Open-o3-Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence
On the map: LLM reasoning → Multimodal reasoning
Abstract (arXiv)
Most video reasoning models only generate textual reasoning traces without indicating when and where key evidence appears. Recent models such as OpenAI-o3 have sparked wide interest in evidence-centered reasoning for images, yet extending this ability to videos is more challenging due to the need for joint temporal tracking and spatial localization across dynamic scenes. We introduce Open-o3-Video, a non-agent framework that integrates explicit spatio-temporal evidence into video reasoning by highlighting key timestamps, objects, and bounding boxes, making the reasoning process traceable and verifiable. To enable this capability, we first construct high-quality datasets STGR that provide unified spatio-temporal supervision, which is absent in existing resources. We further adopt a cold-start reinforcement learning strategy with specially designed rewards that jointly encourage answer accuracy, temporal alignment, and spatial precision. On the V-STAR benchmark, Open-o3-Video achieves state-of-the-art performance, improving mAM by 14.4% and mLGM by 24.2% over the Qwen2.5-VL baseline, and shows consistent gains across a range of video understanding benchmarks. Beyond accuracy, the grounded reasoning traces produced by Open-o3-Video support confidence-aware test-time scaling, improving answer reliability.
Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 55 of its 66 references that are in the map. First posted on arXiv 23 Oct 2025.
Builds on
55 of 66 references.
Closest ancestors · 19
Same direction, newest first
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- 2025Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning9 Jul
- 2025VGR: Visual Grounded Reasoning13 Jun
- 2025CyberV: Cybernetics for Test-time Scaling in Video Understanding9 Jun
- 2025Reinforcing Video Reasoning with Focused Thinking30 May
- 2025GRIT: Teaching MLLMs to Think with Images21 May
- 2025DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning20 May
- 2025VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning18 May
- 2025TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning13 Apr
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- 2025VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning9 Apr
- 2025Video-R1: Reinforcing Video Reasoning in MLLMs27 Mar
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- 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling6 Dec
- 2024VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection22 Nov
- 2024Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning25 Mar
- 2023Look, Remember and Reason: Grounded reasoning in videos with language models30 Jun
Other references · 36
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
- 2025Qwen2.5-VL Technical Reportcited by 5,587
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
- 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
- 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,834
- 2023Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understandingcited by 1,471
- 2024Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysiscited by 1,413
- 2023MVBench: A Comprehensive Multi-modal Video Understanding Benchmarkcited by 1,109
- 2025Group Sequence Policy Optimizationcited by 835
- 2017Localizing Moments in Video with Natural Languagecited by 663
- 2025VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understandingcited by 518
- 2024Long Context Transfer from Language to Visioncited by 515
24 more
- 2019COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysiscited by 318
- 2024LongVILA: Scaling Long-Context Visual Language Models for Long Videoscited by 309
- 2024mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Modelscited by 291
- 2025Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videoscited by 236
- 2024STAR: A Benchmark for Situated Reasoning in Real-World Videoscited by 213
- 2025Adaptive Keyframe Sampling for Long Video Understandingcited by 167
- 2024Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolutioncited by 159
- 2025Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videoscited by 159
- 2025SpaceR: Reinforcing MLLMs in Video Spatial Reasoningcited by 159
- 2025WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMscited by 155
- 2025Time-R1: Post-Training Large Vision Language Model for Temporal Video Groundingcited by 146
- 2025PerceptionLM: Open-Access Data and Models for Detailed Visual Understandingcited by 92
- 2024TRACE: Temporal Grounding Video LLM via Causal Event Modelingcited by 89
- 2025Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodologycited by 69
- 2025Towards Understanding Camera Motions in Any Videocited by 64
- 2025Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awarenesscited by 63
- 2025DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPOcited by 61
- 2025LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understandingcited by 59
- 2024Reconstructive Visual Instruction Tuningcited by 55
- 2022Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Groundingcited by 42
- 2025VideoITG: Multimodal Video Understanding with Instructed Temporal Groundingcited by 29
- 2025Universal Video Temporal Grounding with Generative Multi-modal Large Language Modelscited by 27
- 2025Datasets and Recipes for Video Temporal Grounding via Reinforcement Learningcited by 19
- 2025Fine-grained Spatiotemporal Grounding on Egocentric Videoscited by 11
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Citing papers per year: 2025: 7; 2026: 42.
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12 most cited of 25 All 25 (JSON)
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- 2026VideoScout: Learning Agentic Active Exploration with Adaptive Reasoning Pacing for Long Video Understanding14 Sep 2026
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- 2026DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLMs14 Jul 2026
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- 2026PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models17 Jun 2026
- 2026Find, Fix, Reason: Context Repair for Video Reasoning17 Apr 2026
- 2026VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification2 Apr 2026
- 2026EVA: Efficient Reinforcement Learning for End-to-End Video Agent24 Mar 2026
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- 2026Evidence-Backed Video Question Answering13 Jul 2026
- 2026MotionAtlas: Detailed Region Captioning for Motion-Centric Videos28 Jun 2026
- 2026Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification27 Jun 2026
- 2026Native Active Perception as Reasoning for Omni-Modal Understanding17 Jun 2026
- 2026Towards One-to-Many Temporal Grounding4 Jun 2026
- 2026VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding3 Jun 2026
- 2026One Identity, Many Roles: Multimodal Entity Coreference for Enhanced Video Situation Recognition25 Apr 2026
- 2026StreamingClaw Technical Report23 Mar 2026
- 2026SAMTok: Representing Any Mask with Two Words22 Jan 2026
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