Can LLMs Help Decentralized Dispute Arbitration? A Case Study of UMA-Resolved Markets on Polymarket

2026-04-16

AI Summary

This study addresses the frequent disputes in Web3 prediction markets stemming from flawed mechanisms and proposes an efficient decentralized arbitration solution. For the first time, it introduces large language models (LLMs) with web retrieval capabilities into on-chain dispute resolution, leveraging real-world data from Polymarket and the UMA protocol. The work evaluates LLMs’ ability to replicate UMA’s oracle-based voting outcomes and explores their potential to predict contentious events in advance. Experimental results show that while LLMs struggle to reliably anticipate disputes beforehand, they achieve a stable 89.58% agreement rate with UMA’s final rulings in post-hoc adjudication. These findings demonstrate the feasibility and promise of integrating LLMs as decision-support tools in decentralized governance frameworks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Web
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Web3 prediction markets, exemplified by Polymarket, have gained prominence for leveraging collective intelligence to forecast a wide range of social, political, and sports events. However, among the thousands of prediction market events, consensus disputes still arise due to imperfections in market mechanisms. On Polymarket alone, the trading volume involving disputed events has reached $972,370,804.71, underscoring the critical need for objective and efficient dispute resolution. In this study, we introduce large language models (LLMs) to: (1) evaluate whether web-enabled LLMs can reproduce the decision quality of UMA's on-chain voting process once a dispute has been raised, and (2) predict, based on event rules, which market events are likely to face future disputes before they occur. Our findings show that LLMs are unable to reliably predict which events will become disputed in advance; however, once a dispute is initiated, web-enabled LLMs achieve 89.58% agreement with UMA's final resolutions and demonstrate strong stability.

Problem

• prediction markets
• dispute resolution
• decentralized arbitration
• consensus disputes
• Web3

Innovation

• Large Language Models
• Decentralized Dispute Resolution
• Prediction Markets
• UMA Protocol
• Web3 Governance