多模态 GenAI，展示了向硬件和边缘 AI 扩展的潜力。

在 代币与经济模型 上，ChainOpera 基于 Proof-of-Intelligence 共识，围绕五大价值流（LaunchPad、Agent API、Model Serving、Contribution、Model Training）分配激励，并通过 1% 平台服务费、激励分配和流动性支持形成正向循环，避免单一“炒币”模式，提升了可持续性。

潜在风险

首先，技术落地难度较高。ChainOpera 所提出的五层去中心化架构跨度大，跨层协同（尤其在大模型分布式推理与隐私训练方面）仍存在性能与稳定性挑战，尚未经过大规模应用验证。

其次，生态用户粘性仍需观察。虽然项目已取得初步用户增长，但 Agent Marketplace 与开发者工具链能否长期维持活跃与高质量供给仍有待检验。目前上线的 Agent Social Network 主要以 LLM 驱动的文本对话为主，用户体验与长期留存仍需进一步提升。若激励机制设计不够精细，可能出现短期活跃度高但长期价值不足的现象。

最后，商业模式的可持续性尚待确认。现阶段收入主要依赖平台服务费与代币循环，稳定现金流尚未形成，与 AgentFi或Payment 等更具金融化或生产力属性的应用相比，当前模式的商业价值仍需进一步验证；同时，移动端与硬件生态仍在探索阶段，市场化前景存在一定不确定性。

免责声明：***本文在创作过程中借助了 ChatGPT-5 的 AI 工具辅助完成，作者已尽力校对并确保信息真实与准确，但仍难免存在疏漏，敬请谅解。需特别提示的是，加密资产市场普遍存在项目基本面与二级市场价格表现背离的情况。本文内容仅用于信息整合与学术/研究交流，不构成任何投资建议，亦不应视为任何代币的买卖推荐。

In our earlier research report “The Intelligent Evolution of DeFi: From Automation to AgentFi”, we outlined three stages of DeFi’s intelligence development: Automation, Intent-Centric Copilots, and AgentFi (on-chain autonomous agents). Beyond lending and yield farming—the two most valuable and accessible use cases today—we identified Pendle’s PT/YT yield-rights trading as a high-priority scenario for AgentFi adoption. With its architecture of “yield splitting + maturity mechanism + yield-rights trading”, Pendle naturally provides a programmable space for agents, enabling richer possibilities for automated execution and yield optimization.

1. Fundamentals of Pendle

Pendle is the first DeFi protocol dedicated to yield splitting and yield trading. Its core innovation lies in tokenizing and separating the future yield streams of yield-bearing assets (YBAs) such as LSTs, stablecoin deposit receipts, and lending positions. This enables users to flexibly lock in fixed income, amplify yield expectations, or pursue speculative arbitrage in secondary markets.

In short, Pendle has built a secondary market for the “yield curve” of crypto assets, allowing DeFi users to trade not only the principal but also the yield. This mechanism is highly analogous to the zero-coupon bond + coupon-splitting model in traditional finance, thereby enhancing pricing precision and trading flexibility for DeFi assets.

Pendle’s Yield-Splitting Mechanism

Pendle splits a yield-bearing asset (YBA) into two tradable tokens:

• PT (Principal Token, similar to a zero-coupon bond): represents the principal redeemable at maturity but no longer accrues yield.

• YT (Yield Token, similar to coupon rights): represents all yield generated before maturity, but expires worthless afterward.

• Example: depositing 1 ETH stETH produces PT-stETH (redeemable for 1 ETH at maturity, principal locked) and YT-stETH (entitles the holder to all staking rewards until maturity).

Pendle goes beyond token splitting by introducing a specialized AMM (Automated Market Maker) to provide liquidity for PT and YT, akin to a secondary bond market. Users can buy or sell PT and YT at any time to adjust their yield exposure. PT generally trades below 1, reflecting discounted principal value, while YT is priced based on the market’s expectations of future yield. Importantly, Pendle’s AMM is optimized for expiring assets, enabling PT and YT of different maturities to collectively form an on-chain yield curve—closely resembling bond markets in TradFi.

Stablecoin Strategies: PT vs Pools

Within Pendle’s stablecoin markets, the difference between Stablecoin PT (fixed-income positions) and Stablecoin Pools (liquidity-mining positions) is critical:

• Stablecoin PT (bond-like position):  Functions as an on-chain bond. By purchasing PT at a discount, investors lock in a fixed rate, redeemable 1:1 at maturity. Returns are stable and risks are relatively low, making PT suitable for conservative investors seeking certainty.

Stablecoin Pools (AMM liquidity position):  Function as yield-farming positions. LPs provide liquidity for PT and YT, earning trading fees and incentive rewards. Returns (APY) fluctuate significantly and carry impermanent loss (IL) risk, appealing more to active investors pursuing higher yields who can tolerate volatility.

Pendle’s PT/YT Strategy Paths

Pendle’s PT/YT design supports four primary trading strategies that cater to different risk appetites:

• Fixed Income: Buy PT and hold to maturity to lock in a fixed yield.

• Yield Speculation: Buy YT to bet on rising yields or increased volatility.

• Curve Arbitrage: Exploit pricing differences across PT/YT maturities.

• Leveraged Yield: Use PT or YT as collateral in lending protocols to amplify returns.

Boros and Funding Rate Trading

Beyond Pendle V2’s yield-splitting mechanism, the Boros module further tokenizes funding rates, transforming them from a passive cost of perpetual positions into an independently priced and tradable asset. With Boros, investors can engage in directional speculation, risk hedging, or arbitrage opportunities. In essence, this mechanism brings traditional interest rate derivatives—such as interest rate swaps (IRS) and basis trading—into DeFi, providing institutional capital and risk-averse strategies with a new class of tools.

Additional Pendle V2 Features

In addition to PT/YT trading, AMM pools, and Boros, Pendle V2 also offers several extended features that, while not the focus of this report, form important parts of the ecosystem:

• vePENDLE: A vote-escrow governance and incentive model. Users lock PENDLE to receive vePENDLE, which grants governance rights and boosts yield distribution—serving as the protocol’s long-term incentive and governance backbone.

• PendleSwap: A one-stop asset swap interface that enables efficient conversion between PT/YT and their underlying assets. Functionally a DEX aggregator, it enhances convenience and composability rather than introducing a standalone innovation.

• Points Market: A secondary marketplace for trading project points (e.g., loyalty or airdrop points). It mainly serves speculative and narrative-driven use cases, such as points arbitrage and airdrop capture, rather than core protocol value.

2. Pendle Strategy Landscape: Market Cycles, Risk and Derivative

In traditional financial markets, retail investors are largely limited to equities and fixed-income products, with little access to higher-barrier instruments such as bond derivatives. The same dynamic exists in crypto: retail users are more familiar with token trading and DeFi lending. While Pendle has significantly lowered the entry barrier for “bond-derivative-like” trading in crypto, its strategies still require substantial expertise, as investors must analyze how yield-bearing asset rates shift across different market conditions.

Based on this framework, we argue that in different market phases—early bull market, euphoric bull market, bear market downturn, and sideways consolidation—investors should align their Pendle strategies with their own risk preferences.

• Early Bull Market: Risk appetite begins to recover, lending demand and rates remain low, and YT is relatively cheap. Buying YT here is equivalent to betting on future rate increases. As borrowing demand and LST yields climb, YT appreciates, offering a high-risk, high-reward setup suited to investors seeking early positioning for outsized upside.

• Euphoric Bull Market: Surging market sentiment drives lending demand sharply higher, with DeFi lending rates rising from single digits to 15–30%+. YT valuations soar while PT trades at steep discounts. Buying PT with stablecoins in this phase locks in high fixed yields at a discount—effectively a “fixed-income arbitrage” play that cushions volatility in late bull cycles. The trade-off: security of fixed returns at the cost of potentially larger gains from holding volatile assets.

• Bear Market Downturn: With sentiment depressed and borrowing demand collapsing, rates fall and YT approaches zero value, while PT behaves more like a risk-free asset. Buying and holding PT to maturity secures predictable returns even in low-rate environments, functioning as a defensive allocation for conservative investors.

• Sideways Market: Rates lack clear direction, market expectations are divided, and PT/ YT often display short-term mispricing. Investors can exploit these mismatches through inter-temporal arbitrage across different PT/YT maturities or by trading mispriced yield rights, generating stable spread returns. These strategies require sharper analysis and execution, but they can offer steady alpha in trendless conditions.

Pendle Strategy Market Cycle Matrix

The Pendle Strategy Tree for Conservative vs. Aggressive Investors

Overall, Pendle strategies lean toward stable yield generation, with the core logic being to balance risk and return across market cycles by buying PT, buying YT, or participating in stablecoin pools. For investors with higher risk tolerance, more aggressive strategies—such as shorting PT or YT to bet on rate movements or market mispricings—can be employed. These require sharper judgment and execution capabilities, and come with greater risk exposure. As such, this report does not expand on them in detail; a reference decision tree is provided below.

Pendle Coin-Denominated Strategies: stETH, uniBTC, and Stablecoin Pools

The above Pendle strategy analysis has so far been framed from a USD-denominated perspective, focusing on how to lock in high yields or capture rate volatility for excess returns. Beyond this, Pendle also offers BTC- and ETH-denominated strategies.

ETH is widely regarded as the best asset for coin-denominated strategies due to its ecosystem dominance and long-term value certainty. As Ethereum’s native asset, ETH underpins most DeFi protocols and generates a sustainable cash flow through staking yields. In contrast, BTC has no native yield, and its returns on Pendle depend primarily on protocol incentives, making its coin-denominated logic relatively weaker. Stablecoin pools, meanwhile, are better suited as defensive allocations—acting as “preserve & wait” positions.

Across different market cycles, the three pool types display distinct strategy characteristics:

• Bull Market: stETH pools are the most aggressive — YT positions amplify ETH accumulation via leveraged staking yields; uniBTC can serve as a speculative supplement; stablecoin pools lose relative appeal.

• Bear Market: Discounted YT in stETH pools offers the best ETH accumulation opportunity; stablecoin pools serve as the main defensive allocation; uniBTC is only suitable for small-scale short-term arbitrage.

Sideways Market: stETH pools provide arbitrage potential via PT-YT mispricing and AMM fees; uniBTC fits short-term speculation; stablecoin pools act as a stable, low-volatility supplement.

Boros Strategy Landscape: Rate Swaps, Hedging, and Arbitrage

Beyond PT/YT trading and AMM pools, Pendle has launched Boros, a dedicated funding-rate trading module. Boros effectively “tokenizes” funding rates—akin to importing interest rate swaps (IRS) and basis trading/carry trades into DeFi—transforming what is usually an uncontrollable cost into a tradable, configurable investment instrument. Its core primitive, the Yield Unit (YU), supports three major categories of strategies: speculation, hedging, and arbitrage.

• Speculation: Long YU (pay fixed, receive floating) to bet on higher funding rates; Short YU (receive fixed, pay floating) to bet on lower funding rates—similar to traditional interest rate derivatives.

• Hedging: Boros allows institutions with large perpetual positions to swap floating funding payments/receipts into fixed cash flows.

  • Funding Cost Hedge: Long Perp + Long YU → lock floating funding payments into fixed costs.

  • Funding Income Hedge: Short Perp + Short YU → lock floating funding income into fixed revenues.

• Arbitrage: Investors can build delta-neutral enhanced yield or arbitrage/spread trades by exploiting mispricings across markets (Futures Premium vs. Implied APR) or across maturities (term arbitrage).

Overall: Boros is designed for professional capital, with primary value in risk management and steady yield enhancement, but limited retail friendliness.

Boros Strategy Matrix:

3. Complexity of Pendle Strategies, Value of AgentFi

As outlined earlier, Pendle’s trading strategies essentially function as complex bond derivatives. Even the simplest approach—buying PT to lock in fixed yield—still involves multiple layers of consideration, including rollover at maturity, interest rate fluctuations, opportunity cost, and liquidity depth. More advanced strategies, such as YT speculation, term arbitrage, leveraged combinations, or dynamic comparisons with external lending markets, add further complexity. Unlike lending or staking products, where users can “deposit once and earn continuously” with floating returns, Pendle’s PT (Principal Token) must carry a fixed maturity (typically several weeks to months). At maturity, the principal redeems 1:1 into the underlying asset, and a new position must be established to continue earning. This periodicity is a structural necessity of fixed-income markets, and represents Pendle’s fundamental distinction from perpetual lending protocols—certainty comes with the constraint of maturity.

Currently, Pendle does not provide a native auto-rollover feature. Instead, some DeFi strategy vaults offer Auto-Rollover solutions to balance user experience with protocol simplicity, typically in three forms:

• Passive Auto-Rollover: Simple logic—when PT matures, the vault automatically reinvests principal into a new PT. This delivers a seamless user experience but lacks flexibility. If lending rates on Aave or Morpho rise above Pendle’s fixed rate, forced rollover creates opportunity cost.

• Smart Auto-Rollover: The vault dynamically compares Pendle’s fixed rate with floating lending rates, avoiding “blind rollover” and achieving better yield optimization with flexibility:

  • If Pendle fixed rate > floating lending rate → reinvest into PT, locking in higher certainty.

  • If floating lending rate > Pendle fixed rate → allocate to Aave/Morpho to capture higher returns.

• Hybrid Allocation: A split strategy, with part of the capital in PT for fixed yield and part in lending markets for floating yield. This balances stability and flexibility, reducing the risk of being disadvantaged under extreme rate environments.

This is precisely where AgentFi adds unique value: it can automate these complex rate arbitrage dynamics. Pendle’s fixed PT rates and external floating rates fluctuate in real time, making continuous manual monitoring and switching impractical. While passive Auto-Rollover offers only mechanical reinvestment, AgentFi can dynamically compare rates, auto-adjust allocations, and optimize portfolios according to each user’s risk preferences. In more advanced use cases, such as Boros strategies, AgentFi can also manage funding rate hedging, cross-market arbitrage, and term-structure trades—further unlocking the potential of professional-grade yield management.

4. Pulse: The First AgentFi Product Built on Pendle PT Strategies

In our previous AgentFi research report “The New Paradigm of Stablecoin Yields: From AgentFi to XenoFi”, we introduced ARMA(https://app.arma.xyz/), a stablecoin yield optimization agent built on Giza’s infrastructure layer, which empowers the creation of financial agents across diverse use cases. ARMA, deployed on Base, reallocates funds across AAVE, Morpho, Compound, Moonwell, and other lending protocols to maximize returns — establishing itself as the first lending-focused AgentFi product and a long-standing leader in the sector. Giza is an infrastructure

In September 2025 the Giza team launched Pulse Optimizer(https://app.usepulse.xyz/), the industry’s first AgentFi automation system for Pendle’s fixed-yield PT markets.  Unlike ARMA, which targets stablecoin lending, Pulse focuses on Pendle’s fixed-income opportunities: it leverages deterministic algorithms (not LLMs) to continuously monitor multi-chain PT markets, dynamically reallocates positions via linear programming while factoring in cross-chain costs, maturity management, and liquidity constraints, and automates rollovers, cross-chain transfers, and compounding. Its goal is to maximize portfolio APY under controlled risk, abstracting the complex manual process of “finding APY / rolling maturities / bridging / timing trades” into a one-click fixed-income agent experience.

Pulse Core Architecture

• Data Collection: Continuously tracks multi-chain Pendle markets, including active markets, APYs, maturities, liquidity depth, and bridge fees, while modeling slippage and price impact to provide accurate inputs for optimization.

• Wallet Manager: Acts as the operational hub, generating portfolio snapshots, standardizing cross-chain assets, and enforcing risk controls (e.g., minimum APY improvement thresholds, historical value checks).

• Optimization Engine: Uses linear programming to model allocations across chains, integrating capital distribution, bridge fee curves, slippage costs, and market maturities to output optimal strategies under risk constraints.

Execution Planning: Converts optimization results into transaction sequences, including liquidating inefficient positions, planning bridges/swaps, rebuilding positions, and triggering full portfolio exit if needed — forming a closed optimization loop.

5. Core Features and Product Progress

Pulse currently focuses on ETH-denominated yield optimization, managing ETH and liquid staking derivatives (wstETH, weETH, rsETH, uniETH, etc.) across multiple Pendle PT markets. The system uses ETH as the base asset, handling cross-chain token conversions automatically to achieve optimal allocation. Pulse is live on Arbitrum mainnet and will expand to Ethereum mainnet, Base, Mantle, Sonic, and more, with Stargate integration enabling broader interoperability.

User Experience Flow

Agent Activation & Fund Management: Users can activate Pulse at www.usepulse.xyz with one click. The process includes wallet connection, Arbitrum network verification, allowlist confirmation, and a minimum initial deposit of 0.13 ETH (~$500). Once activated, funds are automatically deployed into the optimal PT markets, entering continuous optimization cycles. Users can add funds at any time, triggering rebalancing and reallocation; no minimum applies for subsequent deposits, though larger capital improves diversification and efficiency.

• Dashboard & Performance Monitoring: Pulse provides a full analytics dashboard to track investment performance in real time:

  • Key Metrics: Total balance, principal invested, cumulative returns, and allocation across PT tokens and chains.

  • Yield & Risk Analysis: Trends at daily/weekly/monthly/yearly horizons; real-time APR monitoring, annualized projections, and benchmark comparisons to assess agent outperformance.

  • Multi-Layer Views: By PT tokens (PT-rETH, PT-weETH, etc.), underlying LST/LRT assets, or cross-chain allocations.

  • Execution Transparency: Complete logs of all agent actions — timestamps, operations, fund sizes, yield impacts, on-chain hashes, and gas costs.

  • Optimization Impact: Metrics on rebalancing frequency, APR improvements, diversification, and market responsiveness, benchmarked against static strategies to reflect true risk-adjusted performance.

Exit & Withdrawal: Users can deactivate Pulse at any time. The agent liquidates PT positions, converts them back to ETH, and returns funds to the user’s wallet. Only a 10% success fee is charged on profits (principal is fully returned). Clear breakdowns of yields and fees are shown before exit, with withdrawals usually processed within minutes. Users may reactivate anytime with preserved historical performance records.

6. Swarm Finance: The Incentive Layer for Active Liquidity

In September 2025, Giza launched Swarm Finance — an incentive distribution layer purpose-built for Active Capital. Its mission is to connect protocol incentives directly to agent networks via standardized APR feeds (sAPR), effectively making capital “intelligent.”

• For Users: Funds are automatically and optimally allocated across chains and protocols in real time, capturing the best yields without manual monitoring or compounding.

• For Protocols: Swarm Finance solves Pendle’s “maturity-driven TVL churn” by enabling automatic rollovers, delivering stickier liquidity, and lowering governance overhead for liquidity management.

• For Ecosystems: Capital flows faster across chains and protocols, enhancing efficiency, price discovery, and utilization.

• For Giza: Incentive flows routed through Swarm Finance feed back into $GIZA tokenomics via fee capture and buyback mechanisms.

According to Giza, when Pulse launched ETH PT markets on Arbitrum, it delivered ~13% APR, More importantly, Pulse’s automated rollover mechanism resolved Pendle’s TVL churn problem, creating more stable liquidity growth. As the first live application of Swarm Finance, Pulse demonstrates the power of agent-driven optimization and signals the beginning of a new paradigm for Active Capital in DeFi.

7. Conclusion and Outlook

As the first AgentFi product built on Pendle PT strategies, Pulse marks a milestone for both Giza and the broader AgentFi sector. By abstracting complex PT fixed-income trading into a one-click agent experience, it lowers the user barrier significantly while boosting Pendle’s liquidity efficiency.

That said, Pulse remains early-stage, focused primarily on ETH PT strategies. Looking ahead, we anticipate:

• Stablecoin PT AgentFi products — catering to more risk-averse investors.

• Smarter Auto-Rollover — dynamically comparing Pendle fixed yields with lending market floating rates for more flexible optimization.

• Cycle-Aware Strategy Coverage — modularizing Pendle strategies for bull/bear/sideways markets, including YT, stablecoin pools, shorts, and arbitrage.

• Boros-Integrated AgentFi products — enabling smarter delta-neutral yields and cross-market/term arbitrage beyond what Ethena offers today.

Of course, Pulse faces the same risks inherent to any DeFi product, including protocol and contract security (potential vulnerabilities in Pendle or cross-chain bridges), execution risks (failed rollovers at maturity or cross-chain rebalancing), and market risks (rate volatility, insufficient liquidity, diminishing incentives). Moreover, Pulse’s returns are tied to ETH and its LST/LRT markets: if Ethereum’s price experiences a sharp decline, even an increase in ETH-denominated holdings may still translate into losses when measured in USD terms.

Overall, the launch of Pulse not only expands the boundaries of AgentFi but also opens new possibilities for the automation and scaling of Pendle strategies across different market cycles, marking an important step forward in the intelligent evolution of DeFi fixed income.

Disclaimer:* This article was prepared with the assistance of ChatGPT-5, an AI-based tool. While the author has exercised due diligence in reviewing and verifying the accuracy of the information presented, inadvertent errors or omissions may remain. Readers are further advised that the fundamentals of crypto assets often diverge materially from their performance in secondary markets. The content herein is provided strictly for informational consolidation and academic or research discussion. It does not constitute investment advice, nor should it be construed as a recommendation to purchase or sell any digital tokens.*

在《DeFi 的智能进化：从自动化到 AgentFi 的演进路径》研报中，我们系统梳理并比较了 DeFi 智能化发展的三个阶段：自动化工具（Automation）、意图驱动助手（Intent-Centric Copilot） 与 AgentFi（链上智能体）。除借贷（Lending）与流动性挖矿（Yield Farming）这两类最具价值且易于落地的场景之外，在我们对 AgentFi 的高阶设想中，Pendle 的 PT/YT 收益权交易被视为极度契合 AgentFi 的高优先级应用。Pendle 以其独特的“收益拆分 + 到期机制 + 收益权交易”架构，为智能体提供了天然的策略编排空间，使自动化执行与收益优化具备了更丰富的可能性。

一、Pendle的基本原理

Pendle 是 DeFi 领域首个专注于收益率拆分与交易的协议。其核心创新在于：将链上收益型资产（如 LST、稳定币存款凭证、借贷头寸等）的未来收益流进行代币化并分离，从而让用户能够在市场中灵活地锁定固定收益、放大收益预期或进行投机套利。

简而言之，Pendle 为加密资产的“收益率曲线”构建了二级市场，使 DeFi 用户不仅可以交易“本金”，还能够交易“收益”。这一机制与传统金融中的零息债券 + 票息拆分高度相似，为DeFi 资产提升了定价精度与交易灵活性。

Pendle的收益拆分机制

Pendle 将一份带收益的基础资产（Yield-Bearing Asset, YBA）拆分为两种可交易的代币：

• PT（Principal Token，本金代币，类似于零息债券）：代表在到期时可以赎回的本金价值，但不再享有收益。

• YT（Yield Token，收益代币，类似于票息权利）：代表该资产在到期前产生的全部收益，但到期后将归零。

• 例如存入 1 ETH stETH 后，会被拆分为 PT-stETH（到期可赎回 1 ETH，本金锁定）和 YT-stETH（获取到期前全部质押收益）。

Pendle 并非只是单纯的代币拆分，还通过专门设计的 **AMM（自动化做市商）**为 PT 与 YT 提供了流动性市场(相当于债券市场的二级流动性池)。用户可以随时买卖 PT 或 YT，以灵活调整自身的收益风险敞口；其中，PT 的价格通常低于 1，反映其“折现后的本金价值”，而 YT 的价格则取决于市场对未来收益的预期。更重要的是，Pendle 的 AMM 针对带有到期日的资产进行了优化，使不同期限的 PT/YT 得以在市场中形成一条收益率曲线，与传统金融的债券市场高度相似。

需要特别说明的是，在 Pendle 的稳定币资产中，PT（本金代币，固定收益型仓位） 相当于链上债券，买入时通过折价锁定固定利率，到期可 1:1 兑回稳定币，收益稳健、风险较低，适合追求确定性回报的保守型投资者；而 Stablecoin Pool（流动性挖矿型仓位） 本质上是 AMM 做市，LP 收益来自手续费与激励，APY 浮动较大，同时伴随无常损失风险，更适合能承受波动、追求更高收益的主动型投资者。在交易量活跃且激励丰厚的市场中，Pool 收益有可能显著高于 PT 固收；而在交易冷清、激励不足时，Pool 收益往往低于 PT，甚至可能因无常损失出现亏损。

Pendle 的 PT/YT 交易策略主要涵盖 固定收益、收益投机、跨期套利与杠杆化收益 四大路径，能够满足不同风险偏好的投资需求。用户可以通过买入 PT 并持有至到期来锁定固定收益，相当于获得确定利率；也可选择买入 YT，押注收益率上升或波动加大，从而进行收益投机。同时，投资者还可利用不同期限 PT/YT 的价格差开展跨期套利，或将 PT、YT 用作抵押物叠加借贷协议，从而放大收益敞口。

Boros 的资金费率交易机制

在 Pendle V2 的收益拆分之外，Boros 模块进一步将 资金费率（Funding Rate）资产化，使其不再只是永续合约持仓的被动成本，而是可独立定价和交易的工具。通过 Boros，投资者可以方向性投机、风险对冲或套利机会，这一机制实质上将 传统利率衍生品（IRS、基差交易） 引入 DeFi，为机构级资金管理和稳健收益策略提供了新的工具。

除 PT/YT 交易与 AMM 池 以及 Boros 资金费率交易机制 之外，Pendle V2 还提供了若干扩展功能，尽管并非本文的重点，但仍构成协议生态的重要补充：

• vePENDLE：基于投票锁仓（Vote-Escrow）机制的治理与激励模型，用户通过锁定 PENDLE 获得 vePENDLE，从而参与治理投票并提升收益分配权重，是协议长期激励与治理的核心。

• PendleSwap：一站式资产交换入口，帮助用户在 PT/YT 与原生资产之间高效切换，提升资金使用的便捷性与协议可组合性，本质上是 DEX 聚合器而非独立创新。

Points Market：允许用户在二级市场提前交易各类项目积分（Points），为空投捕获与积分套利提供流动性，更偏向于投机与话题性场景，而非核心价值。

二、Pendle 策略全景：市场周期、风险分层与衍生扩展

在传统金融市场中，散户的投资渠道主要集中在股票交易与固定收益类理财产品，通常难以直接参与门槛较高的债券衍生品交易。对应在 Crypto 市场，零售用户同样更容易接受代币交易与Defi借贷，虽然 Pendle 的出现极大地降低了散户进入“债券衍生品”交易的门槛，但Pendle 的策略仍对专业度有较高要求，需要投资者对收益性资产利率在不同市场环境下的变化进行深入分析。基于此，我们认为在牛市初期、牛市亢奋期、熊市下行期以及区间震荡期等不同市场阶段，投资者应结合自身的风险偏好，匹配差异化的 Pendle 交易策略。

• 牛市上升期：市场风险偏好逐步恢复，借贷需求和利率仍处在低位，Pendle 上的 YT 定价相对便宜。此时，买入 YT 相当于押注未来收益率上升，一旦市场进入加速上涨阶段，借贷利率和 LST 收益都会抬升，从而推高 YT 的价值。这是典型的高风险高回报策略，适合愿意提前布局、捕捉牛市放大收益的投资者。

• 牛市亢奋期，市场情绪高涨推动借贷需求飙升，Defi借贷协议的利率往往从个位数攀升至 15–30% 以上，使 Pendle 上的 YT 价值水涨船高、PT 出现显著折价。此时投资者若用稳定币买入 PT，相当于折价锁定高利率，到期即可 1:1 兑回标的资产，实质上是在牛市后期通过“固收套利”抵御波动性风险。该策略的优势在于稳健、理性，能够在市场回调或熊市来临时确保固定收益与本金安全，但其代价是放弃了继续持有波动性资产可能带来的更大涨幅。

• 熊市下行期，市场情绪低迷，借贷需求骤减，利率大幅回落，YT 收益趋近于零，而 PT 则更接近无风险资产的表现。此时，买入 PT 并持有到期，意味着在低利率环境下依旧能锁定一个确定回报，相当于建立防御性仓位；对于保守型投资者而言，这是规避收益波动、保存本金的主要策略。

• 区间震荡期，市场利率缺乏趋势性，市场预期分歧较大，Pendle 的 PT 与 YT 经常出现短期错配或定价偏差。投资者可以通过在不同期限的 PT/YT 之间进行跨期套利，或者捕捉因市场情绪波动导致的收益权错价，从中获取稳定的价差收益。这类策略对分析和执行能力要求更高，有望在无趋势行情中获取稳健收益。

全局视角：Pendle 策略全市场周期对照表

风险分层：稳健 vs 激进策略下的Pendle决策树

当然，以上策略总体以稳健收益为主，核心逻辑是在不同市场周期下通过 买入 PT、买入 YT 或参与稳定币池挖矿 来实现风险与收益的平衡。对于风险偏好较高的激进型投资者，也可以选择更具进攻性的 卖出 PT 或 YT 策略，用于押注利率走势或博弈市场错配。此类操作对专业判断和执行力要求更高，风险敞口也更大，因此本文不做过多延展，仅供参考，具体可见下方决策树。

Pendle 币本位策略：stETH、uniBTC 与稳定币池对比

当然，以上 Pendle 策略的分析均基于 U 本位视角。策略重点在于如何通过 锁定高利率 或 捕捉利率波动 来获取超额收益；除此之外，Pendle亦提供BTC 和 ETH 的 币本位策略。

ETH 被普遍认为是币本位策略的最佳标的，原因在于其生态地位与长期价值确定性：作为以太坊网络的原生资产，ETH 不仅是大多数 DeFi 协议的结算基础，还具备质押收益（Staking Yield）这一稳定的现金流来源。与之相比，BTC 并无原生利率，其在 Pendle 上的收益主要依赖协议激励，因此币本位逻辑相对较弱；而稳定币池则更适合作为防御性配置，承担“保值+等待”的作用。

在不同市场周期下，三类资产池的策略差异显著：

• 牛市：stETH 池最具进攻性，YT 是杠杆化 ETH 增持的最佳策略；uniBTC 可作为补充，但更偏投机；稳定币池吸引力相对下降。

• 熊市：stETH 低价 YT 提供增持 ETH 的核心机会；稳定币池承担主要防御功能；uniBTC 仅适合小规模短期套利。

震荡市：stETH 的 PT-YT 错配与 AMM 手续费提供套利机会；uniBTC 适合短期博弈；稳定币池则提供稳健补充。

Boros 策略全景：利率掉期、对冲与跨市场套利

Boros 将资金费率（Funding Rate）这一浮动变量资产化，相当于把传统金融的 利率掉期（Interest Rate Swaps, IRS） 与 基差交易（Basis Trading / Carry Trade） 引入 DeFi，使资金费率从不可控的成本项转化为可配置的投资工具。其核心凭证 Yield Units (YU) 支撑了三类主要策略路径：投机、对冲与套利。

• 投机方面，投资者可通过 Long YU（付固定利率 Implied APR，收浮动利率 Underlying APR）押注资金费率上升，或 Short YU（收固定利率 Implied APR，付浮动利率Underlying APR）押注资金费率下降，类似于传统的利率衍生品交易。

• 对冲方面，Boros 为持有大额永续合约仓位的机构提供了将浮动资金费率转化为固定利率的工具；

  • 对冲资金费率风险(Funding Rate Hedging)：Long Perp + Long YU ,将浮动资金费率支出锁定为固定成本。

  • 锁定收取的资金费率(Funding Rate Income Hedging): Short Perp + Short YU → 将浮动资金费率收入锁定为固定收益。

• 套利方面，投资者可以通过稳健增益组合(Delta-Neutral Enhanced Yield) 或稳健套利(Arbitrage / Spread Trade)，利用跨市场（Futures Premium vs Implied APR）或跨期限定价差，获取相对稳健的利差收益。

整体而言，Boros适合专业资金用于 风险管理与稳健增益，但对零售用户的友好度有限。

三、Pendle 策略复杂性与 AgentFi独特价值

依据前文分析，Pendle 的交易策略本质上是复杂的债券衍生品交易，即便是最简单的 买入 PT 锁定固定收益，仍需考虑到期换仓、利率波动、机会成本与流动性深度等多重因素，更不用说 YT 投机、跨期套利、杠杆化组合或与外部借贷市场的动态比较。与借贷或 Staking 这类“存入一次即可持续生息”的浮动收益产品不同，Pendle 的 PT（本金代币）必须设定明确的到期日（通常为数周至数月），到期后本金按 1:1 兑回标的资产，若要继续获得收益则需重新建仓。这种“定期性”期限约束是固定收益市场的必要前提，也是 Pendle 与永续型借贷协议的根本差异。

目前，Pendle 官方并未内置自动续期机制，而部分Defi策略金库提供“Auto-Rollover”方案，以在用户体验与协议简洁性之间取得平衡。目前分为被动、智能和混合三种Auto-Rollover模式

• 被动 Auto-Rollover：逻辑简单，PT 到期后本金自动续投新 PT，用户体验顺畅。但缺乏灵活性，一旦Aave、Morpho的浮动利率更高，强制续期便会带来机会成本。

• 智能 Auto-Rollover：由 Vault 动态比较 Pendle 固定利率与借贷市场浮动利率，避免“盲目续期”，在提升收益的同时保持灵活性，更符合收益最大化需求

  • 若 Pendle 固定利率 > 借贷浮动利率 → 续投 PT，锁定确定性更高的固定收益；

  • 若 借贷浮动利率 > Pendle 固定利率 → 转入 Aave/Morpho 等借贷协议，获取更高的浮动利率。

• 混合配置：部分资金锁定 PT 固定利率，部分资金流向借贷市场，形成稳健与灵活兼顾的组合，避免在极端情况下被单一利率环境“甩开”。

因此，AgentFi 在 Pendle 交易策略中具有独特价值：它能将复杂的利率博弈自动化。Pendle 的 PT 固定利率与借贷市场浮动利率实时波动，人工难以持续监控与切换；普通 Auto-Rollover 仅是被动续期，而 AgentFi 则可动态比较利率水平、自动调仓，并根据用户风险偏好优化仓位配置。在更复杂的 Boros 策略中，AgentFi 还能承担资金费率对冲、跨市场套利与期限套利等操作，进一步释放专业化收益管理的潜力。

四、Pulse：首个基于 Pendle PT 策略的 AgentFi 产品

在此前的 AgentFi 系列研报《稳定币收益的新范式：AgentFi到XenoFi》中，我们介绍过基于 Giza基建层推出的稳定币收益优化代理 ARMA(https://app.arma.xyz/)。该产品部署在 Base 链，能在 AAVE、Morpho、Compound、Moonwell 等借贷协议间自动切换，实现跨协议收益最大化，并长期稳居 AgentFi 的第一梯队。

2025 年 9 月，Giza 团队正式推出 Pulse Optimizer(https://app.usepulse.xyz/) ——业内首个基于 Pendle PT 固定收益市场 的 AgentFi 自动化优化系统。与聚焦稳定币借贷的 ARMA 不同，Pulse 专注于 Pendle 固收场景：通过确定性算法（非 LLM）实时监控多链 PT 市场，在考虑跨链成本、到期管理与流动性约束的前提下，利用线性规划动态分配仓位，并自动完成续作（rollover）、跨链调度与复利。其目标是在风险可控的条件下最大化组合 APY，将复杂的“找/APY/换仓/跨链/择时”过程抽象为一键式的固收体验。

Pulse 核心架构组件

• 数据采集（Data Collection）：实时抓取 Pendle 多链市场数据，包括活跃市场、APY、到期时间、流动性和跨链桥费用，并建模滑点与价格冲击，为优化引擎提供精准输入。

• 钱包管理（Wallet Manager）：作为资产与逻辑中枢，生成投资组合快照，管理跨链资产标准化，执行风险控制（如最小 APY 改善阈值、历史价值对比）。

• 优化引擎（Optimization Engine）：基于线性规划建模，综合考虑资金分配、跨链来源、桥费曲线、滑点和市场到期，输出风险约束下的最优配置方案。

执行规划（Execution Planning）：将优化结果转化为交易序列，包括清算低效仓位、规划桥接与 Swap 路径、重建新仓位，并在必要时触发全额退出机制，形成完整闭环。

五、Pulse 核心功能与产品进展

Pulse 当前专注于 ETH 本位收益优化，自动化管理 ETH 及其流动性质押衍生品（wstETH、weETH、rsETH、uniETH 等），并在多个 Pendle PT 市场中进行动态分配。系统以 ETH 作为基础资产，自动完成跨链代币转换，实现最优配置。当前已上线 Arbitrum 主网，后续将扩展至以太坊主网、Base、Mantle、Sonic 等，并通过 Stargate 桥接实现多链互操作性。

Pulse用户体验全流程

Agent 激活与资金管理：用户可在官方网站（www.usepulse.xyz）一键启动 Pulse Agent，流程包括连接钱包、网络认证、白名单验证，并存入最低 0.13 ETH（约 $500）。完成激活后，资金即自动部署至最优 PT 市场并进入持续优化循环。用户可随时追加资金，系统会自动再平衡与重新分配，后续存入不设最低门槛，大额资金则可提升组合多样化与优化效果。

数据看板与绩效监控Pulse 提供可视化数据看板，实时跟踪并评估投资表现：

• 关键指标：总资产余额、累计投入、本金与收益增长率，不同 PT 代币和跨链头寸的仓位分布。

• 收益与风险分析：支持日/周/月/年维度的趋势跟踪，结合 APR 实时监控、年度预测和市场对比，帮助衡量自动化优化带来的超额回报。

• 多维度拆解：按 PT Token（如 PT-rETH、PT-weETH）、Underlying Token（LST/LRT 协议）及跨链分布进行展示。

• 执行透明度：完整保留操作日志，包括调仓时间、操作类型、资金规模、收益影响及链上哈希，确保可验证性。

• 优化成效：提示再平衡频率、APR 改善幅度、分散化程度及市场响应速度，并与静态持仓或市场基准对比，评估风险调整后的真实收益。

退出与资产提取：用户可随时关闭 Agent，Pulse 会自动清算 PT 代币并兑换回 ETH，仅对利润部分收取 10% success fee，本金全额返还。退出前系统将透明展示收益与费用明细，提现通常数分钟内完成。用户退出后可随时重新激活，历史收益记录将被完整保留。

六、Swarm Finance：主动流动性激励层

2025年9月，Giza 正式推出 Swarm Finance —— 一个专为 主动流动性（Active Capital） 设计的激励分发层。其核心使命是通过 标准化 APR feeds（sAPR） 将协议激励直接接入智能体网络，从而让资本真正实现“智能化”。

• 对用户而言：资金能够在多链、多协议间实现 实时、自动化的最优分配，无需手动监控或复投，即可捕捉最高收益机会。

• 对协议而言：Swarm Finance 解决了 Pendle 等项目的 到期赎回—TVL 流失 痛点，带来更稳定、黏性的流动性，同时显著降低了流动性管理的治理成本。

• 对生态而言：资本在更短时间内完成 跨链与跨协议迁移，提升了市场效率、价格发现能力与资金利用率。

• 对 Giza 自身而言：所有通过 Swarm Finance 路由的激励流量，部分将回流至 $GIZA，通过 fee capture → buyback 机制启动 Tokenomics 飞轮。

根据 Giza 官方数据，Pulse 在 Arbitrum 上线 ETH PT 市场时实现了约 13% APR。更重要的是，Pulse 通过 自动 rollover 机制 解决了 Pendle 到期赎回导致的 TVL 流失问题，为 Pendle 建立了更稳健的资金沉淀与增长曲线。作为 Swarm Finance 激励网络的首个落地实践，Pulse 不仅展现了智能代理化的潜力，更标志着 DeFi 主动流动性（Active Capital）的新范式正式开启。

七、总结与展望

作为业内首款基于 Pendle PT 策略的 AgentFi 产品，Giza 团队推出的 Pulse 无疑具有里程碑式意义。它首次将复杂的 PT 固定收益交易流程抽象为一键式的智能代理体验，在跨链配置、到期管理和自动复利等环节实现了全面自动化，显著降低了用户的操作门槛，同时提升了 Pendle 市场的资金利用效率与流动性。

Pulse 目前仍主要聚焦于 ETH PT 策略。展望未来，随着产品的不断迭代和更多 AgentFi 团队的加入，我们有望看到：

• 稳定币 PT 策略型产品 —— 为风险偏好更稳健的投资者提供匹配方案；

• 智能化 Auto-Rollover —— 动态比较 Pendle 固定利率与借贷市场浮动利率，在提升收益的同时保持灵活性；

• 基于市场周期的全景化策略覆盖 —— 将 Pendle 在牛熊不同阶段的交易策略模块化，覆盖 YT、稳定币池，甚至做空和套利等更高级的玩法；

• Boros 策略型 AgentFi 产品 —— 实现比 Ethena 更智能的 Delta-Neutral 固定收益及跨市场/期限套利，推动 DeFi 固收市场的进一步专业化与智能化。

当然，Pulse同样面临任何Defi产品都面临的风险，包括协议与合约安全（Pendle 或跨链桥潜在漏洞）、策略执行风险（到期 rollover 或跨链再平衡失败）、市场风险（利率波动、流动性不足、激励衰减）。此外，Pulse 收益依托于 ETH 及其 LST/LRT 市场，若以太坊价格大幅下跌，即便 ETH 本位数量增加，美元计价下仍可能出现亏损。

总体而言，Pulse 的诞生不仅拓展了 AgentFi 的产品边界，也为 Pendle 策略在不同市场周期下的自动化与规模化应用打开了新的想象空间，代表了 DeFi 固收智能化发展的重要一步。

***免责声明：***本文在创作过程中借助了 ChatGPT-5 的 AI 工具辅助完成，作者已尽力校对并确保信息真实与准确，但仍难免存在疏漏，敬请谅解。需特别提示的是，加密资产市场普遍存在项目基本面与二级市场价格表现背离的情况。本文内容仅用于信息整合与学术/研究交流，不构成任何投资建议，亦不应视为任何代币的买卖推荐。

I. The Evolution of the ZK Track

The development of Zero-Knowledge Proofs has been neither instantaneous nor accidental, but rather the result of decades of theoretical accumulation and engineering breakthroughs. Broadly, it can be divided into the following stages:

1. **Theoretical Foundations & Technical Breakthroughs (1980s–2010s)**The ZK concept was first proposed by MIT scholars Shafi Goldwasser, Silvio Micali, and Charles Rackoff, initially limited to interactive proof theory. In the 2010s, the emergence of Non-Interactive Zero-Knowledge proofs (NIZKs) and zk-SNARKs significantly improved proof efficiency, though they still relied on trusted setup.

2. **Blockchain Applications (Late 2010s)**Zcash introduced zk-SNARKs to enable private payments, marking the first large-scale blockchain deployment of ZK. However, due to the high cost of proof generation, real-world applications remained relatively limited.

3. **Explosive Growth & Expansion (2020s–present)**During this period, ZK technology entered the industry mainstream:

   • ZK Rollups: Off-chain batch computation with on-chain proofs enabled high throughput and security inheritance, becoming the core Layer 2 scaling path.

   • zk-STARKs: StarkWare introduced zk-STARKs, eliminating trusted setup while enhancing transparency and scalability.

   • zkEVMs: Teams like Scroll, Taiko, and Polygon advanced EVM bytecode-level proofs, enabling seamless migration of existing Solidity applications.

   • General-purpose zkVMs: Projects such as RISC Zero, Succinct SP1, and Delphinus zkWasm supported verifiable execution of arbitrary programs, extending ZK from a scaling tool to a “trustworthy CPU.”

   • zkCoprocessors: Wrapping zkVMs as coprocessors to outsource complex logic (e.g., RISC Zero Steel, Succinct Coprocessor).

   • zkMarketplaces: Marketizing proof computation into decentralized prover networks (e.g., Boundless), pushing ZK toward becoming a universal compute layer.

Today, ZK technology has evolved from an esoteric cryptographic concept into a core component of blockchain infrastructure. Beyond supporting scalability and privacy, it is also demonstrating strategic value in interoperability, financial compliance, and frontier fields such as ZKML (zero-knowledge machine learning). With the continuous improvement of toolchains, hardware acceleration, and proof networks, the ZK ecosystem is rapidly moving toward large-scale and universal adoption.

II. The Application Landscape of ZK Technology: Scalability, Privacy, and Interoperability Scalability, Privacy, and Interoperability & Data Integrity form the three fundamental application scenarios of ZK-based “trusted computation.” They directly address blockchain’s native pain points: insufficient performance, lack of privacy, and trust across multiple chains.

1. Scalability: Scalability is both the earliest and most widely deployed use case for ZK. The core idea is to move transaction execution off-chain and only verify succinct proofs on-chain, thereby significantly increasing TPS and lowering costs without compromising security. Representative paths include: zkRollups (zkSync, Scroll, Polygon zkEVM): compressing batches of transactions for scaling. zkEVMs: building circuits at the EVM instruction level for native Ethereum compatibility. General-purpose zkVMs (RISC Zero, Succinct): enabling verifiable outsourcing of arbitrary logic.

2. Privacy: Privacy aims to prove the validity of a transaction or action without revealing sensitive data. Typical applications include: Private payments (Zcash, Aztec): ensuring transfer validity without disclosing amounts or counterparties. Private voting & DAO governance: enabling governance while keeping individual votes confidential. Private identity / KYC (zkID, zkKYC): proving “eligibility” without disclosing unnecessary information.

3. Interoperability & Data Integrity: Interoperability is the critical ZK path for solving trust issues in a multi-chain world. By generating proofs of another chain’s state, cross-chain interactions can eliminate reliance on centralized relays. Representative approaches include: zkBridges: cross-chain state proofs. Light client verification: efficient verification of source-chain headers on the target chain. Key projects: Polyhedra, Herodotus. Meanwhile, ZK is also widely used in data and state proofs, such as:Axiom, Space and Time’s zkQuery/zkSQL for historical data and SQL queries.and IoT and storage integrity verification, ensuring off-chain data is verifiably trusted on-chain.

4. 
   

Future Extensions On top of these three foundational scenarios, ZK technology has the potential to extend into broader industries: AI (zkML): generating verifiable proofs for model inference or training, enabling “trustworthy AI.” Financial compliance: proof-of-reserves (PoR), clearing, and auditing, reducing reliance on trust. Gaming & scientific computing: ensuring fairness in GameFi or the integrity of experiments in DeSci.At its core, all of these represent the expansion of “verifiable computation + data proofs” into diverse industries.

III. Beyond zkEVM: The Rise of General-Purpose zkVMs and Proof Markets

In 2022, Ethereum co-founder Vitalik Buterin introduced the four types of zkEVMs (Type 1–4), highlighting the trade-offs between compatibility and performance:

• Type 1 (Fully Equivalent): Bytecode identical to Ethereum L1; lowest migration cost but slowest proving. Example: Taiko.

• Type 2 (Fully Compatible): Maintains high EVM equivalence with minimal low-level optimizations; strongest compatibility. Examples: Scroll, Linea.

• Type 2.5 (Quasi-Compatible): Slight modifications to the EVM (e.g., gas costs, precompile support), sacrificing limited compatibility for better performance. Examples: Polygon zkEVM, Kakarot (EVM on Starknet).

• Type 3 (Partially Compatible): Deeper architectural modifications allow most applications to run but cannot fully reuse Ethereum infrastructure. Example: zkSync Era.

• Type 4 (Language-Level Compatible): Abandons bytecode compatibility, compiling directly from high-level languages into zkVM. Delivers the best performance but requires rebuilding the ecosystem. Example: Starknet (Cairo).

This stage has often been described as the “zkRollup wars,” aimed at alleviating Ethereum’s execution bottlenecks. However, two key limitations soon became apparent: (1) the difficulty of circuitizing the EVM, which constrained proving efficiency, and (2) the realization that ZK’s potential extends far beyond scaling—into cross-chain verification, data proofs, and even AI computation.

Against this backdrop, general-purpose zkVMs have risen, replacing the zkEVM’s “Ethereum-compatibility mindset” with a shift toward chain-agnostic trusted computation. Built on universal instruction sets (e.g., RISC-V, LLVM IR, Wasm), zkVMs support mainstream languages such as Rust and C/C++, allowing developers to build arbitrary application logic with mature libraries, and then generate proofs for on-chain verification. Representative projects include RISC Zero (RISC-V) and Delphinus zkWasm (Wasm). In essence, zkVMs are not merely Ethereum scaling tools, but rather the “trusted CPUs” of the ZK world.

• RISC-V Approach: Represented by Risc Zero, this path directly adopts the open RISC-V instruction set as the zkVM core. It benefits from an open ecosystem, a simple and circuit-friendly instruction set, and broad compatibility with Rust, C, and C++. Well-suited for building a “general-purpose zkCPU,” though it lacks native compatibility with Ethereum bytecode and therefore requires coprocessor integration.

• LLVM IR Approach: Represented by Succinct SP1, this design uses LLVM IR as the front-end for multi-language support, while the back-end remains a RISC-V zkVM. In essence, it is “LLVM front-end + RISC-V back-end.” This makes it more versatile than pure RISC-V, but LLVM IR’s complexity increases proving overhead.

• Wasm Approach: Represented by Delphinus zkWasm, this route leverages the mature WebAssembly ecosystem, which is widely known to developers and inherently cross-platform. However, WASM instructions are more complex and less circuit-friendly, which limits proving efficiency compared to RISC-V and LLVM IR.

As ZK technology evolves, it is trending toward modularization and marketization:

• zkVMs provide the universal, trusted execution environment—the CPU/compiler layer of zero-knowledge computation—supplying the foundational verifiable compute for applications.

• zk-Coprocessors encapsulate zkVMs as accelerators, enabling EVM and other chains to outsource complex computations off-chain, then verify them on-chain through proofs. Examples include RISC Zero Steel and Lagrange, which play roles analogous to “GPUs/coprocessors.”

• zkMarketplaces push this further by decentralizing the distribution of proving tasks. Global prover nodes compete to complete workloads, creating a compute marketplace for zero-knowledge proofs. Boundless is a prime example.

Thus, the zero-knowledge technology stack is gradually forming a progression: zkVM → zk-Coprocessor → zkMarketplace. This evolution marks the transformation of ZK proofs from a narrow Ethereum scaling tool into a general-purpose trusted computing infrastructure. Within this trajectory, RISC Zero’s adoption of RISC-V as its zkVM kernel strikes the optimal balance between openness, circuitization efficiency, and ecosystem compatibility. It not only delivers a low-barrier developer experience but also extends through layers like Steel, Bonsai, and Boundless to evolve zkVMs into zk-Coprocessors and decentralized proof markets—unlocking far broader application horizons.

IV. RISC Zero’s Technical Path and Ecosystem Landscape

RISC-V is an open, royalty-free instruction set architecture not controlled by any single vendor, inherently aligned with decentralization. Building on this open architecture, RISC Zero has developed a zkVM compatible with general-purpose languages like Rust, breaking through the limitations of Solidity within the Ethereum ecosystem. This allows developers to directly compile standard Rust programs into applications capable of generating zero-knowledge proofs. As a result, ZK technology extends beyond blockchain smart contracts into the broader domain of general-purpose computation.

RISC0 zkVM: A General-Purpose Trusted Computing EnvironmentUnlike zkEVM projects that must remain compatible with the complex EVM instruction set, the RISC0 zkVM is built on the simpler, more flexible RISC-V architecture. Applications are structured as Guest Code, compiled into ELF binaries. The Host runs these programs through the Executor, recording the execution process as a Session. A Prover then generates a verifiable Receipt, which contains both the public output (Journal) and the cryptographic proof (Seal). Any third party can verify the Receipt to confirm the correctness of the computation—without needing to re-execute it.

The Release of R0VM 2.0 (April 2025): Entering the Real-Time zkVM EraIn April 2025, the launch of R0VM 2.0 marked the beginning of real-time zkVMs: Ethereum block proving time was reduced from 35 minutes to 44 seconds, costs dropped by up to 5x, user memory was expanded to 3GB, enabling more complex application scenarios. Two critical precompiles—BN254 and BLS12-381—were also added, fully covering Ethereum’s mainstream needs. More importantly, R0VM 2.0 introduced formal verification for security, with most RISC-V circuits already deterministically verified. The target is to achieve the first block-level real-time zkVM (<12-second proofs) by July 2025.

zkCoprocessor Steel: A Bridge for Off-Chain ComputationThe core idea of a zkCoprocessor is to offload complex computational tasks from on-chain execution to off-chain environments, returning only a zero-knowledge proof of the result. Smart contracts need only verify the proof rather than recompute the entire task, thereby significantly reducing gas costs and breaking performance bottlenecks. RISC0’s Steel provides Solidity with an external proof interface, enabling outsourcing of large-scale historical state queries or cross-block batch computations—allowing even tens of Ethereum blocks to be verified with a single proof.

Bonsai: SaaS-Based Proving Service

RISC Zero’s Bonsai is an officially hosted Prover-as-a-Service platform that distributes proving tasks across its GPU clusters, delivering high-performance proofs without developer-managed hardware. With the Bento SDK, Solidity contracts can interact seamlessly with zkVM. By contrast, Boundless decentralizes the proving process through an open marketplace, making the two approaches complementary.

RISC Zero’s Full Product MatrixRISC Zero’s ecosystem extends upward from the zkVM, gradually forming a complete matrix that spans the execution, network, marketplace, and application layers:

V. The ZK Marketplace: Decentralized Commoditization of Trusted Computation

The ZK marketplace decouples the costly and complex process of proof generation, transforming it into a decentralized, tradable commodity of computation. Through globally distributed prover networks, tasks are outsourced via competitive bidding, dynamically balancing cost and efficiency. Economic incentives continuously attract GPU and ASIC participants, creating a self-reinforcing cycle. Boundless and Succinct are leading representatives of this emerging sector.

5.1 Boundless: A General-Purpose Zero-Knowledge Compute Marketplace

Concept & PositioningBoundless is a general-purpose ZK protocol developed by RISC Zero, designed to provide scalable verifiable compute capabilities for all blockchains. Its core innovation lies in decoupling proof generation from blockchain consensus, distributing computational tasks through a decentralized marketplace mechanism.

Developers submit proof requests, and prover nodes compete to execute them via decentralized incentive mechanisms. Rewards are issued based on Proof of Verifiable Work, where unlike traditional PoW’s wasteful energy expenditure, computational power is directly converted into useful ZK results for real applications. In this way, Boundless transforms raw compute resources into assets of intrinsic value.

V. The ZK Marketplace: Decentralized Commoditization of Trusted Computation

The ZK marketplace decouples the costly, complex process of proof generation and transforms it into a decentralized, tradable commodity of computation. Through globally distributed prover networks, tasks are outsourced via competitive bidding, dynamically balancing cost and efficiency. Economic incentives continuously attract GPU and ASIC participants, forming a self-reinforcing cycle. Boundless and Succinct are representative pioneers in this sector.

5.1 Boundless: General-Purpose ZK Compute Marketplace

Architecture & MechanismThe Boundless workflow consists of:

1. Request submission – Developers submit zkVM programs and inputs to the marketplace.

2. Node bidding – Prover nodes evaluate the task and place bids; once locked, the winning node gains execution rights.

3. Proof generation & aggregation – Complex computations are broken into subtasks, each generating zk-STARK proofs, which are then recursively aggregated into a single succinct proof, dramatically reducing on-chain verification costs.

4. Cross-chain verification – Boundless provides unified verification interfaces across multiple chains, enabling “build once, reuse everywhere.”

This architecture allows smart contracts to confirm computations by verifying a short proof—without re-executing heavy tasks—thereby breaking through gas and block capacity limits.

Ecosystem & ApplicationsAs a marketplace-layer protocol, Boundless complements other RISC Zero products:

• Steel – An EVM zkCoprocessor for outsourcing complex Solidity execution to off-chain environments with proof-backed verification.

• OP Kailua – A ZK upgrade path for OP Stack chains, improving both security and finality.

Boundless targets sub-12s real-time proofs on Ethereum, enabled by FRI optimizations, polynomial parallelization, and VPU hardware acceleration. As prover nodes and demand scale, Boundless aims to form a self-reinforcing compute network—reducing gas costs while unlocking new application frontiers such as verifiable on-chain AI, cross-chain liquidity, and unbounded computation.

5.2 Boundless for Apps: Breaking the Gas Ceiling

Boundless for Apps provides Ethereum and L2 applications with “infinite compute capacity” by offloading complex logic to the decentralized proving network, then verifying results on-chain. Its advantages include: unlimited execution, constant gas costs, Solidity/Vyper compatibility, and native cross-chain support.

At the core is Steel, the zkCoprocessor for EVM, enabling developers to build contracts with large-scale state queries, cross-block computations, and event-driven logic. Combined with the R0-Helios light client, Steel also supports cross-chain data verification between Ethereum and OP Stack. Projects including EigenLayer are already exploring integrations, highlighting its potential in DeFi and multi-chain interoperability.

Steel: EVM’s Scalable Compute LayerSteel’s primary goal is to overcome Ethereum’s limits on gas, single-block execution, and historical state access. By migrating heavy logic off-chain and returning only proofs, Steel delivers near-unlimited compute with fixed verification costs.

In Steel 2.0, developers gain three major capabilities to expand contract design:

• Event-driven logic – Using event logs as inputs directly, removing reliance on centralized indexers.

• Historical state queries – Accessing any storage slot or account balance since Ethereum’s Dencun upgrade.

• Cross-block computation – Performing calculations spanning multiple blocks (e.g., moving averages, cumulative metrics) and committing them on-chain with a single proof.

This design significantly lowers costs and makes previously infeasible applications—such as high-frequency computation, state backtracking, and cross-block logic—possible. Steel is emerging as a key bridge between off-chain computation and on-chain verification.

5.3 Boundless for Rollups: ZK-Accelerated Rollup Settlement

Boundless for Rollups leverages the decentralized proving network to provide OP Stack-based L2s with faster and more secure settlement. Its core advantages include:

• Faster finality – Reducing settlement from 7 days to ~3 hours (Hybrid mode) or under 1 hour (Validity mode).

• Stronger security – Gradual upgrades from ZK Fraud Proofs to full Validity Proofs, achieving cryptographic guarantees.

• Decentralized progression – Powered by a distributed prover network with low collateral requirements, enabling rapid Stage 2 decentralization.

• Native scalability – Maintaining stable performance and predictable costs even on high-throughput chains.

OP Kailua: The ZK Upgrade Path for OP ChainsLaunched by RISC Zero, OP Kailua is the flagship Boundless-for-Rollups solution. It allows OP Stack chains to surpass the performance and security limitations of traditional optimistic rollups.

Kailua supports two modes for progressive upgrading:

• Hybrid Mode (ZK Fraud Proofs) – Replaces multi-round interactive fault proofs with ZK Fraud Proofs, simplifying dispute resolution and cutting costs. The malicious actor bears the proving fees, reducing finality to ~3 hours.

• Validity Mode (ZK Validity Proofs) – Transitions to full ZK Rollup, eliminating disputes entirely with validity proofs, achieving sub-1-hour finality and the highest security guarantees.

Kailua enables OP chains to evolve smoothly from Optimistic → Hybrid → ZK Rollup, meeting Stage 2 decentralization requirements while lowering costs in high-throughput scenarios. Applications and tooling remain intact, ensuring ecosystem continuity while unlocking fast finality, reduced staking costs, and cryptographic security. Already, Eclipse has integrated Kailua for ZK Fraud Proofs, while BOB has migrated fully to ZK Rollup architecture.

5.4 The Signal: ZK Consensus Layer for Cross-Chain Interoperability

Positioning & MechanismThe Signal is Boundless’ flagship application—an open-source ZK consensus client. It compresses Ethereum Beacon Chain finality events into a single ZK proof, verifiable by any chain or contract. This enables trust-minimized cross-chain interoperability without multisigs or oracles. Its core value lies in giving Ethereum’s final state “global readability”, establishing a foundation for cross-chain liquidity and logic, while reducing redundant computation and gas costs.

Operating Mechanism

• Boost The Signal – Users can submit proof requests to “boost” the signal; all ETH is directed toward funding new proofs, extending signal longevity and benefiting all chains and apps.

• Prove The Signal – Anyone can run a Boundless prover node to generate and broadcast Ethereum block proofs, replacing multisig verification with a “mathematics over trust” consensus layer.

Expansion Path

1. Generate continuous proofs of Ethereum’s finalized blocks, forming the “Ethereum Signal.”

2. Extend to other blockchains, creating a unified multi-chain signal.

3. Interconnect chains on a shared cryptographic signal layer, enabling cross-chain interoperability without wrapped assets or centralized bridges.

Already, 30+ teams are contributing to The Signal. More than 1,500 prover nodes are active on the Boundless marketplace, competing for 0.5% token rewards. Any GPU owner can join permissionlessly. The Signal is live on Boundless Mainnet Beta, with production-grade proof requests already supported on Base.

VI. Boundless Roadmap, Mainnet Progress, and Ecosystem

Boundless has followed a clear phased development path:

• Phase I – Developer Access: Early access for developers with free proving resources to accelerate application experimentation.

• Phase II – Public Testnet 1: Launch of the first public testnet, introducing a two-sided marketplace where developers and prover nodes interact in real environments.

• Phase III – Public Testnet 2: Activation of incentive structures and the full economic model to test a self-sustaining decentralized proving network.

Phase IV – Mainnet: Full mainnet launch, providing universal ZK compute capacity for all blockchains.

On July 15, 2025, the Boundless Mainnet Beta officially went live, with production deployment first integrated on Base. Users can now submit proof requests with real funds, while prover nodes join permissionlessly, with each node supporting up to 100 GPUs for bidding. As a showcase application, the team released The Signal, an open-source ZK consensus client that compresses Ethereum Beacon Chain finality events into a single proof verifiable by any chain or contract. This effectively gives Ethereum’s finalized state “global readability”, laying the foundation for cross-chain interoperability and secure settlement.

Boundless Explorer data highlights the network’s rapid growth and resilience:

• As of August 18, 2025, the network had processed 542.7 trillion compute cycles, completed 399,000 orders, and supported 106 independent programs.

• The largest single proof exceeded 106 billion compute cycles (August 18).

• The compute throughput peak reached 25.93 MHz (August 14), setting industry records.

• Daily order volume surpassed 15,000 orders in mid-August, with daily peak compute exceeding 40 trillion cycles, showing exponential growth momentum.

• Order fulfillment success rate consistently stayed between 98%–100%, demonstrating a mature and reliable marketplace mechanism.

• As prover competition intensified, unit compute costs dropped to nearly 0 Wei per cycle, signaling the arrival of a high-efficiency, low-cost era of large-scale verifiable computation.

Boundless has also attracted strong participation from leading mining players. Major firms such as Bitmain have begun developing dedicated ASIC miners, while 6block, Bitfufu, Powerpool, Intchain, and Nano Labs have integrated existing mining pool resources into ZK proving nodes. This influx of miners marks Boundless’ progression toward an industrial-scale ZK marketplace, bridging the gap between cryptographic research and mainstream compute infrastructure.

VII. ZK Coin Tokenomics Design

ZK Coin (ZKC) is the native token of the Boundless protocol, serving as the economic and security anchor of the entire network. Its design goal is to build a trusted, low-friction, and sustainably scalable marketplace for zero-knowledge computation.

The total supply of ZKC is 1 billion, with a declining annual inflation model: initial annual inflation at ~7%, gradually decreasing to 3% by year eight, and remaining stable thereafter. All newly issued tokens are distributed through Proof of Verifiable Work (PoVW), ensuring that issuance is directly tied to real computational tasks.

Proof of Verifiable Work (PoVW) is Boundless’ core innovation. It transforms verifiable computation from a technical capability into a measurable, tradable commodity. Traditional blockchains rely on redundant execution by all nodes, constrained by single-node compute bottlenecks. PoVW, by contrast, enables single execution with network-wide verification via zero-knowledge proofs. It also introduces a trustless metering system that converts computational work into a priced resource. This allows computation to scale on demand, discover fair pricing through markets, formalize service contracts, and incentivize prover participation—creating a demand-driven positive feedback loop. For the first time, blockchains can transcend compute scarcity, enabling applications in cross-chain interoperability, off-chain execution, complex computation, and privacy-preserving use cases. PoVW thus establishes both the economic and technical foundation for Boundless as a universal ZK compute infrastructure.

Token Roles & Value Capture

ZKC functions as the native token and economic backbone of Boundless:

• Staking & Collateral – Provers must stake ZKC (≥10× the maximum request fee) before accepting jobs. If they fail to deliver proofs on time, penalties apply: 50% slashed (burned) and 50% redistributed to other provers.

• Proof of Verifiable Work (PoVW) – Provers earn ZKC rewards for generating proofs, analogous to mining. Reward distribution: 75% to provers, 25% to protocol stakers.

• Universal Payment Layer – Applications pay proof fees in native tokens (ETH, USDC, SOL, etc.), but provers are required to stake ZKC. Thus, all proofs are ultimately collateralized by ZKC.

• Governance – ZKC holders participate in protocol governance, including marketplace rules, zkVM integrations, and ecosystem funding.

Token Distribution (Initial Supply: 1B ZKC)

• Ecosystem Growth (49%)

  • 31% Ecosystem Fund: supports app development, developer tools, education, and infra maintenance; linear unlock over 3 years.

  • 18% Strategic Growth Fund: for enterprise integrations, BD partnerships, and institutional prover clusters; gradual unlock within 12 months, milestone-based.

• Core Team & Early Contributors (23.5%)

  • 20% Core team and early contributors: 25% cliff after 1 year, remainder vesting linearly over 24 months.

  • 3.5% allocated to RISC Zero for zkVM R&D and research grants.

• Investors (21.5%): Strategic capital and technical backers; 25% cliff after 1 year, remainder vesting linearly over 24 months.

• Community (6%)

  • Public Sale & Airdrop: strengthens community participation.

  • Public sale: 50% unlocked at TGE, 50% after 6 months.

Airdrops: 100% unlocked at TGE.

**ZKC is the core economic and security anchor of the Boundless protocol.**It secures proof delivery through staking, ties issuance to real computational output via PoVW, underpins the universal ZK demand layer through collateralization, and empowers holders to guide protocol evolution. As proof demand grows and slashing/burning reduces circulating supply, more ZKC will be locked and removed from circulation—creating long-term value support through the dual forces of rising demand and contracting supply.

VIII. Team Background and Project Fundraising

RISC Zero was founded in 2021. The team consists of engineers and entrepreneurs from leading technology and crypto organizations such as Amazon, Google, Intel, Meta, Microsoft, Coinbase, Mina Foundation, and O(1) Labs. They built the world’s first zkVM capable of running arbitrary code and are now building a universal zero-knowledge computing ecosystem on top of it.

Core Team

• Jeremy Bruestle – Co-founder & CEO, RISC ZeroJeremy is a veteran technologist and serial entrepreneur with over 20 years of experience in systems architecture and distributed computing. He previously served as Principal Engineer at Intel, co-founder and Chief Scientist at Vertex.AI, and co-founder and board member at Spiral Genetics. In 2022, he founded RISC Zero and became CEO, leading zkVM research and strategy to drive the adoption of zero-knowledge proofs in general-purpose computation.

• Frank Laub – Co-founder & CTO, RISC ZeroFrank has deep expertise in deep learning compilers and virtual machine technologies. He worked on deep learning software at Intel Labs and Movidius and gained extensive engineering experience at Vertex.AI and Peach Tech. Since co-founding RISC Zero in 2021, he has served as CTO, leading the development of the zkVM core, the Bonsai network, and the developer tooling ecosystem.

• Shiv Shankar – CEO, BoundlessShiv has more than 15 years of experience in technology and engineering management, spanning fintech, cloud storage, compliance, and distributed systems. In 2025, he became CEO of Boundless, where he leads product and engineering teams to drive the marketization of zero-knowledge proofs and the development of cross-chain compute infrastructure.

• Joe Restivo – COO, RISC ZeroJoe is an entrepreneur and operations expert with three successful exits. Two of his companies were acquired by Accenture and GitLab. He also teaches risk management at Seattle University’s Business School. Joe joined RISC Zero in 2023 as COO, overseeing company operations and scaling.

• Brett Carter – VP of Product, RISC ZeroBrett brings extensive product management and ecosystem experience. He previously worked as a senior product manager at O(1) Labs. Since joining RISC Zero in 2023, he has served as VP of Product, responsible for product strategy, ecosystem adoption, and integration with Boundless’ marketplace initiatives.

Fundraising

In July 2023, RISC Zero completed a $40 million Series A round, led by Blockchain Capital. Seed round lead investor Bain Capital Crypto also participated, alongside Galaxy Digital, IOSG, RockawayX, Maven 11, Fenbushi Capital, Delphi Digital, Algaé Ventures, IOBC, Zero Dao (Tribute Labs), Figment Capital, a100x, and Alchemy.

IX. Competitive Analysis: zkVMs and ZK Marketplaces

A key competitor that combines both a zkVM and a zkMarketplace is Succinct, which consists of the SP1 zkVM and the Succinct Prover Network (SPN).

• SP1 zkVM is a general-purpose zero-knowledge virtual machine built on RISC-V with an LLVM IR front-end, designed to support multiple languages, lower development barriers, and improve performance.

• Succinct Prover Network (SPN) is a decentralized proving marketplace deployed on Ethereum, where tasks are allocated through staking and bidding, and the $PROVE token is used for payments, prover incentives, and network security.

In contrast, RISC Zero follows a “dual-engine” strategy: Bonsai provides an officially hosted Prover-as-a-Service with high performance and enterprise-grade stability, while Boundless builds an open decentralized proving marketplace that allows any GPU/CPU node to participate, maximizing decentralization and coverage though with less consistent performance.

Comparison of RISC-V and Wasm

RISC-V and Wasm represent two major approaches to general-purpose zkVMs:

• RISC-V is an open hardware-level instruction set with simple rules and a mature ecosystem, making it well-suited for circuit performance optimization and future verifiable hardware acceleration. However, it has limited integration with traditional Web application ecosystems.

• Wasm, by contrast, is a cross-platform bytecode format with native multi-language support and strong compatibility for Web application migration. Its runtime ecosystem is mature, though its stack-based architecture imposes lower performance ceilings compared to RISC-V.

Overall, RISC-V zkVMs are better suited for high-performance and general-purpose compute expansion, while zkWasm holds stronger advantages in cross-language and Web-oriented use cases.

X. Conclusion: Business Logic, Engineering Implementation, and Potential Risks

Zero-knowledge (ZK) technology is evolving from a single-purpose scaling tool into a general foundation for trusted computation in blockchain. By leveraging the open RISC-V architecture, RISC Zero breaks free from EVM dependency, extending zero-knowledge proofs to general-purpose off-chain computation. This, in turn, has given rise to zk-Coprocessors and decentralized proof marketplaces such as Bonsai and Boundless. Together, these components form a scalable, tradable, and governable layer of computational trust, unlocking higher performance, stronger interoperability, and broader application scenarios for blockchain systems.

That said, the ZK sector still faces significant near-term challenges. Following the peak of ZK hype in the primary market in 2023, the 2024 launch of mainstream zkEVM projects absorbed much of the secondary market’s attention. Meanwhile, leading L2 teams largely rely on in-house prover designs, while applications such as cross-chain verification, zkML, and privacy-preserving computation remain nascent, with limited matching demand. This suggests that open proving marketplaces may struggle to sustain high order volumes in the short term, with their value lying more in aggregating prover supply in advance to capture demand when it eventually surges.

Similarly, while zkVMs offer lower technical barriers, they face difficulty in directly penetrating the Ethereum ecosystem. Their unique value may lie instead in off-chain complex computation, cross-chain verification, and integrations with non-EVM chains.

Overall, the evolutionary path of ZK technology is becoming clear: from zkEVMs’ compatibility experiments, to the rise of general-purpose zkVMs, and now to decentralized proving marketplaces represented by Boundless. Zero-knowledge proofs are rapidly advancing toward commoditization and infrastructuralization. For both investors and developers, today may still be a phase of validation—but within it lies the foundation of the next industry cycle.

Disclaimer: This article includes content assisted by AI. While I have made every effort to ensure the accuracy and reliability of the information provided, there may still be errors or omissions. This article is for research and reference purposes only and does not constitute investment advice, solicitation, or any form of financial service. Please note that tokens and related digital assets carry significant risks and high volatility. Readers should exercise independent judgment and assume full responsibility before making any investment decisions.*

一、ZK 赛道的发展历程 零知识证明（ZK）技术的发展并非一蹴而就，而是经历了长达数十年的理论积累与工程探索。整体可以划分为以下几个关键阶段：

1. 理论奠基与技术突破期（1980s–2010s） ZK 概念由 MIT 学者Shafi Goldwasser、Silvio Micali 和 Charles Rackoff 提出，最初停留在交互式证明理论。2010s 随着 非交互式零知识证明（NIZK） 与 zk-SNARK 出现，证明效率大幅提升，但早期仍依赖可信设置。

2. 区块链应用（2010s 末期） Zcash 将 zk-SNARK 引入隐私支付，首次实现大规模区块链落地。但受限于证明生成开销高昂，实际落地场景依然较为有限。

3. 爆发式增长与扩展（2020s 至今）这一时期 ZK 技术全面进入产业主流： ZK Rollup：通过链下批量计算及链上证明，实现高吞吐与安全继承，成为 Layer2 扩容核心路径。 zk-STARKs：StarkWare 推出 zk-STARK，消除可信设置，提升透明性与扩展性。 zkEVM：Scroll、Taiko、Polygon 等团队致力于 EVM 字节码级证明与现有 Solidity 应用无缝迁移。 通用 zkVM：RISC Zero、Succinct SP1、Delphinus zkWasm 等支持任意程序可验证执行，把 ZK 从扩容工具拓展为“可信 CPU”。 zkCoprocessor 将 zkVM 封装为协处理器，支持复杂逻辑外包（如 RISC Zero Steel、Succinct Coprocessor）； zkMarketplace则市场化证明算力，形成去中心化 prover 网络（如 Boundless），推动 ZK 成为普适计算层。 至今，ZK 技术已从晦涩的密码学概念，成长为区块链基础设施中的核心模块。它不仅支撑扩容与隐私保护，更在跨链互操作、金融合规、人工智能（ZKML）等前沿场景中展现出战略价值。随着工具链、硬件加速与证明网络的持续完善，ZK 生态正快速走向规模化与普适化。

二、ZK 技术应用全景：扩容、隐私与互操作

扩容（Scalability）、隐私（Privacy）与互操作与数据证明（Interoperability & Data Integrity）是当下 ZK “可信计算”技术的三大基础场景，对应区块链性能不足、隐私缺失与多链互信的原生痛点

• 扩容（Scalability） 是 ZK 最早落地、也是应用最广的场景。其核心思想是将交易执行移到链下，再用简短的证明在链上验证，从而在不牺牲安全性的前提下显著提升 TPS、降低成本。典型路径包括：zkRollup（zkSync、Scroll、Polygon zkEVM），通过批量交易压缩实现扩容；zkEVM，在 EVM 指令级别构建电路，实现以太坊原生兼容；以及更通用的 zkVM（RISC Zero、Succinct），支持任意逻辑的可验证外包。

• 隐私保护（Privacy） 旨在证明交易或行为的合法性，同时避免暴露敏感数据。典型应用包括：隐私支付（Zcash、Aztec），保证资金转移有效性而不公开金额与对手方；隐私投票与 DAO 治理，在不泄露投票内容的情况下完成治理；以及 隐私身份/KYC（zkID、zkKYC），仅证明“符合条件”，而不披露额外信息。

• 互操作与数据证明（Interoperability & Data Integrity） 则是 ZK 技术解决“多链世界”信任问题的关键路径。通过生成另一条链状态的证明，跨链交互可摆脱中心化中继。典型形式包括 zkBridge（跨链状态证明）与 轻客户端验证（在目标链上高效验证源链区块头），代表项目有 Polyhedra、Herodotus 等。同时，ZK 也被广泛用于 数据与状态证明，如 Axiom、Space and Time 的 zkQuery/zkSQL，或 IoT 与存储场景的数据完整性验证，确保链下数据可信上链。

在这三大基础场景之上，未来ZK 技术有机会逐渐延伸至更广阔的行业应用：包括 AI（zkML），为模型推理或训练生成可验证证明，实现“可信 AI”；金融合规，如交易所储备证明（PoR）、清算与审计，降低信任成本；以及 游戏与科学计算，在 GameFi 或 DeSci 中确保逻辑与实验结果的真实性。本质上，它们都是“可验证计算 + 数据证明”在不同行业的落地扩展。

三、超越zkEVM: 通用 zkVM 与证明市场的崛起

以太坊创始人 Vitalik 在 2022 年提出的 ZK-EVM 四类分类（Type 1–4），揭示了 兼容性与性能之间的权衡：

• Type 1（完全等价）：字节码与以太坊 L1 完全一致，迁移成本最低，但证明最慢。代表项目：Taiko。

• Type 2（完全兼容）：保持高度 EVM 等价，仅做极少底层优化，兼容性最强。代表项目：Scroll、Linea。

• Type 2.5（准兼容）：小幅修改 EVM（如 gas 成本、预编译支持），牺牲少量兼容性换取性能提升。代表项目：Polygon zkEVM、Kakarot（运行在 Starknet 上的 EVM）。

• Type 3（部分兼容）：对底层修改更彻底，能跑大多数应用，但无法完全复用以太坊基础设施。 代表项目：zkSync Era。

• Type 4（语言级兼容）：放弃字节码兼容，直接从高级语言编译至 zkVM，性能最佳但需重建生态。代表项目：Starknet（Cairo）。

这一阶段的主题是“zkRollup 战争”，目标在于缓解以太坊的执行瓶颈。但随之暴露出两大局限：一是 EVM 电路化难度高、证明效率受限，二是 ZK 的潜力远超扩容，可延伸至跨链验证、数据证明甚至 AI 计算。

在此背景下，通用 zkVM 崛起，取代 zkEVM 的“以太坊兼容思维”，转向“链无关的可信计算”。zkVM 基于通用指令集（如 RISC-V、LLVM IR、Wasm），支持 Rust、C/C++ 等语言，允许开发者用成熟生态库构建任意应用逻辑，再通过证明在链上验证。RISC Zero（RISC-V）、Delphinus zkWasm（Wasm）即为典型代表。其意义在于：zkVM 不只是以太坊的扩容工具，而是 ZK 世界的“可信 CPU”。

• RISC-V 路线：以 RISC Zero为代表，直接选择开放通用指令集 RISC-V 作为 zkVM 的执行内核。优点是生态开放、指令集简洁、易于电路化，能够承接 Rust、C/C++ 等主流语言编译结果，适合做“通用 zkCPU”。缺点是与以太坊字节码没有天然兼容，需要通过协处理器模式嵌入。

• LLVM IR 路线：以 Succinct SP1 为代表：前端用 LLVM IR 兼容多语言，后端仍基于 RISC-V zkVM，本质是“LLVM 前端 + RISC-V 后端”，比纯 RISC-V 模式更通用，但LLVM IR 指令复杂，证明开销更大。

• Wasm 路线：以 Delphinus zkWasm为代表。WebAssembly 生态成熟，开发者熟悉度高，且天然跨平台，但 Wasm 指令集相对复杂，证明性能受限。

进一步的演进中，ZK 技术正走向 模块化与市场化。首先，zkVM 提供了通用可信的执行环境，相当于零知识计算的“CPU/编译器”，为应用提供底层的可验证计算能力。在此之上，zk-coprocessor 将 zkVM 封装为协处理器，使 EVM 等链能够将复杂计算任务外包到链下执行，并通过零知识证明回链验证，典型案例包括 RISC Zero Steel 与 Lagrange，其角色可类比为“GPU/协处理器”。 再进一步，zkMarketplace 则通过去中心化网络实现证明任务的市场化分发，全球 prover 节点通过竞价完成任务，如 Boundless ，即是构建零知识计算的算力市场。

由此，零知识技术栈逐步呈现出从 zkVM → zk-coprocessor → zkMarketplace 的演进链条。这一体系标志着零知识证明从单一的以太坊扩容工具，进化为 通用可信计算基础设施。而这一演进链条中，以RISC-V 作为 zkVM 内核的RISC Zero，在“开放性、可电路化效率、生态适配”之间做了最优平衡。使得它既能提供低门槛的开发体验，又能通过 Steel、Bonsai、Boundless 等扩展层，将 zkVM 演进为 zk-coprocessor 与去中心化证明市场，从而打开更广阔的应用空间。

四、RISC Zero 的技术路径与生态版图

RISC-V 是一种开放、免版税的指令集架构，不受单一厂商控制，具备天然的去中心化特质。RISC Zero 依托这一开放架构，构建出兼容 Rust 等通用语言的 zkVM，突破了以太坊生态内 Solidity 的局限，使开发者能够直接将标准 Rust 程序编译为可生成零知识证明的应用。这种路径让 ZK 技术的应用范围从区块链合约扩展到更广阔的通用计算领域。

RISC0 zkVM：通用可信计算环境

与需要兼容复杂 EVM 指令集的 zkEVM 项目不同，RISC0 zkVM 基于 RISC-V 架构，设计更为开放和通用。其应用由 Guest Code 构成并编译为 ELF 二进制文件，Host 通过 Executor 运行并记录执行过程（Session），Prover 随后生成可验证的 Receipt，其中包含公开输出（Journal）与加密证明（Seal）。第三方仅需验证 Receipt，即可确认计算正确性，而无需重复执行。

2025 年 4 月发布的 R0VM 2.0 标志着 zkVM 进入实时时代：以太坊区块证明时间由 35 分钟缩短至 44 秒，成本降低最高 5 倍，用户内存扩展至 3GB，支持更复杂的应用场景。同时新增 BN254 与 BLS12-381 两个关键预编译，全面覆盖以太坊主流需求。更重要的是，R0VM 2.0 在安全性上引入形式化验证，已完成大部分 RISC-V 电路的确定性验证，目标在 2025 年 7 月实现首个 区块级实时 zkVM（<12 秒证明）。

zkCoprocessor Steel：链下计算的桥梁

zkCoprocessor 的核心理念是将复杂计算任务从链上卸载至链下执行，再通过零知识证明返回结果。智能合约只需验证 Proof，而无需重算整个任务，从而显著降低 Gas 成本并突破性能瓶颈。例如 RISC0 的 Steel，为 Solidity 提供外部证明接口，可以外包大规模历史状态查询或跨区块批量计算，甚至能用一个 Proof 验证数十个以太坊区块。

Bonsai：SaaS 化的高性能证明服务

为满足产业级应用需求，RISC Zero 推出了 Bonsai ，官方托管的 Prover-as-a-Service 平台，通过 GPU 集群分发证明任务，让开发者无需自建硬件即可获得高性能证明。与此同时，RISC Zero 提供 Bento SDK，帮助开发者在 Solidity 与 zkVM 之间实现无缝交互，显著降低 zkCoprocessor 的集成复杂度。相比之下，Boundless 通过开放市场实现去中心化证明，两者形成互补。

RISC Zero 全产品矩阵

RISC Zero 的产品生态围绕 zkVM 向上延展，逐步形成了覆盖执行、网络、市场与应用层的完整矩阵：

五、ZK 市场：信任计算的去中心商品化

零知识证明（ZK）市场将高成本、复杂的证明生成过程解耦，并转化为去中心化、可交易的计算商品。通过全球分布的 prover 网络，计算任务以竞价方式外包，在成本与效率间动态平衡，并以经济激励不断吸引 GPU 与 ASIC 参与者，形成自我强化的循环。Boundless 与 Succinct 是该赛道的代表。

5.1 Boundless：通用零知识计算市场

概念定位Boundless 是 RISC Zero 推出的通用 ZK 协议，旨在为所有区块链提供可扩展的 verifiable compute 能力。其核心在于将证明生成与区块链共识解耦，并通过去中心化市场机制分发计算任务。开发者提交证明请求后，Prover 节点通过去中心化的激励机制竞争执行，并凭借“可验证工作量证明（Proof of Verifiable Work）”获得奖励。不同于传统 PoW 的无意义算力消耗，Boundless 将算力转化为真实应用的 ZK 结果，使计算资源具备实际价值。

架构与机制Boundless 市场的工作流程包括：

• 请求提交：开发者提交 zkVM 程序与输入至市场；

• 节点竞价：Prover 节点评估任务并出价，锁定任务后获得执行权；

• 证明生成与聚合：复杂计算被拆解为子任务，每个子任务生成 zk-STARK 证明，再通过递归与聚合电路压缩为统一的终极证明，大幅降低链上验证成本；

• 跨链验证：Boundless 在多条链上提供统一验证接口，实现一次构建、跨链复用

这种架构使智能合约无需重复执行复杂计算，仅需验证简短证明即可完成确认，从而突破 Gas 上限与区块容量限制。

生态与应用： 作为市场层协议，Boundless 与 RISC Zero 其他产品互补：

• Steel：EVM 的 ZK Coprocessor，可将 Solidity 复杂执行迁移到链下并回链验证；

• OP Kailua：为 OP Stack 链提供 ZK 升级路径，实现更高安全性与更快终结性。

Boundless 的目标是在以太坊实现亚 12 秒实时证明，路径包括 FRI 优化、多项式并行化及 VPU 硬件加速。随着节点和需求增长，Boundless 将形成自增强算力网络，不仅降低 Gas 成本，还将开启链上可验证 AI、跨链流动性与无限计算等新应用场景。

5.2 Boundless for Apps：突破 Gas 限制

Boundless for Apps 旨在为以太坊和 L2 应用提供“无限算力”，将复杂逻辑卸载到去中心化证明网络执行，再以 ZK 证明回链验证。其优势包括：无限执行、恒定 Gas 成本、兼容 Solidity/Vyper、跨链原生支持。

其中 Steel 作为 EVM 的 ZK Coprocessor，让开发者能够在 Solidity 合约中实现大规模状态查询、跨区块计算与事件驱动逻辑，并通过 R0-Helios 轻客户端实现 ETH 与 OP Stack 的跨链数据验证。目前已有包括 EigenLayer 在内的项目探索集成，展现其在 DeFi 与多链交互中的潜力。

Steel：EVM 的可扩展计算层

Steel 的核心目标是突破以太坊在 Gas 上限、单区块执行、历史状态访问等方面的限制，将复杂逻辑迁移至链下，再通过零知识证明回链验证。在保证安全性的同时，以恒定验证开销提供近乎无限的算力支持。

在 Steel 2.0 中，开发者可利用三大能力扩展合约设计空间：

• 事件驱动逻辑：直接以 Event logs 为输入，避免依赖中心化 indexer；

• 历史状态查询：访问自 Dencun 升级以来任意区块的存储槽或账户余额；

• 跨区块计算：执行跨多个区块的运算（如移动平均、累积指标），并以单个证明提交链上。

这一设计显著降低了成本，Steel 的出现，使得原本受限于 EVM 的应用（如高频计算、状态回溯或跨区块逻辑）得以落地，并逐步成为连接链下计算与链上验证的关键桥梁。

5.3 Boundless for Rollups：ZK 驱动的 Rollup 加速方案

Boundless for Rollups 通过去中心化证明网络，为 OP Stack 等二层链提供更快、更安全的结算路径。其核心优势体现在：

• 加速终结性：将 7 天的结算时间缩短约 3 小时（Hybrid 模式）或 <1 小时（Validity 模式）；

• 更强安全性：通过 ZK Fraud Proof 与 Validity Proof 渐进式升级，提供密码学级安全

• 去中心化演进：依托分布式 Prover 网络与低抵押需求，快速迈向 Stage 2 去中心化；

• 原生可扩展性：在高吞吐链上保持稳定性能与可预测成本。

OP Kailua：为 OP 链提供 ZK 升级路径

作为 Boundless for Rollups 的核心方案，OP Kailua 由 RISC Zero 推出，专为基于 Optimism 的 Rollup 设计，使团队能够在 性能与安全性 上超越传统 OP 架构。

Kailua 提供两种模式，支持渐进式升级：

• Hybrid 模式（ZK Fraud Proof）：用 ZK Fraud Proof 替代多轮交互式 Fault Proof，大幅降低争议解决复杂度和成本。证明费用由作恶方承担，最终性缩短至约 3 小时。

• Validity 模式（ZK Validity Proof）：直接转型为 ZK Rollup，利用零知识有效性证明彻底消除争议，实现 <1 小时最终性，并提供最高级别的安全性。

Kailua 支持 OP 链从乐观 → 混合 → ZK Rollup 的平滑升级，符合 Stage 2 去中心化要求，降低了升级门槛并提升高吞吐场景的经济性。在保持现有应用与工具链连续性的同时，OP 生态可逐步获得快速最终性、更低质押成本和更强安全性。Eclipse 已借助 Kailua 实现 ZK Fraud Proof，加速升级；BOB 则完成向 ZK Rollup 的转型。

5.4 The Signal：跨链互操作的 ZK 信号层

定位与机制The Signal 是 Boundless 推出的核心应用 —— 一个开源 ZK 共识客户端。它将以太坊信标链的最终性事件压缩为单个零知识证明，任何链或合约都能直接验证该证明，从而实现无需多签或预言机的信任最小化跨链交互。其价值在于赋予以太坊最终状态“全球可读性”，为跨链流动性与逻辑交互奠定基础，并显著降低冗余计算和 Gas 成本。

运行机制

• Boost The Signal：用户可通过提交证明请求来“增强信号”，所有 ETH 直接用于请求新的证明，延长信号持续时间，惠及所有链与应用。

• Prove The Signal：任何人都可运行 Boundless Prover 节点，生成以太坊区块的 ZK 证明并广播，取代传统的多签验证，形成“用数学替代信任”的跨链共识层。

• 扩展路径：先为以太坊最终确定区块生成连续证明，形成“以太坊信号”；再推广至其他公链，构建多链统一信号；最终在同一密码学信号层上互联，形成“共享波长”，实现无包裹资产、无中心化桥的跨链互操作。

目前已有 30+ 团队参与 The Signal 推进，Boundless 市场上已聚合 1,500+ Prover 节点，竞争 0.5% 代币激励，任何拥有 GPU 的用户均可无许可加入。The Signal 已在 Boundless 主网 Beta 上线，并支持基于 Base 的生产级证明请求。

六、Boundless 路线图、主网进展与生态

Boundless 的发展遵循清晰的阶段式路径：Phase I – Developer Access：面向开发者开放早期接入，提供免费证明资源，加速应用探索；Phase II – Public Testnet 1：开启公开测试网，引入双边市场机制，开发者与 Prover 节点在真实环境中交互；Phase III – Public Testnet 2：引入市场激励与完整经济机制，测试自我维持的去中心化证明网络；Phase IV – Mainnet：全面主网上线，为所有链提供通用 ZK 计算能力。

在 2025 年 7 月 15 日，Boundless 主网 Beta 已正式上线，率先在 Base 上进入生产环境。用户可用真实资金请求证明，Prover 节点则以无许可方式接入，单节点最多支持 100 块 GPU 并参与竞价。作为展示性应用，团队推出了 The Signal，这一开源 ZK 共识客户端能将以太坊信标链最终性事件压缩为单个零知识证明，任何链与合约均可直接验证。由此，以太坊的最终状态实现了“全球可读性”，为跨链互操作与安全结算提供基础。

Boundless 浏览器的运行数据显示，整体网络已展现出高速增长与强大韧性。截至 2025 年 8 月 18 日，累计处理 542.7 万亿计算周期，完成 39.9 万笔订单，覆盖 106 个独立程序。单笔最大证明规模突破 1060 亿计算周期（8 月 18 日），网络算力峰值达到 25.93 MHz（8 月 14 日），均刷新了行业纪录。从订单履约情况看，日均订单数在 8 月中旬一度突破 1.5 万笔，每日算力峰值超过 40 万亿周期，展现了指数级增长态势。同时，订单履约成功率始终维持在 98%–100% 的高水准，证明市场机制已相当成熟。更值得注意的是，随着 prover 竞争加剧，单周期成本已下降至接近 0 Wei，意味着网络正进入高效、低成本的大规模计算时代。

此外，Boundless吸引了一线矿工的积极参与。比特大陆等头部厂商已着手研发专用 ASIC 矿机；6block、Bitfufu、原力区、Intchain、Nano Labs 等厂商加入网络将既有矿池资源转化为ZK证明计算节点，矿工群体的加入使得 Boundless的ZK市场进一步迈向规模产业化阶段。

七、ZK Coin代币经济模型设计

ZK Coin（ZKC）是 Boundless 协议的原生代币，也是整个网络的经济与安全锚点。其设计目标是构建一个可信、低摩擦、可持续扩展的零知识计算市场。ZKC 总量为 10 亿枚，采用逐年递减的通胀机制：首年年化通胀率约为 7%，逐步下降至第 8 年的 3%，并在此水平保持长期稳定。所有新发行的代币通过 可验证工作量证明（Proof of Verifiable Work, PoVW） 分配，确保发行直接与真实的计算任务绑定。

Proof of Verifiable Work（PoVW） 是 Boundless 的核心创新机制，它将“可验证计算”从一种技术能力转变为可度量、可交易的商品。传统区块链依赖所有节点的重复执行，受限于单节点算力瓶颈，而 PoVW 通过零知识证明实现单次计算、全网验证，并引入无信任的计量体系，将计算工作量转化为可定价的资源。由此，计算不仅能按需扩展，还能通过市场发现价格、签订服务合约、激励 Prover 节点，形成需求驱动的正循环。PoVW 的引入让区块链首次摆脱算力稀缺，支持跨链互操作、链下执行、复杂计算与隐私保护等应用场景，为 Boundless 打造普适的 ZK 计算基础设施奠定了经济与技术双重基础。

代币角色与价值捕获

ZK Coin（ZKC） 是 Boundless 的原生代币，也是整个网络的经济支柱：

• 质押抵押：Prover 必须在接单前质押 ZKC（通常 ≥10× 最大请求费用），若超时未交付则被罚没（50% 销毁，50% 奖励其他 prover）。

• Proof of Verifiable Work (PoVW)：Provers 通过生成零知识证明获得 ZKC 激励，类似挖矿机制。奖励分配为：75% 给 prover、25% 给协议质押者。

• 通用支付层：应用方用自身原生代币（如 ETH、USDC、SOL）支付证明费用，但 prover 需用 ZKC 质押，因此所有证明都由 ZKC 背书。

• 治理功能：ZKC 持有者可参与 Boundless 治理，包括市场机制、zkVM 集成、基金拨款等。

代币分配（初始供应 10 亿枚）

• 生态增长（49%）

  • 31% 生态基金：支持应用开发、开发者工具、教育与基础设施维护；线性解锁至第 3 年。

  • 18% 战略增长基金：用于企业级集成、BD 合作与机构 prover 集群引入；12 个月内逐步解锁，与合作成果挂钩。

• 核心团队与早期贡献者（23.5%）

  • 20% 给核心团队与早期贡献者，25% 一年 cliff，剩余 24 个月线性解锁。

  • 3.5% 分配给 RISC Zero，用于 zkVM 研发与研究基金。

  • 投资者（21.5%）：战略资本与技术支持者；25% 一年 cliff，剩余两年线性解锁。

  • 社区（约 6%）：社区公募与空投，增强社区参与度；公募 50% TGE 解锁，50% 6 个月后解锁；空投 100% TGE 解锁。

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ZKC 是 Boundless 协议的核心经济与安全锚点，既作为抵押担保保障证明交付，又通过 PoVW 将发行与真实工作量绑定，同时充当支付背书层承载全链 ZK 需求，并在治理层面赋能持币者参与协议演进。随着证明请求增加与惩罚销毁机制叠加，更多 ZKC 被锁定并退出流通，在需求增长与供给收缩的双重作用下形成长期价值支撑。

八、团队背景及项目融资

RISC Zero 团队成立于 2021 年。团队由来自 Amazon、Google、Intel、Meta、Microsoft、Coinbase、Mina Foundation、O(1) Labs 等知名科技与加密机构的工程师与创业者组成，已打造出全球首个可运行任意代码的 zkVM，并正基于此构建通用零知识计算生态。

Jeremy Bruestle – Co-founder & CEO, RISC ZeroJeremy 是一位资深技术专家与连续创业者，拥有超过二十年的系统架构与分布式计算经验。曾任 Intel Principal Engineer、Vertex.AI 联合创始人兼首席科学家，并在 Spiral Genetics 担任联合创始人及董事会成员。他于 2022 年创立 RISC Zero 并担任 CEO，主导 zkVM 技术的研发与战略，推动零知识证明在通用计算领域的落地。

Frank Laub – Co-founder & CTO, RISC ZeroFrank 长期深耕深度学习编译器与虚拟机技术，曾在 Intel Labs 与 Movidius 从事深度学习软件研发，也曾在 Vertex.AI、Peach Tech 等公司积累了丰富的工程经验。自 2021 年共同创立 RISC Zero 以来，担任 CTO，主导 zkVM 内核、Bonsai 网络和开发者工具链的建设。

Shiv Shankar – CEO, BoundlessShiv 拥有超过十五年的科技与工程管理经验，涉足金融科技、云存储、合规与分布式系统等多个领域。2025 年起担任 Boundless CEO，领导产品与工程团队，推动零知识证明市场化与跨链计算基础设施建设。

Joe Restivo – COO, RISC ZeroJoe 是三次成功退出的创业者与运营专家，具备丰富的组织管理与风控经验。两家公司先后被 Accenture 与 GitLab 收购。他在西雅图大学商学院教授风险管理课程。2023 年加入 RISC Zero，现任 COO，负责全公司运营与规模化管理。

Brett Carter – VP of Product, RISC ZeroBrett 具备丰富的产品管理与生态经验。曾在 O(1) Labs 担任高级产品经理。2023 年加入 RISC Zero，现任产品副总裁，负责产品战略、生态应用落地以及与 Boundless 的市场对接。

在融资方面，RISC Zero 于 2023 年 7 月完成 4,000 万美元的 A 轮融资，由 Blockchain Capital 领投，种子轮领投方 Bain Capital Crypto 继续参投，其他投资方还包括 Galaxy Digital、IOSG、RockawayX、Maven 11、Fenbushi Capital、Delphi Digital、Algaé Ventures、IOBC、Zero Dao（Tribute Labs）、Figment Capital、a100x 与 Alchemy 等。

九、ZKVM及ZK市场竞品分析

目前市场上同时具备 zkVM 与 zkMarketplace 的代表性项目是 Succinct，由 SP1 zkVM 与 Succinct Prover Network (SPN) 组成。SP1 基于 RISC-V 构建，并通过 LLVM IR 前端兼容多语言；SPN 部署在以太坊上，通过 staking 与竞价机制分配任务，并以 $PROVE 代币承担支付、激励与安全功能。相比之下，RISC Zero 采取“双引擎”战略：一方面由 Bonsai 提供官方托管的 Prover-as-a-Service，高性能、稳定，面向企业级应用；另一方面通过 Boundless 构建开放的去中心化证明市场，允许任何 GPU/CPU 节点自由加入，最大化去中心化与节点覆盖，但性能一致性相对不足。

Risc Zero 同时兼顾开放与工业化落地，而 Succinct 更聚焦于高性能与标准化路径。

Risc Zero(zkVM + Bonsai + Boundless) 与Succinct (SP1 zkVM + SPN) 区别与定位

RISC-V 与Wasm 的比较

RISC-V 与 WASM 是通用 zkVM 的两条主要路线，前者是硬件级开放指令集，规则简洁、生态成熟，利于电路性能优化和未来可验证硬件加速；但与传统Web应用生态结合有限。WASM 则是跨平台字节码，天然支持多语言和 Web 应用迁移，运行时成熟，但因栈式架构性能上限低于 RISC-V。总体而言，RISC-V zkVM 更适合追求性能与通用计算扩展，zkWasm 则在跨语言与 Web 场景中具备优势。

十、总结：商业逻辑、工程实现及潜在风险

ZK 技术正在从单一扩容工具演进为区块链可信计算的通用基石。RISC Zero 以开放的 RISC-V 架构突破 EVM 依赖，将零知识证明扩展到通用链下计算，并催生了 zk-Coprocessor 与去中心化证明市场（如 Bonsai、Boundless）。它们共同构建起一个可扩展、可交易、可治理的计算信任层，为区块链带来更高性能、更强互操作性与更广阔应用场景。

当然ZK 赛道短期内仍面临不少挑战：2023 年一级市场炒作ZK概念见顶后，2024年主流zkEVM项目上线亦消耗二级市场热度。此外，L2 头部团队多采用自研 prover，跨链验证、zkML、隐私计算等应用场景仍处早期，可撮合的任务有限。这意味着开放 proving marketplace 的订单量难以支撑庞大网络，其价值更多在于前置聚合 prover 供给，以在未来需求爆发时抢占先机。与此同时，zkVM 虽然技术门槛低，但难以直接切入以太坊生态，未来可在链下复杂计算、跨链验证及非 EVM 链对接等场景具备独特补充价值。

总体来看，ZK 技术的演进路径已逐渐明晰：从 zkEVM 的兼容性探索，到通用 zkVM 的出现，再到以 Boundless 为代表的去中心化证明市场，零知识证明正在加速商品化与基础设施化。对于投资者与开发者而言，当前或许仍是验证期，但它孕育着下一轮产业周期的核心机遇。

免责声明：本文部分内容包含 AI 辅助创作，本人已尽力确保资料与信息的真实与准确，如有偏差或疏漏，敬请谅解。本文仅供研究与参考之用，不构成任何投资建议、邀约或其他形式的金融服务。请注意，代币及相关数字资产价格存在高度风险与剧烈波动，读者在做出投资决策前应自行审慎判断并承担全部风险。

In our vision for the future of AgentFi, beyond Lending and Yield Farming—two scenarios that currently offer the most value and easiest implementation—Swap Strategy Composition also represents a promising direction. When multiple swaps are sequenced or conditionally linked, they form a “strategy”, such as in arbitrage or yield-shifting. This approach requires a state machine to manage positions, trigger conditions, and execute multi-step operations automatically—fully embodying the closed-loop characteristics of AgentFi: perception → decision-making → execution → rebalancing.

1. DeFi Quantitative Strategy Landscape and Feasibility

Traditional quantitative finance relies on mathematical models, statistical methods, and algorithms, using data such as historical prices, trading volumes, and macroeconomic indicators to drive data-based decision-making. Execution is typically programmatic, enabling low-latency, high-frequency, and automated trading, complemented by strict risk controls (stop-losses, position management, VaR, etc.).  Key applications include high-frequency trading (HFT), trend-following and mean-reversion strategies (CTA), cross-market/cross-asset arbitrage, and derivative pricing and hedging. In traditional markets, these strategies are supported by a mature infrastructure, exchange systems, and rich data ecosystems.

On-chain quantitative finance inherits the logic of traditional quant, but operates within the programmable market structure of blockchains. Data sources include on-chain transaction records, DEX price feeds, and DeFi protocol states. Execution takes place through smart contracts (AMMs, lending protocols, derivatives platforms), with trading costs including gas fees, slippage, and MEV risk. The composability of DeFi protocols enables the construction of automated strategy chains.

At present, on-chain quant finance remains in its early stage, with several structural constraints limiting the deployment of complex quant strategies:

1. Market structure – Limited liquidity depth and the absence of ultra-fast matching in AMMs hinder the feasibility of high-frequency or large-scale trades.

2. Execution and cost – On-chain block confirmation delays and high gas costs make frequent trading difficult to profit from.

3. Data and tooling – Development and backtesting environments are underdeveloped, data dimensions are narrow, and there is a lack of multi-source information, such as corporate financials or macroeconomic data.

Among currently feasible DeFi quant strategies, the main directions are:

• Mean reversion / trend-following – Buy/sell decisions based on technical indicator signals (e.g., RSI, moving averages, Bollinger Bands).

• Term structure arbitrage – Represented by protocols like Pendle, profiting from the spread between fixed and floating yields.

• Market making + dynamic rebalancing – Actively managing AMM liquidity ranges to earn fees.

• Leverage loop farming – Using lending protocols to boost capital efficiency.

Potential growth areas in the future include:

• Maturation of the on-chain derivatives market, especially the widespread adoption of options and perpetual contracts.

• More efficient integration of off-chain data via decentralized oracles, enriching model input dimensions.

• Multi-agent collaboration, enabling automated execution of multi-strategy portfolios with balanced risk.

2. Almanak’s Positioning and Vision: Exploring AgentFi in On-Chain Quantitative Finance

In our previous Crypto AI research reports, we have covered many outstanding AgentFi projects, but most remain focused on intent-driven DeFi execution, lending, or liquidity management in fully automated workflows. Few teams have gone deep into quantitative trading strategies. Currently, Almanak is one of the very few projects in the market that explicitly positions quantitative trading as its core focus—namely, vibecoding quantitative strategies in DeFi.

The project focuses on no-code quantitative strategy development, offering a complete toolchain that covers strategy scripting (Python), deployment, execution, permission management, and vault-based asset management. This gives Almanak a unique position within the AgentFi landscape, making it a flagship example of on-chain quantitative finance.

In traditional finance, **Inclusive Finance **aims to lower participation barriers and reach long-tail users. Almanak extends this philosophy on-chain, with the goal of democratizing access to quantitative trading capabilities. By using AI-driven agents to execute strategies, the platform significantly reduces capital, technical, and time costs. It serves active DeFi traders, financial developers, and institutional investors, providing end-to-end support from strategy ideation to on-chain execution. This allows everyday users—with no professional technical background—to participate in crypto asset trading and yield optimization using fully automated, transparent, and customizable on-chain quantitative strategies.

Almanak introduces AI multi-agent collaboration (Agentic Swarm) into strategy research, execution, and optimization, enabling users to rapidly create, test, and deploy Python-based automated financial strategies in a no-code environment, while ensuring that the execution environment remains non-custodial, verifiable, and scalable. Supported by a State Machine strategy framework, Safe + Zodiac permission management, multi-chain protocol integration, and vault-based asset custody, Almanak retains institutional-grade security and scalability while dramatically lowering the barriers to strategy development and deployment.

This report will provide a systematic analysis of its product architecture, technical features, incentive mechanisms, competitive positioning, and future development path, as well as explore its potential value in inclusive finance and on-chain quantitative trading.

3. Almanak’s Product Architecture and Technical Features

Almanak’s product architecture follows the flow “Strategy Logic → Execution Engine → Security Assurance → Assetization & Expansion”, building a full-stack on-chain quantitative finance system tailored for AI Agent scenarios. Within this framework:

• The Strategies module provides the development and management infrastructure for strategies from ideation to live execution. It currently supports a Python SDK and will in the future support natural language-based strategy generation.

• The Deployments module serves as the execution engine, running strategies automatically within authorized parameters, and leveraging AI decision-making capabilities for adaptive optimization.

• The Wallets module uses a Safe + Zodiac non-custodial architecture to secure funds and permissions, enabling institutional-grade key management and fine-grained access control.

• The Vaults module transforms strategies into tokenized financial products, leveraging standardized vault contracts (ERC-7540) to enable capital raising, yield distribution, and strategy sharing—making strategies fully composable and seamlessly integrated into the broader DeFi ecosystem.

3.1 Strategy Infrastructure

Almanak’s strategy infrastructure covers the full lifecycle from ideation to live execution — including Ideation, Creation, Evaluation, Optimization, Deployment, and Monitoring. Compared to a traditional quantitative trading stack, it differs in three key ways:

1. Built for AI-Agent-led strategy development rather than human-operated workflows.

2. Incorporates Trusted Execution Environments (TEE) to protect the privacy of strategy alpha.

3. Adopts a Safe Wallet + Zodiac-based non-custodial execution model to ensure secure and controllable fund management from the ground up.

Key Features

• Python-based – Strategies are written in Python, offering high flexibility and rich programming capabilities.

• State Machine Architecture – Enables complex decision trees and branching logic based on market conditions.

• High Reliability – Runs on dedicated Almanak infrastructure with robust monitoring and failover mechanisms.

• Default Privacy – All strategy code is encrypted to safeguard proprietary trading logic.

• Abstracted Trading Logic – Developers do not need to directly handle low-level blockchain interactions, wallet management, or transaction signing.

Under this architecture, the strategy framework is built on a persistent state machine design, fully encapsulating on-chain interactions and execution layers. Users only need to write business logic within the Strategy component. Developers can either perform highly customized Python development via the SDK or, in the future, use a natural language strategy generator to describe objectives in plain English—after which multi-agent systems will propose code for review. Users retain full discretion to approve, reject, or adjust strategies before deployment, and can choose whether to launch them as standalone strategies or vaults. Vaults may also be permissioned through whitelisting, enabling controlled access for institutions or liquid funds

Strategy code is encrypted by default, protecting users’ proprietary logic, while all transaction construction, signing, and broadcasting are handled by the official infrastructure to ensure reliability and consistency.

In AI KITCHEN, Almanak’s strategy library is currently open only to whitelisted users. We can see its strategic blueprint: existing live strategies include Tutorials and Technical Analysis. Internally developed strategies cover Liquidity Provisioning, Automated Looping, and Custom Strategies. The roadmap includes the launch of Arbitrage, Advanced Yield Farming, and Derivatives & Structured Products, reflecting a complete product evolution path—from beginner-friendly basics to professional quant systems, and from single-strategy setups to complex cross-protocol portfolios.

3.2 Deployment System

The Deployments module is the core execution layer connecting strategy logic to on-chain operations, responsible for automatically carrying out trades and actions within the scope of user-authorized permissions.Its current primary form, StrategyDeployment, runs at preset intervals or upon triggers according to defined logic, making it suitable for clearly defined and reproducible trading strategies, with an emphasis on stability and controllability.

The upcoming LLMDeployment will integrate one or more large language models (LLMs) as decision engines, enabling strategies to adapt to market changes and continuously learn and optimize. This will allow exploration of new trading opportunities within a tightly controlled permissions framework.

The Deployment workflow covers the full process—from authentication and authorization, strategy execution, transaction construction, and permission verification to transaction signing, submission, and execution monitoring.The underlying execution is handled by core classes maintained by the official infrastructure:

• TransactionManager converts strategy actions into valid on-chain transactions and performs simulation/verification.

• AccountManager generates transaction signatures.

• ExecutionManager broadcasts transactions, tracks their status, and retries if needed—forming a highly reliable closed loop from strategy to on-chain execution.

In the future, Almanak will expand toward multi-Deployment collaboration, cross-chain execution, and enhanced analytics capabilities, supporting the operation of more complex multi-agent strategies.

3.3 Wallet System and Security Mechanism

The wallet system is central to ensuring fund security and controllable strategy execution. Almanak adopts a Safe + Zodiac non-custodial setup, guaranteeing users full ownership of their funds while granting only the precise, controlled permissions necessary for strategy execution to an automated execution account (Deployment EOA).

Users control the Safe Wallet directly through their User Wallet (either an EOA or an ERC-4337 smart account). The Safe Wallet embeds the Zodiac Roles Modifier module, which enforces strict function whitelists and parameter constraints for the Deployment EOA, ensuring it can “only do what is allowed” and that permissions can be revoked at any time.

The Deployment EOA is platform-managed, with its private key stored in enterprise-grade encrypted form and hosted within Google’s secure infrastructure—completely inaccessible to any human. In extreme situations, the platform will immediately notify the user to revoke permissions and generate a new EOA for replacement, ensuring uninterrupted strategy execution.

To keep strategies running, users must purchase an Autonomous Execution Fees service package to cover on-chain operating costs (including gas fees).

Through complete separation of funds and execution permissions, fine-grained access control, institutional-grade key security, and rapid incident response, this architecture achieves an institutional-level security standard—laying the trust foundation for the large-scale adoption of automated DeFi strategies.

3.4 On-Chain Quantitative Strategy Vaults

Almanak Vaults are user-deployable, fully on-chain, permissionless vault contracts that transform trading strategies into tokenized, composable financial products. Rather than static “closed-loop” containers, these vaults—built on the ERC-7540 asynchronous extension of ERC-4626—are designed as programmable capital allocators that integrate natively into the DeFi ecosystem.

By tokenizing AI-crafted strategies, vaults introduce a new DeFi primitive: strategies themselves become ERC-20 assets that can be LP’d, used as collateral, traded, transferred, or composed into structured products. This composability unlocks “DeFi Lego” at the strategy layer, enabling seamless integration with protocols, funds, and structured products.

A vault may be owned by an individual curator or a community. Almanak Vaults are implemented on Lagoon Finance’s open-source contracts (MIT license), inherit Lagoon’s audit and security guarantees, and remain compliant with the ERC-7540 standard. Permission management is consistent with Almanak Wallets, leveraging the Zodiac Roles Modifier to enforce function whitelists and parameter restrictions, ensuring all operations stay within strictly authorized scopes.

Operational workflow:

1. Strategy binding – Link an existing Python strategy or AI-generated strategy to the vault.

2. Capital raising – Investors purchase vault tokens to obtain proportional ownership.

3. On-chain execution & rebalancing – The vault trades and adjusts positions according to the strategy logic.

4. Profit distribution – Profits are distributed based on token holdings, with management fees and performance fees automatically deducted.

Key advantages:

• Each vault position is tokenized as an ERC-20, ensuring portability and interoperability.

• Strategies are deterministic, auditable, and executed on-chain.

• Capital is secure but mobile—security and composability are no longer trade-offs.

• Builders can permissionlessly integrate vault tokens into their own protocols, while allocators can move capital fluidly across the ecosystem.

In short, Almanak Vaults evolve DeFi capital management from isolated wrappers into intelligent, composable systems. By turning AI-generated strategies into tokenized financial primitives, they extend DeFi beyond passive yield containers toward responsive, modular capital networks—fulfilling the long-standing vision of programmable, interoperable finance.

3.5 DeFi Agentic Swarm

The Almanak AI Swarm architecture is a one-stop platform covering the entire strategy development lifecycle. It enables the autonomous research, testing, creation, and deployment of complex financial strategies while ensuring full user control and non-custodial asset management.

Its goal is to simulate and replace the full operational workflow of a traditional quantitative trading team—with the important distinction that every “team member” in the AI Swarm is an AI agent, not a human.

Strategy Team – Transforms a user’s natural language instructions into deployable on-chain strategies. Roles include: Strategist, Programmer , Auditor, Debugger, Quality Engineer, Permission Manager, UI Designer, Deployer,  Together, these roles ensure a complete workflow from ideation to on-chain implementation.

The Strategy Team uses LangGraph for deterministic workflow orchestration, persistent state sharing (TeamState), human-in-the-loop (HITL) verification, parallel processing, and interruption recovery. While the system can fully automate the process, manual confirmation is enabled by default to ensure reliability.

Alpha Seeking Team – Continuously scans the entire DeFi market to identify inefficiencies, explore new ideas and alpha opportunities, and propose new logic and strategy concepts to the Strategy Team.

Optimization Team – Uses large-scale simulations on historical and forecast market data to rigorously evaluate strategy performance. This includes hypothetical stress tests and cycle performance analysis before deployment, identifying potential drawdowns and vulnerabilities to ensure strategy stability and robustness across different market environments.

Additional AI support tools include:

• Stack Expert AI – Answers user questions about the Almanak tech stack and platform operations, providing instant technical support.

• Troubleshooting AI – Focuses on real-time monitoring and issue diagnosis during strategy execution to ensure stable and continuous strategy operation.

Core Principles of Almanak:

• All AI actions are logged, reviewed, and structurally processed.

• No AI operates independently.

• All strategy logic must undergo complete human–AI dual verification before going live.

• Users retain ultimate control and custody rights over their strategies.

4. Almanak Product Progress & Development Roadmap

Autonomous Liquidity USD Vault

Almanak community curators launched the Autonomous Liquidity USD (alUSDC Vault) on Ethereum mainnet—a stablecoin yield optimization vault. Similar to lending-yield AgentFi products such as Giza and Axal, its core is a Stable Rotator Agent that continuously scans the DeFi ecosystem to identify and capture the highest available yield opportunities. It automatically rebalances the portfolio based on customizable risk parameters.

Before executing any trade, the strategy runs an intelligent transaction cost analysis and will only adjust positions if the projected yield increase can cover all associated costs. It also integrates advanced route optimization and auto-compounding to maximize capital efficiency. The vault currently connects to a range of USDC derivative assets from protocols including Aave v3, Compound v3, Fluid, Euler v2, Morpho Blue, and Yearn v3.

Almanak Liquidity Strategies & Swap Trading Strategies

The currently deployed strategies fall into two main categories:

1. LP Series – Dynamic LP Blue Chip, Dynamic LP Degen

Indicator-Based Spot Strategies – MyAmazingStrat, PENDLERSI_Momentum, VIRTUALBollingerBandsMeanReversion

AI-assisted strategy code analysis yields the following conclusions:

• Dynamic LP (Blue Chip / Degen) – Suitable for pools with sustained volume and acceptable IL; Blue Chip focuses on steady fee income, Degen targets higher-frequency fee capture.

• Indicator-based Spot (EMA / RSI / BB) – Lightweight; supports multi-asset, multi-parameter grid experiments; requires strict cooldown, slippage, and min-trade controls, with close monitoring of pool depth and MEV.

• Capital & Scale – Long-tail assets (Degen / ANIME / VIRTUAL) are better suited for small-size, multi-instance deployments for risk diversification; blue-chip LPs are better for mid/long-term strategies with higher TVL.

• Minimum viable live combo – Dynamic LP Blue Chip (steady fees) + RSI/BB-type spot strategy (volatility capture) + small-scale Degen LP or EMA Cross for “high-volatility test fields.”

Almanak Development Roadmap

• Phase 1 – Focus on infrastructure and early community building. Launch a public beta covering the full quant trading stack, with core user formation and private test access distribution via the Legion platform. Begin onboarding capital into AI-designed community vault strategies to achieve the first batch of on-chain automated asset management.

• Phase 2 – Almanak plans to fully open AI Swarm capabilities to the public by the end of the year. Until then, access is being gradually expanded as the system scales toward mass usage. This will mark the official rollout of the platform’s token economy and incentive system.

• Phase 3 – Shift focus to onboarding global retail users. Launch user-friendly products targeting savings and retirement accounts, integrate with centralized exchanges (e.g., Binance, Bybit) to enable seamless CeFi–DeFi connectivity. Expand asset classes with low-risk, high-capacity RWA strategies, and roll out a mobile app to further lower the participation barrier.

In addition, Almanak will continue expanding multi-chain support (including Solana, Hyperliquid, Avalanche, Optimism), integrate more DeFi protocols, and introduce multi-agent collaboration systems and Trusted Execution Environments (TEE) to enable AI-driven alpha discovery—building the world’s most comprehensive AI-powered DeFi intelligent execution network.

5. Almanak Tokenomics & Points Incentive Program

Almanak’s token economy is designed to build an efficient and sustainable value exchange network for AI-driven financial strategies, enabling high-quality strategies and liquidity capital to be efficiently matched on-chain. The ecosystem is structured around two core roles: Strategy & Vault Curators and Liquidity Providers.

• Curators use the Agentic AI Swarm in a no-code environment to design, optimize, and manage verifiable deterministic strategies. They deploy permissionless Vaults to attract external capital, charging management fees and performance-based profit shares.

Liquidity Providers deposit funds into these Vaults to obtain tokenized exposure to strategies and participate in profit distribution.

Strategy Privacy Options: Curators can choose between: Private Mode – Closed-source, not listed in the strategy library, accessible only to the creator. Or Public Mode – Open-source and listed in the library for community and third-party protocol use.  This provides a balance between IP protection and knowledge sharing.

Economic Model: The tokenomics design draws on: Hedge fund-style dynamic capital allocation, combined with Bittensor’s demand-driven emission distribution, and Curve Finance’s governance incentive model.

Emissions are weighted by TVL and strategy ROI, encouraging capital to flow toward the highest-performing strategies. The “veToken + Bribe” model allows protocols to boost emissions for specific Vaults through governance voting, directing agentic traffic (AI execution flow) toward designated protocols.

The emission formula is weighted by AUM + ROI, ensuring that a Vault’s ability to attract capital and generate returns is directly translated into token rewards. A governance boost mechanism (Almanak Wars) can grant up to 3× weighting for a target Vault, creating a competitive incentive market among projects, curators, and liquidity providers.

To ensure sustainability, a portion of protocol fees (Vault fees, compute surcharges, etc.) will flow back into the emission pool, gradually offsetting inflationary pressure as the ecosystem matures.

Token Utility

• Staking – Token holders can stake to get compute resource discounts, increase voting weight, and direct emissions toward specific Vaults.

• Governance – Participate in DAO governance to decide key parameters such as compute surcharges, Vault fee ratios, and emission curves.

Governance will be managed by:

• Ecosystem Parameters Committee – Protocol parameters, revenue distribution, and fund management.

Innovation & Development Committee – Ecosystem grants and strategic initiatives.

Token Distribution

• Team – 54-month linear vesting, 12-month lock-up

• Institutional Investors – 48-month linear vesting, 12-month lock-up

• Advisors – 48-month linear vesting, 12-month lock-up

• Innovation & Ecosystem Development – 35% unlocked at TGE, remainder vesting over 36 months

• Community & Early Participants – 45% unlocked at TGE

• Legion Community Round – Two rounds:

  • Round 1: 30% TGE + 24-month linear vesting

  • Round 2: 100% TGE

The Emission Pool is used to reward network participants and early adopters, following a halving annual inflation model to maintain long-term incentive and governance activity.

Almanak Points: Quantified Participation Incentives

Almanak Points measure and reward user engagement and contribution to the platform, designed to encourage capital lock-up, strategy usage, and community growth.

Points are distributed in seasons and stages. Each season and stage has its own emission amount, eligible activities, and calculation rules.

Ways to earn Points:

1. Deposit funds into listed community Vaults – points are based on deposit size and duration (current Vault deposits earn 2× points).

2. Hold assets in the Almanak Wallet – points based on balance and holding time.

3. Activate & manage Deployments – earn extra points based on managed asset size, duration, and strategy complexity.

4. Refer new users – earn 20% of the referred user’s points.

Points are non-transferable and non-tradable, but will convert 1:1 into tokens at issuance. They will also serve as the basis for governance rights, feature access, and broader ecosystem benefits.

Fundraising & Token Launch Strategy

Almanak works with Cookie.fun and Legion.cc, introducing the Snaps/cSnaps mechanism. Using the Attention Capital Formation model, on-chain contribution scores (from community promotion, content engagement, capital support, etc.) are directly linked to token distribution—creating a transparent, structured “contribution = ownership” allocation model.

Capital Background:

• Early incubation from Delphi Digital and NEAR Foundation

• Almanak raised $8.45M in total. Other Investors include Hashkey Capital, Bankless Ventures, Matrix Partners, RockawayX, AppWorks, Artemis Capital, SParkle Ventures, etc

Key Milestones:

• Jan 8, 2025 – Completed $1M IDO via Legion at a $43M valuation; 30% unlocked at TGE, remaining 70% with 6-month lock-up and 24-month linear vesting

• Aug 21, 2025 – Launching the community round on Legion at a $90M FDV, raising $2M (hard cap $2.5M). The round is open to all verified accounts, with tokens 100% unlocked at TGE. The TGE is targeted for late September to early October.

• Cookie DAO Priority – Top 25 Snappers & Top 50 cSnappers can invest at $75M FDV with 100% TGE unlock

• Campaign Incentives – 0.55% of total supply allocated: 0.4% to the top 500 cSnappers (80% of pool), 0.1% to the top 250 Snappers, and 0.05% to $COOKIE stakers.

This issuance model enhances fairness and accessibility in token distribution while tightly binding capital raising, community building, and long-term governance, creating a sustainable shared-interest network and laying the foundation for Almanak’s long-term expansion in the AI × DeFi space.

6. Investment Thesis & Potential Risk Analysis

Investment Thesis

Almanak currently aligns well with the positioning of “the most user-friendly retail DeFi strategy platform”, with notable advantages in user experience, security architecture, and low entry barriers—particularly suited for retail users with no coding background or on-chain strategy experience to get started quickly.

1. Its core competitive edge lies in the deep integration of an AI multi-agent architecture with a non-custodial execution framework, delivering institutional-grade security and strategy privacy protection while maintaining high performance. The technology stack—comprising Trusted Execution Environments (TEE), Safe Wallet, and Zodiac Roles Modifier—enables fully automated on-chain execution with permission granularity down to specific smart contract function parameters, significantly outperforming most AgentFi models that rely solely on delegated EOA signing.

2. The technical architecture forms a complete closed loop: Data acquisition (Sensors), Strategy logic execution (state machine framework with Prepare / Validate / Sadflow stages), Transaction execution (TransactionManager / AccountManager / ExecutionManager), Monitoring & metrics, Productization (ERC-7540 Vault) and external fundraising/fee charging.  This streamlined pipeline supports commercial scalability. In particular, the vault productization capability allows strategies to evolve from personal-use tools into publicly investable financial products—generating scalable management fees and performance-based revenue for the platform.

3. On the operations side, the Points incentive system is already live, with transparent rules oriented toward AUAM (Assets under Agentic Management), effectively driving TVL growth and platform engagement. The integration of the Attention Capital Formation model—linking on-chain measurable contribution scores to capital raising, community building, and long-term governance—creates a sustainable interest-aligned network, laying the groundwork for Almanak’s long-term expansion in the AgentFi sector.

Potential Risks

Despite its strong technical and functional completeness, Almanak still faces several key challenges:

1. Protocol testing has not yet been fully opened – At present, it only supports Ethereum mainnet lending protocols and Uniswap V3-based LP and swap strategies. However, the underlying Kitchen already supports Ethereum, Base, and Arbitrum, with the capability to extend to 8+ EVM chains and over 200 protocols. The pace of broader expansion—across multiple chains (e.g., Base, Solana, Hyperliquid), protocol integrations, and CEX connectivity—will directly impact strategy diversity, yield opportunities, and overall market competitiveness.

2. Strategy sophistication still basic – Existing strategies are primarily entry-level technical analysis (TA)-based, falling short of institutional-grade or advanced quantitative standards. To become the go-to retail entry point for DeFi quant, Almanak must expand into a richer library of advanced strategies (covering on-chain liquidity management, funding rate arbitrage, cross-pool spreads, and multi-signal fusion), enhance backtesting and paper-trading tools, embed a cost-optimization engine, and support multi-strategy portfolio management.

3. Ecosystem and user base still early-stage – While the points program and vault mechanism have launched, and Almanak is already in the first tier of AgentFi projects, AI-driven vault-managed TVL growth still needs time to validate. User engagement and retention will be critical medium- to long-term indicators.

Overall, Almanak combines mature technical architecture, a clear commercialization path, and a strong incentive engine, making it a rare asset in the AI-driven on-chain quant and asset management vertical. However, ecosystem expansion speed, competitive dynamics, and execution stability will be the three key variables determining whether it can sustain a long-term leadership position.

Disclaimer: The cryptocurrency market often exhibits a disconnect between project fundamentals and secondary market price performance. This content is for information consolidation and research discussion only. It does not constitute investment advice and should not be considered a recommendation to buy or sell any tokens.

在我们对 AgentFi 未来的高阶畅想中，除借贷（Lending）与流动性挖矿（Yield Farming）这两大近期最具价值且易落地的场景外，Swap 组合策略同样是潜力方向。当多个 Swap 按顺序或条件组合时，便形成“策略链路”，例如套利或收益搬砖。这种模式需要状态机管理持仓、条件触发与多步骤自动执行，具备了 AgentFi 的完整闭环特征——感知 → 决策 → 执行 → 再平衡。

一、DeFi 量化策略图谱及可行性分析

传统量化金融（Quantitative Finance）以数学模型、统计方法和算法为核心，依赖历史价格、交易量、宏观指标等数据进行数据驱动决策，并通过程序化执行实现低延迟、高频、自动化交易，辅以严格的风险控制（止损、仓位管理、VaR 等）。其主要应用涵盖高频交易（HFT）、趋势跟随与均值回归（CTA）、跨市场/跨品种套利以及衍生品定价与对冲等，在传统市场中已形成成熟的基础设施、交易所体系和数据生态。

链上量化金融（On-Chain Quantitative Finance）延续了传统量化的逻辑，但运行环境迁移至区块链的可编程市场结构。其数据来自链上交易记录、DEX 报价、DeFi 协议状态，执行在智能合约（AMM、借贷、衍生品协议）中，交易成本包含 Gas、滑点与 MEV 风险，并可通过 DeFi 协议的可组合性构建自动化策略链路。

当前链上量化金融仍处于早期阶段，受多重因素制约难以支撑复杂量化策略的落地：一是市场结构方面，流动性深度不足且 AMM 缺乏超高速撮合机制，限制了高频与大额交易的可行性；二是执行与成本方面，链上出块延迟与高额 Gas 费用使频繁交易难以盈利；三是数据与工具方面，开发与回测环境不完善，且数据维度单一，缺乏企业财务、宏观经济等多源信息。在可实际落地的 DeFi 量化策略中，目前的主流方向集中于：

1. 均值回归 / 趋势跟随 —— 基于技术指标信号（如 RSI、均线、布林带）进行买卖决策；

2. 跨期套利 —— 以 Pendle 等协议为代表，通过固收与浮动收益差获取利润；

3. 做市 + 动态调仓 —— 主动管理 AMM 流动性区间赚取手续费；

4. 杠杆循环收益 —— 依托借贷协议提升资金利用率。

未来的潜在增长空间包括：

• 链上衍生品市场成熟化，尤其是期权与永续合约的广泛应用；

• 更高效的链下数据接入，通过去中心化预言机丰富模型输入维度；

• 多 Agent 协作，实现多策略组合的自动化执行与风险平衡。

二、Almank定位与愿景：链上量化金融的AgentFi探索

在过往的 Crypto AI 研报中，我们曾介绍过诸多优秀的 AgentFi 项目，但大多数仍聚焦于意图驱动的 DeFi 执行、借贷或流动性管理等全自动化操作，鲜有团队深耕量化交易策略。目前市场上明确以量化交易为核心方向的项目几乎只有 Almanak。该项目切入无代码量化策略开发，提供涵盖策略编写（Python）、部署、执行、权限管理与金库化（Vault）的完整工具链，在 AgentFi 领域中具有独特定位，可视为链上量化金融的核心代表案例。

在传统金融中，Inclusive Finance（普惠金融） 旨在降低参与门槛、覆盖更多长尾用户。Almanak 将这一理念延伸至链上，目标是将量化交易能力普惠化。平台通过 AI 驱动的智能体执行策略，显著降低资金、技术与时间成本，面向 DeFi 生态的活跃交易者、金融开发者及机构级投资者，提供从策略构想到链上执行的全链路支持，使普通用户无需专业技术背景，也能使用全自动、链上透明且可定制的量化策略参与加密资产交易与收益优化。

Almanak 平台引入 AI 多智能体协作（Agentic Swarm），在策略研发、执行与优化环节中，令到用户能在无代码环境中快速创建、测试与部署基于 Python 的自动化金融策略，同时确保执行环境的非托管、可验证与可扩展。借助 State Machine 策略框架、Safe+Zodiac 权限管理、多链协议接入及 Vault 资产托管等模块，Almanak 既保留了机构级的安全与可扩展性，又大幅降低了策略开发与部署门槛。本报告将系统分析其产品架构、技术特点、激励机制、竞争定位与未来发展路径，并探讨其在普惠金融与链上量化领域的潜在价值。

三、Almank的产品架构与技术特点

Almanak 的产品架构以 “策略逻辑 → 执行引擎 → 安全保障 → 资产化与扩展” 为主线，构建了一个面向 AI Agent 场景的链上量化金融全栈体系。在这一体系中，Strategies 模块提供从构想到落地执行的策略开发与管理框架，目前支持 Python SDK 且未来会支持自然语言生成方式；Deployments 模块作为执行引擎，将策略逻辑在授权范围内自动化运行，并通过 AI 决策能力实现自适应优化；Wallets 模块以 Safe + Zodiac 的非托管架构保障资金与权限安全，实现机构级密钥管理与细粒度权限控制；Vaults 模块将策略转化为代币化的金融产品，依托标准化的金库合约（ERC-7540），实现资金募集、收益分配与策略共享——使策略具备完全的可组合性，并能够无缝融入更广泛的 DeFi 生态体系。

**1. 策略基础设施（Strategies）**Almanak 的策略基础设施覆盖从构想到执行的完整链路，包括策略构思(Ideation)、创建(Creation)、评估(Evaluation)、优化(Optimization)、部署(Deployment)与监控(Monitoring)等环节。相较传统量化交易栈，其在设计上有三大核心差异：首先，面向 AI Agent 主导的策略开发，而非依赖人工操作的工作流程；其次，引入可信执行环境（TEE）以保护策略 Alpha 的隐私；最后，采用 Safe Wallet + Zodiac 权限管理的非托管执行模式，从底层确保资金与执行的安全可控。

核心特性

• 基于 Python：使用 Python 编写，具备高度灵活且功能强大的编程能力。

• 状态机架构：可根据市场状况实现复杂的决策树与分支逻辑。

• 高可靠性：运行于 Almanak 专用基础设施，配备完善的监控与故障切换机制。

• 默认私有化：所有策略代码均加密存储，保护用户的专有交易逻辑。

• 交易逻辑抽象化：无需直接处理底层区块链交互、钱包管理或交易签名。

在这一架构下，策略框架基于持久化状态机设计，完整封装了链上交互与执行层。用户仅需在 Strategy 组件中编写业务逻辑。开发者既可以通过 SDK 进行高度定制化的 Python 开发，也可在未来借助自然语言策略生成器，用英文直接描述目标——随后多智能体系统会生成代码供用户审阅。用户拥有完全的自主权，可以在部署前批准、拒绝或调整策略，并选择将其作为独立策略或 Vault 发布。Vault 还可通过白名单进行权限管理，从而为机构或流动性基金等主体提供受控的访问方式。策略代码默认加密存储，保护用户专有逻辑；底层交易构建、签名与广播均由官方维护完成，确保执行的高可靠性与一致性。

在目前仅对白名单用户开放的 Almanak 策略库AI KITCHEN 中，可以窥见其策略版图：当前已上线的策略包括 教程策略（Tutorials）与 技术分析（Technical Analysis），而内部开发的策略涵盖 流动性挖矿（Liquidity Provisioning）、自动循环加杠（Automated Looping） 及自定义策略（Custom Strategy），未来路线图则规划推出套利（ Arbitrage）、高级流动性挖矿（Advanced Yield Farming）与 衍生品与结构化产品（Derivatives & Structured Product）等高阶策略，体现出从基础入门到专业量化、从单一策略到跨协议复杂组合的完整产品演进路径。

1. 部署系统（Deployments） Deployments 是连接策略逻辑与链上执行的核心执行层，负责在用户授权的权限范围内自动化完成交易与操作。当前主力形态为 StrategyDeployment，按预设逻辑定时或触发运行，适合执行逻辑明确、可复现的交易策略，强调稳定性与可控性。即将上线的 LLMDeployment 将引入一个或多个大型语言模型（LLM）作为决策引擎，使策略具备自适应市场变化与持续学习优化的能力，在严格权限控制的框架下探索新的交易机会。 Deployment 工作链路涵盖从认证授权、策略运行、交易构建、权限校验，到签名提交与执行监控的全流程。底层执行由官方维护的核心类完成：TransactionManager 将策略动作转换为合规链上交易并模拟验证；AccountManager 生成交易签名；ExecutionManager 广播交易、追踪状态并在必要时重试，形成从策略到链上执行的高可靠闭环。未来，Almanak 将扩展至多 Deployment 协作、跨链执行与增强分析能力，支持更复杂的多智能体策略运行。

2. 钱包体系与安全机制（Wallets） 钱包体系是确保资金安全与策略执行可控的核心。Almanak 采用 Safe + Zodiac 的非托管方案，确保用户对资金的完全所有权，并将策略执行所需的权限精准、可控地委派给自动化执行账户（Deployment EOA）。用户通过 User Wallet（EOA 或 ERC-4337 智能账户）直接控制 Safe Wallet。Safe Wallet 内嵌 Zodiac Roles Modifier 模块，允许为 Deployment EOA 设定严格的函数白名单与参数限制，确保“只能做被允许的事”，且权限可随时撤销。 Deployment EOA 由平台托管，其私钥采用企业级加密静态存储，并由 Google 安全基础设施托管，全程无人可访问。在极端情况下，平台会立即通知用户撤销权限，并生成新的 EOA 完成替换，确保策略不中断运行。为保障策略持续执行，用户需购买 Autonomous Execution Fees 服务套餐，覆盖链上运行成本（包括 Gas）。该架构通过资金与执行权限的彻底隔离、精细化权限管理、机构级密钥安全与快速异常响应，达成了机构级安全标准，为自动化 DeFi 策略的大规模普及奠定了信任基础。

4. 链上量化策略金库（Vaults）

Almanak Vaults 是用户可部署、完全链上、无需许可的金库合约，它们将交易策略转化为代币化、可组合的金融产品。不同于静态的“封闭容器”，这些金库基于 ERC-4626 的异步扩展标准 ERC-7540 构建，被设计为可编程的资本分配器，能够原生融入 DeFi 生态。

通过将 AI 生成的策略代币化，金库引入了一种全新的 DeFi 原语：策略本身成为 ERC-20 资产，可以用于 LP、抵押、交易、转让，或组合成结构化产品。这种可组合性在策略层面解锁了“DeFi 乐高”，使其能够与协议、基金和结构化产品无缝集成。

金库可以由个人策展人或社区拥有。Almanak Vaults 基于 Lagoon Finance 的开源合约（MIT 许可） 实现，继承 Lagoon 的审计与安全保障，并符合 ERC-7540 标准。其权限管理机制与 Almanak Wallets 保持一致，依赖 Zodiac Roles Modifier 来执行函数白名单和参数限制，确保所有操作严格在授权范围内完成。

运行流程包括：

1. 策略绑定 – 将现有的 Python 策略或 AI 生成的策略绑定至金库；

2. 资金募集 – 投资者购买金库代币以获得按比例的所有权；

3. 链上执行与再平衡 – 金库根据策略逻辑进行交易并动态调整仓位；

4. 利润分配 – 按代币持有比例分配收益，管理费和绩效费用将自动扣除。

核心优势：

• 每个金库仓位均以 ERC-20 代币形式存在，保证可移植性与互操作性；

• 策略具备确定性、可审计性，并在链上执行；

• 资本既安全又具流动性——安全性与可组合性不再对立；

• 开发者可无许可地将金库代币集成到自己的协议中，资金分配者则可在生态内灵活调配资本。

简而言之，Almanak Vaults 将 DeFi 资本管理从孤立的包装容器进化为智能、可组合的系统。通过将 AI 生成的策略转化为代币化金融原语，它推动 DeFi 超越被动收益容器，迈向响应式、模块化的资本网络，实现了长期以来对可编程、可互操作金融的愿景。

5. DeFi 智能体集群（DeFi Agentic Swarm ）

Almanak AI Swarm 架构作为覆盖完整策略开发周期的一站式平台，能够在用户完全掌控与资产非托管的前提下，自主完成复杂 DeFi 策略的研究、测试、创作与部署，旨在模拟并替代传统量化交易团队的全流程作业。值得注意的是，AI Swarm“团队”均由 AI 智能体构成并非真人。

策略团队（Strategy Team）：将用户的自然语言指令转化为可直接部署的链上策略，涵盖策略师（设计逻辑）、程序员（编写智能合约代码）、审计员（校验正确性）、调试员（修复错误）、质量工程师（运行模拟测试）、权限管理员（配置执行权限）、UI 设计师（构建可视化面板）以及部署员（执行主网部署），确保从构想到落地的完整链路。

策略团队通过 LangGraph 实现确定性流程编排、持久化状态共享（TeamState）、人机双重验证（HITL）、并行处理及中断恢复机制。可自动执行全流程但默认启用人工确认以确保可靠性。

Alpha 搜寻团队（Alpha Seeking Team）：持续扫描整个 DeFi 市场，识别市场低效之处，探索新思路与 Alpha 机会，并向策略团队提出新的逻辑与策略构想。

优化团队（Optimization Team）：通过对历史与预测市场数据进行大规模模拟，严格评估策略表现，在部署前开展假设压力测试、周期表现分析，识别潜在回撤与脆弱点，确保策略在不同市场环境下的稳定性与鲁棒性。

此外辅助型 AI 工具包括 Stack Expert AI 与 Troubleshooting AI：前者专注解答用户关于 Almanak 技术栈与平台操作的各类问题，提供即时技术支持；后者则聚焦于策略运行过程的实时监控与问题定位，确保策略执行的稳定性与连续性。

Almanak 的核心原则是：所有 AI 操作都会被记录、审查并结构化处理，任何 AI 都不会独立运行，且所有策略逻辑在上线前必须经过完整的人机双重验证且用户拥有最终的控制权与托管权。

四、Almank产品进度与发展路线图

Autonomous Liquidity USD Vault金库

目前Almanak通过社区已正式上线部署在以太坊主网的Autonomous Liquidity USD（alUSDC Vault）稳定币收益优化金库，同Giza、Axal等借贷收益类AgentFi产品一样，其核心是 Stable Rotator Agent，会持续扫描 DeFi 生态，识别并捕捉最高可得收益机会，并根据可自定义的风险参数自动再平衡投资组合。策略在执行前会进行智能交易成本分析，仅在收益增幅足以覆盖所有成本时才会调整仓位，并结合高级路由优化自动复利功能最大化资金效率。目前该金库底层接入 Aave v3、Compound v3、Fluid、Euler v2、Morpho Blue、Yearn v3 等协议的多种 USDC 衍生资产。

Almanak 流动性策略与Swap交易策略

Almanak 已上线的策略可分为两大类：LP 系列（Dynamic LP Blue Chip、Dynamic LP Degen）与指标现货策略（MyAmazingStrat、PENDLERSI_Momentum、VIRTUALBollingerBandsMeanReversion），详细策略内容如下表：

通过AI工具对以上策略代码的分析，我们可以得出以下结论：

• LP 动态做市（Blue Chip / Degen）适合有持续交易量且能接受无常损失的池子；Blue Chip 追求稳态费收，Degen 追求更高频收益捕获。

• 指标现货（EMA/RSI/BB）实现轻、可做多标的多参数网格实验；但要严格冷却/滑点/最小成交量控制，并关注池子深度与 MEV。

• 资金与规模：长尾资产（Degen/ANIME/VIRTUAL）更适合小额/多实例策略以分散风控；蓝筹 LP 更适合中长期/更高 TVL。

• 实盘最小可行组合：Dynamic LP Blue Chip（稳态费收）+ RSI/BB 一类现货策略（做波动捕捉）+ 小额部署 Degen LP 或 EMA 交叉 做「高波动试验田」。

Almanak 发展路线图Almanak 的平台演进分为三个阶段，逐步实现从技术底座到全链路普及扩张。

• Phase 1 聚焦基础设施与早期社区构建，推出涵盖完整量化交易栈的公测版本，并通过 Legion 平台完成核心用户群体的形成与私测资格分发。同时，开始接纳资金进入由 AI 设计的社区金库策略，实现第一批资产的链上自动化管理。

• Phase 2 Almanak 计划在今年年底前向公众全面开放 AI Swarm 功能。在此之前，将随着系统扩展至大规模使用而逐步放宽访问权限，这一阶段将是平台代币经济与激励体系的正式落地期。

• Phase 3 将重点转向全球零售用户的引入，推出面向储蓄与退休账户的友好型产品，并与中心化交易所（如 Binance、Bybit）打通，实现 CeFi 与 DeFi 的无缝衔接。同时，利用低风险高容量的 RWA 策略拓展资产类别，并上线移动端应用，进一步降低用户参与门槛。

此外，Almanak 还将持续扩展多链支持（包括 Solana、Hyperliquid、Avalanche、Optimism 等）、集成更多 DeFi 协议，以及引入 多智能体协作系统（Multi-Agent System） 与 可信执行环境（TEE），通过 AI 驱动自动发现Alpha ，构建全球最全面的 AI DeFi 智能执行网络。

五、Almank的代币经济学与Points积分激励

Almanak 的代币经济体系旨在为 AI 驱动的金融策略构建高效、可持续的价值交换网络，使高质量的策略与流动性资本在链上实现高效匹配。平台通过双核心角色策略与金库策展人（Strategy & Vault Curators）和流动性提供者（Liquidity Providers）构建生态闭环：前者利用 AI 智能体集群（Agentic AI-Swarm）在无代码环境中设计、优化并管理可验证的确定性策略，通过部署无许可金库（Vault）引入外部资金并收取管理费与业绩分成；后者则向这些 Vault 存入资金，以获得代币化的策略敞口并参与收益分配。

在策略隐私层面，策略策展人可选择私有模式（闭源、不列入策略库，仅本人可访问）或公开模式（开源并列入策略库，供社区和第三方协议使用），从而在 IP 保护与知识共享之间取得平衡。

代币经济模型借鉴了传统对冲基金的动态资金分配逻辑，并融合 Bittensor 的需求驱动型排放分配机制与 Curve Finance 的治理激励模型：前者通过 TVL 与策略收益率加权分配排放，鼓励资本向高绩效策略集中；后者则引入 “veToken + Bribe” 模式，允许协议方通过投票提升特定 Vault 的排放倍数，从而引导 AI 智能体流量（agentic traffic）向指定协议聚集。

排放分配采用基于 AUM 与 ROI 的加权公式，确保 Vault 在吸引资本和创造收益方面的贡献直接转化为代币奖励；而治理加成机制（Almanak Wars）可为目标 Vault 叠加最高 3 倍权重，形成项目方、策展人和流动性方三方博弈的激励市场。为维持长期可持续性，协议费用（Vault 收费抽成、计算资源加价等）将部分回流至排放池，从而在生态成熟后逐步抵消新增排放压力。

代币功能涵盖 质押（Staking） 与 治理（Governance）：持币者可通过质押获取平台计算资源折扣、提升投票权重、为特定 Vault 引流排放，并参与 DAO 治理，决定计算资源加价率、Vault 收费比例、排放曲线等关键参数。治理架构预期包含 生态参数委员会 与 创新发展委员会，分别负责协议参数、收入分配与资金管理、生态资助等事务。

在代币分配上，Almanak 设置了团队（54 个月线性释放，12 个月锁仓）、机构投资人（48 个月线性释放，12 个月 锁仓）、顾问（48 个月线性释放，12 个月 锁仓）、创新与生态发展（TGE 释放 35%，余下 36 个月线性释放）、社区与早期参与者（TGE 释放 45%）、Legion 社区轮（分两轮，第一轮 30% TGE + 24 个月线性释放，第二轮 100% TGE）。排放池（Emission）用于奖励网络参与者及早期激励，并按年度减半的通胀模型分配，保持长期的激励与治理活跃度。

Almanak Points：平台参与度量化激励机制

Almanak Points 是衡量奖励用户在平台中参与度与贡献度的核心机制，旨在通过积分体系驱动资产沉淀、策略使用和社区增长。积分按赛季（Season）分阶段发放，每个赛季的排放量、可参与活动及计算方式都会调整。

用户可通过多种方式获取 Points：① 将资金存入 Almanak 平台列出的社区金库（Vault），按存入规模与持有时间计算积分（当前 Vault 存款享 2× 积分倍数）；② 在 Almanak Wallet 中持有资产，按余额与持有时长累计积分；③ 启用并活跃管理 Deployments，根据管理资产规模、时长及策略复杂度获得额外积分；④ 通过推荐新用户参与平台活动，按被推荐用户积分的 20% 额外奖励推荐人。Points不可转让、不可交易，但将在代币发行时以 1:1 的比例转换为代币。同时，积分还将作为治理权、功能使用权限以及生态系统福利的基础。

项目融资与发币策略

Almanak 与 Cookie.fun 和 Legion.cc 深度合作，引入 Snaps/cSnaps 机制，通过注意力资本形成(Attention Capital Formation)模式分析链上可追踪贡献度积分，将用户在社区传播、内容互动、资金支持等多维度的活跃度与代币分配直接挂钩，实现 “贡献即所有权” 的透明化、结构化分配逻辑。

在资本背景方面，Almanak 早期获得 Delphi Digital 与 NEAR Foundation 的孵化支持，并先后引入 Hashkey Capital、Bankless Ventures、Matrix Partners、RockawayX、AppWorks、Artemis Capital、SParkle Ventures 等知名机构投资,累计融资金额达8.45M美金。

• 2025 年 1 月 8 日：通过 Legion 完成 $1M IDO，估值 $43M，TGE 解锁 30%，剩余 70% 设 6 个月锁仓期，并在 24 个月内线性释放。

• 2025 年 8 月 21 日：即将启动 Legion 社区轮融资，估值为 9000 万美元 FDV，目标融资 200 万美元，最高上限 250 万美元。本轮对所有已验证账户开放，代币将在 TGE 时 100% 全额解锁。TGE 预计在 9 月底至 10 月初进行。

• Cookie DAO 优先权益：前 25 名 Snappers 和前 50 名 cSnappers 可享有以 7500 万美元 FDV 投资的优先权，且同样在 TGE 时 100% 全额解锁。

• 活动激励 – 总供应量的 0.55% 将用于激励分配：其中 0.4% 分配给前 500 名 cSnappers（占奖励池 80%）、0.1% 分配给前 250 名 Snappers、0.05% 分配给 $COOKIE 质押者。

这一发行机制不仅优化了代币分配的公平性与参与门槛，还将资本募集、社区建设与长期治理深度绑定，形成可持续的利益共同体，为 Almanak 在 AI × DeFi 赛道的长期扩张奠定基础。

六、投资逻辑与潜在风险分析

投资逻辑Almanak 目前更契合“最易用的散户 DeFi 策略沙盒”定位，其在用户体验、安全架构与低门槛上具备显著优势，尤其适合从未编写代码或缺乏链上策略经验的散户快速入门。Almanak 的核心竞争力在于将 AI 多智能体架构 与 非托管执行体系 深度融合，在保证性能的同时提供机构级安全与策略隐私保护。其技术栈由 TEE（可信执行环境）+ Safe 钱包 + Zodiac Roles Modifier 共同构成，可在粒度精确到合约函数参数的权限管理下实现全自动化链上执行，显著优于多数仅依赖 EOA 代签的 AgentFi 模式。

技术架构已形成完整闭环：从数据获取（Sensors）、策略逻辑执行（持久化状态机架构，Prepare / Validate / Sadflow）、交易执行（TransactionManager / AccountManager / ExecutionManager）、到监控与指标系统，再到产品化（ERC-7540 Vault）与外部募资收费，链路顺畅且具备商业化延展性。特别是 Vault 产品化能力，使策略从自用工具直接升级为可对外发行的金融产品，为平台带来规模化的管理费和业绩分成收入。

在运营端，Points 激励体系已启动，规则透明且以 AUAM（Assets under Agentic Management）为导向，可有效驱动锁仓量与活跃度；通过注意力资本形成(Attention Capital Formation)模式分析链上可追踪贡献度积分，将资本募集、社区建设与长期治理深度绑定，形成可持续的利益共同体，为 Almanak 在 AgentFi赛道的长期扩张奠定基础。

潜在风险

尽管 Almanak 在技术与功能体系上已具备较高完备度，但仍面临若干关键挑战：

首先，协议测试尚未完全开放。目前仅支持以太坊主网的借贷协议，以及基于 Uniswap V3 的 LP 与 Swap 策略，底层的 Kitchen 已经支持以太坊、Base 和 Arbitrum，并具备扩展至 8+ 条 EVM 链和 200+ 协议的能力，多链扩展（如 Base、Solana、Hyperliquid）、多协议接入以及 CEX 融合等更大范围的开放节奏将直接影响策略的多样性、收益机会以及市场竞争力。

其次，策略层级仍偏基础。现有策略以入门级技术分析（TA）为主，距离机构级或专业量化水准仍有差距。未来需引入更丰富的高级策略库（涵盖链上流动性管理、资金费率套利、跨池价差、以及多信号融合等），并完善回测与模拟交易工具、内置成本优化引擎及多策略组合管理功能，方能成长为散户进入 DeFi 量化的首选入口。

此外，生态与用户基础仍处早期阶段。尽管积分计划与 Vault 机制已上线并进入 AgentFi 第一梯队，但由 AI 驱动的 Vault 管理 TVL 增长仍需时间验证，且用户活跃度与留存率将是中长期关键指标。

总体来看，Almanak 兼具技术架构成熟度、商业化路径清晰度与激励机制驱动力，在 AI 驱动的链上量化与资产管理赛道中具备稀缺性。然而，生态扩张速度、竞争格局演变与技术落地稳定性将是决定其能否长期保持领先的三大核心变量。

**免责声明：当前加密资产市场普遍存在项目基本面与二级市场价格表现背离的现象。本文内容仅用于信息整合与研究交流，不构成任何投资建议，亦不应被视为代币的买卖推荐。

Among the various sectors in the current crypto landscape, stablecoin payments and DeFi applications stand out as two verticals with verified real-world demand and long-term value. At the same time, the flourishing development of AI Agents is emerging as the practical user-facing interface of the AI industry—acting as a key intermediary between AI and users.

In the convergence of Crypto and AI, particularly in how AI feeds back into crypto applications, exploration has centered around three typical use cases:

• Conversational Agents: These include chatbots, companions, and assistants. While many are still wrappers around general-purpose large models, their low development threshold and natural interaction—combined with token incentives—make them the earliest form to reach users and attract attention.

• Information Integration Agents: These focus on aggregating and interpreting both on-chain and off-chain data. Projects like Kaito and AIXBT have seen success in web-based information aggregation, though on-chain data integration remains exploratory with no clear market leaders yet.

• Strategy Execution Agents: Centered on stablecoin payments and DeFi strategy automation, these agents give rise to two key categories: (1) Agent Payment and (2) DeFAI. These agents are deeply embedded in the logic of on-chain trading and asset management, with the potential to move beyond speculative hype and establish sustainable, efficient financial automation infrastructure.

This article focuses on the evolutionary path of DeFi and AI integration, outlining the transition from automation to intelligent agents, and analyzing the underlying infrastructure, application space, and core challenges of strategy-executing agents.

Three Stages of DeFi Intelligence: From Automation to Copilot to AgentFi

The evolution of intelligence in DeFi can be categorized into three progressive stages:

1. Automation (Automation Infra) –  These systems function as rule-based triggers, executing predefined tasks such as arbitrage, rebalancing, or stop-loss orders. They lack the ability to generate strategies or operate autonomously.

2. Intent-Centric Copilot – This stage introduces intent recognition and semantic parsing. Users input commands via natural language, and the system interprets and decomposes them to suggest execution paths. However, the execution loop is not autonomous and still relies on user confirmation.

3. AgentFi – This represents a complete intelligence loop: perception → reasoning/strategy generation → on-chain execution → continuous evolution. AgentFi embodies on-chain agents with autonomous execution and adaptive learning, capable of closing the loop and evolving with experience.

To determine whether a project truly qualifies as AgentFi, it must meet at least three of the following five core criteria:

1. Autonomous perception of on-chain state/market signals  (Not static inputs, but real-time monitoring)

2. Capability for strategy generation and composition (Not just preset strategies, but the ability to create contextual action plans autonomously)

3. Autonomous on-chain execution (Able to perform complex actions like swap/lend/stake without human interaction)

4. Persistent state and evolutionary capability (Agents have lifecycles, can run continuously, and adjust behavior based on feedback)

5. Agent-native architecture (dedicated Agent SDKs, specialized execution environments, and intent routing or execution middleware designed to support autonomous agents)

In other words:  Automated trading ≠ Copilot, and certainly ≠ AgentFi.

• Automated trading is merely a rule-based trigger system;

• Copilots can interpret user intent and offer actionable suggestions, but still depend on human input;

True AgentFi refers to agents with perception, reasoning, and autonomous on-chain execution, capable of closing the strategy loop and evolving over time without human intervention.

DeFi Use Case Suitability Analysis:

Within decentralized finance (DeFi), core applications generally fall into two categories:

• Token Capital Markets: Asset circulation and exchange

• Fixed Income: Yield-generating financial strategies

We believe these two categories differ significantly in their compatibility with intelligent execution:

1. Token Capital Markets: Asset Circulation & Exchange Scenarios

These involve atomic interactions, such as Swap transactions, Cross-chain bridging, Fiat on/off ramps, Their core characteristics are "intent-driven + single-step atomic execution", and they do not involve yield strategies, state persistence, or evolution logic. Thus, they are better suited for Intent-Centric Copilots and do not constitute AgentFi.

Due to their lower technical barriers and simple interactions, most current DeFAI projects fall into this category. These do not form closed-loop AgentFi systems.

However, more advanced swap strategies—like Cross-asset arbitrage, Perpetual hedge LP, Leverage rebalancing, may require the capabilities of AI Agents, though such implementations are still in the early exploratory phase.

2. Yield-Generating Financial ScenariosYield-generating financial scenarios are naturally aligned with the AgentFi model of “strategy closed-loop + autonomous execution,” as they feature clear return objectives, complex strategy composition, and dynamic state management. Key characteristics include:

• Quantifiable return targets (APR / APY), enabling Agents to build optimization functions;

• A wide strategy design space involving multi-asset, multi-duration, multi-platform, and multi-step interactions;

Frequent management and real-time adjustments, making them well-suited for execution and maintenance by on-chain Agents.

Due to multiple constraints such as yield duration, volatility frequency, on-chain data complexity, cross-protocol integration difficulty, and compliance limitations, different yield-generating scenarios vary significantly in terms of AgentFi compatibility and engineering feasibility. The following prioritization is recommended:

High-Priority Implementation Scenarios:

• Lending / Borrowing:  Interest rate fluctuations are easy to track, with standardized execution logic—ideal for lightweight agents.

• Yield Farming:   Pools are highly dynamic with broad strategy combinations and volatile returns. AgentFi can significantly enhance APY and interaction efficiency, though engineering implementation is relatively challenging.

Mid-to-Long-Term Exploration Directions:

• Pendle Yield Trading:  Time dimensions and yield curves are clear, making it suitable for agents to manage rollovers and inter-pool arbitrage.

• Funding Rate Arbitrage:  Theoretical returns are attractive but require solving cross-market execution and off-chain interaction challenges—high engineering complexity.

• LRT (Liquid Restaking Token) Dynamic Portfolio Management: Static staking is not suitable; potential lies in combining LRT with LP, lending, and automated rebalancing strategies.

• RWA Multi-Asset Portfolio Management:  Difficult to implement in the short term. Agents can assist with portfolio optimization and maturity planning.

Intelligentization of DeFi Scenarios:

1. Automation Tools: Rule Triggers and Conditional Execution

Gelato is one of the earliest infrastructures for DeFi automation, once supporting conditional task execution for protocols like Aave and Reflexer. It has since pivoted to a Rollup-as-a-Service provider. Currently, the main battleground for on-chain automation has shifted to DeFi asset management platforms (e.g., DeFi Saver, Instadapp) , which offer standardized automation modules like limit orders, liquidation protection, auto-rebalancing, DCA, and grid strategies.

Some more advanced DeFi automation platforms include:

Mimic.fi – https://www.mimic.fi/

An on-chain automation platform for DeFi developers and projects, supporting programmable automation across chains like Arbitrum, Base, and Optimism. Its architecture includes: Planning(task and trigger definition), Execution(intent broadcasting and competitive execution), Security(triple verification and risk control). Currently SDK-based and still in early-stage deployment.

AFI Protocol – https://www.afiprotocol.ai/

An algorithm-driven agent execution network supporting 24/7 non-custodial automation. It targets fragmented execution, high strategy barriers, and poor risk response in DeFi.It offers programmable strategies, permission controls, SDK tools, and a native yield-bearing stablecoin afiUSD.  Currently in private testing under Sonic Labs, not yet open to the public or retail users.

2. Intent-Centric Copilot: Intent Expression & Execution Suggestions

The DeFAI narrative that surged in late 2024, excluding speculative meme-token projects, mostly falls under Intent-Centric Copilot—where users express intent in natural language, and the system suggests actions or performs basic on-chain operations.

The core capability is still at the stage of:  “Intent Recognition + Copilot-Assisted Execution”,  without a closed-loop strategy cycle or continuous optimization.  Due to current limitations in semantic understanding, cross-protocol interaction, and response feedback, most user experiences are suboptimal and functionally constrained.

Notable projects include:

HeyElsa – https://app.heyelsa.ai/

HeyElsa is the AI Copilot of Web3, empowering users to execute actions like trading, bridging, NFT purchases, stop-loss settings, and even creating Zora tokens—all through natural language. As a powerful conversational crypto assistant, it targets beginner to super advanced  users as well as degen traders and is fully live and functional across 10+ chains. With 1 million in daily trading volume, 3,000 to 5,000 daily active users, and integrated yield optimization strategies alongside automated intent execution, HeyElsa establishes a robust foundation for AgentFi.

Bankr – https://bankr.bot/

An intent-based trading assistant integrating AI, DeFi, and social interaction. Users can issue natural language commands via X or its dedicated terminal to perform swap, limit order, bridging, token launches, NFT minting, etc., across Base, Solana, Polygon, and Ethereum mainnet.  Bankr builds a full Intent → Compilation → Execution pipeline, emphasizing minimal UI and seamless integration within social platforms. It uses token incentives and revenue sharing to fuel growth.

Griffain – https://griffain.com/

A multifunctional AI Agent platform built on Solana. Users interact with the Griffain Copilot via natural language to query assets, trade NFTs, manage LPs, and more.The platform supports multiple agent modules and encourages community-built agents. It’s built using Anchor Framework and integrates with Jupiter, Tensor, etc., emphasizing mobile compatibility and composability.  Currently supports 10+ core agent modules with strong execution capabilities.

Symphony – https://www.symphony.io/

An execution infrastructure for AI agents, building a full-stack system with intent modeling, intelligent route discovery, RFQ execution, and account abstraction.  Their conversational assistant Sympson is live with market data and strategy suggestions, though full on-chain execution isn’t yet available.  Symphony provides the foundational components for AgentFi and is positioned to support collaborative agent execution and cross-chain operations in the future.

HeyAnon – https://heyanon.ai/

A DeFAI platform that combines intent interaction, on-chain execution, and intelligence analysis.Supports multi-chain deployment (Ethereum, Base, Solana, etc.) and cross-chain bridges (LayerZero, deBridge).  Users can use natural language to swap, lend, stake, and analyze market sentiment and on-chain dynamics.  Despite attention from founder Sesta, it remains in the Copilot phase, with incomplete strategy and execution intelligence. Long-term viability is still being tested.

The above scoring system is primarily based on the usability of the product at the time the author reviewed it, the user experience, and the feasibility of the publicly available roadmap. It reflects a high degree of subjectivity. Please note that this evaluation does not include any code security audits and should not be considered investment advice.

3. AgentFi Agents: Strategy Closed-Loop and Autonomous Execution

We believe AgentFi represents a more advanced paradigm in the intelligent evolution of DeFi, compared to Intent-Centric Copilots. These agents possess independent yield strategies and on-chain autonomous execution capabilities, significantly improving execution efficiency and capital utilization for users.

In 2025, we are excited to see a growing number of AgentFi projects already live or under development, mainly focused on lending and liquidity mining. Notable examples include Giza ARMA, Theoriq AlphaSwarm, Almanak, Brahma, the Olas agent series, and more.

🔹 Giza ARMA – https://arma.xyz/

ARMA is an intelligent agent product launched by Giza, designed for cross-protocol stablecoin yield optimization. Deployed on Base, ARMA supports major lending protocols like Aave, Morpho, Compound, and Moonwell, with key capabilities such as cross-protocol rebalancing, auto-compounding, and smart asset switching. The ARMA strategy system monitors stablecoin APRs, transaction costs, and yield spreads in real time, adjusting allocations autonomously—resulting in significantly higher yields compared to static holdings.

Its modular architecture includes: Smart accounts, Session keys, Core agent logic, Protocol adapters, Risk management, Accounting modules. Together, these ensure non-custodial yet secure and efficient automation.ARMA is now fully launched and iterating rapidly, making it one of the most practical AgentFi products in DeFi yield automation.

📚 Reference report: A New Paradigm for Stablecoin Yields: AgentFi to XenoFi

🔹 Theoriq – https://www.theoriq.ai/

Theoriq Alpha Protocol is a multi-agent collaboration protocol focused on DeFi, with AlphaSwarm as its core product targeting liquidity management.

It aims to build a full automation loop of perception → decision-making → execution, made up of Portal Agents (on-chain signal detection), Knowledge Agents (data analytics and strategy selection), LP Assistants (strategy execution), These agents can dynamically manage asset allocation and optimize yield without human intervention.

The base-layer Alpha Protocol provides agent registration, communication, parameter configuration, and development tools, serving as an “Agent Operating System” for DeFi. Via AlphaStudio, users can browse, invoke, and compose agents into modular, scalable automated strategies.

Theoriq recently raised $84M in community funding via Kaito Capital Launchpad and is preparing for its TGE. The AlphaSwarm Community Beta Testnet is now live, with mainnet launch imminent.

📚 Reference report: Theoriq: The Evolution of AgentFi in Liquidity Mining Yields

🔹 Almanak – https://almanak.co/

Almanak is an intelligent agent platform for DeFi strategy automation, combining non-custodial security architecture with a Python-based strategy engine to help traders and developers deploy sustainable on-chain strategies.

Its core modules include:  Deployment (execution engine), Strategy (logic layer), Wallet (Safe + Zodiac security), Vault (asset tokenization). It supports yield optimization, cross-protocol interactions, LP provisioning, and automated trading. Compared to traditional tools, Almanak emphasizes AI-powered market perception and risk control, offering 24/7 autonomous operation. It plans to introduce multi-agent and AI decision systems as next-gen AgentFi infrastructure.

The strategy engine is a Python-based state machine—the “decision brain” of each agent—capable of responding to market data, wallet status, and user-defined conditions to autonomously execute on-chain actions. A full Strategy Framework enables users to build and deploy actions like trading, lending, or LP provisioning without writing low-level contract code. It ensures privacy and safety through encrypted isolation, permission control, and monitoring. Users can write strategies via SDKs, with future support for natural language strategy creation.

Currently, a USDC lending vault on Ethereum mainnet is live; more complex strategies are in testing (whitelist required). Almanak will soon join the cookie.fun cSNAPS campaign for a public raise.

🔹 Brahma – https://brahma.fi/

Brahma positions itself as "The Orchestration Layer for Internet Finance," abstracting on-chain accounts, execution logic, and off-chain payment flows to help users and developers manage on/off-chain assets efficiently. Its architecture includes: Smart Accounts, Persistent On-Chain Agents, Capital Orchestration Stack.  This enables a backend-free smart capital management experience.

Deployed agents include:

• Felix Agent: Optimizes feUSD vault rates to avoid liquidation and save interest

• Surge & Purge Agent: Tracks volatility and executes auto-trading

• Morpho Agent: Deploys and rebalances Morpho vault capital

• ConsoleKit Framework: Supports integration of any AI model for unified strategy execution and asset orchestration

🔹 Olas – https://olas.network/

Olas has launched a suite of on-chain AgentFi products—specifically Modius Agent and Optimus Agent, both part of the BabyDegen family,—across chains like Solana, Mode, Optimism, and Base. Each supports full on-chain interaction, strategy execution, and autonomous asset management.

• BabyDegen: An AI trading agent on Solana using CoinGecko data and community strategies to auto-trade. Integrated with Jupiter DEX and in Alpha.

• Modius Agent: Portfolio manager for USDC/ETH on Mode, integrated with Balancer, Sturdy, and Velodrome, running 24/7 based on user preferences.

• Optimus Agent: Multichain agent for Mode, Optimism, and Base, supporting broader protocol integrations like Uniswap and Velodrome, suitable for intermediate and advanced users building automated portfolios.

🔹 Axal – https://www.getaxal.com/

Axal’s flagship product Autopilot Yield provides a non-custodial, verifiable yield management experience, integrating protocols like Aave, Morpho, Kamino, Pendle, and Hyperliquid. It offers on-chain strategy execution + risk control through three tiers:

• Conservative Strategy:Focused on low-risk, stable yield from trusted protocols like Aave and Morpho (5–7% APY). Uses TVL monitoring, stop-losses, and top-tier strategies for long-term growth.

• Balanced Strategy:Mid-risk strategies with higher returns (10–20% APY), using wrapped stablecoins (feUSD, USDxL), LP provision, and neutral arbitrage. Axal dynamically monitors and adjusts exposure.

• Aggressive Strategy:High-risk strategies (up to 50%+ APY) including high-leverage LPs, cross-platform plays, low-liquidity market making, and volatility capture. Smart agents enforce stop-loss, auto-exit, and redeployment logic to protect users.

This resembles the structure of a traditional wealth management risk assessment product.

🔹 Fungi.ag – https://fungi.ag/

Fungi.ag is a fully autonomous AI agent designed for USDC yield optimization, auto-allocating funds across Aave, Morpho, Moonwell, Fluid, etc. Based on APR, fees, and risk, it maximizes capital efficiency with no manual interaction—just Session Key authorization.

Currently supports Base, with plans to expand to Arbitrum and Optimism. Fungi also offers the Hypha strategy scripting interface, enabling the community to build DCA, arbitrage, and other strategies. DAO and social tools foster a co-built ecosystem.

🔹 ZyFAI – https://www.zyf.ai/

ZyFAI is a smart DeFi assistant deployed on Base and Sonic, combining on-chain interfaces with AI modules to help users manage assets across risk preferences.

Three core strategy types:

• Safe Strategy: For conservative users. Focused on secure protocols (Aave, Morpho, Compound, Moonwell, Spark) offering stable USDC deposits and safe returns.

• Yieldor Strategy: For high-risk users (requires 20,000 $ZFI to unlock). Involves Pendle, YieldFi, Harvest Finance, Wasabi, supporting complex strategies like LPs, reward splitting, leverage vaults, with future plans for looping and delta-neutral products.

Airdrop Strategy (in development): Aimed at maximizing airdrop farming opportunities.

The above scoring system is primarily based on the usability of the product at the time the author reviewed it, the user experience, and the feasibility of the publicly available roadmap. It reflects a high degree of subjectivity. Please note that this evaluation does not include any code security audits and should not be considered investment advice.

AgentFi: Practical Paths and Advanced Horizons

Undoubtedly, lending and liquidity mining are the most valuable and most readily implementable business scenarios for AgentFi in the near term. Both are mature sectors in the DeFi ecosystem and naturally well-suited for intelligent agent integration due to the following common features:

• Extensive strategy space with many optimization dimensions:Lending goes beyond chasing high yields—it includes rate arbitrage, leverage loops, debt refinancing, liquidation protection, etc.Yield farming involves APR tracking, LP rebalancing, auto-compounding, and multi-layered strategy composition.

• Highly dynamic environments that require real-time perception and response:Fluctuations in interest rates, TVL, incentive structures, the launch of new pools, or emergence of new protocols can all shift the optimal strategy, requiring dynamic adjustments.

• Significant execution window costs where automation creates clear value:Funds not allocated to optimal pools result in opportunity cost; automation enables real-time migration.

Lending vs Yield Farming: Readiness for AgentFi