OracleProto: A Reproducible Framework for Benchmarking LLM Forecasting via Knowledge Cutoffs and Temporal Masking

Forecasting is a composite capability encompassing information gathering, evidence integration, situational assessment, and decision-making. Large language models (LLMs) combine reasoning and tool use, offering new avenues for automated forecasting and decision support. Yet LLM forecasting evaluation faces a structural challenge: live benchmarks support prospective assessment, but limited question reuse and changing information conditions make results difficult to reproduce and compare; retrospective benchmarks offer reproducibility but risk information leakage. We introduce OracleProto, a reproducible framework for evaluating LLM forecasting in a controlled, temporally masked environment...

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