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Agentic Physics-Grounded Video Generation via Memory-Augmented In-Context Reasoning

Paper recorded by Signals 4 on 2026-10-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19

Category: cs.CV · 计算机视觉 · first seen 2026-10-19

Abstract

Physical plausibility is essential for realistic video generation. Existing methods impose physical guidance before synthesis via simulations or refined prompts, but this can limit dynamic diversity, miss errors that emerge only after generation, and require repeated regeneration when inconsistencies remain. We therefore study a generate-diagnose-repair paradigm that preserves the original generat

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#241 most recent of 756 cs.CV papers we have recorded · ↑ newer: FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models · ↓ older: PCDFusion: Proposal-Context-Detail Bayesian Rendering for Infrared-Vis
Cite this page: Agentic Physics-Grounded Video Generation via Memory-Augmented In-Context Reasoning: the #241 most recent of 756 cs.CV papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/agentic-physics-grounded-video-generation-via-memory-augmented-in-context-reason.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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