本期我们将通过几篇最新论文，看看研究者如何给机器人装上“快慢双脑”以实现实时反应，又如何用“二阶思维”为大模型精准剪枝、保留专家的协作默契。我们还将破解Adam优化器参数背后的“悬崖地图”，并派出一个小巧的“侦察兵”模型，去揪出长任务AI悄悄犯下的隐藏错误。最后，我们要警惕一碗“毒鸡汤”考题，看看被污染的基准测试是如何诱导自我进化的AI，把坏习惯固化成肌肉记忆的。00:00:34 机器人也需要条件反射00:05:04 裁员的智慧，你以为的庸才，可能是团队的粘合剂00:10:16 你手里的工具，藏着一张秘密地图00:16:28 你的AI助手，可能正在悄悄搞破坏00:22:15 一碗“毒鸡汤”，如何带歪一个自我进化的AI本期介绍的几篇论文：[RO] Reinforcement Learning for Real-Time Vision-Language-Action Policies[Stanford University]https://arxiv.org/abs/2609.18207 ---[LG] Higher-order pruning of experts in mixture-of-experts language models[AWS Agentic AI]https://arxiv.org/abs/2609.18916 ---[LG] Beyond Quadratic Loss：The Stability Phase Diagram of Adam[Tsinghua University]https://arxiv.org/abs/2609.18314 ---[LG] Locating Hidden Failures Makes Long-Horizon Agents More Reliable[Google DeepMind & University of California, Los Angeles & Google Research]https://arxiv.org/abs/2609.17930 ---[AI] Reflections on Trusting Trust，Revisited：Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks[University of Washington & Georgetown University]https://arxiv.org/abs/2609.17817 在小宇宙查看该单集文稿

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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