Reward-Guided Autoregressive Graph Generation for Efficient Multi-Agent Communication Topology Design
3.40T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.20099.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA research paper introducing RGA-Designer, a method using reward-guided autoregressive graph generation to design more efficient communication topologies for LLM-based multi-agent systems. Inspired by RLHF, the approach trains a reward model balancing task correctness and structural compactness, reducing token consumption by 20.5% on average while maintaining accuracy versus ARG-Designer.
Why it mattersConcrete method for cutting multi-agent LLM token costs by a fifth without losing task accuracy, via RLHF-style reward shaping on graph topologies. Useful for anyone designing LLM agent systems where token spend is a constraint.
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