BRIEF: Belief Revision for Iterative Evidence-based Forecasting

Most forecasting agents that retrieve evidence use it in the same way. Once the search stops, everything collected is placed in a single context, and the model reads a forecast off it. Holding the evidence corpus fixed, we show that this practice makes poor use of it. Presenting the corpus all at once yields a Brier loss of 19.34, processing it in rounds lowers this to 18.13, and revising an explicit forecast belief as evidence arrives lowers it further to 16.52. Motivated by this finding, we introduce BRIEF (Belief Revision for Iterative Evidence-based Forecasting), a framework in which a belief state is initialized from multi-perspective research, revised under a Bayesian-inspired principle as new evidence arrives, and used to direct further research through the gaps and conflicts it leaves unresolved. A final refinement stage integrates candidate judgments and applies calibration and target-specific structural adjustments. Compared with twelve baselines spanning reasoning models, prompting and search strategies, and deep research systems, BRIEF achieves the best aggregate performance on FutureX, ForecastBench, and Prophet Arena, with the lowest variance across three runs. Relative to the strongest baseline, it improves the FutureX score by 7.86 points and reduces ForecastBench Brier loss by 5.3%, and it achieves the lowest Brier loss on Prophet Arena. These results indicate that how evidence is used is itself a design decision for forecasting agents, and that a maintained belief state offers an effective way to connect research decisions, evidence interpretation, and final forecast construction.