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Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

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

Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10

Category: cs.CL · 自然语言处理 · first seen 2026-09-10

Abstract

Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained large language models and condition generation on a textual prompt. Their strong performance on captioning, question answering, retrieval and temporal grounding comes at a computation and memory cost that grows with frame count and context length, limiti

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#82 most recent of 186 cs.CL papers we have recorded · ↑ newer: Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware D · ↓ older: On-Policy Distillation for Vision-Language Model Adaptation, an Effect
Cite this page: Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs: the #82 most recent of 186 cs.CL papers we have recorded (as of 2026-09-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/why-is-video-still-so-expensive-a-survey-of-inference-efficiency-mechanisms-in-v.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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