PolySwarm: A Multi-Agent Large Language Model Framework for Prediction Market Trading and Latency Arbitrage
arXiv:2604.03888v1 Announce Type: new Abstract: This paper presents PolySwarm, a novel multi-agent large language model (LLM) framework designed for real-time prediction market trading and latency arbitrage on decentralized platforms such as Polymarket. PolySwarm deploys a swarm of 50 diverse LLM personas that concurrently evaluate binary outcome markets, aggregating individual probability estimates through confidence-weighted Bayesian combination of swarm consensus with market-implied probabilities, and applying quarter-Kelly position sizing for risk-controlled execution. The system incorporates an information-theoretic market analysis engine using Kullback-Leibler (KL) divergence and Jensen-Shannon (JS) divergence to detect cross-market inefficiencies and negation pair mispricings. A latency arbitrage module exploits stale Polymarket prices by deriving CEX-implied probabilities from a log-normal pricing model and executing trades within the human reaction-time window. We provide a f
arXiv:2604.03888v1 Announce Type: new Abstract: This paper presents PolySwarm, a novel multi-agent large language model (LLM) framework designed for real-time prediction market trading and latency arbitrage on decentralized platforms such as Polymarket. PolySwarm deploys a swarm of 50 diverse LLM personas that concurrently evaluate binary outcome markets, aggregating individual probability estimates through confidence-weighted Bayesian combination of swarm consensus with market-implied probabilities, and applying quarter-Kelly position sizing for risk-controlled execution. The system incorporates an information-theoretic market analysis engine using Kullback-Leibler (KL) divergence and Jensen-Shannon (JS) divergence to detect cross-market inefficiencies and negation pair mispricings. A latency arbitrage module exploits stale Polymarket prices by deriving CEX-implied probabilities from a log-normal pricing model and executing trades within the human reaction-time window. We provide a full architectural description, implementation details, and evaluation methodology using Brier scores, calibration analysis, and log-loss metrics benchmarked against human superforecaster performance. We further discuss open challenges including hallucination in agent pools, computational cost at scale, regulatory exposure, and feedback-loop risk, and outline five priority directions for future research. Experimental results demonstrate that swarm aggregation consistently outperforms single-model baselines in probability calibration on Polymarket prediction tasks.
Executive Summary
The paper introduces PolySwarm, a groundbreaking multi-agent LLM framework engineered for high-frequency trading in decentralized prediction markets and latency arbitrage. Utilizing 50 diverse LLM personas, the system aggregates probabilistic forecasts via confidence-weighted Bayesian methods and employs Kelly criterion-based position sizing for risk management. It integrates an information-theoretic engine to detect market inefficiencies and executes arbitrage by exploiting stale prices on Polymarket relative to centralized exchanges (CEXs). Benchmarking against human superforecasters, PolySwarm demonstrates superior calibration and predictive accuracy, though it acknowledges challenges such as hallucination risks, computational scalability, regulatory uncertainties, and feedback-loop vulnerabilities. The study provides a detailed architecture, implementation roadmap, and future research directions, positioning PolySwarm as a paradigm shift in AI-driven financial prediction and arbitrage strategies.
Key Points
- ▸ PolySwarm employs a swarm of 50 diverse LLM agents for real-time probability estimation in prediction markets, leveraging confidence-weighted Bayesian aggregation to refine consensus forecasts.
- ▸ The framework incorporates an information-theoretic module using KL and JS divergence to identify inefficiencies and negation pair mispricings, enhancing cross-market arbitrage opportunities.
- ▸ A latency arbitrage module exploits stale Polymarket prices by deriving CEX-implied probabilities and executing trades within human reaction-time windows, achieving superior performance over single-model baselines.
- ▸ The system integrates quarter-Kelly position sizing for risk-controlled capital allocation and is benchmarked against human superforecasters using Brier scores, calibration analysis, and log-loss metrics.
- ▸ Open challenges include hallucination risks in agent pools, computational scalability, regulatory exposure, and feedback-loop dynamics, with five priority research directions proposed.
Merits
Novelty and Innovation
PolySwarm pioneers the integration of multi-agent LLM systems with Bayesian aggregation and Kelly criterion-based sizing for prediction markets, offering a novel approach to AI-driven arbitrage and market efficiency analysis.
Empirical Rigor
The study provides a comprehensive evaluation framework using multiple metrics (Brier scores, calibration, log-loss) and benchmarks against human superforecasters, demonstrating robust outperformance and methodological transparency.
Scalability and Adaptability
The architecture is modular and scalable, with a swarm-based design that can be extended to other asset classes or markets, and the use of information-theoretic tools enhances adaptability to dynamic market conditions.
Interdisciplinary Integration
The paper effectively bridges AI/ML techniques (LLMs, Bayesian methods) with financial economics (arbitrage, Kelly criterion) and information theory (KL/JS divergence), offering a holistic solution to complex problems.
Demerits
Hallucination and Reliability Risks
The reliance on diverse LLM personas introduces potential hallucination risks, where agents may generate plausible but inaccurate predictions, undermining the reliability of the swarm consensus, especially in low-liquidity or highly volatile markets.
Computational and Cost Barriers
Deploying 50 LLM agents in real-time for high-frequency trading incurs significant computational costs, including inference, data processing, and energy consumption, which may limit accessibility for smaller firms or researchers.
Regulatory and Compliance Uncertainty
The framework operates in a nascent regulatory environment for decentralized prediction markets and AI-driven trading, with potential exposure to evolving legal frameworks, anti-gaming rules, and market manipulation allegations.
Feedback-Loop Vulnerabilities
The system’s reliance on iterative feedback and swarm consensus may amplify biases or errors over time, creating feedback-loop risks where incorrect predictions reinforce themselves, particularly in illiquid or thinly traded markets.
Expert Commentary
PolySwarm represents a significant leap forward in the convergence of AI, financial economics, and decentralized markets. The integration of multi-agent LLM systems with Bayesian aggregation and Kelly criterion sizing is particularly innovative, as it leverages the strengths of diverse agents while mitigating individual biases through probabilistic consensus. The information-theoretic engine for detecting inefficiencies is a notable contribution, offering a rigorous method for identifying arbitrage opportunities that could be extended to other asset classes. However, the paper’s acknowledgment of hallucination risks and feedback-loop vulnerabilities is critical, as these challenges could undermine the framework’s reliability in practice. The regulatory exposure is another salient concern, given the nascent state of DeFi regulations and the potential for AI-driven trading to be perceived as manipulative. While the empirical results are compelling, the scalability and cost barriers may limit adoption to well-resourced institutions, raising questions about market fairness and accessibility. Overall, PolySwarm sets a new benchmark for AI-driven trading systems but underscores the importance of robust governance, risk management, and regulatory alignment to realize its full potential.
Recommendations
- ✓ Conduct further research on hallucination mitigation techniques tailored to multi-agent LLM systems, such as ensemble validation, consistency checks, and adversarial testing, to enhance the reliability of swarm-generated predictions.
- ✓ Develop industry standards or regulatory sandboxes for AI-driven trading in decentralized markets, focusing on transparency, disclosure requirements, and stress-testing protocols to address compliance risks.
- ✓ Explore hybrid models that combine PolySwarm’s swarm intelligence with traditional econometric methods or rule-based systems to reduce reliance on LLM-generated content and improve interpretability.
- ✓ Investigate the long-term feedback-loop dynamics of swarm-based trading systems, including potential bias amplification and market impact, to develop safeguards against destabilizing market outcomes.
- ✓ Expand the framework’s applicability to other asset classes (e.g., equities, forex) and market structures (e.g., traditional exchanges), while adapting the information-theoretic and arbitrage modules to account for structural differences.
Sources
Original: arXiv - cs.AI
