Yann LeCun and Brown researchers unveil counterfactual world models for robots

What happened

A team of machine learning researchers thinks it has found a cleaner way to close that gap.

Their new technique helps world models, the internal simulators used to train physical AI, predict how objects would behave under actions the model never actually observed. Ask them to handle something new and things tend to go sideways.

The work comes from Randall Balestriero of Brown University, Kerda Varaku, and Yann LeCun of NYU. It is laid out in a pre-print paper titled “Counterfactual World Models: Natura Non Facit Saltus” , published on November 9, 2026.

It forecasts the consequences of an action before the action happens. That capability matters most for physical AI systems like robots.

A robot that can anticipate outcomes can plan, rather than flailing through trial and error in the real world. The catch is that world models learn from what they observe.

If a particular action never shows up in the training data (the material a model is built from), the model has little to go on when asked what it would do. The trick: subtract the surprise The researchers’ core idea is surprisingly compact.