Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
We introduce M3-Agent, a novel multimodal agent framework equipped with long-term memory. Like humans, M3-Agent can process real-time visual and auditory inputs to build and update episodic and semantic memories, gradually accumulating world knowledge. Its memory is organized in an entity-centric, multimodal manner, enabling deeper and more consistent understanding of the environment. Given an instruction, M3-Agent autonomously performs multi-turn reasoning and retrieves relevant memories to complete tasks. To evaluate memory effectiveness and memory-based reasoning in multimodal agents, we develop M3-Bench, a long-video question answering benchmark comprising 100 newly recorded robot-perspective videos (M3-Bench-robot) and 920 diverse web-sourced videos (M3-Bench-web). We annotate QA pairs designed to test capabilities essential for agent applications, such as person understanding, general knowledge extraction, and cross-modal reasoning. Experimental results show that M3-Agent, trained via reinforcement learning, outperforms the strongest baseline, a prompting agent using Gemini-1.5-pro and GPT-4o, achieving 6.7%, 7.7%, and 5.3% higher accuracy on M3-Bench-robot, M3-Bench-web and VideoMME-long, respectively. Our work advances multimodal agents toward more human-like long-term memory and provides insights for their practical design. Model, code and data are available at https://github.com/bytedance-seed/m3-agent.
Key facts. Cited by 88 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 60 of its 62 references that are in the map. First posted on arXiv 13 Aug 2025.
Builds on
60 of 62 references.
Closest ancestors · 2
Same direction, newest first
- 2024VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding18 Mar
- 2023Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models23 Oct
Other references · 58
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Citing papers per year: 2025: 12; 2026: 76.
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