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
भाषा, धारणा और कार्रवाई के माध्यम से अनुभव को एकीकृत करने की मानव क्षमता से प्रेरित, हम UniWALM को पेश करते हैं, एक एकीकृत वर्ल्ड-एक्शन-भाषा मॉडल जो वीडियो, कार्रवाई और भाषा को एक साझा प्रतिनिधित्व में सीखता है, जहां प्रत्येक मॉड्यूल दोनों एक कंडीशनर सिग्नल और एक पर्यवेक्षण लक्ष्य है।
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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