RAAS: LLM Agentic System Architecture Search with GRPO
Yang, Jiayi, Wan, Guancheng, Zhang, Man, Ye, Mang
原文摘要
Large Language Model (LLM) agentic systems solve complex tasks through coordinated workflows, but designing them remains labor-intensive. The Agentic Supernet paradigm automates this by optimizing a probabilistic architecture space, yet suffers from critical evaluation instabilities: absolute performance scores entangle architectural merit with query difficulty, while single-execution protocols capture execution randomness rather than true capability. These instabilities lead to unreliable search dynamics where simple queries inflate weak designs and challenging queries suppress strong ones.We introduce RAAS (Robust Architecture Adaptive Search), which establishes more stable and fair evaluation through two synergistic mechanisms. Contextual Architecture Orchestration (CAO) disentangles quality from task difficulty by evaluating cohorts of candidate architectures on identical queries, deriving context-aware merit signals through peer-group comparison. Multi-Trial Assessment Synthesis (MTAS) reduces execution variance by aggregating performance across multiple independent trials, producing statistically robust capability estimates.Together, these mechanisms provide more reliable signals for architecture discovery. Experiments on six benchmarks spanning mathematical reasoning, code generation, and one multi-step tool-use benchmark show that RAAS consistently improves over strong baselines, improving HumanEval pass@1 from 92.23% to 96.31% and MATH accuracy from 52.08% to 60.87%, while maintaining practical efficiency. These results suggest that robust evaluation is a useful ingredient for agentic architecture search in the studied settings.
cs.CV · cs.CL · cs.AI
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表