Position: Multi-Agent Explainability Needs Contracts Before Methods

摘要

Multi-Agent Systems (MAS) are deployed at unprecedented scale—from warehouse robot fleets to autonomous vehicle networks to collaborative LLM agents—yet methods for explaining their behavior remain fragmented and underspecified. We analyze 2,381 MAS-related papers from top machine learning venues (2021–2025) and find systematic gaps: 65% omit stakeholder specifications, 76% lack quantitative evaluation bounds, and 99% ignore auditability requirements. These gaps render current MAS XAI research non-comparable, non-reproducible, and disconnected from deployment requirements. We argue that MAS XAI research requires explicit specification of two contracts before developing methods. The **Research Contract** defines six elements: explanandum, stakeholder, intervention unit, evaluation bounds, adversarial context, auditability. The **Agent Contract** defines expected behaviors through obligations, permissions, prohibitions, violation criteria, and accountability chains—providing the baseline against which deviations are explained. These contracts are method-agnostic and architecture-agnostic, applicable to LLM-based, learning-based, and hybrid MAS. Through case studies spanning warehouse robotics, autonomous vehicles, and LLM agent systems, we demonstrate that contracts transform vague post-hoc descriptions into verifiable, actionable, and comparable explanations. We call on researchers to adopt contracts in their work, conferences to encourage specification in submissions, and platforms to integrate contract templates into MAS benchmarks.