Semantic Trading: Agentic AI for Clustering and Relationship Discovery in Prediction Markets

Prediction markets allow users to trade on outcomes of real-world events, but are prone to fragmentation with overlapping questions, implicit equivalences, and hidden contradictions across markets. We present an agentic AI pipeline that autonomously (i) clusters markets into coherent topical groups using natural-language understanding over contract text and metadata, and (ii) identifies within-cluster market pairs whose resolved outcomes exhibit strong dependence, including "same-outcome" (correlated) and "different-outcome" (anti-correlated) relationships. Using a historical dataset of resolved markets on Poly-market, we evaluate the accuracy of the agent's relational predictions. We then synthesize discovered relationships into a simple trading strategy to quantify how discovered relationships translate into actionable strategies. Results show that agent-identified relationships have around 60-70% accuracy, and their induced trading strategies have an average return of 20% over week-long horizons, highlighting the ability of agen-tic AI and large language models to uncover latent semantic structure within prediction markets.