发表机构
The University of Hong Kong; School of Artificial Intelligence, Jiangxi Science and Technology Normal University; The Hong Kong University of Science and Technology; University of Science and Technology of China; New York University（香港大学; 江西科技师范大学人工智能学院; 香港科技大学; 中国科学技术大学; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对智能工作流优化中计算冗余和预算分配低效问题，提出Agent-UCT算法，结合RAGSpace框架和WTB，通过重用感知正则化项减少冗余执行，实验证明其能有效识别最佳配置，实现成本感知、可重现且组合高效的智能工作流优化。
AI 中文摘要
优化智能工作流，如检索增强生成（RAG）管道，需要在严格的评估预算下在离散组件选择的组合空间中导航。现有方法未明确利用这些工作流的组合结构，导致计算冗余和预算分配低效。我们引入Agent-UCT，一种通过从二分前缀重用图导出的重用感知正则化项扩展UCT的树搜索算法。它偏向于利用先前实现的配置前缀的分支，减少冗余执行。我们的框架RAGSpace将来自不同RAG组件统一到五维配置空间，WTB提供确定性重放等。实验表明Agent-UCT能识别出最佳配置，二分前缀重用降低搜索成本，采样评估加速运行。Agent-UCT、RAGSpace和WTB共同提供了一个用于成本感知、可重现且组合高效的智能工作流优化的统一框架。
英文摘要
Optimizing agentic workflows, such as retrieval-augmented generation (RAG) pipelines, requires navigating a combinatorial space of discrete component choices under tight evaluation budgets. Existing approaches - heuristic search, black-box optimization, and standard tree search methods - do not explicitly exploit the compositional structure of these workflows, leading to redundant computation and inefficient budget allocation. We introduce Agent-UCT (Agent-based Cost-Aware Upper Confidence Bounds Applied to Trees), a tree search algorithm that extends UCT with a reuse-aware regularization term derived from a bipartite prefix reuse graph. Agent-UCT biases selection toward branches that leverage previously materialized configuration prefixes, reducing redundant execution while maintaining effective exploration. Our framework, RAGSpace, unifies heterogeneous RAG components from LongRAG, LightRAG, and Self-RAG into a five-dimensional configuration space, enabling systematic cross-framework recombination. WTB (Workflow Test Bench) provides deterministic replay, content-addressable caching, and transactional consistency, ensuring that intermediate states are materialized once and reused across the search. Experiments on HotpotQA and UltraDomain demonstrate that Agent-UCT identifies configurations with the highest out-of-sample performance among the evaluated fixed framework presets. Under full-pool evaluation, bipartite prefix reuse reduces logical search cost by 73.6% relative to the no-prefix-sharing cost upper bound. Compared with full-pool evaluation, sampling-based evaluation further achieves a 4.2x wall-clock speedup. Agent-UCT, RAGSpace, and WTB together provide a unified framework for cost-aware, reproducible, and compositionally efficient agentic workflow optimization.
