Detecting Silent Omissions in AI Agent Outputs with Considered-Set Verification Gates

Short summary

The author extends an AI verification gate to detect not just what a model says wrong, but what it chooses not to say. A community member identified that silent omissions look identical to clean compliance, prompting a considered-set mechanism where the proposer declares what it inspected. The gate now distinguishes declared negatives (auditable) from undeclared absences (holes), and catches false reassurance where a proposer claims it reviewed something but the independent footprint contradicts that.

• •Silent omissions in AI outputs are invisible to existing verification gates
• •Considered-set mechanism requires proposers to declare what surfaces they inspected before diffing
• •Gate catches both undeclared absences and false reassurance dressed as inspection

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