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Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding

Paper recorded by Signals 4 on 2026-09-03 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04

Category: cs.CV · 计算机视觉 · first seen 2026-09-04

Abstract

Streaming video understanding requires multimodal large language models (MLLMs) to process continuous visual inputs and respond to user queries under strict causality and bounded memory. Existing approaches typically compress historical observations into an external memory bank and retrieve query-relevant evidence as additional visual context. Though effective, this store-and-retrieve paradigm kee

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#149 most recent of 237 cs.CV papers we have recorded · ↑ newer: Persistent Identity Preservation in Generative Image Models: A Benchma · ↓ older: BooM-VVT: Boosting Mask-Free Video Virtual Try-On with Image-Level Pse
Cite this page: Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding: the #149 most recent of 237 cs.CV papers we have recorded (as of 2026-09-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/beyond-retrieval-progressive-latent-memory-evolution-for-streaming-video-underst.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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