RSI-forecaster: A Self-Improving Agent for Event Forecasting

Accurate event forecasting across real-world domains requires task-specific strategies whose development has long relied on substantial human expertise and effort. We formulate autonomous forecasting strategy development as a recursive selfimprovement (RSI) process, where an LLM iteratively executes, evaluates, and revises its forecasting strategy. Yet forecasting creates a fundamental feedback deadlock: the target outcome remains unknown when a prediction is made. We empirically show that resolving this deadlock is non-trivial: naive historical tasks and imperfect backtesting provide little target improvement, and existing RSI methods improve historical performance in backtesting but struggle to reliably transfer gains to the unresolved targets. We introduce RSI-FORECASTER, an end-to-end RSI system that constructs target-relevant historical proxy tasks, replays them in a leakage-controlled open-web environment, and uses predictive and process feedback to hierarchically improve strategies, followed by held-out selection and transfer the resulting gains to the unresolved target. Across three months of evaluation on two popular living forecasting benchmarks, RSI-FORECASTER maintains leading performance and ranks second among 400 submissions on ForecastBench’s preliminary cross-round leaderboard as of the paper submission deadline. Moreover, it substantially outperforms forecasts from the general public and prediction-market crowds, while performing on par with human superforecasters. RSI-FORECASTER consistently improves Brier Index across five LLM backbones, up to 15.54 points.