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AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction

Paper recorded by Signals 4 on 2026-08-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-28 on arXiv · recorded by Signals 4 on 2026-08-31

Category: cs.AI · 人工智能 · first seen 2026-08-31

Abstract

Predicting robot videos requires both precise motion reasoning and preservation of high-frequency appearance, yet monolithic pixel models entangle these objectives and often conceal their progress behind a strong last-frame baseline. We present AcrossVAM1.0, a lightweight, text-assisted video action model that factorizes future prediction into object-centric motion and dense appearance. A frozen S

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#293 most recent of 300 cs.AI papers we have recorded · ↑ newer: On the Maintenance and Co-evolution of Agent Plugins: An Empirical Stu · ↓ older: LLM-Based Agents for Software and Systems Security: Approaches, Applic

Cite this page: AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction: the #293 most recent of 300 cs.AI papers we have recorded (as of 2026-08-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/acrossvam1-0-particle-world-modeling-for-text-assisted-robot-video-prediction.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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