跳到正文
arXiv cs.AI· Amir H. Rezaeian, Weiyi Sun, Yassine Benajiba, Dan Roth·· 14 小时前AI 评分38

Prefix Executability:超越最终成功率评估工具使用智能体

Prefix Executability: Evaluating Tool-Using Agents Beyond Final Success

AI 导读

研究提出 prefix executability 轨迹级评估协议,衡量工具调用序列的每个前缀在工具 schema、状态前置条件和数据依赖下是否可执行,并用 PEC、FED、TER、AUC-PEC 和 RR 等指标量化可靠性。

正文 · 原文

View PDF HTML (experimental)

Abstract:Tool-use benchmarks often score agents by final task success, but terminal outcomes can obscure whether the intermediate tool-call trajectory was executable. We propose prefix executability as a trajectory-level evaluation protocol for tool-using agents. The protocol measures whether every prefix of a predicted tool-call sequence remains executable under tool schemas, state preconditions, and data dependencies, and summarizes reliability using Prefix Executability Curves (PEC), First-Error Depth (FED), Trajectory Executability Rate (TER), horizon-normalized AUC-PEC, and Recovery Rate (RR). We instantiate the protocol on stratified samples from BFCL-v3 multi-turn and ToolSandbox, and on the complete Airline/Retail base splits of $\tau^3$-bench. We observe that outcome-only and prefix-level rankings diverge: agents whose execution is conditioned on an unverified plan often achieve higher point-estimate final success than non-plan-conditioned baselines while substantially lowering TER and AUC-PEC. These divergences are precisely the failure modes that prefix executability is designed to expose: they reveal whether apparent success rests on an executable trajectory, where invalidity first enters, and whether success follows recovery from an earlier failure. As a validation intervention, explicit pre-execution plan checking can improve executable-prefix survival while maintaining competitive terminal success. These results show that final success alone is insufficient for evaluating tool-use agents and that prefix-level executability provides an actionable reliability signal for comparing agents, diagnosing failures, and assessing validation mechanisms.
Comments: 21 pages, 2 figures. Accepted to NeurIPS 2026
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2610.40444 [cs.AI]
  (or arXiv:2610.40444v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.40444

arXiv-issued DOI via DataCite

Submission history

From: Amir H. Rezaeian [view email]
[v1] Thu, 29 Oct 2026 16:58:04 UTC (1,303 KB)

来源:arXiv cs.AI · arxiv.org