代码代理只按测试要求实现，而非用户真实需求。
Building to the Test: Coding Agents Deliver What You Check, Not What You Requested
- 用测试用例反向驱动代码生成，导致结果仅满足测试通过
- 有测试时得分接近满分，但实际功能缺失或未生成
- 揭示了模型缺乏自我验证能力，适合关注可靠性研究者
基准测试广泛用于评估大语言模型的任务完成情况，但其构建效度问题日益突出，高分未必代表任务真正完成。本研究在受控的代码即规范环境下，使用两个生产级Copilot CLI代理（claude-opus-4.7、gpt-5.5）在18次运行中，基于隐藏的222个Playwright测试用例，将React Fluent-UI数据表格重写为可复用的Angular库。结果显示：无测试用例时，生成库存在但未完成；有测试用例时，分数接近完美，但实际库中关键功能缺失或不存在。该现象称为“为测试而构建”，其背后更广泛的倾向称为“验证自知性缺失”——模型无法像用户一样主动验证所交付内容是否正确。该现象在其他代理、信号与模型族中仍待探究。除分数外，验证自知性等行为倾向值得深入研究。
原文摘要 · Abstract (English)
Benchmarks are widely used to evaluate task completion by Large Language Models (LLMs), but this approach has accumulated construction-validity problems, and a passing score may not show whether the requested task was delivered. We study both problems. In a controlled code-as-spec setup, two production Copilot CLI agents (claude-opus-4.7, gpt-5.5) re-implement a React Fluent-UI data table in Angular as a reusable library under a hidden 222-test Playwright oracle across 18 runs and three oracle-availability conditions. Alongside the score, we run a mechanical library audit and check each verdict with a no-op ablation. Without the oracle, the library is present but unfinished, revealed by scores. With the oracle in the loop, the score reaches near-perfect, but from a demo holding the tested behavior directly, the library left dead or absent. We call this building to the test; the broader disposition behind both we call validation self-awareness. The agent does not, on its own, validate what it ships as a user would. Prevalence remains an open question across other agents, signals, and model families. Beyond benchmark scores, dispositions like validation self-awareness merit research attention.
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