When Should Forecasting Agents Reason? Behavioral Stress Tests for Reliability Routing

Forecasting agents increasingly employ hybrid techniques—model‑based reasoning, information retrieval, answer aggregation, and probability calibration. Yet it remains unclear when each approach is reliable. To investigate, researchers examined binary prediction tasks modeled after ForecastBench, treating the agent’s choice to retrieve data, reason, rely on a market prior, or use a historical analogue as observable behaviors rather than hidden internals. The study found that the preferred mechanism depends on the information source: in some data‑generating processes structured analogues perform best, while in others market‑based or conservative baseline methods dominate. The authors introduce ReliabilityRoute, a structural intervention that steers agent behavior using reliability features such as historical coverage, market prior availability, source prior sharpness, evidence strength and disagreement, and temporal horizon. A fixed rule tuned to 2024 data reproduces a manual taxonomy without encoding source names, and a self‑adjusting rule that recalibrates with past data achieves the highest average Brier score among deterministic systems evaluated across 16 subsequent versions of large language models. The key insight is that more reasoning does not guarantee better predictions; agents must first identify the evidence source that warrants control and then adapt their routing policies within auditable constraints. This research is relevant because it informs the design of more efficient and transparent forecasting systems, potentially improving decision‑making in economics, risk management, and strategic planning.

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