✨Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination

📝 Summary:
Video-R4 is a video reasoning LMM that improves text-rich video QA through iterative visual rumination. It simulates human behavior by iteratively selecting, zooming, and re-encoding frames to update its reasoning. This approach achieves state-of-the-art results on various QA tasks.

🔹 Publication Date: Published on Nov 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17490
• PDF: https://arxiv.org/pdf/2511.17490
• Project Page: https://yunlong10.github.io/Video-R4/
• Github: https://github.com/yunlong10/Video-R4

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#VideoReasoning #LMM #MultimodalAI #DeepLearning #VideoQA

✨WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning

📝 Summary:
WorldMM is a novel multimodal memory agent for long video reasoning. It uses episodic, semantic, and visual memories with adaptive retrieval across multiple temporal scales, significantly outperforming prior methods on long video question-answering benchmarks.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02425
• PDF: https://arxiv.org/pdf/2512.02425
• Project Page: https://worldmm.github.io
• Github: https://github.com/wgcyeo/WorldMM

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#MultimodalAI #VideoReasoning #MemoryNetworks #DeepLearning #AI

✨Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning

📝 Summary:
Video generation models empower visual reasoning by using generated frames as intermediate steps. They demonstrate robust zero-shot generalization, effectively utilize visual context, and improve planning with increased generated video length.

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21037
• PDF: https://arxiv.org/pdf/2601.21037
• Github: https://thinking-in-frames.github.io/

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#VideoReasoning #VideoGeneration #ComputerVision #AI #DeepLearning

✨Think While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models

📝 Summary:
Think While Watching is a memory-anchored framework enabling multimodal large language models to perform continuous multi-turn video reasoning. It maintains long-range dependencies and boosts efficiency for streaming, significantly outperforming existing benchmarks.

🔹 Publication Date: Published on Mar 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11896
• PDF: https://arxiv.org/pdf/2603.11896
• Github: https://github.com/wl666hhh/Think_While_Watching

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#MLLM #VideoReasoning #StreamingAI #AIMemory #AIResearch

✨Structured Causal Video Reasoning via Multi-Objective Alignment

📝 Summary:
This paper introduces Structured Event Facts for explicit causal video reasoning, moving beyond unstructured methods. It uses a multi-objective reinforcement learning pipeline to balance training goals, leading to Factum-4B. This model achieves reliable, stronger performance on complex temporal v...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04415
• PDF: https://arxiv.org/pdf/2604.04415

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#CausalAI #VideoReasoning #ReinforcementLearning #ComputerVision #AIResearch

🔥 A Very Big Video Reasoning Suite

💡 The paper introduces a large scale video reasoning dataset and benchmark to study video intelligence capabilities beyond visual quality. The problem addressed is that current video models have focused on visual quality and their reasoning capabilities have been underexplored. Video reasoning involves understanding spatiotemporal structure such as continuity, interaction, and causality, which is essential for intelligent systems. However, the lack of large scale training data has hindered systematic study of video reasoning.

To address this gap, the authors introduce the Very Big Video Reasoning Dataset, which is an unprecedentedly large scale resource consisting of 200 curated reasoning tasks and over one million video clips. This dataset is approximately three orders of magnitude larger than existing datasets. The authors also present VBVR-Bench, a verifiable evaluation framework that incorporates rule-based, human-aligned scorers to enable reproducible and interpretable diagnosis of video reasoning capabilities.

The results of the study show early signs of emergent generalization to unseen reasoning tasks, indicating that the proposed dataset and benchmark can be used to develop more generalizable video reasoning models. The dataset, benchmark toolkit, and models are publicly available, laying a foundation for the next stage of research in generalizable video reasoning. The contributions of the paper are the introduction of a large scale video reasoning dataset and benchmark, and the demonstration of their effectiveness in studying video reasoning capabilities and enabling the development of more generalizable models.

📅 Published on Feb 23

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2602.20159
• PDF: https://arxiv.org/pdf/2602.20159
• Project Page: https://video-reason.com/

🤖 Models citing this paper:
• https://huggingface.co/Video-Reason/VBVR-Wan2.2
• https://huggingface.co/Video-Reason/VBVR-LTX2.3-diffsynth
• https://huggingface.co/Video-Reason/VBVR-Wan2.1-diffsynth

📊 Datasets citing this paper:
• https://huggingface.co/datasets/Video-Reason/VBVR-Dataset
• https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data
• https://huggingface.co/datasets/Video-Reason/video-mcp

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard

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📢 By: https://xn--r1a.website/PaperNexus

#VideoIntelligence #VideoReasoning #SpatiotemporalAnalysis #CausalityInAI #ComputerVision