WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning
On the map: LLM reasoning → Multimodal reasoning
also in 1 more
Abstract (arXiv)
Recent advances in video large language models have demonstrated strong capabilities in understanding short clips. However, scaling them to hours- or days-long videos remains highly challenging due to limited context capacity and the loss of critical visual details during abstraction. Existing memory-augmented methods mitigate this by leveraging textual summaries of video segments, yet they heavily rely on text and fail to utilize visual evidence when reasoning over complex scenes. Moreover, retrieving from fixed temporal scales further limits their flexibility in capturing events that span variable durations. To address this, we introduce WorldMM, a novel multimodal memory agent that constructs and retrieves from multiple complementary memories, encompassing both textual and visual representations. WorldMM comprises three types of memory: episodic memory indexes factual events across multiple temporal scales, semantic memory continuously updates high-level conceptual knowledge, and visual memory preserves detailed information about scenes. During inference, an adaptive retrieval agent iteratively selects the most relevant memory source and leverages multiple temporal granularities based on the query, continuing until it determines that sufficient information has been gathered. WorldMM significantly outperforms existing baselines across five long video question-answering benchmarks, achieving an average 8.4% performance gain over previous state-of-the-art methods, showing its effectiveness on long video reasoning.
Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 40 of its 49 references that are in the map. First posted on arXiv 2 Dec 2025.
Builds on
40 of 49 references.
Closest ancestors · 7
Same direction, newest first
- 2025Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence23 Oct
- 2025Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understanding15 Oct
- 2025ReWatch-R1: Boosting Complex Video Reasoning in Large Vision-Language Models through Agentic Data Synthesis28 Sep
- 2025Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding28 Aug
- 2025E-VRAG: Enhancing Long Video Understanding with Resource-Efficient Retrieval Augmented Generation3 Aug
- 2025Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning9 Jul
- 2024VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos29 May
Other references · 33
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
- 2025Qwen3-VL Technical Reportcited by 2,534
- 2024Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysiscited by 1,375
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- 2024LLaVA-Video: Video Instruction Tuning With Synthetic Datacited by 550
- 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
- 2024LVBench: An Extreme Long Video Understanding Benchmarkcited by 402
- 2024LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understandingcited by 341
- 2024InternVideo2: Scaling Foundation Models for Multimodal Video Understandingcited by 310
- 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 310
- 2024VideoAgent: A Memory-augmented Multimodal Agent for Video Understandingcited by 224
- 2024MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understandingcited by 223
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- 2025Adaptive Keyframe Sampling for Long Video Understandingcited by 153
- 2025Time-R1: Post-Training Large Vision Language Model for Temporal Video Groundingcited by 138
- 2024Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehensioncited by 134
- 2024HourVideo: 1-Hour Video-Language Understandingcited by 110
- 2025VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documentscited by 110
- 2024LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecturecited by 100
- 2025Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memorycited by 88
- 2023Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labellingcited by 87
- 2025VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videoscited by 76
- 2024Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QAcited by 68
- 2025Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoningcited by 67
- 2025VideoRAG: Retrieval-Augmented Generation over Video Corpuscited by 55
- 2024InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactionscited by 48
- 2025HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understandingcited by 19
- 2025UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularitiescited by 18
- 2022EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokenscited by 15
- 2025∞-Video: A Training-Free Approach to Long Video Understanding via Continuous-Time Memory Consolidationcited by 14
- 2025AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understandingcited by 14
- 2025Movie Facts and Fibs (MF2): A Benchmark for Long Movie Understandingcited by 4
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Citing papers per year: 2025: 2; 2026: 48.
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- 2026Search over the Visual World: Persistent Visual Memory, Layered Indexes, and Source-Grounded Evidence8 Aug 2026
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- 2026FOLIO: Focused Semantic Memory for Streaming Video Understanding14 Jul 2026
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