AI RESEARCH
Design and Evaluation of Multi-Agent AI Oracle Systems for Prediction Market Resolution
arXiv CS.AI
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ArXi:2605.30802v1 Announce Type: cross 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.