arXiv:2605.30802v1 Announce Type: cross Abstract: Prediction markets aggregate collective intelligence to forecast uncertain events, but their utility depends on reliable outcome resolution. Existing oracle systems tradeoff fast but brittle automation against accurate but costly human arbitration. Single-LLM oracles achieve meaningful accuracy but inherit all failure modes of their underlying model with no self-correction mechanism. We evaluate whether multi-agent LLM architectures can improve oracle resolution accuracy over single-model baselines. We compare independent aggregation and delibe
The rapid advancement of large language models is leading to exploration of more sophisticated, multi-agent architectures for complex tasks like prediction market resolution.
Reliable oracle systems are critical for the growth and trustworthiness of prediction markets, which represent a significant tool for aggregating collective intelligence and incentivizing accurate forecasting.
The accuracy and reliability of automated prediction market resolution systems could significantly improve, reducing reliance on costly human arbitration and expanding market utility.
- · Decentralized finance (DeFi) platforms
- · Forecasting and intelligence communities
- · AI agent developers
- · Prediction market participants
- · Human arbitrators for prediction markets
- · Single-model AI oracle providers
- · Projects dependent on imprecise oracle data
Multi-agent LLM architectures become the standard for automated prediction market resolution due to enhanced accuracy.
Increased trust and participation lead to a significant expansion in the size and scope of prediction markets, influencing various sectors from finance to policy.
Prediction markets become a real-time 'truth discovery' layer, challenging traditional information sources and potentially accelerating innovation and decision-making for corporations and governments.
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Read at arXiv cs.AI