你有没有想过，AI要如何像高手一样，同时“试驾”多种思路？我们又该如何给狂飙的AI装上“定速巡航”，让它在学习时永不“翻车”？今天，我们就从几篇最新的AI论文出发，聊一聊AI要如何学会“分身术”思考，如何跳出“思维定式”的陷阱，甚至，我们以后可能再也不用费劲地给AI设定KPI，直接“说人话”就能让它们完美协作。准备好了吗？让我们一起探索AI思考方式的深层变革。

00:00:35 如何像高手一样思考？答案可能在“分身术”里

00:05:07 给狂飙的AI装上定速巡航

00:09:57 思维定式是怎么炼成的？AI给了我们一个新答案

00:15:23 怎么让AI大模型学会“左右互搏”？

00:21:37 AI界的“KPI”革命，未来我们不用再跟机器打哑谜

本期介绍的几篇论文：

[CL] Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge

[Microsoft Research & University of Pennsylvania]

https://arxiv.org/abs/2601.08808

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[LG] Controlled LLM Training on Spectral Sphere

[Microsoft Research Asia & Renmin University]

https://arxiv.org/abs/2601.08393

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[LG] Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs

[MIT & NUS]

https://arxiv.org/abs/2601.08763

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[LG] Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies

[MIT]

https://arxiv.org/abs/2601.08136

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[LG] The End of Reward Engineering: How LLMs Are Redefining Multi-Agent Coordination

[New York University & Lerna AI]

https://arxiv.org/abs/2601.08237

[人人能懂] 从“分身术”思考，到“反向”学习，再到“说人话”的KPI

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