AI

MEA: A Reward-Driven Multi-Agent System for Faithful Model Explanations

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Briefings

MEA is a multi-agent system designed to generate faithful model explanations by having an Agent optimize against faithfulness rewards. The system outperforms post-hoc explainers and closed-source baselines across six datasets, achieving +28% faithfulness gains on tabular data, +21% on text, and +34% on vision. The framework allows Proposer agents to select tools while optimizing Actors end-to-end, addressing the knowledge barrier in high-stakes domains.

Key points

• MEA uses a Proposer agent to select explanation tools and an Agent optimized end-to-end against faithfulness rewards.
• The system achieves +28% faithfulness gains on tabular data, +21% on text, and +34% on vision compared to untrained backbones.

Context

This research addresses the lack of expertise needed to wield post-hoc explanation methods effectively in high-stakes domains, proposing MEA as a scalable interface for natural-language explainability.

Limitations

Findings are based on six datasets and reward-driven optimization; specific performance metrics for each dataset are provided but no further details on dataset composition or evaluation protocols are available.

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