Xulong Zhang

Foundation models already encode whether they are right or wrong: a lightweight attention layer over frozen internal token states reliably predicts classification errors across both text and multimodal domains.

Unified multimodal models suffer from internal conflict, but this work shows how to turn that interference into a surprisingly effective source of performance gains.

Forget reinforcement learning: robots can now evolve manipulation skills simply by reflecting on their successes and failures.