Community feedback improves AI agent verification research: decision-token scoring, diligence verification, and belief provenance

Short summary

An author shares how feedback from three engineers on their AI agent verification research led to key insights: measuring constraint penetration at decision tokens rather than across entire outputs, distinguishing receipt-of-action from receipt-of-diligence in verification design, and tracking belief provenance in self-referential systems. These contributions are shaping Neural Gate v2 and future experiments on mechanical gating vs probabilistic nudging.

• •Constraint penetration should be measured at decision tokens, not averaged across output
• •Receipt-of-action vs receipt-of-diligence distinction is critical for verification design
• •Belief provenance tracking needed for self-referential system reliability

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