When Should Forecasting Agents Reason? Behavioral Stress Tests

Overview

arXiv:2609.28475 [cs.AI] — Submitted 9 July 2026

Author: Yufeng Wang

Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

DOI: https://doi.org/10.48550/arXiv.2609.28475

Abstract

Forecasting agents increasingly combine language-model reasoning, retrieval, ensembling, and calibration, but it remains unclear when each behavior should be trusted. This work studies that question on ForecastBench-style binary forecasting tasks, treating the decision to retrieve, reason, defer to a market prior, or use a historical analog as an observable agent behavior rather than a hidden implementation detail.

The central finding is that mechanism choice is source-dependent: structured analogs dominate for some data-generating processes, while market/crowd-style and conservative baselines are better for others.

The paper introduces ReliabilityRoute, a structural intervention that steers forecasting-agent behavior using reliability features such as:

Historical coverage
Market-prior availability
Source-prior sharpness
Evidence strength
Evidence disagreement
Forecast horizon

A fixed 2024-fitted rule closely matches a hand-built taxonomy without hard-coded source-name decisions, while a walk-forward self-adjusting rule refits thresholds from previously resolved vintages and achieves the best mean Brier score among the deterministic systems tested across 16 later LLM vintages.

The gain is modest, and historical/search baselines remain highly competitive. The main contribution is therefore a behavioral stress test demonstrating three principles:

More reasoning is not always better.
Forecasting agents should first estimate which evidence source deserves control.
Routing policies should themselves adapt under auditable constraints.

Reproducibility artifacts are available at: https://github.com/louiswang524/forcastagent

Key Contributions

Behavioral framing of forecasting pipelines: retrieval, reasoning, market-prior deference, and analog use are treated as explicit, inspectable agent decisions.
Source-dependent mechanism selection: no single mechanism dominates across all data-generating processes.
ReliabilityRoute: a reliability-feature-driven routing intervention with both a fixed 2024-fitted rule and a walk-forward self-adjusting variant.
Auditable adaptability: routing thresholds update from resolved vintages without relying on hard-coded source identities.
Open reproducibility: code and artifacts publicly released.

Citation

Wang, Y. (2026). When Should Forecasting Agents Reason?
Behavioral Stress Tests for Reliability Routing.
arXiv:2609.28475 [cs.AI].
https://doi.org/10.48550/arXiv.2609.28475

Submission History

v1: 9 July 2026 — Yufeng Wang

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