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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Discovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference
TL;DR
E2-Explainer is a model-agnostic framework that enhances the interpretability of communication topologies in large language model (LLM)-based multi-agent systems. It identifies critical communication subgraphs, reducing redundancy while maintaining task performance.
✦ Why It Matters
Implement E2-Explainer to improve communication efficiency in your LLM-based multi-agent systems today.
Key Takeaways
- Utilize E2-Explainer for better communication topology insights.
- Apply causal attribution to identify critical communication paths.
- Reduce communication costs without sacrificing performance.
- Leverage budgeted subgraphs for efficient post-hoc explanations.
How It Works
E2-Explainer formulates the explanation of communication topologies as a causal attribution problem. It uses a Granger-style objective to evaluate the impact of masking communication channels on task outcomes, allowing it to identify which communication edges are essential for maintaining performance.
The resulting compact communication subgraphs are then distilled into an amortized explainer, which provides efficient explanations without the need for repeated evaluations.