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VideoLoop: Looped Working Memory Against Semantic Thrashing in Long-Form Video Agents

Paper recorded by Signals 4 on 2026-09-29 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-29 on arXiv · recorded by Signals 4 on 2026-09-30

Category: cs.CV · 计算机视觉 · first seen 2026-09-30

Abstract

Long-form video understanding requires multimodal agents to iteratively gather evidence over many reasoning steps. However, most existing agentic methods suffer from semantic thrashing: as append-only working memory grows, attention to key evidence collapses, and the agent loses access to what it has already found. First, we provide a structural argument showing that append-only memory can incorpo

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#12 most recent of 357 cs.CV papers we have recorded · ↑ newer: HelixWorld: A Real-time Interactive Audio-Visual World Model · ↓ older: GA-EIRFS: A Geometry-Augmented Repeat-Factor Sampling Method for Long-
Cite this page: VideoLoop: Looped Working Memory Against Semantic Thrashing in Long-Form Video Agents: the #12 most recent of 357 cs.CV papers we have recorded (as of 2026-09-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/videoloop-looped-working-memory-against-semantic-thrashing-in-long-form-video-ag.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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