From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events. However, current agentic forecasting often relies on implicit narrative aggregation: agents collect evidence, discuss it in prose, and often assign a probability without an explicit update path from evidence to forecast. This limits both forecasting accuracy and auditability. We propose AuditForecast, an agentic scaffold for structured probabilistic forecasting. AuditForecast first anchors the forecast with a suitable quantitative baseline model, uses model-guided data retrieval to derive a base probability, and then applies situational fact...
| Authors | Yuanpu Cao, Yongkang Du, Yurui Chang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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