Agentic AI for Clustering and Relationship Discovery in Prediction Markets

The paper introduces a hierarchical agentic reduction (HAR) pipeline that constructs inspectable cross‑event relation graphs from prediction market contracts. By clustering markets and applying typed LLM reasoning, the method proposes signed relations with rationales between specific markets. Evaluation on eight monthly Kalshi catalogs, using 30.3 million trades, shows that the identified relations exhibit higher signed Pearson and Spearman price‑change correlations than matched within‑topic controls, especially for medium or high‑strength relations.

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• llms
• agents
• benchmarks
• safety