AIA Forecaster: Technical Report
This study addresses the challenge of enhancing large language models’ (LLMs) judgmental forecasting capabilities on unstructured data to match human superforecasters. We propose a proxy-collaborative forecasting architecture integrating agent-driven news retrieval, supervised multi-source forecast ensembling, and behaviorally informed statistical calibration—explicitly modeling cognitive biases. Evaluated on ForecastBench, the first large-scale, verifiable forecasting benchmark, our framework achieves performance parity with human superforecasters—a first for LLMs—and demonstrates incremental information value beyond liquid prediction market consensus. The core contribution lies in the deep coupling of multi-agent collaboration with bias-aware calibration, markedly improving forecast stability and accuracy. This work establishes a novel paradigm and empirical benchmark for AI-augmented, expert-level forecasting.