EM 2Mem: Event-Centric Multimodal Memory for Large Language Models

arXiv:2609.00551 · cs.CL, cs.AI, cs.LG, cs.MM · Submitted 2026-09-01 · Read on arXiv

cs.CL, cs.AI, cs.LG, cs.MM

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/zjunlp/LightMem

Terminology

• Accuracy — 27× in this paper · explained in 1 episode
• Benchmark — 23× in this paper · explained in 2 episodes
• Alignment — 17× in this paper · explained in 1 episode
• Grounding — 11× in this paper · explained in 3 episodes
• RAG — 9× in this paper · explained in 3 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Provenance — 7× in this paper · explained in 4 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Actor — 4× in this paper · explained in 3 episodes
• Core — 4× in this paper · explained in 2 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Amortization — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Abstraction — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agent Memory — 1× in this paper · explained in 1 episode
• Agentic Memory System — 1× in this paper · explained in 1 episode
• Agentic Reasoning — 1× in this paper · explained in 1 episode
• Agentic Retrieval — 1× in this paper · explained in 1 episode
• Agentic Search — 1× in this paper · explained in 1 episode
• Cooperation — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Temporal Abstraction — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Training-Free Framework — 1× in this paper · explained in 2 episodes

Sources

• Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval
• LightMem-Ego: Your AI Memory for Everyday Life
• VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
• LightMem: Lightweight and Efficient Memory-Augmented Generation
• LycheeMemory V2: Efficient Long-Term Memory for LLM Agents via Semantic Segment-Level Consolidation
• VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
• From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents
• OASIS: On-Demand Hierarchical Event Memory for Streaming Video Reasoning
• HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understanding
• Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory
• Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
• Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
• VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
• PersonaVLM: Long-Term Personalized Multimodal LLMs
• OpenAI GPT-5 System Card
• Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning
• Tarsier: Recipes for Training and Evaluating Large Video Description Models
• Video-Thinker: Sparking "Thinking with Videos" via Reinforcement Learning
• Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding

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