Tsinghua Universityresearchers have developed AgentSquare, a groundbreaking framework for automatically searching large language model (LLM) agents within a modular design space. This innovative system empowers researchers to build and optimize AI agents with unprecedented efficiency and adaptability.
AgentSquare’s corestrength lies in its modular design space, encompassing four fundamental modules: task planning, common sense reasoning, tool usage, and memory learning. This standardized modular interface abstraction allows forrapid self-evolution and adaptive evolution of AI agents. By optimizing the agent’s top-level architecture, AgentSquare can effectively recombine existing high-performance modules, exploring optimal agent designs.
Furthermore, AgentSquare goes beyond mererecombination by introducing module evolution. This capability allows the framework to explore and generate novel module designs at the code level, introducing innovative design elements and expanding the design space.
To accelerate the search process, AgentSquare incorporates a surrogate modelfor performance prediction. This model significantly reduces the cost of real-time evaluation, enabling faster exploration of potential agent designs.
AgentSquare’s key features include:
- Modular Design Space: Offers a standardized framework for constructing and optimizing LLM agents.
- Module Recombination: Enables the exploration ofoptimal agent designs by recombining existing high-performance modules.
- Module Evolution: Introduces innovative design elements and expands the design space by exploring and generating new module designs at the code level.
- Performance Prediction: Utilizes a surrogate model to predict agent performance, reducing the need for costly real-timeevaluations.
- Automated Search: Automatically discovers and optimizes LLM agent designs, eliminating the need for manual intervention.
AgentSquare represents a significant advancement in the field of AI agent design. Its modular approach, coupled with automated search and performance prediction capabilities, paves the way for more efficient and adaptable AIsystems. This framework holds immense potential for various applications, including intelligent assistants, decision-making systems, and personalized learning platforms.
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