AI 能自己造自己的“手脚”吗？
我们总在测 AI 能不能答对题；这篇测的是 AI 能不能给自己造一套“答题工具”。研究者让 AI 从零开始搭一套执行系统(相当于给它自己装手脚)，再让它根据跑分结果反复改这套系统。结果很分裂：在写作和机器学习实验上，AI 自己造的工具能赶上甚至超过人类工程师精心设计的；但在写代码和搜索研究上，差距巨大。更关键的是，这套系统换一个 AI 来用，效果就大打折扣——它像是“认主”的。这不是你明天能用上的东西，但它划出了一条新边界：AI 的能力上限，可能不只在它脑子里，还在它能不能给自己造出合手的工具。
📄 原文摘要(英文)
As agents move from research prototypes to deployed tools, their capability increasingly depends on model-external execution infrastructure, commonly termed the agent harness. Changing this harness while holding model weights fixed can substantially alter task performance. Current agent evaluations typically report downstream performance under a chosen harness, leaving a model's ability to develop the harness itself comparatively underexplored. We introduce HarnessDev, a benchmark that shifts the unit of evaluation from task outputs to runnable infrastructure. HarnessDev covers two stages. In Creation, the agent starts from a minimal seed and a small number of cases, then builds a complete execution system. In Evolution, it starts from its own created harness and iteratively revises it using downstream execution feedback, with the goal of improving benchmark performance. We then evaluate each constructed harness on capability (task success on held-out benchmarks) and efficiency (execution-token cost). The reported Creation results cover six creator LLMs, four domains, and five downstream benchmarks totaling 2,207 unique downstream instances, with hidden evaluation tasks withheld from development. We find that generated harnesses remain substantially behind mature human-engineered references on code and on search and research, while matching or exceeding the selected references on writing and machine-learning experimentation, with large variation in execution cost. Evolution produces some performance gains, but they are unstable and transfer only partially to held-out tasks. Experiments with a fixed runtime model further show that the gains depend strongly on the model executing the harness, indicating limited transfer across models.