Judging the Future: Can AI Resolve Disputes in Prediction Markets?

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Author: Denis Avetisyan

A new study explores whether large language models can accurately adjudicate disagreements arising in decentralized prediction markets like Polymarket.

The system navigates disagreement through a stake-weighted dispute lifecycle, where proposed outcomes are challenged via staked assets and resolved through iterative voting rounds-a process now augmented by large language models capable of predicting and enacting resolution.
The system navigates disagreement through a stake-weighted dispute lifecycle, where proposed outcomes are challenged via staked assets and resolved through iterative voting rounds-a process now augmented by large language models capable of predicting and enacting resolution.

The research finds LLMs excel at reproducing final rulings from UMA-resolved markets, but struggle to anticipate which events will require dispute resolution.

Despite the growing sophistication of decentralized prediction markets like Polymarket, disputes inevitably arise, impacting trading volume-which on Polymarket alone totals over \$972,370,804.71 in contested events. This study, ‘Can LLMs Help Decentralized Dispute Arbitration? A Case Study of UMA-Resolved Markets on Polymarket’, investigates whether large language models (LLMs) can assist in resolving these disputes and proactively identifying those likely to occur. Our findings reveal that while LLMs struggle to predict future disputes, they reliably reproduce the resolutions of UMA’s on-chain voting process with 89.58% agreement, suggesting a potential role in post-dispute arbitration-but can LLMs ultimately evolve to anticipate, rather than simply react to, challenges in decentralized governance?

The Inevitable Ambiguity of Forecasts

The functionality of decentralized prediction markets, such as Polymarket, is fundamentally dependent on the reliable determination of real-world outcomes. However, the very nature of forecasting introduces inherent challenges; events are rarely black and white, and subjective interpretation frequently leads to disagreement regarding whether a particular outcome has actually occurred. This ambiguity can trigger disputes among market participants, potentially undermining trust and hindering the efficient operation of the platform. Without a robust and impartial mechanism for resolving these conflicts, the predictive power – and the economic incentives driving these markets – become compromised, highlighting the critical need for clear resolution protocols that address the nuances of real-world event definition and assessment.

Effective resolution of events within decentralized prediction markets hinges on the capacity to swiftly and impartially determine factual outcomes, a process considerably more nuanced than simple yes/no answers. Current mechanisms, though operational, often rely on centralized oracles or subjective human judgment when faced with complex, multi-faceted scenarios – think of forecasting the efficacy of a novel pharmaceutical or the precise impact of a geopolitical event. This introduces potential biases and vulnerabilities, necessitating the development of systems capable of objectively evaluating evidence from diverse sources. The ideal solution isn’t merely about speed, but about establishing a robust framework for truth assessment that minimizes the influence of individual perspectives and maximizes confidence in the final resolution, ultimately preserving the integrity of the entire predictive ecosystem.

Automating truth determination within decentralized systems presents a formidable paradox: how to establish objective reality without recreating the centralized control mechanisms these systems aim to avoid. Current approaches often rely on oracles – entities providing external data – but these introduce potential single points of failure or bias. Researchers are exploring novel solutions, including decentralized networks of human judges incentivized for accurate reporting, and sophisticated statistical models that aggregate information from multiple sources to mitigate individual inaccuracies. However, each method must carefully balance the need for speed and efficiency with the preservation of decentralization, ensuring that no single actor can unduly influence the outcome and that the system remains resilient against manipulation or malicious reporting. The ultimate goal is a self-correcting mechanism where truth emerges not from authority, but from the collective intelligence and verifiable consensus of the network itself.

Polymarket disputes are most frequently associated with Sports (31.5%), Politics (20.4%), and Crypto (16.7%) events.
Polymarket disputes are most frequently associated with Sports (31.5%), Politics (20.4%), and Crypto (16.7%) events.

The Illusion of Objective Interpretation

Large Language Models (LLMs) present a novel method for automating dispute resolution by processing and interpreting textual data related to the incident. This analysis encompasses event descriptions, supporting documentation, and any associated communications. LLMs can identify key details, extract relevant facts, and establish relationships between different pieces of information. By converting unstructured data into a structured format, LLMs facilitate the assessment of claims and counterclaims, ultimately enabling a more efficient and scalable approach to resolving disputes compared to traditional manual processes. The capacity to process high volumes of information and identify patterns makes LLMs particularly suited for handling routine or repetitive disputes.

LLMs enhance dispute resolution objectivity by integrating web search capabilities to corroborate or refute claimant assertions. This process involves formulating search queries based on the details of the dispute, retrieving relevant information from public sources – including news articles, official records, and product documentation – and then analyzing that data to validate or invalidate specific claims. The LLM assesses the credibility of sources and the consistency of information with the presented evidence, effectively functioning as an independent fact-checker. This external data validation reduces reliance on potentially biased or incomplete information provided by involved parties, leading to more impartial and evidence-based resolutions.

The automated dispute resolution process leverages LLMs by translating formalized event rules – which define conditions and expected outcomes – into a machine-readable format. The LLM then analyzes available evidence, including submitted details and externally sourced data, to assess how well the evidence aligns with the semantics of those rules. This analysis doesn’t rely on pre-programmed logic, but rather on the LLM’s ability to understand nuanced language and probabilistic reasoning to determine the most likely outcome based on the strength of evidence supporting each potential rule application. The system effectively assigns a probability score to each outcome, allowing for a determination of the most probable resolution, even in cases with incomplete or ambiguous information.

Mimicking Consensus: A Controlled Experiment

An evaluation was conducted to assess the capacity of several Large Language Models (LLMs) – specifically DeepSeek V3.1, Claude-4.5-Sonnet, Qwen Max, and GPT-4o-Search-Preview – to replicate the decision-making process of UMA’s Optimistic Oracle. This assessment involved presenting the LLMs with data from resolved disputes within UMA’s ecosystem to determine their ability to accurately predict outcomes. The goal was to gauge whether these models could independently arrive at conclusions consistent with those reached through UMA’s established mechanisms of token holder voting and data verification. This evaluation used a dataset comprised of 259 disputed markets and 558 user-initiated dispute events.

The evaluation of Large Language Models (LLMs) utilized a dataset comprising 259 markets that had previously undergone dispute resolution processes, totaling 558 individual dispute events initiated by users. This dataset served as the basis for assessing the LLMs’ capacity to replicate the outcomes determined by UMA’s established resolution mechanisms – specifically, the collective voting results of token holders and the subsequent data verification conducted by UMA. The objective was to quantify the degree to which these models could independently and accurately ascertain the correct resolution for disputed events, effectively mirroring the results of UMA’s existing decentralized governance and data validation procedures.

Evaluation of several large language models (LLMs) against UMA’s Optimistic Oracle dispute resolution process demonstrated a high degree of agreement with finalized outcomes. Specifically, DeepSeek V3.1 achieved 89.58% consistency, while Qwen Max reached 89.19%. Qwen Max also displayed strong internal consistency, with 96.14% of its predictions aligning with its own previous outputs. Claude-4.5-Sonnet achieved 57.01% accuracy in predicting dispute resolutions, a statistically significant improvement over a random baseline of 50%. This assessment was conducted on a dataset comprised of 259 disputed and resolved markets, encompassing 558 user-initiated dispute events.

The Inevitable Scaling of Distributed Judgement

Decentralized prediction markets, while promising, often grapple with costly and time-consuming dispute resolution processes when disagreements arise regarding event outcomes. Recent advancements leverage large language models (LLMs) to automate this critical function, significantly reducing both the financial burden and delays currently experienced by users. Instead of relying on lengthy human arbitration or complex voting mechanisms, LLMs can analyze evidence – such as news articles, data feeds, and event reports – to determine outcomes with remarkable speed and efficiency. This automated approach not only lowers transaction costs but also enables a far greater volume of disputes to be processed concurrently, fostering a more robust and scalable ecosystem for decentralized forecasting and betting. The potential for near-instantaneous resolutions unlocks new possibilities for creating sophisticated prediction markets encompassing a wider range of events and contingencies.

The inherent limitations of manual dispute resolution have long constrained the growth of decentralized prediction markets; traditional processes are slow, expensive, and struggle to accommodate the volume required for truly widespread adoption. However, by automating key stages of the dispute lifecycle – from initial claim filing to evidence review and final judgment – these markets can achieve a level of scalability previously unattainable. This streamlined approach not only reduces costs and latency but also facilitates the creation of more complex and nuanced prediction questions, extending beyond simple binary outcomes to encompass multifaceted scenarios and subjective evaluations. Consequently, a wider range of events can be accurately priced, incentivizing greater participation and unlocking the full potential of decentralized forecasting as a powerful tool for collective intelligence.

The capacity of large language models to facilitate automated dispute resolution extends significantly beyond prediction markets, offering a novel approach to decentralized governance challenges. Areas traditionally hampered by slow, costly, and potentially biased human adjudication – such as decentralized autonomous organizations (DAOs) determining the fulfillment of smart contract terms, or even verifying data integrity in decentralized science initiatives – stand to benefit from this technology. By providing a mechanism for efficient and unbiased truth determination, LLMs can streamline decision-making processes, reduce reliance on centralized authorities, and foster greater trust within these systems. This innovation promises to unlock new possibilities for self-governance and collaborative problem-solving in a variety of decentralized contexts, ultimately enabling more robust and scalable decentralized applications.

The pursuit of predictable systems, as evidenced by this exploration of LLMs and decentralized dispute arbitration, reveals a fundamental truth about complexity. This paper illuminates how even sophisticated models struggle with proactive forecasting – anticipating which Polymarket events will trigger disputes. It echoes a sentiment articulated by Ken Thompson: “There’s no such thing as a perfect system, only systems that have yet to be stressed.” The study doesn’t demonstrate failure, but rather the inevitable evolution of any system towards unforeseen states. Long stability, as the authors implicitly show through the difficulty of predictive accuracy, is often the harbinger of hidden vulnerabilities, not a sign of robust design. The real value, it seems, lies not in prevention, but in elegantly resolving the chaos when it inevitably arises.

The Horizon of Disagreement

The study reveals a familiar truth: systems designed to resolve conflict are not, and cannot be, systems that prevent it. The capacity of Large Language Models to mirror past judgments offers a comforting illusion of control, yet their inability to anticipate dispute is a more honest assessment. Scalability is just the word used to justify complexity; a model that perfectly reproduces yesterday’s answers is, by definition, inflexible to tomorrow’s questions. This isn’t a failure of the technology, but a confirmation of the inherent messiness of prediction itself.

Future work will inevitably focus on expanding the scope of resolved disputes, seeking ever-larger datasets to train these models. However, a more fruitful avenue may lie in understanding why disagreements arise in the first place. The patterns of contention, the nuances of interpretation – these are not simply data points to be ingested, but reflections of fundamental uncertainties within the market itself.

The perfect architecture is a myth to keep sane. Perhaps the true value isn’t in automating dispute resolution, but in designing systems that gracefully accommodate disagreement – systems that recognize conflict not as an error state, but as an intrinsic property of any complex, decentralized ecosystem. Everything optimized will someday lose flexibility, and the most robust systems are those built to bend, not break.

Original article: https://arxiv.org/pdf/2604.15674.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-04-20 22:25