Jun Xiao

A training-free framework that reformulates failure recovery as test-time scaling over causal histories, which decomposes recovery into three coupled decisions: when to revise the causal history, where to recover a reliable history prefix, and which history configuration best supports subsequent execution.

This work revisits joint multimodal representation learning and generation to produce linearly interpolatable embeddings that are directly consumable by generative decoders and ensures its representations function as both discriminative semantic descriptors and generative conditions.