Maat: Independent Deterministic Contract-Based Governance for Multi-Agent LLM Workflows

arXiv:2609.34017 · cs.SE, cs.AI, cs.MA · Submitted 2026-09-27 · Read on arXiv

cs.SE, cs.AI, cs.MA

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/Lorelys/maat-benchmarks

Project page: https://maat.synwe.ai

Terminology

• Benchmark — 45× in this paper · explained in 2 episodes
• Verification — 12× in this paper · explained in 2 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Multi-Agent System — 8× in this paper · explained in 5 episodes
• Provenance — 8× in this paper · explained in 3 episodes
• Failure Modes — 5× in this paper · explained in 3 episodes
• Hallucination — 5× in this paper · explained in 3 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Analyzer — 3× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Hallucination Detection — 2× in this paper · explained in 2 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Agentic AI — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Why Do Multi-Agent LLM Systems Fail?
• Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement
• Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems
• Towards a Science of Scaling Agent Systems
• Aegis: Automated Error Generation and Attribution for Multi-Agent Systems
• MASPrism: Lightweight Failure Attribution for Multi-Agent Systems Using Prefill-Stage Signals
• Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection
• Detecting Silent Failures in Multi-Agentic AI Trajectories
• VerifyMAS: Hypothesis Verification for Failure Attribution in LLM Multi-Agent Systems
• AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security
• From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration
• PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation
• Agent-as-a-Judge
• Agora: Git as Shared Memory for Collective AutoResearch
• Agent-as-a-Judge: Evaluate Agents with Agents

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