arXiv cs.AI· Yuxuan Wang, Yishu Wang, Jiaqi Deng·· 1 天前AI 评分44
FABench：面向预测智能体的可扩展回测基准
Benchmarking Forecasting Agents on Yesterday's Tomorrows
AI 导读
研究者推出 FABench，一个面向预测智能体的可扩展回测环境，首个版本包含覆盖九个领域的 287 道问题。该基准通过自动化流水线发现事件、生成问题并解析结果，可在离线档案加实时网页搜索（已过滤以防止时间泄漏）的环境中自由研究。
正文 · 原文
Abstract:Digital agents can now gather and organize information, reason over it, and use tools to complete complex tasks. Forecasting puts all of these abilities to the test: it requires integrating incomplete evidence, inferring causal drivers, and committing to calibrated probabilistic judgments. Existing benchmarks are poorly suited to measuring and improving this ability. Live benchmarks provide feedback only after events resolve, so every evaluation cycle waits on the calendar; fixed-corpus evaluations return results immediately but confine research to pre-collected documents. We introduce FABench, a scalable backtesting environment for Forecasting Agents. An automated pipeline discovers events, generates questions, and resolves outcomes for any past forecast date, so the benchmark can be renewed to stay ahead of model training cutoffs; its first release poses 287 questions across nine domains. Agents research each question freely in an archive environment that combines an offline archive with live web search screened to prevent temporal leakage. Evaluating agents built on eight recent models, we find that retrieval improves the Brier score of all eight in every retrieval setting and lifts the leading agents to the market consensus on the 54 questions linked to Polymarket, with the best three scoring slightly above it. Research traces reveal recurring failures in how agents weigh and apply evidence, leaving ample room for improvement. We release our code, data, and archive environment as a foundation for community research on forecasting agents.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.40940 [cs.AI] |
| (or arXiv:2610.40940v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40940 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuxuan Wang [view email]
[v1]
Thu, 29 Oct 2026 20:18:39 UTC (347 KB)
来源：arXiv cs.AI · arxiv.org