rank-communication-edges-masking-effect-then-prune-budget TEC-0288 · proposedRank communication edges by masking effect, then prune to bu
Mask each communication edge in a generated multi-agent graph, score it by the drop in task score plus the rise in semantic entropy of the final response, keep the top-ranked edges under a pruning budget, and distill those subgraphs into a
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Mask each communication edge in a generated multi-agent graph, score it by the drop in task score plus the rise in semantic entropy of the final response, keep the top-ranked edges under a pruning budget, and distill those subgraphs into a Filed by the drafting stage from Discovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference (2608.12921) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Accuracy and measured online token usage versus each unpruned generator across 24 generator-dataset cells: tokens fell in all 24 (10.6%-44.0%), accuracy rose in 16; ablations show removing the causal masking signal costs about 4 accuracy points on MMLU and HumanEval and most of the token saving, and removing semantic entropy costs about 2-3 points.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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Sources
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