arXiv cs.AI· Zhengxin Zhang, Erkang Zhu, Yin Lin, Claire Cardie, Bolin Ding, Jingren Zhou·· 2 小时前AI 评分59
arXiv 论文提出 Agent Harness 验证框架,独立审查以更优成本换准确率提升
Rethinking Verification in Agent Harnesses
AI 导读
arXiv 论文将验证作为测试时扩展的一个维度,研究 agent harness 中何时干预、谁来检查、如何修正,框架覆盖初始指令、执行中提醒、完成门控和独立审查四层。
正文 · 原文
Abstract:Agent harnesses enable large language models to act through tools, observe the results, and iteratively work toward a task goal. Verification checks and revises agents' work to improve output quality. Yet there is limited guidance on how harnesses should organize this effort. We study verification as an axis of test-time scaling by examining when to intervene, who checks the work, and how checking leads to revision. Our framework spans four layers of the agent lifecycle: initial instructions, mid-turn reminders, completion gates, and independent review. We compare 17 designs across seven frontier coding models on Terminal-Bench (TB) and SWE-bench Verified (SWE), and introduce VisualBench for expert evaluation of visual and interactive artifacts. Initial instructions and execution-time reminders yield modest, model-dependent average gains on the coding benchmarks, without substantially increasing average output token use. Completion gates can yield larger gains but may substantially increase cost. Independent review by a critic offers a more favorable accuracy-cost tradeoff, improving average accuracy by 5.9-9.5 percentage points on TB and 3.9-6.6 on SWE at 1.13-1.25 and 1.00-1.04 times baseline cost, respectively. On VisualBench, independent review achieves net preferences of 13.2-23.8 points over the baseline across its four designs, exceeding the evaluated L0-L2 designs. Additional review rounds or independent critics further improve average TB accuracy by up to 3.5 percentage points. Together, these results provide empirical guidance for verification design in agent harnesses and support independent review as a practical axis of test-time scaling.
| Comments: | 65 pages, including appendices |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34577 [cs.AI] |
| (or arXiv:2610.34577v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34577 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhengxin Zhang [view email]
[v1]
Sun, 25 Oct 2026 21:17:26 UTC (4,119 KB)
来源:arXiv cs.AI · arxiv.org