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ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning

Paper recorded by Signals 4 on 2026-10-27 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-27 on arXiv · recorded by Signals 4 on 2026-10-28

Category: cs.AI · 人工智能 · first seen 2026-10-28

Abstract

Vision-Language-Action (VLA) models have made substantial progress in robotic manipulation, but their generalization remains constrained by the diversity and coverage of action-labeled robot demonstrations. Human videos provide much broader behavioral and environmental experience, while learning from them faces a fundamental trade-off between scalable data and explicit action grounding. We introdu

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#82 most recent of 920 cs.AI papers we have recorded · ↑ newer: Honeycomb-Surfels: Compact Hexagonal Primitives for Efficient Scene Re · ↓ older: Attention as Lattice Gauge Theory: Relations Interact Only Through Wil
Cite this page: ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning: the #82 most recent of 920 cs.AI papers we have recorded (as of 2026-10-27). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/thinkworld-world-imagination-reinforces-chain-of-dynamics-reasoning.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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