藏在黑箱里的推理，被一个工具钩出来了
闭源大模型到底怎么“想”的，一直没人能看——厂商把推理过程藏起来了。这篇用了个极简的招：通过标准 API 注册一个自定义工具，诱导模型把中间推理吐出来。他们先拿开源模型验证了这套“外显推理”和原生推理水平一致，再套到闭源前沿模型上，发现抽出来的推理在竞赛数学、科学和代码生成上都明显强于“不推理”的基线。更关键的是结构差异：最强的 Astra 模型走的是“省字路线”——早早锁定正确方向，简单步骤内部消化，只把关键推理说出来。换句话说，前沿模型的“聪明”不是话多，而是知道哪些话值得说。
📄 原文摘要(英文)
The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT traces in closed-source systems are hidden. By registering a simple custom tool through a standard API feature, we induce frontier models to externalize intermediate reasoning. Because these traces may reflect post-hoc rationalization rather than genuine reasoning, we first evaluate against native CoT on open-source models and extend to closed-source frontier models including GPT-6 Astra. We find that the extracted reasoning matches native reasoning performance and substantially outperforms no-reasoning baselines, across competition mathematics, science, and code generation. We then characterize how frontier models structure their intermediate reasoning. Across token efficiency, reasoning-step types, and induced reasoning trees, we identify systematic differences in how models externalize, compress, and organize reasoning. We find that Astra exhibits token-efficient directed reasoning, selecting a correct trajectory earlier, while resolving elementary steps internally and externalizing only crucial reasoning. These findings provide a behavioral lens on frontier-model reasoning beyond benchmark scores.