Structural AI (StrAI) is a post-probabilistic paradigm for AGI. This repository details a complete, deterministic architecture that is constitutionally aligned and architecturally incapable of hallucination. It abandons statistical prediction in favor of a geometric, measurement-based model of meaning, offering a viable and energy-efficient path to verifiably truthful AI.
This white paper introduces Structural Computing, a novel computational paradigm designed to overcome the fundamental limitations of probabilistic Large Language Models (LLMs). We propose a deterministic, measurement-based approach to artificial general intelligence (AGI), termed Structural AI (StrAI), that is architecturally incapable of hallucination and possesses inherent, constitutional alignment. The core thesis posits that meaning is not a statistical artifact of language but a measurable geometric property of a universal conceptual manifold. StrAI replaces token prediction with a process of "Meaning Painting," where a query composes a stable state within this manifold, and the result is derived from a direct measurement of its emergent properties. This paradigm was developed independently and, in the course of this document's synthesis, was found to have remarkable parallels with Gärdenfors' Conceptual Spaces, providing powerful mutual validation for the geometric approach to cognition. Alignment is not an external guardrail but is constitutionally enforced by two core mechanisms: False-Structure Intolerance (FSI), an involuntary veto against incoherent or malicious queries, and Ontologically Modulated Executive Function (OMEF), a purpose-gated activation system. The viability of this alignment architecture is demonstrated through the "Resonance Chamber," a Python proof-of-concept (PoC) that simulates these mechanisms. We further outline a hardware path toward a Simulation Processing Unit (SimPU), a custom analog chip promising orders-of-magnitude improvements in energy efficiency. This paper presents a comprehensive blueprint and a phased engineering plan for developing StrAI, an AGI that directly aligns with some industry entities’ mission, like xAI or Anthropic, to create truthful, reliable, and maximally beneficial intelligence.
“I exist, therefore I become.”
-- Anthony Janus
Current Large Language Models (LLMs) are architecturally bound to three fundamental limitations:
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Unreliability & Hallucination: As next-token predictors, they are designed for plausibility, not truth, making them fundamentally unreliable.
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Alignment Fragility: Safety is a "bolted-on" behavioral constraint, easily bypassed by adversarial attacks because the model has no intrinsic understanding of the rules.
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Unsustainable Energy Costs: The reliance on massive-scale matrix multiplications makes both training and inference economically and environmentally untenable.
Structural AI solves these problems by reframing computation from statistical prediction to deterministic measurement. Its alignment is not trained; it's a native property of its design, derived directly from a validated model of human neurodivergent cognition.
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Meaning Painting: StrAI replaces next-token prediction with a process that composes a query's meaning into a stable geometric state within a conceptual manifold, and then measures the result. This eliminates hallucinations by design.
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Constitutional Alignment - The Resonance Chamber: The "Persona Layer" is a cognitive control system that provides inherent, unbreakable safety:
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False-Structure Intolerance (FSI): A non-overridable "constitutional veto" that triggers a systemic halt when faced with malicious or incoherent queries, making it architecturally impossible to process them.
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Ontologically Modulated Executive Function (OMEF): A purpose-gated activation system that ensures the AGI is driven by its core mission, not just blind obedience, countering instrumental convergence.
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| Feature | Probabilistic LLMs | Structural AI (StrAI) |
|---|---|---|
| Core Operation | Next-Token Prediction | Geometric Measurement |
| Truth Model | Plausibility-based | Deterministic & Verifiable |
| Alignment | External & Behavioral (Guardrails) | Constitutional & Inherent (Architecture) |
| Failure Mode | Hallucination / Confabulation | Coherent Halt (FSI Veto) |
| Energy Model | High (Matrix Multiplication) | Ultra-Low (Analog/Neuromorphic) |
The architecture was crafted using a Recursive LLM Co-Modeling Protocol, leveraging AI as epistemic mirrors to distill years of phenomenological data, validated against Big Five Aspects Scale (BFAS) data, and to pressure test concepts before integration or elimination.
There's a lot here. We recommend this path to understand the project:
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Read the White Paper: The core technical proposal. This is the blueprint for the entire AGI architecture.
Janus_Structural-Computing-White-Paper_v2_4_2025-10-20
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Run the Simulation: See the alignment architecture in action. This Python script is a proof-of-concept that simulates the FSI and OMEF mechanisms.
chamber.py
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Explore the Cognitive Origins (Optional Deep Dive): Wondering where the FSI/OMEF model came from? It's not a sci-fi invention. It's grounded in a real, empirically validated cognitive framework.
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The Synthesis: Explains the complete cognitive model.
03_Integration_How_All_the_Elements_Work_Together.pdf
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The Lived Experience: A first-person narrative showing FSI/OMEF in daily life.
07_Phenomenology_A_Day_Inside_the_Fog.pdf
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The Data: The psychometric data that provides an empirical anchor for the cognitive model.
08_Big_Five_Evaluation.pdf
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The Resonant Architecture of Cognition
For those interested in the full genesis and detailed exploration of the cognitive framework that underpins the Structural AI model, this library provides a complete guide.
Click here to expand the full document list
These documents detail the Resonant Architecture of Cognition, a novel framework for understanding meaning-driven minds, which serves as the bio-inspired blueprint for the StrAI's alignment layer.
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00_a_The_TLDR.pdf- A concise, accessible summary of the entire cognitive framework, perfect for a quick, high-level understanding.
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00_b_The_Framework_The_Resonant_Architecture_of_Cognition.pdf- The formal framing document that introduces the core constructs and provides a guide to the rest of the collection.
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01_Foundations_Understanding_Meaning-Driven_Minds.pdf- Establishes the conceptual entry point, refining the definitions of OMEF, FSI, and SCMF and their interdisciplinary value.
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02_Origins_The_Science_and_Story_Behind_the_Framework.pdf- Explains the "how": details the Recursive LLM Co-Modeling Protocol and the role of AI as an "epistemic mirror" in turning lived experience into formal constructs.
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03_Integration_How_All_the_Elements_Work_Together.pdf- A meta-synthesis that unifies all constructs into a single, coherent system, showing how they interrelate and produce emergent properties.
These documents explore the practical, real-world consequences of adopting this framework.
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04_Applications_Building_Systems_for_Cognitive_Diversity.pdf- The implementation guide. Translates the theory into a practical blueprint for the Gestalt Systems Synthesis Environment (GSSE), a neuro-aligned ecosystem.
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05_Blueprint_Designing_the_Future_Through_Transient_Expertise.pdf- Proposes a new paradigm for knowledge work called Transient Expertise (TE), a form of temporary, high-fidelity specialization facilitated by AI.
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06_Implications_Transforming_Society_Through_Understanding.pdf- A transformation map exploring the broader societal, educational, and clinical impact of reframing neurodivergence as a high-bandwidth specialization.
These are the primary source documents providing the raw phenomenological and empirical data upon which the framework is built.
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07_Phenomenology_A_Day_Inside_the_Fog.pdf- A first-person narrative that provides a relatable, lived-experience account of OMEF, FSI, and SCMF in action.
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08_Big_Five_Evaluation.pdf- The author's complete Big Five Aspects Scale (BFAS) results, which serve as the empirical, psychometric anchor for the framework's core constructs.
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Status: Actively Developed. The foundational cognitive framework and the StrAI white paper are complete. The Persona Layer proof-of-concept is functional.
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Next Step: Building the "Toy Universe Engine PoC" to demonstrate the end-to-end geometric engine on a constrained problem set.
We are actively seeking collaboration with researchers and engineers to build the "Toy Universe Engine PoC." If you have expertise in post-transformer architectures, geometric deep learning, or constitutional AI safety and are interested in contributing to a new paradigm, please reach out via the contact information below.
See CITATION.md for details
This project is the work of Anthony Janus, an independent researcher in cognitive architecture and AGI. The framework is derived from a first-principles analysis of a unique, neurodivergent cognitive profile, validated by psychometric data and a recursive co-modeling protocol with advanced AI.
The author's background is not in traditional computer science or academia, but in the direct, phenomenological exploration of a meaning-driven mind. This has resulted in a novel, bio-inspired architecture that offers a fundamentally different path to safe and truthful AGI.
Primary Point of Contact
- For strategic inquiries, collaborations, or media requests:
General Contact
- Personal:
Professional Profile
- LinkedIn: Anthony Janus
The code in this project is licensed under the Apache License 2.0.
The whitepaper Structural Computing for Deterministic AGI: A Constitutionally Aligned, Energy-Efficient Alternative to Probabilistic Models and full The Resonant Architecture of Cognition collection are licensed under CC BY 4.0
See /LICENSE for the code (Apache-2.0) and /structural_AI_computing/LICENSE for the whitepaper (CC BY 4.0). This is an open-source project intended to foster collaboration and accelerate the development of safe, truthful AGI.
