PolySwarm: A Multi-Agent Large Language Model Framework for Prediction Market Trading and Latency Arbitrage

📰 ArXiv cs.AI

PolySwarm is a multi-agent LLM framework for prediction market trading and latency arbitrage

advanced Published 7 Apr 2026
Action Steps
  1. Deploy a swarm of diverse LLM personas to evaluate binary outcome markets
  2. Aggregate individual probability estimates through confidence-weighted Bayesian combination
  3. Utilize swarm consensus with market-implied probabilities for prediction market trading and latency arbitrage
  4. Evaluate the performance of PolySwarm on decentralized platforms such as Polymarket
Who Needs to Know This

AI engineers and researchers on a team can benefit from PolySwarm's novel approach to aggregating probability estimates, while traders and financial analysts can utilize the framework for making informed decisions

Key Insight

💡 PolySwarm's use of a swarm of diverse LLM personas can improve the accuracy of probability estimates in prediction markets

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🤖 PolySwarm: a multi-agent LLM framework for prediction market trading & latency arbitrage

Key Takeaways

PolySwarm is a multi-agent LLM framework for prediction market trading and latency arbitrage

Full Article

Title: PolySwarm: A Multi-Agent Large Language Model Framework for Prediction Market Trading and Latency Arbitrage

Abstract:
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 probabili
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