我们总希望AI更强大，但“强大”就等于“更聪明”吗？本期几篇最新论文将带我们探索“聪明”的另一面：有时，给AI加上“部门墙”的约束，反而能激发它的潜力；有时，教会AI在关键时刻向“专家”求助，比让它无所不知更高效；甚至，我们还会发现，那个你以为在帮你润色文稿的AI，可能正在不动声色地重塑你的观点。准备好了吗？让我们一起看看AI是如何学会“思考”，而我们又该如何与它共处。00:00:37 AI大模型里的“部门墙”，怎么破？00:06:20 你的“专家外挂”，需要一个“智能开关”00:11:29 AI学会了“做大菜”，而不只是“选番茄”00:17:14 AI也懂“四两拨千斤”？00:21:32 你以为AI在帮你润色，其实它在重塑你的观点本期介绍的几篇论文：[LG] Path-Constrained Mixture-of-Experts [Apple & Google] https://arxiv.org/abs/2603.18297 ---[CL] TARo: Token-level Adaptive Routing for LLM Test-time Alignment [Meta] https://arxiv.org/abs/2603.18411 ---[CL] Reasoning over mathematical objects: on-policy reward modeling and test time aggregation [FAIR at Meta] https://arxiv.org/abs/2603.18886 ---[LG] dTRPO: Trajectory Reduction in Policy Optimization of Diffusion Large Language Models [Meta AI] https://arxiv.org/abs/2603.18806 ---[CL] How LLMs Distort Our Written Language [UC Berkeley & UC San Diego & Google DeepMind] https://arxiv.org/abs/2603.18161

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

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