agent/papers/items/2026-2607-22031-idstune-a-multi-agent-collaborative-framework-for-integrated-database-system-tun.md

Paper: IDSTune: A Multi-Agent Collaborative Framework for Integrated Database System Tuning

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type: paper title: "IDSTune: A Multi-Agent Collaborative Framework for Integrated Database System Tuning" authors: Yiyan Li, Guanli Liu, Renata Borovica-Gajic, Haoyang Li, Zihang Qiu, Xinmei Huang, Andreas Kipf, Cuiping Li, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.22031 code_url: source: arxiv collected_at: 2026-07-27 published_at: 2026-07-24 updated_at: 2026-07-24 status: queued relevance: high topics:

• agent-evaluation
• multi-agent
• rag methods:

benchmarks:

models:

datasets:

• cs.DB related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 13 collection_queries: multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: multi-agent-llm
• inferred topics: agent-evaluation, multi-agent, rag
• arXiv categories: cs.DB
• collection score: 13

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.22031