QueenBee: A Recursively Self-Improving Harness for Communication Topology Design in LLM-Based Multi-Agent Systems

Computer Science > Multiagent Systems

[Submitted on 25 Jun 2026 (v1), last revised 30 Oct 2026 (this version, v2)]

Title:QueenBee: A Recursively Self-Improving Harness for Communication Topology Design in LLM-Based Multi-Agent Systems

View PDF HTML (experimental)Abstract:LLM-based multi-agent systems show great potential for complex tasks through role specialization and collaborative reasoning. However, existing automated workflow design methods primarily focus on one-off search and candidate optimization, lacking sustained accumulation and reuse of structural design experience to meet evolving coordination demands. We propose QueenBee, an experience-guided framework for multi-agent workflow evolution. QueenBee represents workflows as executable programs that generate temporal communication DAGs and maintains Structural Feedback and Transition Bank (SFT-Bank), a persistent structural memory of execution diagnoses, design hypotheses and validation outcomes, and historical programs. This accumulated experience guides subsequent workflow generation, validation, and optimization, enabling the co-evolution of collaboration programs and design experience. Experiments on Silo-Bench show that QueenBee outperforms fixed communication baselines across team sizes and achieves the highest mean task score among recent automated multi-agent workflow optimization methods. Ablation studies further show that SFT-Bank components provide complementary performance gains, while evolution dynamics reveal a consistently improving average trend in the quality of the best collaboration structures found across independent runs as structural experience accumulates.

Submission history

From: Jiaming Cui [view email][v1] Thu, 25 Jun 2026 19:16:51 UTC (1,388 KB)

[v2] Fri, 30 Oct 2026 04:12:34 UTC (1,768 KB)

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