把大模型的隐藏思考偷出来看
闭源大模型到底怎么推理的，一直是个黑箱——厂商只给你答案，不给你过程。这篇论文用了个巧办法：通过一个标准的 API 功能注册一个自定义工具，诱导模型把中间推理过程吐出来。研究者先拿开源模型验证了这套方法提取出的推理和原生推理效果一致，再应用到 GPT-6 Astra 等闭源模型上，发现提取出的推理在竞赛数学、科学和代码生成上都明显优于不推理的基线。更关键的是，他们看清了 Astra 的推理风格：它很省 token，会早早选中正确路径，把基础步骤在内部消化掉，只把关键推理外显出来。这不是你明天能用上的技术，但它给了我们一个窥探前沿模型思考方式的窗口——原来最强的模型不是想得最多，而是想得最省。
📄 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.