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.

Proposes 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.

Latency 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.

Cited by

No citations on record.