Yann LeCun's lab borrowed a trick from the human brain and more than doubled how often an AI found its way to a goal. The paper is called Te…

Yann LeCun's lab borrowed a trick from the human brain and more than doubled how often an AI found its way to a goal. The paper is called Temporal Straightening for Latent Planning, from NYU with Brown and the University of Toronto. It was accepted at ICML this year. LeCun has argued for years that chatbots won't get AI to human-level intelligence. He thinks the path runs through world models, AI that learns how the world moves by watching it. A world model turns each moment of a video into a point in its own internal map, then learns to predict where the next point will land. To plan a route, the AI imagines moves and heads for the point that matches the goal. The problem is that its internal map is warped. Two spots can look close on the map and sit far apart in real life, like two rooms on either side of a wall. The AI aims straight at the goal and walks into the wall. The team found the fix in neuroscience. A 2019 study in Nature Neuroscience showed that your visual system takes the messy, twisting paths of what you see and straightens them out inside your brain, which makes it easier to predict what happens next. So the team trained their model with one extra rule, a penalty every time its internal path bends. Here's what the straighter map did. In a two-room task with one door in the wall, the AI reached the goal 90.7% of the time, up from 52.7%. In a U-shaped maze, it reached the goal 94% of the time, up from 44%. When it could re-plan along the way, it hit 100% in both. A simple planner using the straightened map kept up with a much heavier one and ran about 10 times faster. They used the same kind of model on the same data. The only change was the shape of the map it learned. The tests are small. The AI moves through mazes and rooms and pushes a T-shaped block into place. The authors say mistakes add up over long plans. Chatbots learn to predict the next word. This model learned to predict the next moment, and it got better by seeing the world the way your eyes do. LLMs gave AI language. World models could teach it to move through the world!

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Source: Yann LeCun (X) | 2026-09-26