Countering LeCun: LLMs Store Shapes, Not Facts, and Do Solve New Problems
teortaxesTex · x · 2026-09-25
Responding to Yann LeCun's claim that scaling LLMs can't reach AGI because they're 'gigantic memory and retrieval systems,' the author argues that von Neumann fit the same description yet was creative. LLM weights encode the shape of representations, not stored facts — facts are recomputed on the fly, even in engram-style memory modules. This is why LLMs aren't slavishly bound to training data and can genuinely find solutions to new problems.
Related event: Debate Erupts Over LeCun's Claim That LLMs Are Mere Memory Retrieval(3 posts)→
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