Astra is our most aligned model, with substantial improvements in understanding user intent and model behavior—you can delegate tasks with greater confidence in Astra's judgment.
【非共识】这一关于模型对齐的声明是主观的，没有提供客观衡量标准。'对齐'是一个复杂且多方面的概念，仅凭OpenAI的自我评估不足以证明这一点。需要第三方验证和更具体的评估方法来支持这一说法。
_Self-reported score with custom Anthropic scaffold._ SWEPro were evaluated with the mini-swe-agent scaffold. However, we use the scores reported by Anthropic for Opus with the max thinking efforts due to frequent timeouts during our evaluation trials.
脚注2揭示了重要数据点：Opus 4.6的53.4分是Anthropic的自报分数，因为作者在评估过程中频繁遇到超时问题，无法自行验证。这表明性能比较中存在数据可靠性问题，特别是对于Opus的评估依赖于厂商自报数据，可能存在偏差。
To enable true process-level verification, we audit fine-grained intermediate states rather than just final answers, and quantify efficiency via an overthinking metric relative to human trajectories.
主流评估方法通常只关注最终答案的正确性，而作者提出了一种革命性的评估方法：关注中间过程状态并引入'过度思考'指标来衡量效率。这一观点与当前AI评估领域的传统做法背道而驰，暗示单纯追求正确答案可能掩盖了AI系统在效率和推理路径上的严重缺陷。