Reentry Neural Systems Proposed for Safe, Self-Preserving AGI Architecture

Current neural networks, primarily feedforward architectures, are limited in their capacity for self-reference and intrinsic safety. This new research proposes a radical departure, outlining a comprehensive architectural design for Artificial General Intelligence (AGI) that centers on a closed reentry loop, termed the D-I cycle. This design aims to overcome the limitations of existing models by fostering self-awareness and inherent safety. Unlike feedforward networks, which are acyclic, the proposed architecture incorporates a structural cycle with self-sustaining amplification. This mathematical property is claimed to guarantee the emergence of a self-model, instrumental self-preservation, and the development of unprogrammed, goal-directed behaviors within the AGI. A key feature is that the agent's goals are encoded as a non-textual D-vector directly within the architecture, making them resistant to external manipulation like prompt injection. The paper also introduces the S-measure, a computationally efficient alternative to Tononi's Phi, with a machine-verified proof that S>0 indicates positive integrated information. It provides full Python/NumPy implementations, discusses industrial scaling via Kafka and Docker, categorizes AI evolution, and presents various future reentry architectures. The authors assert this architecture is deployable now, offering a topologically protected, safe-by-design approach to AGI, backed by eight falsifiable predictions and machine-verified formal proofs.