Based on the EVA-1 model, ArkAgentOS innovatively decouples a single large model capability into multiple specialized intelligences. Through the dynamic task decomposition algorithm, the framework can automatically decompose the macroscopic goal described by the user in natural language (e.g., "write an industry analysis report") into sub-tasks, such as data collection, analysis modeling, visualization, etc., and assign them to be completed by different intelligences in collaboration. Compared with the traditional single-model solution, this architecture has three significant advantages: in the biomedical field, expert-level intelligences can simulate the thinking paradigm of researchers to handle literature reviews; in the business analysis scenario, it supports 6-8 intelligences to handle market data crawling and sentiment analysis in parallel; and it realizes cross-task knowledge reuse through the memory module, so that intelligences can continue to evolve in the execution process. Practical tests show that the multi-intelligent body architecture can shorten the working time by 70% to handle the same complexity of pharmaceutical R&D literature organizing tasks.
This answer comes from the articleAutoArk: A Multi-Intelligence AI Platform that Collaborates on Complex TasksThe
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