Discovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference
4.00T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.12921.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryThe paper introduces E2-Explainer, a framework that applies causal inference to identify critical communication subgraphs in LLM-based multi-agent systems. A Granger-style objective measures how masking each communication channel affects task outcomes, producing budgeted subgraphs that can be executed directly to prune redundant edges and reduce communication costs.
Why it mattersOffers a concrete, experiment-backed method for trimming communication overhead in LLM multi-agent setups while preserving task performance. Useful for anyone running agent ensembles at scale.
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