Sukai Huang

Embodied VLMs routinely succeed for the wrong reasons, answering visual queries correctly despite completely missing the critical timestamps and viewpoints required to ground their answers.

Unearthing two distinct planning competencies in LLMs reveals that scaling up models enhances operational reasoning but leaves structural enumeration largely unchanged.