Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness

• URL: http://arxiv.org/abs/2607.24162v2
• Date: Tue, 28 Jul 2026 18:08:04 GMT
• Title: Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness
• Abstract summary: We introduce Agent-based Cost-Aware Upper Bounds Applied to Trees, a tree search algorithm that extends UCT with a reuse-aware regularization term derived from a bipartite prefix reuse graph.<n>Agent-UCT biases selection toward branches that leverage previously materialized configuration prefixes, reducing redundant execution while maintaining effective exploration.<n>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 computation.
• Score: 11.231092673447735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows [72.9051182568928]
  We present ReASearch, a unified framework for reasoning-driven optimization.<n>With a shared agent loop and domain-specific tools, ReASearch instantiates the exact same scaffold to optimize prompts, programs, and ML.<n>We observe that complex search behaviors emerge naturally from the agent's reasoning process.
  arXiv  Detail & Related papers  (2026-08-07T02:15:39Z)
• GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG [0.5566372336864309]
  We introduce GRADRAG, a framework for cross-component prompt adaptation.<n>An Evaluator critiques downstream answers and supporting evidence, producing actionable feedback.<n>We evaluate GRADRAG on the SQUALITY and QMSUM benchmarks.
  arXiv  Detail & Related papers  (2026-07-23T13:54:38Z)
• CAMI: Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval [7.07006462273528]
  CAMI (Cost-Aware Multi-Indexing) is a framework that formalizes multi-index construction as a budgeted, multi-objective portfolio selection problem.<n>Our findings reveal that the framework systematically isolates high-recall portfolios under strict budget constraints.<n> CAMI is able to systematically identify these high-recall portfolios using up to 5x less budget compared to random search baselines.
  arXiv  Detail & Related papers  (2026-06-14T16:59:18Z)
• TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning [52.67756371231985]
  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.<n>We introduce Tree Rollout Allocation for Contrastive Exploration (TRACE), a unified rollout allocation framework.<n>Technically, TRACE allocates rollout budget to both prompt roots and intermediate prefixes that are most likely to yield mixed terminal rewards.
  arXiv  Detail & Related papers  (2026-06-09T17:16:03Z)
• Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval [0.0]
  Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding their responses in external knowledge.<n>This paper presents an Agent-Orchestrated Adaptive RAG framework that introduces dynamic query decomposition, iterative retrieval, and a bounded self-reflective evaluation loop.
  arXiv  Detail & Related papers  (2026-06-04T03:38:46Z)
• Why Search When You Can Transfer? Amortized Agentic Workflow Design from Structural Priors [51.95306551061392]
  Automated agentic workflow design currently relies on per-task iterative search.<n>We present SWIFT, a framework that amortizes workflow design into reusable structural priors.<n> SWIFT outperforms the state-of-the-art search-based method on five benchmarks.
  arXiv  Detail & Related papers  (2026-04-27T21:25:00Z)
• Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration [14.88759517020146]
  Existing RAG pipelines incur substantial retrieval and generation overhead when applied to large-scale entity matching.<n>We introduce CE-RAG4EM, a cost-efficient RAG architecture that reduces computation through blocking-based batch retrieval and generation.
  arXiv  Detail & Related papers  (2026-02-05T14:33:00Z)
• TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG [71.06073770344732]
  Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval.<n>We present TreePS-RAG, an online, tree-based RL framework for agentic RAG that enables step-wise credit assignment while retaining outcome-only rewards.
  arXiv  Detail & Related papers  (2026-01-11T14:07:30Z)
• TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework [62.66056331998838]
  TeaRAG is a token-efficient agentic RAG framework capable of compressing both retrieval content and reasoning steps.<n>Our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps.
  arXiv  Detail & Related papers  (2025-11-07T16:08:34Z)
• AgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search [58.98450205734779]
  Large language model (LLM) agents have demonstrated strong capabilities across diverse domains.<n>Existing agent search methods suffer from three major limitations.<n>We introduce a comprehensive framework to address these challenges.
  arXiv  Detail & Related papers  (2025-06-06T12:07:23Z)
• syftr: Pareto-Optimal Generative AI [40.80352098169579]
  syftr is a framework that performs efficient multi-objective search over a broad space of agentic and non-agentic RAG configurations.<n>Syftr finds flows which are on average approximately 9 times cheaper while preserving most of the accuracy of the most accurate flows.
  arXiv  Detail & Related papers  (2025-05-26T17:43:13Z)
• Efficient Non-Parametric Optimizer Search for Diverse Tasks [93.64739408827604]
  We present the first efficient scalable and general framework that can directly search on the tasks of interest. Inspired by the innate tree structure of the underlying math expressions, we re-arrange the spaces into a super-tree. We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent- form detection.
  arXiv  Detail & Related papers  (2022-09-27T17:51:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.