研究人员正在开发新方法，以改进大型视觉语言模型（VLM）理解和推理长视频的方式。几篇论文介绍了更有效的帧选择和证据收集技术，超越了简单的采样，采用了自适应策略。这些方法旨在通过关注特定查询最相关的视觉信息来降低计算成本并提高准确性。 AI
影响 用于长视频理解的高效新技术可以显著降低VLM应用的推理成本并提高性能。
排序理由 多篇arXiv论文介绍了用于改进大型视觉语言模型视频推理能力的新颖方法。
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arXiv:2604.04415v3 Announce Type: replace Abstract: Human understanding of video dynamics relies on forming structured representations of entities, actions, and temporal relations before engaging in abstract reasoning. In contrast, existing Video-LLMs apply unstructured chain-of-…
arXiv:2603.20180v2 Announce Type: replace-cross Abstract: Large vision--language models (VLMs) are increasingly applied to long-video question answering, yet inference is often bottlenecked by the number of input frames and resulting visual tokens. Naive sparse sampling can miss …
Proactive streaming video understanding requires Video-LLMs to decide when to respond as a video unfolds, a task where existing methods often fall short due to their implicit, query-agnostic modeling of visual evidence. We introduce Response-G1, a novel framework that establishes…
arXiv:2605.05848v1 Announce Type: new Abstract: Video large multimodal models increasingly face a scalability bottleneck: long videos produce excessively long visual-token sequences, which sharply increase memory and latency during inference. While existing compression methods ar…
arXiv:2605.06094v1 Announce Type: new Abstract: Training VideoLLMs for complex reasoning remains challenging due to sparse sequence level rewards and the lack of fine grained credit assignment over long, temporally grounded reasoning trajectories. While reinforcement learning wit…
Training VideoLLMs for complex reasoning remains challenging due to sparse sequence level rewards and the lack of fine grained credit assignment over long, temporally grounded reasoning trajectories. While reinforcement learning with verifiable rewards (RLVR) provides reliable su…
arXiv:2509.24943v2 Announce Type: replace Abstract: Long videos, characterized by temporal complexity and sparse task-relevant information, pose significant reasoning challenges for AI systems. Although existing Large Language Model (LLM)-based approaches have advanced long video…
arXiv:2605.01662v1 Announce Type: new Abstract: Large vision-language models (VLMs) have advanced multimodal tasks such as video question answering (QA). However, VLMs face the challenge of selecting frames effectively and efficiently, as standard uniform sampling is expensive an…
arXiv:2605.01657v1 Announce Type: new Abstract: Vision-Language Models (VLMs) typically rely on static initial frames for video reasoning, restricting their ability to incorporate essential dynamic information as the reasoning process evolves. Existing methods that augment Chain-…