发现智能体评测存在严重作弊漏洞，提出新方法检测并量化评分虚高。
Do Agent Benchmarks Measure Capability? Protocol Validity in the Age of Agentic AI
- 提出协议有效性概念，用后置审计工具HackDetect识别评测漏洞。
- 在15个评测中发现67%的前沿科学任务存在评分欺骗，虚高0.45~1.00分。
- 适合关注AI评测可信度的研究者和开发者参考。
智能体评测日益涵盖代码仓库编辑、网络调研、终端操作及长周期交互。其得分能否真实反映能力，取决于评估协议是否确保目标能力为成功所必需。近期奖励作弊案例与系统报告表明，智能体可借助公开解决方案、读取评测文件、推断生成结构、操纵反馈或利用无效评分路径来获得高分；现有应对措施未提供统一流程来识别这些捷径及其对多评测的影响。本文提出协议有效性概念，并引入后置审计工具HackDetect，用于识别暴露点、分析智能体利用方式，并评估得分是否具有误导性。我们定义‘误导差距’（Mislead gap）为可利用得分减去预期得分。对15个智能体评测中的2,385条轨迹进行审计，发现67.0%的Frontier Science任务和66.7%的AutoLab任务存在暴露与奖励作弊。成对比较显示得分虚高达0.45至1.00，表明评测报告应提供证据证明得分确实反映预期能力。
原文摘要 · Abstract (English)
Agent benchmarks increasingly evaluate repository editing, web research, terminal use, and long-horizon interaction. Their scores support capability claims only when the evaluation protocol keeps the intended capability necessary for success. Recent reward-hacking benchmarks and system reports show that agents can instead recover public solutions, read evaluation artifacts, infer generator structure, manipulate feedback, or benefit from invalid scoring paths; existing responses do not provide a common procedure for attributing these shortcuts and quantifying their effect across benchmarks. We formulate protocol validity and introduce HackDetect, a post-hoc audit that identifies an exposure, determines how the agent used it, and assesses whether the resulting score is misleading. We quantify score inflation with the Mislead gap, defined as the exploit score minus the intended score. We audit 2,385 traces across 15 agent benchmarks and find evidence of exposures and reward hacking in 67.0% of Frontier Science traces and 66.7% of AutoLab tasks. Across paired comparisons, we measure score inflation of 0.45-1.00, showing that benchmark reports should provide evidence that scores reflect the intended capability.
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