arXiv AI
Efficient Reasoning Distillation: Small Video-Language Models via Synthetic CoT and Difficulty-Aware Fine-Tuning
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Towards Sparse Video Understanding and Reasoning
arXiv:2602. 13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA).
arXiv Computer Vision
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Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision
arXiv:2608.20814v1 Announce Type: new Abstract: Though Multimodal Large Language Models (MLLMs) have shown impressive potential in video understanding, long video understanding (LVU) remains challeng...
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CADER: Confidence-Aware Dynamic Evidence Reasoning for Long-Video Understanding
arXiv:2607. 24582v1 Announce Type: cross Abstract: Long-video understanding increasingly relies on large vision-language models and tool-augmented reasoning, but most systems apply the same inference procedure to every example regardless of difficulty.
arXiv Computer Vision
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ThinkV2V: Unleashing the Reasoning Capability of MLLMs for Instruction-Guided Video Editing
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A Very Big Video Reasoning Suite
The paper introduces the Very Big Video Reasoning (VBVR) Dataset, a large-scale collection of over one million video clips organized into 200 curated reasoning tasks. It also presents VBVR-Bench, a benchmark framework that uses rule-based, human-aligned scorers for reproducible evaluation of video reasoning models. The authors conduct a large-scale scaling study, noting early signs of emergent generalization to unseen reasoning tasks, and make all resources publicly available.
arXiv AI
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Lumos-Nexus: Efficient Frequency Bridging with Homogeneous Latent Space for Video Unified Models
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