Yibo Li

LLM agents can improve their decision-making by learning from their own diagnosed failures, with first-proposal corrections boosting success rates by over 30 percentage points.

High-level robot planners routinely fail because they treat low-level skills as opaque black boxes; turning training-time inductive biases into explicit compositional interfaces bridges the gap to robust out-of-distribution execution.