Learning Latency-Aware Orchestration for Multi-Agent Systems
3.40T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2607.13359.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryProposes LAMaS, a framework for learning-based multi-agent LLM systems that reduces end-to-end latency by over 50% on four benchmarks. It uses critical-path-aware credit assignment during training and a lightweight runtime controller that prunes redundant agent interactions during inference, without sacrificing accuracy.
Why it mattersLatency is an underexamined axis in MAS orchestration; the critical-path framing and runtime pruning are concrete mechanisms readers running multi-agent pipelines can evaluate for their own setups.
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