arXiv cs.AI· Egor Pakhomov, Erik Nijkamp·· 11 小时前AI 评分31
将智能体失败归因于其历史需要可执行的干预验证：迈向一种报告标准
Attributing Agent Failures to Their History Needs an Executed Counterfactual: Toward a Reporting Standard
AI 导读
针对长程智能体失败解释普遍依赖对完整轨迹打标签的分类法，论文主张把智能体行为归因于其历史时必须附带干预验证证据：冻结可执行的中途状态、只改动智能体可见的历史并继续运行，且需提供与估计目标匹配的重放证书。
正文 · 原文
Abstract:Published explanations of why long-horizon agents fail are commonly taxonomies labeled over completed trajectories: a judge reads a transcript and names the failure. We argue that a claim attributing an agent's behavior to its history should carry intervention-verified evidence, per instance or via a validated estimator: freeze an executable mid-trajectory state, alter only the history the agent sees, and continue - executed rather than inferred from the transcript, at a state whose restoration has been measured, with a replay certificate matched to the estimand. Each of the four manipulation classes we name has a precedent, and several methods already execute interventions from checkpoints, re-executed prefixes or recorded conversation states (for example DoVer, REFLECT, Min et al., Who&When Pro, Causal Agent Replay), but none reports a measured replay certificate. Two cautionary measurements, neither applying the standard, follow: a carrier taxonomy whose "concentration" could not be separated from benchmark construction (adjusted odds ratio 1.15, interval crossing 1), and hash-identical snapshots that, restored into identically seeded processes, agreed with one another in 24 of 24 states while differing from the live continuation in 9 and 20 of 24. Restoring the captured position of a random stream made all 24 agree with the live run.
| Comments: | Accepted at the IAB Workshop (Interpreting Agent Behavior) at NeurIPS 2026 (non-archival). 20 pages |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.05650 [cs.AI] |
| (or arXiv:2611.05650v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05650 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Egor Pakhomov [view email]
[v1]
Tue, 3 Nov 2026 21:33:48 UTC (52 KB)
来源：arXiv cs.AI · arxiv.org