这篇案例研究揭示了AI编码代理在科学软件中的关键盲点——无法区分预测充分性与解释正确性，做科学计算或AI辅助研究的开发者看完会重新审视自己的测试策略。
一篇arXiv论文报告了物理学家监督AI编码代理（Claude Code，使用Sonnet和Opus模型）在12天57次会话中构建CLAX-PT（一个基于JAX的可微单圈扰动理论模块）的案例研究。研究者记录了15次监督干预事件，其中代理自主解决了10次，2次借助物理学家领域知识，3次未能解决。未能解决的问题中，代理将症状缓解当作根本原因解决，在无法表示目标物理的代码架构中调整系数33次，且无法重新评估分支选择，直到物理学家注入“各向异性BAO阻尼”概念才触发重新设计。代理还引入了一个通过所有测试但无理论意义的校准修正，在不同宇宙学参数下预测错误值。研究强调，监督设计（如多样化参数测试、共享变更日志、禁止非物理数值补丁）比模型能力更能决定输出可信度。
Physics Is All You Need? A Case Study in Physicist-Supervised AI Development of Scientific Software
Are AI agents tools, co-authors, or researchers? We present a quantified case study ($N=1$): a physicist supervising an AI coding agent (Claude Code, Sonnet and Opus models) over 12 work days and 57 sessions to build CLAX-PT, a differentiable one-loop perturbation theory module in JAX. We documented and classified 15 supervision events by intervention level. The agent resolved ten autonomously by iterating against oracle tests. Two more by the physicist's domain knowledge. The three it could not -- all evaded oracle detection -- share a common property: the agent treated symptom reduction as root-cause resolution. It spent 33 of the 57 sessions adjusting coefficients within a code architecture that could not represent the target physics, and could not re-evaluate its CLASS-PT branch choice even when prompted to reconsider; only an injected physics concept (anisotropic BAO damping) triggered the redesign. Separately, the agent committed a calibrated correction that passed all oracle tests but corresponded to no quantity in the theory, predicting wrong values at any other cosmology. The fudge factor was caught and replaced within the same session. Three supervision practices proved critical for catching what oracle tests missed: testing at diverse parameter points beyond the fiducial calibration; shared changelogs that surfaced stalled exploration across sessions; and an explicit rule against unphysical numerical patches. In this case, supervision design, not model capability, determined whether the agent's output was trustworthy. Closing the gap would require agents that propose architectural alternatives rather than optimize within a given structure, and distinguish predictive adequacy from explanatory correctness -- capabilities not exhibited here, not obviously addressed by scaling alone. [Abridged.]