Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization
3.60T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2610.29276.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryPaper proposing a Bayesian Evolutionary Optimization framework that combines evolutionary graph generation with a graph-aware Gaussian Process and Expected Improvement to automatically discover task-specific multi-agent system topologies. Evaluated on BigCodeBench and TabPFN tabular preprocessing tasks.
Why it mattersReplaces LLM-based topology selection with a principled BO-plus-evolutionary search, giving an explicit selection criterion under costly evaluations for MAS design.
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