Rahul Sajnani, Diego Romeres, Mingyuan Zhang, Kai Gao, Carl Qi, Baichuan Huang, Srinath Sridhar, Dylan Randle · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28626 · open-access PDF
언어, 인식, 행동을 통한 경험을 통합하는 인간의 능력에 의해 동기를 부여, 우리는 단일 공유 표현에서 비디오, 액션 및 언어를 배우는 통합 세계 액션 언어 모델 인 UniWALM을 소개합니다, 각 모듈은 조건 신호와 감독 목표입니다.우리는 질문합니다 : 공동 생성 언어가이 공유 표현을 풍부하게하고 조작을 향상시키고 기억에 의존적 인 행동을 지원합니까?
Machine translation (Meta M2M100); the English original follows.
Motivated by the human ability to integrate experience across language, perception, and action, we introduce UniWALM, a unified world-action-language model that learns video, action, and language in a single shared representation, where each modality is both a conditioning signal and a supervision target. We ask: can jointly generating language enrich this shared representation, improving manipulation and supporting memory-dependent behavior? Unlike prior approaches that attach a reasoning or vision-language model to a video-action backbone and run them as distinct stages, UniWALM unifies all
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