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This paper proposes the Explicit Belief Multi-Agent System (EB-MAS), which externalizes belief states into a structured substrate, uses belief operators to propose candidate updates, and applies a Cognitive Firewall to bound or reject updates that are weakly evidenced but strongly driven by vulnerability cues.
This work introduces PrefixGuard, a neuro-symbolic method that uses learned events to connect online failure warning with finite-state execution diagnosis with finite-state execution diagnosis in large language model (LLM) agents.
FragileFlow is introduced, a plug-in regularizer that uses a calibrated margin buffer to identify correct-but-fragile predictions and organize their off-class probability mass into a class-wise vulnerable-risk matrix and provides the first PAC-Bayes upper bound for this margin-aware error-flow object.
The Reasoning Backroom is established as a general AI provenance problem whose audit requires intervention because of a pervasive provenance failure in multi-agent systems.
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