Researchers introduce MEA, a Reward-Driven Multi-Agent system for faithful model explanations
Researchers present MEA, a multi-agent framework designed to improve the faithfulness of model explanations across tabular, text, and vision modalities. The system uses a Proposer agent to select and configure explanation tools and an Actor agent optimized end-to-end against faithfulness, transforming outputs into natural language explanations grounded in model behavior. The approach introduces…
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Key points
- MEA uses a Proposer agent to select explanation tools and an Actor agent optimized for faithfulness
- Faithfulness gains of +28% on tabular, +21% on text, and +34% on vision over untrained backbone
- Outperforms post-hoc explainers, agentic, and closed-source baselines across six datasets
Read the original at arXiv cs.AI · by Yuyang Cheng, Raghav Kaushik Ravi, Srivarshinee Sridhar, Sriparna Saha, Akash Ghosh, Chirag Agarwal primary sourceOpen source ↗
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