InternLM Composer 2.5: Comprehensive Multimodal System for Long-term Optimization

AI systems that can interact with environments over long periods, similar to human cognition, have been a hot topic in research. One of the latest advancements in this field is the development of InternLM-XComposer2.5-OmniLive (IXC2.5-OL), a cutting-edge system that allows for long-term streaming video and audio interactions.

Traditional large language models (LLMs) have made great progress in understanding open-world scenarios. However, they face challenges when it comes to continuous and simultaneous processing of streaming data. This is where IXC2.5-OL steps in with its disentangled streaming perception, reasoning, and memory mechanisms.

The system is divided into three main modules. The Streaming Perception Module handles real-time processing of multimodal information, storing important details in memory, and triggering reasoning responses. The Multi-modal Long Memory Module efficiently integrates short-term and long-term memories for better accuracy and retrieval. Lastly, the Reasoning Module executes tasks and coordinates with perception and memory to provide continuous and adaptive service.

By simulating human-like cognition, InternLM-XComposer2.5-OmniLive sets the stage for multimodal large language models to offer dynamic and evolving interactions. This project represents a significant leap forward in the quest to create AI systems that can seamlessly engage with streaming content over extended periods.