In our previous Crypto AI series research reports, we have consistently emphasized the view that the most practical application scenarios in the current crypto field are mainly concentrated in stablecoin payments and DeFi, while Agents are the key interface for the AI industry facing users. Therefore, in the trend of Crypto and AI integration, the two most valuable paths are: AgentFi, based on existing mature DeFi protocols (basic strategies like lending and liquidity mining, as well as advanced strategies like Swap, Pendle PT, and funding rate arbitrage) in the short term; and Agent Payment, centering on stablecoin settlement and relying on protocols such as ACP/AP2/x402/ERC-8004 in the medium to long term.

Prediction markets have become an undeniable new industry trend in 2025, with their total annual trading volume surging from approximately $9 billion in 2024 to over $40 billion in 2025, achieving a year-over-year growth of more than 400%. This significant growth is driven by multiple factors: uncertainty demand brought by macro-political events (such as the 2024 US election), the maturity of infrastructure and trading models, and the thawing of the regulatory environment (Kalshi's lawsuit victory and Polymarket's return to the US). Prediction Market Agents are showing early embryonic forms in early 2026 and are poised to become a continuously emerging product form in the agent field over the coming year.

A prediction market is a financial mechanism for trading on the outcomes of future events. Contract prices essentially reflect the market's collective judgment on the probability of an event occurring. Its effectiveness stems from the combination of crowd wisdom and economic incentives: in an environment of anonymous, real-money betting, scattered information is quickly integrated into price signals weighted by financial willingness, thereby significantly reducing noise and false judgments.

By the end of 2025, prediction markets have basically formed a duopoly dominated by Polymarket and Kalshi. According to Forbes, the total trading volume in 2025 reached approximately $44 billion, with Polymarket contributing about $21.5 billion and Kalshi about $17.1 billion. Relying on its legal victory in the previous election contract case, its first-mover compliance advantage in the US sports prediction market, and relatively clear regulatory expectations, Kalshi has achieved rapid expansion. Currently, the development paths of the two have shown clear differentiation:

• Polymarket adopts a mixed CLOB architecture with "off-chain matching, on-chain settlement" and a decentralized settlement mechanism, building a globalized, non-custodial high-liquidity market. After returning to the US with compliance, it formed an "onshore + offshore" dual-track operating structure.

• Kalshi integrates into the traditional financial system, accessing mainstream retail brokerages via API, attracting Wall Street market makers to participate deeply in macro and data-type contract trading. Its products are constrained by traditional regulatory processes, and long-tail demands and sudden events lag relatively behind.

Apart from Polymarket and Kalshi, other competitive players in the prediction market field are developing mainly along two paths:

• First is the compliance distribution path, embedding event contracts into the existing account systems of brokerages or large platforms, relying on channel coverage, clearing capabilities, and institutional trust to build advantages (e.g., ForecastTrader by Interactive Brokers and ForecastEx, and FanDuel Predicts by FanDuel and CME).

• Second is the on-chain performance and capital efficiency path. Taking the Solana ecosystem's perpetual contract DEX Drift as an example, it added a prediction market module B.E.T (prediction markets) on top of its original product line.

The two paths—traditional financial compliance entry and crypto-native performance advantages—together constitute the diversified competitive landscape of the prediction market ecosystem.

Prediction markets appear similar to gambling on the surface and are essentially zero-sum games. However, the core difference lies not in the form, but in whether they possess positive externalities: aggregating scattered information through real-money trading to publicly price real-world events, forming a valuable signal layer. Despite limitations such as entertainment-focused participation, the trend is shifting from gaming to a "Global Truth Layer"—with the access of institutions like CME and Bloomberg, event probabilities have become decision-making metadata that can be directly called by financial and enterprise systems, providing a more timely and quantifiable market-based truth.

Currently, Prediction Market Agents are entering an early practice stage. Their value lies not in "AI predicting more accurately," but in amplifying information processing and execution efficiency in prediction markets. The essence of a prediction market is an information aggregation mechanism, where price reflects the collective judgment of event probability; market inefficiencies in reality stem from information asymmetry, liquidity, and attention constraints. The reasonable positioning of a Prediction Market Agent is Executable Probabilistic Portfolio Management: converting news, rule texts, and on-chain data into verifiable pricing deviations, executing strategies in a faster, more disciplined, and lower-cost manner, and capturing structural opportunities through cross-platform arbitrage and portfolio risk control.

An ideal Prediction Market Agent can be abstracted into a four-layer architecture:

• Information Layer: Aggregates news, social media, on-chain, and official data.

• Analysis Layer: Uses LLMs and ML to identify mispricing and calculate Edge.

• Strategy Layer: Converts Edge into positions through the Kelly criterion, staggered entry, and risk control.

• Execution Layer: Completes multi-market order placement, slippage and Gas optimization, and arbitrage execution, forming an efficient automated closed loop.

The ideal business model design for Prediction Market Agents has different exploration spaces at different levels:

• Bottom Infrastructure Layer: Provides multi-source real-time data aggregation, Smart Money address libraries, unified prediction market execution engines, and backtesting tools. Charges B2B/B2D fees to obtain stable revenue unrelated to prediction accuracy.

• Middle Strategy Layer: Precipitates modular strategy components and community-contributed strategies in an open-source or Token-Gated manner, forming a composable strategy ecosystem and achieving value capture.

• Top Agent Layer: Directly runs live trading through trusted managed Vaults, realizing capabilities with transparent on-chain records and a 20–30% performance fee (plus a small management fee).

The ideal Prediction Market Agent is closer to an "AI-driven probabilistic asset management product," gaining returns through long-term disciplined execution and cross-market mispricing gaming, rather than relying on single-time prediction accuracy. The core logic of the diversified revenue structure of "Infrastructure Monetization + Ecosystem Expansion + Performance Participation" is that even if Alpha converges as the market matures, bottom-layer capabilities such as execution, risk control, and settlement still have long-term value, reducing dependence on the single assumption that "AI consistently beats the market."

Theoretically, Agents have advantages in high-speed, 24/7, and emotion-free execution. However, in prediction markets, this is often difficult to convert into sustainable Alpha. Its effective application is mainly limited to specific structures, such as automated market making, cross-platform mispricing capture, and information integration of long-tail events. These opportunities are scarce and constrained by liquidity and capital.

1. Market Selection: Not all prediction markets have tradable value. Participation value depends on five dimensions: settlement clarity, liquidity quality, information advantage, time structure, and manipulation risk. It is recommended to prioritize the early stages of new markets, long-tail events with few professional players, and fleeting pricing windows caused by time zone differences; avoid high-heat political events, subjective settlement markets, and varieties with extremely low liquidity.

2. Order Strategy: Adopt strict systematic position management. The prerequisite for entry is that one's own probability judgment is significantly higher than the market implied probability. Positions are determined based on the fractional Kelly criterion (usually 1/10–1/4 Kelly), and single event risk exposure does not exceed 15%, to achieve robust growth with controllable risk, bearable drawdowns, and compoundable advantages in the long run.

3. Arbitrage Strategy: Arbitrage in prediction markets is mainly manifested in four types: cross-platform spread (be wary of settlement differences), Dutch Book arbitrage (high certainty but strict liquidity requirements), settlement arbitrage (relies on execution speed), and correlated asset hedging (limited by structural mismatch). The key to practice lies not in discovering spreads, but in strictly aligning contract definitions and settlement standards to avoid pseudo-arbitrage caused by subtle rule differences.

4. Smart Money Copy-Trading: On-chain "Smart Money" signals are not suitable as a main strategy due to lagging, inducement risks, and sample issues. A more reasonable usage is as a confidence adjustment factor, used to assist core judgments based on information and pricing deviations.

As an early exploration of Prediction Market Agents, NOYA's core philosophy is "Intelligence That Acts." In on-chain markets, pure analysis and insight are not enough to create value—although dashboards, data analysis, and research tools can help users understand "what might happen," there is still a large amount of manual operation, cross-chain friction, and execution risk between insight and execution. NOYA is built based on this pain point: compressing the complete link of "Research → Form Judgment → Execution → Continuous Monitoring" in the professional investment process into a unified system, enabling intelligence to be directly translated into on-chain action.

NOYA achieves this goal by integrating three core levels:

• Intelligence Layer: Aggregates market data, token analysis, and prediction market signals.

• Abstraction Layer: Hides complex cross-chain routing; users only need to express Intent.

• Execution Layer: AI Agents execute operations across chains and protocols based on user authorization.

In terms of product form, NOYA supports different participation methods for passive income users, active traders, and prediction market participants. Through designs like Omnichain Execution, AI Agents & Intents, and Vault Abstraction, it modularizes and automates multi-chain liquidity management, complex strategy execution, and risk control.

The overall system forms a continuous closed loop: Intelligence → Intent → Execution → Monitoring, achieving efficient, verifiable, and low-friction conversion from insight to execution while ensuring users always maintain control over their assets.

Omnivaults is NOYA's capital deployment layer, providing cross-chain, risk-controlled automated yield strategies. Users hand over assets to the system to run continuously across multiple chains and protocols through simple deposit and withdrawal operations, without the need for manual rebalancing or monitoring. The core goal is to achieve stable risk-adjusted returns rather than short-term speculation.

Omnivaults cover strategies like standard yield and Loop, clearly divided by asset and risk level, and support optional bonding incentive mechanisms. At the execution level, the system automatically completes cross-chain routing and optimization, and can introduce ZKML to provide verifiable proof for strategy decisions, enhancing the transparency and credibility of automated asset management. The overall design focuses on modularity and composability, supporting future access to more asset types and strategy forms.

NOYA Vault Technical Architecture: Each vault is uniformly registered and managed through the Registry; the AccountingManager is responsible for user shares (ERC-20) and NAV pricing; the bottom layer connects to protocols like Aave and Uniswap through modular Connectors and calculates cross-protocol TVL, relying on Value Oracle (Chainlink + Uniswap v3 TWAP) for price routing and valuation; trading and cross-chain operations are executed by Swap Handler (LiFi); finally, strategy execution is triggered by Keeper Multi-sig, forming a composable and auditable execution closed loop.

NOYA's most imaginative module: the Intelligence layer continuously tracks on-chain fund behavior and off-chain narrative changes, identifying news shocks, emotional fluctuations, and odds mismatches. When probability deviations are found in prediction markets like Polymarket, the Execution layer AI Agent can mobilize vault funds for arbitrage and rebalancing under user authorization. At the same time, Token Intelligence and Prediction Market Copilot provide users with structured token and prediction market analysis, directly converting external information into actionable trading decisions.

NOYA is committed to upgrading prediction markets from single-event betting to systematically manageable probabilistic assets. Its core module integrates diverse data such as market implied probability, liquidity structure, historical settlements, and on-chain smart money behavior. It uses Expected Value (EV) and scenario analysis to identify pricing deviations and focuses on tracking position signals of high-win-rate wallets to distinguish informed trading from market noise. Based on this, Copilot supports cross-market and cross-event correlation analysis and transmits real-time signals to AI Agents to drive automated execution such as opening and rebalancing positions, achieving portfolio management and dynamic optimization of prediction markets.

Core Strategy Mechanisms include:

• Multi-source Edge Sourcing: Fuses Polymarket real-time odds, polling data, private and external information flows to cross-verify event implied probabilities, systematically mining information advantages that have not been fully priced in.

• Prediction Market Arbitrage: Builds probabilistic and structural arbitrage strategies based on pricing differences across different markets, different contract structures, or similar events, capturing odds convergence returns while controlling directional risk.

• Auto-adjust Positions (Odds-Driven): When odds shift significantly due to changes in information, capital, or sentiment, the AI Agent automatically adjusts position size and direction, achieving continuous optimization in the prediction market rather than a one-time bet.

NOYA's institutional-grade research and decision hub aims to automate the professional crypto investment research process and directly output decision-level signals usable for real asset allocation. This module presents clear investment stances, comprehensive scores, core logic, key catalysts, and risk warnings in a standardized report structure, continuously updated with real-time market and on-chain data. Unlike traditional research tools, NOYA's intelligence does not stop at static analysis but can be queried, compared, and followed up by AI Agents in natural language. It is directly fed to the execution layer to drive subsequent cross-chain trading, fund allocation, and portfolio management, thereby forming a "Research—Decision—Execution" integrated closed loop, making Intelligence an active signal source in the automated capital operation system.

The NOYA AI Agent is the platform's execution layer, whose core role is to directly translate user intent and market intelligence into authorized on-chain actions. Users can express goals via text or voice, and the Agent is responsible for planning and executing cross-chain, cross-protocol operations, compressing research and execution into a continuous process. It is a key product form for NOYA to lower the threshold for DeFi and prediction market operations.

Users do not need to understand the underlying links, protocols, or transaction paths. They only need to express their goals through natural language or voice to trigger the AI Agent to automatically plan and execute multi-step on-chain operations, achieving "Intent as Execution." Under the premise of full-process user signing and non-custody, the Agent operates in a closed loop of "Intent Understanding → Action Planning → User Confirmation → On-chain Execution → Result Monitoring." It does not replace decision-making but is only responsible for efficient implementation and execution, significantly reducing the friction and threshold of complex financial operations.

Verifiable Execution aims to build a verifiable closed loop for the entire process of strategy, decision-making, and execution. NOYA introduces ZKML as a key mechanism to reduce trust assumptions: strategies are calculated off-chain and verifiable proofs are generated; corresponding fund operations can only be triggered after on-chain verification passes. This mechanism can provide credibility for strategy output without revealing model details and supports derivative capabilities such as verifiable backtesting. Currently, relevant modules are still marked as "under development" in public documents, and engineering details remain to be disclosed and verified.

• Prediction Market Advanced Order Capabilities: Improve strategy expression and execution precision to support Agent-based trading.

• Expansion to Multi-Prediction Markets: Access more platforms beyond Polymarket to expand event coverage and liquidity.

• Multi-source Edge Information Collection: Cross-verify with handicap odds to systematically capture underpriced probability deviations.

• Clearer Token Signals & Advanced Reports: Output trading signals and in-depth on-chain analysis that can directly drive execution.

• Advanced On-chain DeFi Strategy Combinations: Launch complex strategy structures to improve capital efficiency, returns, and scalability.

Currently, Omnichain Vaults are in the early stage of ecosystem development, and their cross-chain execution and multi-strategy framework have been verified.

• Strategy & Coverage: The platform has integrated mainstream DeFi protocols such as Aave and Morpho, supports cross-chain allocation of stablecoins, ETH, and their derivative assets, and has preliminarily built a layered risk strategy (e.g., Basic Yield vs. Loop Strategy).

• Development Stage: The current TVL volume is limited. The core goal lies in functional verification (MVP) and risk control framework refinement. The architectural design has strong composability, reserving interfaces for the subsequent introduction of complex assets and advanced Agent scheduling.

NOYA has built a growth flywheel deeply binding content narrative and liquidity anchored on "Real Contribution."

1. Ecosystem Partnership (Kaito Yaps): NOYA landed on Kaito Leaderboards with a composite narrative of "AI × DeFi × Agent," configuring an unlocked incentive pool of 5% of the total supply, and reserving an additional 1% for the Kaito ecosystem. Its mechanism deeply binds content creation (Yaps) with Vault deposits and Bond locking. User weekly contributions are converted into Stars that determine rank and multipliers, thereby synchronously strengthening narrative consensus and long-term capital stickiness at the incentive level.

2. Growth Engine (Space Race): Space Race constitutes NOYA's core growth flywheel, replacing the traditional "capital scale first" airdrop model by using Stars as long-term equity credentials. This mechanism integrates Bond locking bonuses, two-way 10% referral incentives, and content dissemination into a weekly Points system, filtering out long-term users with high participation and strong consensus, and continuously optimizing community structure and token distribution.

3. Community Building (Ambassador): NOYA adopts an invitation-only ambassador program, providing qualified participants with community round participation qualifications and performance rebates based on actual contributions (up to 10%).

Currently, Noya.ai has accumulated over 3,000 on-chain users, and its X platform followers have exceeded 41,000, ranking in the top five of the Kaito Mindshare list. This indicates that NOYA has occupied a favorable attention niche in the prediction market and Agent track.

In addition, Noya.ai's core contracts have passed dual audits by Code4rena and Hacken, and have accessed Hacken Extractor.

NOYA adopts a Single-token ecosystem model, with $NOYA as the sole value carrier and governance vehicle.

NOYA employs a Buyback & Burn value capture mechanism. The value generated by the protocol layer in products such as AI Agents, Omnivaults, and prediction markets is captured through mechanisms like staking, governance, access permissions, and buyback & burn, forming a value closed loop of Use → Fee → Buyback, converting platform usage into long-term token value.

The project takes Fair Launch as its core principle. It did not introduce angel round or VC investment but completed distribution through a public community round (Launch-Raise) with a low valuation ($10M FDV), Space Race, and airdrops. It deliberately reserves asymmetric upside space for the community, making the chip structure more biased towards active users and long-term participants; team incentives mainly come from long-term locked token shares.

Token Distribution:

• Total Supply: 1 Billion (1,000,000,000) NOYA

• Initial Float (Low Float): ~12%

• Valuation & Financing (The Raise): Financing Amount: $1 Million; Valuation (FDV): $10 Million

Currently, the Prediction Market Agent track is still in its early stages with a limited number of projects. Representative ones include Olas (Pearl Prediction Agents), Warden (BetFlix), and Noya.ai.

From the perspective of product form and user participation, each represents three types of paths in the current prediction market agent track:

1. Olas (Pearl Prediction Agents): Agent Productization & Runnable Delivery. Participated by "running an automated prediction Agent," encapsulating prediction market trading into a runnable Agent: users inject capital and run it, and the system automatically completes information acquisition, probability judgment, betting, and settlement. The participation method requiring additional installation has relatively limited friendliness for ordinary users.

2. Warden (BetFlix): Interactive Distribution & Consumer-grade Betting Platform. Attracts user participation through a low-threshold, highly entertaining interactive experience. Adopts an interaction and distribution-oriented path, lowering participation costs with gamified and content-based frontends, emphasizing the consumption and entertainment attributes of prediction markets. Its competitive advantage mainly comes from user growth and distribution efficiency, rather than strategy or execution layer depth.

3. NOYA.ai: Centered on "Fund Custody + Strategy Execution on Behalf," abstracting prediction markets and DeFi execution into asset management products through Vaults, providing a participation method with low operation and low mental burden. If the Prediction Market Intelligence and Agent execution modules are superimposed later, it is expected to form a "Research—Execution—Monitoring" integrated workflow

Compared with AgentFi projects that have achieved clear product delivery such as Giza and Almanak, NOYA's DeFi Agent is currently still in a relatively early stage. However, NOYA's differentiation lies in its positioning and entry level: it enters the same execution and asset management narrative track with a fair launch valuation of about $10M FDV, possessing significant valuation discount and growth potential at the current stage.

• NOYA: An AgentFi project encapsulating asset management centered on Omnichain Vault. Current delivery focus is on infrastructure layers like cross-chain execution and risk control. Upper-layer Agent execution, prediction market capabilities, and ZKML-related mechanisms are still in the development and verification stage.

• Giza: Can directly run asset management strategies (ARMA, Pulse). Currently has the highest AgentFi product completion.

• Almanak: Positioned as AI Quant for DeFi, outputting strategy and risk signals through models and quantitative frameworks. Mainly targets professional fund and strategy management needs, emphasizing methodological systematicness and result reproducibility.

• Theoriq: Centered on multi-agent collaboration (Agent Swarms) strategy and execution framework, emphasizing scalable Agent collaboration systems and medium-to-long-term infrastructure narratives, leaning more towards bottom-layer capability construction.

• Infinit: An Agentic DeFi terminal leaning towards the execution layer. Through process orchestration of "Intent → Multi-step on-chain operation," it significantly lowers the execution threshold of complex DeFi operations, and users' perception of product value is relatively direct.

Business Logic:

NOYA is a rare target in the current market that superimposes multiple narratives of AI Agent × Prediction Market × ZKML, and further combines the product direction of Intent-Driven Execution. At the asset pricing level, it launches with an FDV of approximately $10M, significantly lower than the common $75M–$100M valuation range of similar AI / DeFAI / Prediction related projects, forming a certain structural price difference.

Design-wise, NOYA attempts to unify Strategy Execution (Vault / Agent) and Information Advantage (Prediction Market Intelligence) into the same execution framework, and establishes a value capture closed loop through protocol revenue return (fees → buyback & burn). Although the project is still in its early stages, under the combined effect of multi-narrative superposition and low valuation starting point, its risk-return structure is closer to a type of high-odds, asymmetric betting target.

Engineering Implementation:

At the verifiable delivery level, NOYA's core function currently online is Omnichain Vaults, providing cross-chain asset scheduling, yield strategy execution, and delayed settlement mechanisms. The engineering implementation is relatively foundational. The Prediction Market Intelligence (Copilot), NOYA AI Agent, and ZKML-driven verifiable execution emphasized in its vision are still in the development stage and have not yet formed a complete closed loop on the mainnet. It is not a mature DeFAI platform at this stage.

Potential Risks & Key Focus Points:

1. Delivery Uncertainty: The technological span from "Basic Vault" to "All-round Agent" is huge. Be alert to the risk of Roadmap delays or ZKML implementation falling short of expectations.

2. Potential System Risks: Including contract security, cross-chain bridge failures, and oracle disputes specific to prediction markets (such as fuzzy rules leading to inability to adjudicate). Any single point of failure could cause fund loss.

Disclaimer: This article was created with the assistance of AI tools such as ChatGPT-5.2, Gemini 3, and Claude Opus 4.5. The author has tried their best to proofread and ensure the information is true and accurate, but omissions are inevitable. Please understand. It should be specially noted that the crypto asset market generally has a divergence between project fundamentals and secondary market price performance. The content of this article is only for information integration and academic/research exchange, does not constitute any investment advice, and should not be considered as a recommendation to buy or sell any tokens.

In our previous Crypto AI series research reports, we have consistently emphasized the view that the most practical application scenarios in the current crypto field are mainly concentrated in stablecoin payments and DeFi, while Agents are the key interface for the AI industry facing users. Therefore, in the trend of Crypto and AI integration, the two most valuable paths are: AgentFi, based on existing mature DeFi protocols (basic strategies like lending and liquidity mining, as well as advanced strategies like Swap, Pendle PT, and funding rate arbitrage) in the short term; and Agent Payment, centering on stablecoin settlement and relying on protocols such as ACP/AP2/x402/ERC-8004 in the medium to long term.

Prediction markets have become an undeniable new industry trend in 2025, with their total annual trading volume surging from approximately $9 billion in 2024 to over $40 billion in 2025, achieving a year-over-year growth of more than 400%. This significant growth is driven by multiple factors: uncertainty demand brought by macro-political events (such as the 2024 US election), the maturity of infrastructure and trading models, and the thawing of the regulatory environment (Kalshi's lawsuit victory and Polymarket's return to the US). Prediction Market Agents are showing early embryonic forms in early 2026 and are poised to become a continuously emerging product form in the agent field over the coming year.

A prediction market is a financial mechanism for trading on the outcomes of future events. Contract prices essentially reflect the market's collective judgment on the probability of an event occurring. Its effectiveness stems from the combination of crowd wisdom and economic incentives: in an environment of anonymous, real-money betting, scattered information is quickly integrated into price signals weighted by financial willingness, thereby significantly reducing noise and false judgments.

By the end of 2025, prediction markets have basically formed a duopoly dominated by Polymarket and Kalshi. According to Forbes, the total trading volume in 2025 reached approximately $44 billion, with Polymarket contributing about $21.5 billion and Kalshi about $17.1 billion. Relying on its legal victory in the previous election contract case, its first-mover compliance advantage in the US sports prediction market, and relatively clear regulatory expectations, Kalshi has achieved rapid expansion. Currently, the development paths of the two have shown clear differentiation:

• Polymarket adopts a mixed CLOB architecture with "off-chain matching, on-chain settlement" and a decentralized settlement mechanism, building a globalized, non-custodial high-liquidity market. After returning to the US with compliance, it formed an "onshore + offshore" dual-track operating structure.

• Kalshi integrates into the traditional financial system, accessing mainstream retail brokerages via API, attracting Wall Street market makers to participate deeply in macro and data-type contract trading. Its products are constrained by traditional regulatory processes, and long-tail demands and sudden events lag relatively behind.

Apart from Polymarket and Kalshi, other competitive players in the prediction market field are developing mainly along two paths:

• First is the compliance distribution path, embedding event contracts into the existing account systems of brokerages or large platforms, relying on channel coverage, clearing capabilities, and institutional trust to build advantages (e.g., ForecastTrader by Interactive Brokers and ForecastEx, and FanDuel Predicts by FanDuel and CME).

• Second is the on-chain performance and capital efficiency path. Taking the Solana ecosystem's perpetual contract DEX Drift as an example, it added a prediction market module B.E.T (prediction markets) on top of its original product line.

The two paths—traditional financial compliance entry and crypto-native performance advantages—together constitute the diversified competitive landscape of the prediction market ecosystem.

Prediction markets appear similar to gambling on the surface and are essentially zero-sum games. However, the core difference lies not in the form, but in whether they possess positive externalities: aggregating scattered information through real-money trading to publicly price real-world events, forming a valuable signal layer. Despite limitations such as entertainment-focused participation, the trend is shifting from gaming to a "Global Truth Layer"—with the access of institutions like CME and Bloomberg, event probabilities have become decision-making metadata that can be directly called by financial and enterprise systems, providing a more timely and quantifiable market-based truth.

Currently, Prediction Market Agents are entering an early practice stage. Their value lies not in "AI predicting more accurately," but in amplifying information processing and execution efficiency in prediction markets. The essence of a prediction market is an information aggregation mechanism, where price reflects the collective judgment of event probability; market inefficiencies in reality stem from information asymmetry, liquidity, and attention constraints. The reasonable positioning of a Prediction Market Agent is Executable Probabilistic Portfolio Management: converting news, rule texts, and on-chain data into verifiable pricing deviations, executing strategies in a faster, more disciplined, and lower-cost manner, and capturing structural opportunities through cross-platform arbitrage and portfolio risk control.

An ideal Prediction Market Agent can be abstracted into a four-layer architecture:

• Information Layer: Aggregates news, social media, on-chain, and official data.

• Analysis Layer: Uses LLMs and ML to identify mispricing and calculate Edge.

• Strategy Layer: Converts Edge into positions through the Kelly criterion, staggered entry, and risk control.

• Execution Layer: Completes multi-market order placement, slippage and Gas optimization, and arbitrage execution, forming an efficient automated closed loop.

The ideal business model design for Prediction Market Agents has different exploration spaces at different levels:

• Bottom Infrastructure Layer: Provides multi-source real-time data aggregation, Smart Money address libraries, unified prediction market execution engines, and backtesting tools. Charges B2B/B2D fees to obtain stable revenue unrelated to prediction accuracy.

• Middle Strategy Layer: Precipitates modular strategy components and community-contributed strategies in an open-source or Token-Gated manner, forming a composable strategy ecosystem and achieving value capture.

• Top Agent Layer: Directly runs live trading through trusted managed Vaults, realizing capabilities with transparent on-chain records and a 20–30% performance fee (plus a small management fee).

The ideal Prediction Market Agent is closer to an "AI-driven probabilistic asset management product," gaining returns through long-term disciplined execution and cross-market mispricing gaming, rather than relying on single-time prediction accuracy. The core logic of the diversified revenue structure of "Infrastructure Monetization + Ecosystem Expansion + Performance Participation" is that even if Alpha converges as the market matures, bottom-layer capabilities such as execution, risk control, and settlement still have long-term value, reducing dependence on the single assumption that "AI consistently beats the market."

Theoretically, Agents have advantages in high-speed, 24/7, and emotion-free execution. However, in prediction markets, this is often difficult to convert into sustainable Alpha. Its effective application is mainly limited to specific structures, such as automated market making, cross-platform mispricing capture, and information integration of long-tail events. These opportunities are scarce and constrained by liquidity and capital.

1. Market Selection: Not all prediction markets have tradable value. Participation value depends on five dimensions: settlement clarity, liquidity quality, information advantage, time structure, and manipulation risk. It is recommended to prioritize the early stages of new markets, long-tail events with few professional players, and fleeting pricing windows caused by time zone differences; avoid high-heat political events, subjective settlement markets, and varieties with extremely low liquidity.

2. Order Strategy: Adopt strict systematic position management. The prerequisite for entry is that one's own probability judgment is significantly higher than the market implied probability. Positions are determined based on the fractional Kelly criterion (usually 1/10–1/4 Kelly), and single event risk exposure does not exceed 15%, to achieve robust growth with controllable risk, bearable drawdowns, and compoundable advantages in the long run.

3. Arbitrage Strategy: Arbitrage in prediction markets is mainly manifested in four types: cross-platform spread (be wary of settlement differences), Dutch Book arbitrage (high certainty but strict liquidity requirements), settlement arbitrage (relies on execution speed), and correlated asset hedging (limited by structural mismatch). The key to practice lies not in discovering spreads, but in strictly aligning contract definitions and settlement standards to avoid pseudo-arbitrage caused by subtle rule differences.

4. Smart Money Copy-Trading: On-chain "Smart Money" signals are not suitable as a main strategy due to lagging, inducement risks, and sample issues. A more reasonable usage is as a confidence adjustment factor, used to assist core judgments based on information and pricing deviations.

As an early exploration of Prediction Market Agents, NOYA's core philosophy is "Intelligence That Acts." In on-chain markets, pure analysis and insight are not enough to create value—although dashboards, data analysis, and research tools can help users understand "what might happen," there is still a large amount of manual operation, cross-chain friction, and execution risk between insight and execution. NOYA is built based on this pain point: compressing the complete link of "Research → Form Judgment → Execution → Continuous Monitoring" in the professional investment process into a unified system, enabling intelligence to be directly translated into on-chain action.

NOYA achieves this goal by integrating three core levels:

• Intelligence Layer: Aggregates market data, token analysis, and prediction market signals.

• Abstraction Layer: Hides complex cross-chain routing; users only need to express Intent.

• Execution Layer: AI Agents execute operations across chains and protocols based on user authorization.

In terms of product form, NOYA supports different participation methods for passive income users, active traders, and prediction market participants. Through designs like Omnichain Execution, AI Agents & Intents, and Vault Abstraction, it modularizes and automates multi-chain liquidity management, complex strategy execution, and risk control.

The overall system forms a continuous closed loop: Intelligence → Intent → Execution → Monitoring, achieving efficient, verifiable, and low-friction conversion from insight to execution while ensuring users always maintain control over their assets.

Omnivaults is NOYA's capital deployment layer, providing cross-chain, risk-controlled automated yield strategies. Users hand over assets to the system to run continuously across multiple chains and protocols through simple deposit and withdrawal operations, without the need for manual rebalancing or monitoring. The core goal is to achieve stable risk-adjusted returns rather than short-term speculation.

Omnivaults cover strategies like standard yield and Loop, clearly divided by asset and risk level, and support optional bonding incentive mechanisms. At the execution level, the system automatically completes cross-chain routing and optimization, and can introduce ZKML to provide verifiable proof for strategy decisions, enhancing the transparency and credibility of automated asset management. The overall design focuses on modularity and composability, supporting future access to more asset types and strategy forms.

NOYA Vault Technical Architecture: Each vault is uniformly registered and managed through the Registry; the AccountingManager is responsible for user shares (ERC-20) and NAV pricing; the bottom layer connects to protocols like Aave and Uniswap through modular Connectors and calculates cross-protocol TVL, relying on Value Oracle (Chainlink + Uniswap v3 TWAP) for price routing and valuation; trading and cross-chain operations are executed by Swap Handler (LiFi); finally, strategy execution is triggered by Keeper Multi-sig, forming a composable and auditable execution closed loop.

NOYA's most imaginative module: the Intelligence layer continuously tracks on-chain fund behavior and off-chain narrative changes, identifying news shocks, emotional fluctuations, and odds mismatches. When probability deviations are found in prediction markets like Polymarket, the Execution layer AI Agent can mobilize vault funds for arbitrage and rebalancing under user authorization. At the same time, Token Intelligence and Prediction Market Copilot provide users with structured token and prediction market analysis, directly converting external information into actionable trading decisions.

NOYA is committed to upgrading prediction markets from single-event betting to systematically manageable probabilistic assets. Its core module integrates diverse data such as market implied probability, liquidity structure, historical settlements, and on-chain smart money behavior. It uses Expected Value (EV) and scenario analysis to identify pricing deviations and focuses on tracking position signals of high-win-rate wallets to distinguish informed trading from market noise. Based on this, Copilot supports cross-market and cross-event correlation analysis and transmits real-time signals to AI Agents to drive automated execution such as opening and rebalancing positions, achieving portfolio management and dynamic optimization of prediction markets.

Core Strategy Mechanisms include:

• Multi-source Edge Sourcing: Fuses Polymarket real-time odds, polling data, private and external information flows to cross-verify event implied probabilities, systematically mining information advantages that have not been fully priced in.

• Prediction Market Arbitrage: Builds probabilistic and structural arbitrage strategies based on pricing differences across different markets, different contract structures, or similar events, capturing odds convergence returns while controlling directional risk.

• Auto-adjust Positions (Odds-Driven): When odds shift significantly due to changes in information, capital, or sentiment, the AI Agent automatically adjusts position size and direction, achieving continuous optimization in the prediction market rather than a one-time bet.

NOYA's institutional-grade research and decision hub aims to automate the professional crypto investment research process and directly output decision-level signals usable for real asset allocation. This module presents clear investment stances, comprehensive scores, core logic, key catalysts, and risk warnings in a standardized report structure, continuously updated with real-time market and on-chain data. Unlike traditional research tools, NOYA's intelligence does not stop at static analysis but can be queried, compared, and followed up by AI Agents in natural language. It is directly fed to the execution layer to drive subsequent cross-chain trading, fund allocation, and portfolio management, thereby forming a "Research—Decision—Execution" integrated closed loop, making Intelligence an active signal source in the automated capital operation system.

The NOYA AI Agent is the platform's execution layer, whose core role is to directly translate user intent and market intelligence into authorized on-chain actions. Users can express goals via text or voice, and the Agent is responsible for planning and executing cross-chain, cross-protocol operations, compressing research and execution into a continuous process. It is a key product form for NOYA to lower the threshold for DeFi and prediction market operations.

Users do not need to understand the underlying links, protocols, or transaction paths. They only need to express their goals through natural language or voice to trigger the AI Agent to automatically plan and execute multi-step on-chain operations, achieving "Intent as Execution." Under the premise of full-process user signing and non-custody, the Agent operates in a closed loop of "Intent Understanding → Action Planning → User Confirmation → On-chain Execution → Result Monitoring." It does not replace decision-making but is only responsible for efficient implementation and execution, significantly reducing the friction and threshold of complex financial operations.

Verifiable Execution aims to build a verifiable closed loop for the entire process of strategy, decision-making, and execution. NOYA introduces ZKML as a key mechanism to reduce trust assumptions: strategies are calculated off-chain and verifiable proofs are generated; corresponding fund operations can only be triggered after on-chain verification passes. This mechanism can provide credibility for strategy output without revealing model details and supports derivative capabilities such as verifiable backtesting. Currently, relevant modules are still marked as "under development" in public documents, and engineering details remain to be disclosed and verified.

• Prediction Market Advanced Order Capabilities: Improve strategy expression and execution precision to support Agent-based trading.

• Expansion to Multi-Prediction Markets: Access more platforms beyond Polymarket to expand event coverage and liquidity.

• Multi-source Edge Information Collection: Cross-verify with handicap odds to systematically capture underpriced probability deviations.

• Clearer Token Signals & Advanced Reports: Output trading signals and in-depth on-chain analysis that can directly drive execution.

• Advanced On-chain DeFi Strategy Combinations: Launch complex strategy structures to improve capital efficiency, returns, and scalability.

Currently, Omnichain Vaults are in the early stage of ecosystem development, and their cross-chain execution and multi-strategy framework have been verified.

• Strategy & Coverage: The platform has integrated mainstream DeFi protocols such as Aave and Morpho, supports cross-chain allocation of stablecoins, ETH, and their derivative assets, and has preliminarily built a layered risk strategy (e.g., Basic Yield vs. Loop Strategy).

• Development Stage: The current TVL volume is limited. The core goal lies in functional verification (MVP) and risk control framework refinement. The architectural design has strong composability, reserving interfaces for the subsequent introduction of complex assets and advanced Agent scheduling.

NOYA has built a growth flywheel deeply binding content narrative and liquidity anchored on "Real Contribution."

1. Ecosystem Partnership (Kaito Yaps): NOYA landed on Kaito Leaderboards with a composite narrative of "AI × DeFi × Agent," configuring an unlocked incentive pool of 5% of the total supply, and reserving an additional 1% for the Kaito ecosystem. Its mechanism deeply binds content creation (Yaps) with Vault deposits and Bond locking. User weekly contributions are converted into Stars that determine rank and multipliers, thereby synchronously strengthening narrative consensus and long-term capital stickiness at the incentive level.

2. Growth Engine (Space Race): Space Race constitutes NOYA's core growth flywheel, replacing the traditional "capital scale first" airdrop model by using Stars as long-term equity credentials. This mechanism integrates Bond locking bonuses, two-way 10% referral incentives, and content dissemination into a weekly Points system, filtering out long-term users with high participation and strong consensus, and continuously optimizing community structure and token distribution.

3. Community Building (Ambassador): NOYA adopts an invitation-only ambassador program, providing qualified participants with community round participation qualifications and performance rebates based on actual contributions (up to 10%).

Currently, Noya.ai has accumulated over 3,000 on-chain users, and its X platform followers have exceeded 41,000, ranking in the top five of the Kaito Mindshare list. This indicates that NOYA has occupied a favorable attention niche in the prediction market and Agent track.

In addition, Noya.ai's core contracts have passed dual audits by Code4rena and Hacken, and have accessed Hacken Extractor.

NOYA adopts a Single-token ecosystem model, with $NOYA as the sole value carrier and governance vehicle.

NOYA employs a Buyback & Burn value capture mechanism. The value generated by the protocol layer in products such as AI Agents, Omnivaults, and prediction markets is captured through mechanisms like staking, governance, access permissions, and buyback & burn, forming a value closed loop of Use → Fee → Buyback, converting platform usage into long-term token value.

The project takes Fair Launch as its core principle. It did not introduce angel round or VC investment but completed distribution through a public community round (Launch-Raise) with a low valuation ($10M FDV), Space Race, and airdrops. It deliberately reserves asymmetric upside space for the community, making the chip structure more biased towards active users and long-term participants; team incentives mainly come from long-term locked token shares.

Token Distribution:

• Total Supply: 1 Billion (1,000,000,000) NOYA

• Initial Float (Low Float): ~12%

• Valuation & Financing (The Raise): Financing Amount: $1 Million; Valuation (FDV): $10 Million

Currently, the Prediction Market Agent track is still in its early stages with a limited number of projects. Representative ones include Olas (Pearl Prediction Agents), Warden (BetFlix), and Noya.ai.

From the perspective of product form and user participation, each represents three types of paths in the current prediction market agent track:

1. Olas (Pearl Prediction Agents): Agent Productization & Runnable Delivery. Participated by "running an automated prediction Agent," encapsulating prediction market trading into a runnable Agent: users inject capital and run it, and the system automatically completes information acquisition, probability judgment, betting, and settlement. The participation method requiring additional installation has relatively limited friendliness for ordinary users.

2. Warden (BetFlix): Interactive Distribution & Consumer-grade Betting Platform. Attracts user participation through a low-threshold, highly entertaining interactive experience. Adopts an interaction and distribution-oriented path, lowering participation costs with gamified and content-based frontends, emphasizing the consumption and entertainment attributes of prediction markets. Its competitive advantage mainly comes from user growth and distribution efficiency, rather than strategy or execution layer depth.

3. NOYA.ai: Centered on "Fund Custody + Strategy Execution on Behalf," abstracting prediction markets and DeFi execution into asset management products through Vaults, providing a participation method with low operation and low mental burden. If the Prediction Market Intelligence and Agent execution modules are superimposed later, it is expected to form a "Research—Execution—Monitoring" integrated workflow

Compared with AgentFi projects that have achieved clear product delivery such as Giza and Almanak, NOYA's DeFi Agent is currently still in a relatively early stage. However, NOYA's differentiation lies in its positioning and entry level: it enters the same execution and asset management narrative track with a fair launch valuation of about $10M FDV, possessing significant valuation discount and growth potential at the current stage.

• NOYA: An AgentFi project encapsulating asset management centered on Omnichain Vault. Current delivery focus is on infrastructure layers like cross-chain execution and risk control. Upper-layer Agent execution, prediction market capabilities, and ZKML-related mechanisms are still in the development and verification stage.

• Giza: Can directly run asset management strategies (ARMA, Pulse). Currently has the highest AgentFi product completion.

• Almanak: Positioned as AI Quant for DeFi, outputting strategy and risk signals through models and quantitative frameworks. Mainly targets professional fund and strategy management needs, emphasizing methodological systematicness and result reproducibility.

• Theoriq: Centered on multi-agent collaboration (Agent Swarms) strategy and execution framework, emphasizing scalable Agent collaboration systems and medium-to-long-term infrastructure narratives, leaning more towards bottom-layer capability construction.

• Infinit: An Agentic DeFi terminal leaning towards the execution layer. Through process orchestration of "Intent → Multi-step on-chain operation," it significantly lowers the execution threshold of complex DeFi operations, and users' perception of product value is relatively direct.

Business Logic:

NOYA is a rare target in the current market that superimposes multiple narratives of AI Agent × Prediction Market × ZKML, and further combines the product direction of Intent-Driven Execution. At the asset pricing level, it launches with an FDV of approximately $10M, significantly lower than the common $75M–$100M valuation range of similar AI / DeFAI / Prediction related projects, forming a certain structural price difference.

Design-wise, NOYA attempts to unify Strategy Execution (Vault / Agent) and Information Advantage (Prediction Market Intelligence) into the same execution framework, and establishes a value capture closed loop through protocol revenue return (fees → buyback & burn). Although the project is still in its early stages, under the combined effect of multi-narrative superposition and low valuation starting point, its risk-return structure is closer to a type of high-odds, asymmetric betting target.

Engineering Implementation:

At the verifiable delivery level, NOYA's core function currently online is Omnichain Vaults, providing cross-chain asset scheduling, yield strategy execution, and delayed settlement mechanisms. The engineering implementation is relatively foundational. The Prediction Market Intelligence (Copilot), NOYA AI Agent, and ZKML-driven verifiable execution emphasized in its vision are still in the development stage and have not yet formed a complete closed loop on the mainnet. It is not a mature DeFAI platform at this stage.

Potential Risks & Key Focus Points:

1. Delivery Uncertainty: The technological span from "Basic Vault" to "All-round Agent" is huge. Be alert to the risk of Roadmap delays or ZKML implementation falling short of expectations.

2. Potential System Risks: Including contract security, cross-chain bridge failures, and oracle disputes specific to prediction markets (such as fuzzy rules leading to inability to adjudicate). Any single point of failure could cause fund loss.

Disclaimer: This article was created with the assistance of AI tools such as ChatGPT-5.2, Gemini 3, and Claude Opus 4.5. The author has tried their best to proofread and ensure the information is true and accurate, but omissions are inevitable. Please understand. It should be specially noted that the crypto asset market generally has a divergence between project fundamentals and secondary market price performance. The content of this article is only for information integration and academic/research exchange, does not constitute any investment advice, and should not be considered as a recommendation to buy or sell any tokens.