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This paper introduces InfoDelphi, a framework that uses information asymmetry (partitioning evidence into shared public and disjoint private subsets) to improve multi-agent LLM deliberation and forecasting. On the PolyGym benchmark, it outperforms single-agent and multi-agent baselines by 12-18% in Brier score and 4-8 percentage points in accuracy, demonstrating that diverse evidence is key to effective multi-agent reasoning.