ReliabilityRoute steers forecasting agents using reliability features
The paper introduces ReliabilityRoute, a structural intervention that steers forecasting‑agent behavior based on reliability features such as historical coverage, market‑prior availability, source‑prior sharpness, evidence strength, evidence disagreement, and horizon.
Key points
- ReliabilityRoute uses reliability features to steer forecasting agents.
- Mechanism choice depends on data source; structured analogs dominate for some processes.
- Best mean Brier score achieved across 16 LLM vintages with a walk‑forward self‑adjusting rule.
The authors test the method on ForecastBench‑style binary forecasting tasks, treating the choice to retrieve, reason, defer to a market prior, or use a historical analog as an observable agent behavior. They find that mechanism choice is source‑dependent: structured analogs dominate for some data‑generating processes, while market/crowd‑style and conservative baselines perform better for others.
A fixed 2024‑fitted rule closely matches a hand taxonomy without hard‑coded source‑name decisions, and a walk‑forward self‑adjusting rule refits thresholds from previously resolved vintages. This rule obtains the best mean Brier score among deterministic systems across 16 later LLM vintages, though the gain is modest and historical/search baselines remain highly competitive. The authors provide reproducibility artifacts at https://github.com/louiswang524/forcastagent.
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