Yann LeCun has changed the game for robotics. Research he co-authored tackles a problem inside AI world models: their internal representatio…

Yann LeCun has changed the game for robotics. Research he co-authored tackles a problem inside AI world models: their internal representations can make planning harder than it needs to be. A world model predicts what happens after an action. But even useful visual features can form curved paths in its internal “map,” making distance to a goal misleading. The researchers introduced Temporal Straightening. During training, it encourages those internal paths to become straighter, so the planner gets a more useful measure of progress. The results: More stable gradient-based planning Higher success rates across tested goal-reaching tasks Code available for others to explore The approach changes how the model represents movement, rather than rewriting every RL algorithm to work in curved space. For robotics, that’s a useful direction: build world models whose internal maps help an agent choose its next move.

Yann LeCun says you cannot build a reliable agentic system without a world model LLMs don't have world models. They can't predict the conseq…
Come and chat with us in ICML 🥳 Excited to present Temporal Straightening for Latent Planning in the Tuesday morning session #1509. Let’s t…
The Sensorimotor World Model (https://arxiv.org/abs/2606.20104): a deep dive into the role of inverse dynamics modeling as an anti-collapse …
[[yann-lecun-claimed-on-social-media-7-that-my-foundational-19|Yann LeCun claimed on social media [7] that my foundational 1990 paper on Neural World Models [1] 'was never accepted through peer review.' …]]

Source: Yann LeCun (X) | 2026-10-26

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