研究通过 API 工具提取 GPT-6 Astra 等闭源模型的隐藏思维链
Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models
想看闭源模型脑子里在想什么？这篇用 API 小工具撬出了 GPT-6 Astra 的思维链，还对比了各家模型推理方式的差异。
一篇 arXiv 论文提出通过标准 API 注册自定义工具，诱导闭源模型将中间推理外化。作者先在开源模型上用原生 CoT 验证该方法，再扩展到 GPT-6 Astra 等闭源前沿模型。提取的推理在竞赛数学、科学问答和代码生成上与原生推理表现相当，明显优于无推理基线。论文进一步从 token 效率、推理步骤类型和推理树结构刻画模型差异，发现 Astra 呈现 token 高效的有向推理，更早锁定正确轨迹，并把基础步骤放在内部处理，只外化关键推理。
Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models
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.
