Invited: Polymath: Self-Improving Hierarchical Workflow for Multi-Domain Problem Solving

Le résumé fourni par la source

Large language models (LLMs) excel at solving complex tasks by executing agentic workflows composed of detailed instructions and structured operations. However, building agents for diverse applications by manually embedding foundation models into agentic systems such as Chain-of-Thought, Self-Reflection, and ReACT through text interfaces limits scalability and efficiency. Recently, researchers have explored automating workflow generation using code-based representations, but most methods depend on labeled data, limiting their applicability to real-world, dynamic hardware design problems. We introduce Polymath, a self-improving agent with a dynamic hierarchical workflow that combines task flow graphs with code-represented workflows to address these challenges. Polymath employs an experience-driven optimization framework that integrates multi-level graph optimization using surrogate scores from historical evaluations with a self-reflection-guided evolutionary algorithm for workflow refinement, enabling unsupervised self-improvement without labeled data. Experiments show that Polymath outperforms a leading commercial agentic system by 16.23% pass@1 and 11.47% pass@3 on hardware benchmarks, and achieves an average 8.1% improvement over state-of-the-art baselines on coding, math, and multi-turn QA tasks.

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