我们总说AI有知识，但你想过吗，AI的知识该如何称重、如何存储、又该如何溯源？更进一步，AI能否自我修炼、检查作业，它吃的“数据大餐”又藏着怎样的“秘密食谱”？今天，我们就从五篇最新的论文出发，一起探索AI知识世界的台前与幕后。

00:00:24 给AI模型称重，我们终于有了一杆新秤

00:06:34 AI的“记忆宫殿”是如何搭建的？

00:11:56 AI的自我修炼，如何从“检查作业”中获得智慧

00:17:01 AI世界的“亲子鉴定”技术

00:22:37 AI的“隐藏食谱”，为什么数据配比比数量更重要？

本期介绍的几篇论文：

[LG] Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data

[New York University & CMU]

https://arxiv.org/abs/2607.11883

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[LG] MLPs are Hebbians: Constructing Efficient Fact-Storing MLPs for Transformers

[Stanford University]

https://arxiv.org/abs/2607.10034

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[AI] SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning

[University of Illinois Urbana-Champaign & Meta]

https://arxiv.org/abs/2607.10966

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[LG] Reference-Based Distillation Detection in LLMs

[UC Berkeley]

https://arxiv.org/abs/2607.09692

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[LG] Domain-Aware Scaling Laws Uncover Data Synergy

[MIT & Microsoft Research]

https://arxiv.org/abs/2607.11052