Belief-State Management Meets Rubric Rewards for Agentic Forecasting

Current large language model (LLM) agents still struggle to forecast real-world events. This stems from two primary limitations: i) standard ReAct harness accumulates reasoning and evidence within one growing context, and ii) outcome-based rewards in forecasting lead to a slow and noisy signal that cannot distinguish sound reasoning from lucky guesses. To address these, we introduce a forecasting training framework including harness design and post-training. For the harness side, we propose Proactive Belief-State Management (PBM), equipping agents with a fold tool to compress lengthy context into compact belief states and reset the context for subsequent exploration. For the training side, we develop SEIPH, a five-dimensional, outcome-blind rubric, providing process-quality rewards. Using the rubric-based reward, we post-train a 35B model with PPO and GRPO, yielding DeepForecaster-PPO and DeepForecaster-GRPO. Theoretically, we prove that idealized fold-based stopping converges and calibrated local rewards reduce segment-level gradient variance. Empirically, DeepForecaster-PPO (35B) achieves the best Brier score on both ForecastBench and Polymarket-Sim, outperforming frontier agents based on DeepSeek-V4-Pro (1.6T), Kimi-K2.6 (1T), and GPT-5.5. DeepForecaster-GRPO ranks second on both FutureX metrics. Ablations reveal that PBM also improves the untrained base model without any training. Calibration analysis shows that DeepForecaster-PPO makes more accurate high-confidence predictions than Qwen3.6 and GPT-5.5. Trajectory analysis further shows that forecasts generally improve from the first to the final belief state as agents gather evidence and revise their judgments.