E2-Explainer turns multi-agent communication topology from black-box optimization into a causal attribution problem — then prunes the graph

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Topology generators for LLM multi-agent systems are typically optimized against task-level reward alone, so nobody can say why a particular communication edge exists or which subgraph actually carries the collaboration. E2-Explainer is model-agnostic: it uses a Granger-style objective measuring how masking each communication channel changes task outcome and response stability, producing budgeted critical subgraphs, then distills those into an amortized explainer so no edge-level re-evaluation is needed at deployment. The practical payoff is that the identified subgraphs can be executed directly to prune redundant edges, substantially cutting communication cost while holding task performance.