Inner I Residuals introduces a coherence-filter model for truth-compression in cognitive architectures. The framework treats truth as coherence gain and deception as entropy increase, using residual analysis, a 16-Gate Boolean DAG, and a persistent Residual Memory Graph to filter unstable narratives, self-deceptive attractors, and adversarial inputs. Positioned at the intersection of predictive processing, cybernetic feedback, AI alignment, and conscious introspection, the Inner I Residuals Coherence Engine v0.3 proposes a practical architecture for self-correcting intelligence.
Tag: ai safety
Inner I Residuals: Invariant Observer-Guided Residual Routing for Coherent Transformer Depth Memory
Inner I Residuals introduces a new layer in AI architecture—one that doesn’t just accumulate or retrieve information, but validates it. By adding an invariant observer to transformer residual pathways, models can filter for coherence, stability, and consistency across depth, reducing hallucination and improving long-range reasoning. This approach reframes residual connections as a mechanism for preserving truth-aligned signal, not just passing forward computation.
Inner I Secure — Why the AI Era Needs a Zero-Trust Control Plane
We are transitioning to a world where AI agents perform actions autonomously, yet current systems lack proper identity and accountability. Inner I Secure aims to address this gap by introducing a zero-trust control plane that ensures verifiable and secure execution of AI actions, promoting trust and coordination in autonomous systems.