Patent Filing:Triadic Regulatory Architecture for Hallucination- Resistant AI

This document discloses a novel cognitive architecture for generative AI systems designed to suppress hallucination through internal regulatory mechanisms. The proposed model introduces a triadic architecture consisting of two functionally distinct cognitive agents — one abstraction-oriented and one context-sensitive — governed by a third regulatory layer that enforces mutual constraint before any output is finalized. This system treats non-generation as a first-class intelligence function, enabling hesitation, refusal, or delay in response when confidence exceeds contextual grounding. In contrast to ensemble voting or confidence filtering, this architecture implements tension-based regulation rather than consensus. The patent also includes a secondary mechanism for cognitive counterweighting, which identifies a user's dominant cognitive mode and dynamically activates the opposing mode to introduce productive generative friction — especially useful for ideation, synthesis, and recursive problem-solving. The full document includes: Detailed technical summary Two separable patent claim groups (core triadic system + user counterweight interface) Architectural diagrams: Fig. 1: Triadic Cognitive Architecture Fig. 2: Output Gating Flow Fig. 3: Pressure Trigger Response Model Fig. 4: User Cognitive Counterweight Loop Applications include hallucination-resistant LLMs, recursive agent design, creative synthesis tools, and neurodivergent-aligned cognitive assistants. This document constitutes a public disclosure of an invention under international patent law, intended to establish prior art and prevent subsequent patenting by other entities. This publication is timestamped and made permanently available via Zenodo/GitHub/Pinata to preserve authorship and structural origin.