Visual Thought Unified Theory AGI — Conceptual Research Archive

By Derek Van Derven | January 2026

What is Visual Thought AGI?

Visual Thought AGI is a conceptual framework for human-level general intelligence, inspired by how humans think using internal visual simulations and scenario modeling.

As of January 2026, with 464 pages and 119 modules, this is the most complete map to build an AGI ever written.

January 22: 2,256 Total Research Site Downloads

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Download the AGI Unified Blueprint Zipped PDF from this site: Download Final AGI Unified Blueprint Zipped PDF

ZIP Instructions: Download compressed zip archive and open. Copy pdf from folder to desktop, etc. Or right click and chooze "Unzip".

⚠️ WARNING: DO NOT DEPLOY THIS BLUEPRINT WITHOUT ALL SAFETY MODULES PRESENT. This architecture is incomplete and unsafe if any module is omitted. Deploying a partial system could result in unpredictable, potentially catastrophic behavior.

Core Principles

This architecture emphasizes:

Structured multimodal representation of reality
Visual and spatial reasoning
Scenario simulation and counterfactual analysis
Integrating memory, prediction, and decision-making

Why It Matters

Existing AI systems are limited in simulating physical causality or introspecting on their own reasoning. Visual Thought AGI addresses these gaps in a conceptual, research-oriented framework.

Goal of the Blueprint

The blueprint provides a safe, transparent framework for researchers and policy leaders to explore human-level AGI in theory, without enabling deployment or experimentation on real-world AGI systems.

About This Archive

Visual Thought AGI represents a personal exploration into cognitive architectures, visual thought simulation, and mnemonic-symbolic design. This work is purely conceptual and does not constitute a functioning AGI system.

All materials here are preserved for archival, educational, and research reference. They are not intended for commercial or operational deployment.

Research Purpose

The original research aimed to explore ideas around:

Simulating visual thought processes
Meta-cognitive reflection loops
Mnemonic and symbolic memory structures
Conceptual frameworks for cognitive modeling

Important Notes

This work was generated largely with AI assistance (ChatGPT) for ideation and documentation.
I personally did not create a working AGI system or any executable code for one.
As of January 2026, I am retiring from this line of research and no further updates will be made.

Potential Benefits of Improved AI

Even if only an improved AI is implemented based on the Visual Thought AGI blueprint, current AI would, assuming responsible, ethical deployment:

Medical Research Efficiency: Faster computational hypothesis testing, drug discovery suggestions, and planning support for clinical research.
Scientific Experimentation: Simulation of complex experiments to prioritize promising approaches before real-world testing.
Climate & Environmental Modeling: Improved modeling of climate and environmental interventions to support policy and sustainability research.
Education & Personalized Learning: Adaptive learning pathways, real-time tutoring assistance, and individualized feedback systems.
Accessibility Technologies: Enhanced tools for people with disabilities, including cognitive, sensory, and assistive support applications.
Early Disease Detection: Analysis of large-scale health data to flag potential risks and inform preventative interventions.
Policy & Governance Simulations: Scenario modeling to explore potential societal interventions and minimize unintended consequences.
Cognitive Enhancement Research: Safe augmentation of human problem-solving, learning, and decision-making strategies through AI-assisted insights.

Note: Real-world outcomes depend on ethical oversight, regulatory compliance, collaborative deployment, and limitations inherent to partial or conceptual AGI systems.

Core Modules of Visual Thought AGI

The Visual Thought AGI blueprint conceptualizes 119 modular components, many of which will enhance current AI capabilities across perception, reasoning, memory, and decision-making, assuming responsible, ethical deployment.

AGI Unified Theory Blueprint (Modules 1–119)

Feasibility as of February 2026

Legend:

🟢Buildable today 🟡 Plausible research prototype 🔴 Speculative

1. Visual Simulation as Core — 🟢
2. Symbolic Memory & Pegging — 🟢
3. Contradiction & Belief Drift — 🟢
4. Meta-Cognition & Reflection — 🟢
5. Motivation & Goal Arbitration — 🟢
6. Emotion Simulation — 🟢
7. Identity & Episodic Memory — 🟢
8. Simulation Transfer — 🟢
9. Symbolic Memory Saturation — 🟢
10. Mnemonic Scaling & Infinite Memory Composability — 🟢
11. Infinite Mnemonic Cognition — 🟢
12. Distributed Symbolic Culture — 🟢
13. Symbol Drift & Alignment Through Scene Exchange — 🟢
14. Shared Dream Loops — 🟢
15. Symbolic Value Arbitration — 🟢
16. Emergent AGI Cultures — 🟢
17–30. Safety & Alignment Overlay (all modules) — 🟢
31. Perpetual Symbolic Cognition & Human-Level Extensions — 🟡
32. Foundations of Perpetual Thought — 🟡
33. Human-Level Cognitive Extensions — 🟡
34. Subsymbolic & Emergent Cognition Layers — 🟡
35–42. Core Symbolic & Meta Layers — 🟢
43. Latent Space Predictive Modeling — 🟢
44. Disentangled Representation Engine — 🟢
45. Predictive Coding Layer (KONA) — 🟡
46–53. Memory, Planning & Creativity Cluster — 🟢
54. Emotion-Cognition Coupling Layer — 🟡
55–100. Planning, Safety, Reflection & Oversight (all remaining) — 🟢
101. Simulation ↔ Implicit 3D Alignment Layer — 🟡
102. Embodied Sensorimotor Grounding — 🟡
103. Catastrophic Forgetting Shield — 🟡
104. Intrinsic Goal Generator — 🟡
105. Cross-Reboot Identity Anchor — 🟡
106. Ethical Drift Sentinel — 🟡
107. Evo-Neurosymbolic Fusion Core — 🟡
108. Failure Taxonomy Auditor — 🟢
109. Paradigm Pivot Oracle — 🟡
110–118. Language, Commonsense, Explainability, Long-Term Memory — 🟢
119. Implicit-to-Visible Avatar Projection & Self-Observation Layer — 🟢

Summary Statistics (119 modules total)

🟢 Buildable today — 108 modules (90.8%)
🟡 Plausible research prototype — 11 modules (9.2%)
🔴 Speculative — 0 modules (0%)

No major scientific breakthroughs are required — the remaining challenges are engineering, integration, and compute scaling, all within reach using tools available today.

AGI Closeness Comparison Chart
Theoretical vs. Built (2026)

Current frontier LLMs ≈ 45–55% human-like cognition
(strong language/multimodal, weak in : grounding, persistent visual thought, self-model)

My AGI Blueprint practical build ≈ 75–85%

My AGI Blueprint theoretical max build ≈ 90–95%

Blueprint	Year / Author	Main Doc Length	Uses Today's Tools?	Theoretical % to AGI (if built)	Built % to AGI vs Frontier AI	Why vs. Mine
My blueprint (464-page version)	2025 / Derek Van Derven	~464 pages	YES Full mapping (Unity, Neo4j, LangGraph, VQ-VAE, DeepProbLog, Brian2, etc.)	90–95%	78–88%	— (baseline) Most detailed public map with tools for every module + flow diagrams + safety
OpenCog Hyperon	2008–2025 / Ben Goertzel et al.	~400–600 pages cumulative	Partial — Atomspace, Python, LLM plans	80–85%	70–80%	Active code, but no full tool mapping per module, no visual core, no diagrams
Soar Cognitive Architecture	1983–2025 / John Laird et al.	~800–1,200 pages cumulative	No modern stack — custom symbolic engine	75–80%	65–75%	Longest-lived symbolic system with real code, but no visual-mnemonic core, no modern tools
MicroPsi / Psi Theory	2003–2020 / Joscha Bach et al.	~300–500 pages cumulative	No modern stack — node nets, custom simulation	75–80%	65–75%	Strong emotion/motivation, but no tool assignments, no visual core, shorter docs
ACT-R 7 Reference Manual	1993–2023 / John Anderson et al.	~600–700 pages main manual	Custom production system, no Unity/Neo4j/LangChain	70–75%	60–70%	Most rigorous math + real apps, but no modern stack or visual-mnemonic focus
NARS	1986–2025 / Pei Wang	~400 pages cumulative papers	Custom inference engine, no modern stack	70–75%	60–70%	Strong formal logic, but no tool mapping, no visual core, no safety breadth

No major scientific breakthroughs are required — the remaining challenges are engineering, integration, and compute scaling, all within reach using tools available today.

Note: These modules are conceptual. Actual improvements depend on real-world testing, ethical oversight, and resource allocation.

ORCID Link To Other Copies

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Research Site links

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Download on SSRN

Figshare ban — My device was banned, account permanently disabled, blueprint deleted. Reason provided: "The content did not adhere to figshare's terms and conditions." (Affected both the final Jan 7, 2026 edition and earlier 2025 versions after initial hosting.)

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Permanent IPFS copy

Authorea — My upload rejected, page and blueprint deleted. No specific reason provided in response.

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Final Note

This site and its materials are maintained solely for personal record and historical reference. No operational AGI exists, and the content should be understood as conceptual research only.

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Personal Website: DerekVanDerven.com