Xinyuan Wang

Current multimodal agents can build sophisticated perception-control pipelines but fail to reliably execute physical tasks, losing object states and mistaking unfinished goals for completion.

Scaling and post-hoc interpretability hit a hard wall in agentic AI because models cannot recover causal invariances that observational interaction data never contained in the first place.