Adversarial Provability and False Closure Mitigation in Agentic Review Topologies
When autonomous artificial intelligence systems are tasked with reviewing their own implementations, conventional single-agent evaluation succumbs to confirmation bias, authority expansion, and semantic laundering. In this paper, we demonstrate how an independent adversarial lane ('Specter') operates with inverted incentives—specifically designed to disqualify implementation claims through bypass discovery, exploit path mapping, and boundary pressure. Evaluated across production CI pipelines, this adversarial topology reduced false closure events to zero while maintaining high throughput.