Natural Language Query to Configuration for Retrieval Agents
The paper presents a new approach called BRANE for optimizing retrieval agent configurations based on natural language queries. BRANE aims to minimize costs while maximizing accuracy by selecting the best configuration from a predefined catalog. The results demonstrate that BRANE can significantly reduce costs while maintaining high accuracy compared to traditional methods.
- ▪BRANE uses a large language model to convert natural language queries into workload-specific characteristics.
- ▪It selects configurations that maximize predicted correctness while penalizing cost, allowing for a flexible cost-quality tradeoff.
- ▪BRANE has been shown to achieve up to 89% lower costs while matching the accuracy of the best fixed configurations.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27361 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | _KwatbDsjoOY |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.27361 (cs) [Submitted on 26 May 2026] Title:Natural Language Query to Configuration for Retrieval Agents Authors:Melissa Z. Pan, Negar Arabzadeh, Mathew Jacob, Fiodar Kazhamiaka, Esha Choukse, Matei Zaharia View a PDF of the paper titled Natural Language Query to Configuration for Retrieval Agents, by Melissa Z. Pan and 5 other authors View PDF HTML (experimental) Abstract:Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping both answer quality and serving cost. Today, these pipelines are typically hand-tuned once per workload, leaving substantial per-query optimization untapped.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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