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The Convergent Evolution of Automation, AI, and Web3 in the Robotics Industry

This independent research report is supported by IOSG Ventures. The author thanks Hans (RoboCup Asia-Pacific), Nichanan Kesonpat(1kx), Robert Koschig (1kx), Amanda Young (Collab+Currency) , Jonathan Victor (Ansa Research), Lex Sokolin (Generative Ventures), Jay Yu (Pantera Capital) , Jeffrey Hu (Hashkey Capital) for their valuable comments, as well as contributors from OpenMind, BitRobot, peaq, Auki Labs, XMAQUINA, GAIB, Vader, Gradient, Tashi Network and CodecFlow for their constructive feed...

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机器人产业畅想：自动化、人工智能与 Web3 的融合进化

本独立研报由IOSG Ventures支持，感谢*Hans (RoboCup Asia-Pacific) , Nichanan Kesonpat(1kx), Robert Koschig (1kx) , Amanda Young (Collab+Currency) , Jonathan Victor (Ansa Research), Lex Sokolin (Generative Ventures), Jay Yu (Pantera Capital) , Jeffrey Hu (Hashkey Capital) 对本文提出的宝贵建议。撰写过程中亦征询了 OpenMind, BitRobot, peaq, Auki Labs, XMAQUINA, GAIB, Vader, Gradient,Tashi Network 和CodecFlow等项目团队的意见反馈。本文力求内容客观准确，部分观点涉及主观判断，难免存在偏差，敬请读者予以理解。*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...

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Brevis Research Report: The Infinite Verifiable Computing Layer of zkVM and ZK Data Coprocessor

The paradigm of Verifiable Computing—“off-chain computation + on-chain verification”—has become the universal computational model for blockchain systems. It allows blockchain applications to achieve near-infinite computational freedom while maintaining decentralization and trustlessness as core security guarantees. Zero-knowledge proofs (ZKPs) form the backbone of this paradigm, with applications primarily in three foundational directions: scalability, privacy, and interoperability & data int...

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Brevis研报：ZKVM 与数据协处理器的无限可信计算层

“链下计算 + 链上验证”的可信计算（Verifiable Computing）范式，已成为区块链系统的通用计算模型。它让区块链应用在保持去中心化与信任最小化（trustlessness）安全性的前提下，获得几乎无限的计算自由度（computational freedom）。零知识证明（ZKP）是该范式的核心支柱，其应用主要集中在扩容（Scalability）、隐私（Privacy）以及互操作与数据完整性（Interoperability & Data Integrity）三大基础方向。其中，扩容是 ZK 技术最早落地的场景，通过将交易执行移至链下、以简短证明在链上验证结果，实现高 TPS 与低成本的可信扩容。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、Succinc...

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Cysic Research Report: The ComputeFi Path of ZK Hardware Acceleration

Zero-Knowledge Proofs (ZK) — as a next-generation cryptographic and scalability infrastructure — are demonstrating immense potential across blockchain scaling, privacy computation, zkML, and cross-chain verification. However, the proof generation process is extremely compute-intensive and latency-heavy, forming the biggest bottleneck for industrial adoption. ZK hardware acceleration has therefore emerged as a core enabler. Within this landscape, GPUs excel in versatility and iteration speed, ...

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Cysic研报：ZK 硬件加速的ComputeFi之路

零知识证明（ZK）作为新一代加密与扩容基础设施，已在区块链扩容、隐私计算以及zkML、跨链验证等新兴应用中展现出广阔潜力。然而，其证明生成过程计算量巨大、延迟高昂，成为产业化落地的最大瓶颈。ZK 硬件加速正是在此背景下崛起的核心环节，在 ZK 硬件加速路径上，GPU 以通用性和迭代速度见长，ASIC 追求极致能效与规模化性能，而 FPGA 则作为中间形态，兼具灵活可编程性与较高能效，三者共同构成推动零知识证明落地的硬件基础。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...

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GAIB Research Report: The On-Chain Financialization of AI Infrastructure — RWAiFi

As AI becomes the fastest-growing tech wave, computing power is seen as a new “currency,” with GPUs turning into strategic assets. Yet financing and liquidity remain limited, while crypto finance needs real cash flow–backed assets. RWA tokenization is emerging as the bridge. AI infrastructure, combining high-value hardware + predictable cash flows, are viewed as the best entry point for non-standard RWAs — GPUs offer near-term practicality, while robotics represent the longer frontier. GAIB’s...

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GAIB研报：AI 基建的链上金融化之路 - RWAiFi

随着 AI 成为全球增长最快的技术浪潮，算力正被视为新的“货币”，GPU 等高性能硬件也逐渐演化为战略性资产。但长期以来这类资产的融资与流动性受限。与此同时，加密金融亟需接入具备真实现金流的优质资产，RWA（Real-World Assets）链上化正在成为连接传统金融与加密市场的关键桥梁。AI 基础设施资产凭借“高价值硬件 + 可预测现金流”的特性，被普遍视为非标资产 RWA 的最佳突破口，其中 GPU 具备最现实的落地潜力，而机器人则代表更长期的探索方向。在这一背景下，GAIB 提出的 RWAiFi（RWA + AI + DeFi）路径，为“AI 基建的链上金融化之路”提供了全新解法，推动“AI基建 (算力与机器人) x RWA x DeFi”的飞轮效应。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...

From Federated Learning to Decentralized Agent Networks: An Analysis on ChainOpera

In our June report “The Holy Grail of Crypto AI: Frontier Exploration of Decentralized Training”, we discussed Federated Learning—a “controlled decentralization” paradigm positioned between distributed training and fully decentralized training. Its core principle is keeping data local while aggregating parameters centrally, a design particularly suited for privacy-sensitive and compliance-heavy industries such as healthcare and finance. At the same time, our past research has consistently hig...

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从联邦学习到去中心化 Agent 网络：ChainOpera 项目解析

在 6 月份的研报《Crypto AI 的圣杯：去中心化训练的前沿探索》中，我们提及联邦学习（Federated Learning）这一介于分布式训练与去中心化训练之间的“受控去中心化”方案：其核心是数据本地保留、参数集中聚合，满足医疗、金融等隐私与合规需求。与此同时，我们在过往多期研报中持续关注智能体（Agent）网络的兴起——其价值在于通过多智能体的自治与分工，协作完成复杂任务，推动“大模型”向“多智能体生态”的演进。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 Lea...

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Pendle Yield Strategies Unveiled: Pulse’s AgentFi Paradigm

Undoubtedly, Pendle is one of the most successful DeFi protocols in the current crypto cycle. While many protocols have stalled due to liquidity droughts and fading narratives, Pendle has distinguished itself through its unique yield-splitting and trading mechanism, becoming the “price discovery venue” for yield-bearing assets. By deeply integrating with stablecoins, LSTs/LRTs, and other yield-generating assets, it has secured its positioning as the foundational “DeFi yield-rate infrastructur...

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Pendle 收益策略全景解读：Pulse的AgentFi新范式

毫无疑问，Pendle 是本轮 Crypto 周期里最成功的 DeFi 协议之一。在众多协议因流动性枯竭和叙事退潮而陷入停滞时，Pendle 凭借独特的收益率拆分与交易机制，成功成为收益型资产的“价格发现场所”，通过与稳定币、 LST/LRT等收益资产的深度结合，奠定了其“DeFi 收益率基础设施”的独特定位。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 ...

From zkVM to Open Proof Market: An Analysis of RISC Zero and Boundless

In blockchain, cryptography is the fundamental basis of security and trust. Zero-Knowledge Proofs (ZK) can compress any complex off-chain computation into a succinct proof that can be efficiently verified on-chain—without relying on third-party trust—while also enabling selective input hiding to preserve privacy. With its combination of efficient verification, universality, and privacy, ZK has become a key solution across scaling, privacy, and interoperability use cases. Although challenges r...

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