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
Motivados por la capacidad humana de integrar la experiencia a través del lenguaje, la percepción y la acción, presentamos UniWALM, un modelo unificado de lenguaje de acción mundial que aprende vídeo, acción y lenguaje en una única representación compartida, donde cada modalidad es tanto una señal condicionante como un objetivo de supervisión. preguntamos: ¿puede generar conjuntamente el lenguaje enriquecer esta representación compartida, mejorar la manipulación y apoyar el comportamiento dependiente de la memoria?
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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