Computer Science > Artificial Intelligence
arXiv:2603.08877 (cs)
[Submitted on 9 Mar 2026]
Title:Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search
View a PDF of the paper titled Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search, by Kyle McCleary and 1 other authors
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Abstract:Agentic Retrieval-Augmented Generation (RAG) systems combine iterative search, planning prompts, and retrieval backends, but deployed settings impose explicit budgets on tool calls and completion tokens. We present a controlled measurement study of how search depth, retrieval strategy, and completion budget affect accuracy and cost under fixed constraints. Using Budget-Constrained Agentic Search (BCAS), a model-agnostic evaluation harness that surfaces remaining budget and gates tool use, we run comparisons across six LLMs and three question-answering benchmarks. Across models and datasets, accuracy improves with additional searches up to a small cap, hybrid lexical and dense retrieval with lightweight re-ranking produces the largest average gains in our ablation grid, and larger completion budgets are most helpful on HotpotQA-style synthesis. These results provide practical guidance for configuring budgeted agentic retrieval pipelines and are accompanied by reproducible prompts and evaluation settings.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.08877 [cs.AI] |
| (or arXiv:2603.08877v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08877
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View a PDF of the paper titled Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search, by Kyle McCleary and 1 other authors
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