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About this paper
Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
Maestro introduces a framework for jointly optimizing the computational graph and configuration of LLM agents, enhancing reliability and performance across various tasks. It achieves this by iteratively refining both the agent's structure and its parameters, leveraging reflective textual feedback, and demonstrating an average performance increase of 12% over leading prompt optimizers with fewer computational resources.
Key insights
- Jointly optimizing both the agent's computational graph (structure) and its operational configuration (parameters) is crucial for addressing a wider range of failure modes and achieving higher reliability than configuration-only approaches.
- Leveraging qualitative, reflective feedback significantly improves sample efficiency, allowing for targeted fixes for specific errors (e.g., state loss, instruction drift) and reducing the number of costly rollouts required.
- Representing agents as flexible computation graphs with tunable components (LLMs, tools, memory) and conditional logic enables the discovery of more robust architectures and the explicit management of global state.
Summary by alphaXiv.
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