你有没有想过，我们能用一套“知识探针”给大模型做一次精确的“脑容量”CT扫描吗？或者，当AI不再满足于讲一个完美的成功故事，而是把所有失败的教训都记录下来，科学研究会变成怎样一个“活物”？本期节目，我们将从五篇最新论文出发，看看AI如何学会“变脸”戏法，又是如何用“笨办法”实现反超，以及，如何只用一部手机就让一只螃蟹学会跳街舞。00:00:31 如何给AI大模型做一次“脑容量”CT扫描？00:08:25 让一只螃蟹学会跳街舞，总共分几步？00:13:47 让知识“活”起来，科研的下一种形态00:20:47 AI的“变脸”戏法，我们以为的安全，可能只是没对上“暗号”00:27:26 AI进化新思路，为什么“笨办法”反而更聪明？本期介绍的几篇论文：[LG] Incompressible Knowledge Probes: Estimating Black-Box LLM Parameter Counts via Factual Capacity [Pine AI] https://arxiv.org/abs/2604.24827 ---[CV] MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos [Huawei International Pte. Ltd. & Huawei Central Media Technology Institute] https://arxiv.org/abs/2512.10881 ---[LG] The Last Human-Written Paper: Agent-Native Research Artifacts [Orchestra Research & Stanford University & Ohio State University] https://arxiv.org/abs/2604.24658 ---[LG] Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers [Warsaw University of Technology & Truthful AI] https://arxiv.org/abs/2604.25891 ---[CV] Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation [Meta AI] https://arxiv.org/abs/2604.24763

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

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