Recursive Adversarial Engine: A Closed-Loop Cognitive Safety Framework for Autonomous AGI Evolution
Keywords:
hallucination mitigation, multi-agent red teaming, fixed-point convergence, ethical alignment, entropy regulationAbstract
Large language models are approaching general-purpose capability, yet three failure modes—hallucination, ethical misalignment, and cognitive rigidity—continue to block reliable deployment, and existing defenses address them only reactively at the output level. This paper presents the Recursive Adversarial Engine (RAE), a cognitive-safety infrastructure that treats contradiction as a source of negative entropy and organizes defense as a five-stage closed loop: definition, adversarial engagement, iterative verification, convergence, and ethical fusing. RAE formulates model self-correction as a recursive fixed-point problem, quantifies cognitive quality through topological metrics, distributes adversarial pressure across a pool of heterogeneous agents, and enforces ethical boundaries through dual-layer circuit breakers. A system specification and internal deployment observations indicate that the design targets self-referential vulnerability false-alarm rates below 1%, consensus variance at or above ln(2), and parallel adversarial pools exceeding 100 agents. We compare RAE with conventional filtering, instruction tuning, and static red teaming, and discuss the framework's theoretical status, evaluation limitations, and deployment requirements for high-stakes domains.
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Copyright (c) 2026 Jianhua Fang
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