The core innovation of M3-Agent is the use of an entity-centered knowledge graph as a memory storage structure. The system automatically recognizes key entities (e.g., characters, objects) from video and audio inputs and uses these entities as graph nodes. The entities' state changes, behavioral performance, and interactions with other entities at different points in time form the edges connecting the nodes.
This architecture brings three key advantages: first, it enables discrete multimodal information to form an organic linkage network; second, it supports correlated querying and reasoning across time dimensions; and third, it ensures the coherence of memory updates. For example, in a home scenario, the system can establish an association network of 'owner-coffee maker-use time', and when the owner asks for coffee maker maintenance suggestions, personalized suggestions can be given by automatically associating memory nodes such as use frequency.
Visualization tools show that a typical 30-minute home video can generate a knowledge graph containing 50-100 entity nodes and 300-500 relationships, a structural density that far exceeds that of traditional vector database storage.
This answer comes from the articleM3-Agent: a multimodal intelligence with long-term memory and capable of processing audio and videoThe
