Yann LeCun: LLMs Fail to Understand Reality; World Models Needed for Physical Causal Reasoning

1 reports archived1 independent sourcesupdated 10/12/2026, 14:46:27

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Turing Award winner Yann LeCun argues that while current Large Language Models (LLMs) excel in predictable sequences like coding and math, they fundamentally lack the ability to understand real-world physical laws and causal relationships. He advocates for a shift toward 'World Models' in next-generation AI, which simulate physical rules to predict action outcomes and interact with environments, bridging the gap between linguistic understanding and reality.

LATEST/Turing Award winner Yann LeCun argues that while current Large Language Models (LLMs) excel in predictable sequences like coding and math, they fundamentally lack the ability to understand real-world physical laws and causal relationships. He advocates for a shift toward 'World Models' in next-generation AI, which simulate physical rules to predict action outcomes and interact with environments, bridging the gap between linguistic understanding and reality.

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IT之家 · 智能时代T2·Oct 12, 14:4668 pts
Yann LeCun: LLMs Fail to Understand Reality; World Models Needed for Physical Causal Reasoning
- LLMs excel at predictable sequences like language and code but fail to master skills requiring physical world understanding, such as driving.
- There is a significant cognitive gap between machine language comprehension and real-world understanding.
- The proposed solution is developing 'World Models' that learn physical laws and causal relationships to enable AI to predict action consequences.
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