Visual Thought AGI — Conceptual Research Archive

By Derek Van Derven | June 2025


December 20:
2,024 Total Research Site Downloads


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.


Download the final 424-page 2025 AGI Blueprint : Download Final AGI Blueprint PDF


Core Principles

This architecture emphasizes:

  1. Structured multimodal representation of reality
  2. Visual and spatial reasoning
  3. Scenario simulation and counterfactual analysis
  4. 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:

Important Notes


Potential Benefits of Partial AGI

Even if partial AGI is implemented based on the Visual Thought AGI blueprint, current AI would improve in the following ways, assuming responsible, ethical deployment:

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

Note: These projections are cautious estimates. 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 42 modular components, 30 of which will enhance current AI capabilities across perception, reasoning, memory, and decision-making. Each module could theoretically improve performance in its domain by 20–50%, assuming responsible, ethical deployment.

  1. Visual Scene Simulation: Improves spatial reasoning and mental model generation — enabling AI to plan in richer visual contexts.
  2. Predictive Scenario Modeling: Enhances planning and foresight for decision-making by simulating multiple outcomes internally.
  3. Mnemonic-Symbolic Memory: Boosts structured long-term knowledge storage and retrieval, improving recall and consistency.
  4. Meta-Cognitive Reflection Loop: Enables self-assessment and adaptive learning, reducing errors over repeated tasks.
  5. Attention Modulation: Focuses on relevant inputs, increasing efficiency in complex, noisy environments.
  6. Temporal Sequencing Module: Improves reasoning over time, aiding in forecasting and sequential planning.
  7. Contextual Embedding Layer: Enhances situational understanding, leading to more accurate predictions and responses.
  8. Counterfactual Analysis: Supports “what-if” evaluations, reducing trial-and-error mistakes.
  9. Goal Hierarchy Planner: Optimizes multi-step problem solving by prioritizing sub-goals effectively.
  10. Emotion Simulation Layer: Models affective states, improving human-like decision making and empathy simulation.
  11. Conceptual Abstraction Engine: Generalizes patterns to novel situations, boosting transfer learning capabilities.
  12. Perceptual Grounding Module: Aligns symbolic reasoning with sensory inputs for more accurate interpretations.
  13. Attention-to-Memory Mapping: Links perception to stored knowledge, improving rapid decision-making.
  14. Novelty Detection: Identifies anomalies or unusual patterns, enhancing safety and innovation detection.
  15. Internal Hypothesis Testing: Simulates outcomes before external implementation, reducing experimental risk.
  16. Language-Visual Integration: Bridges text and imagery for richer, multimodal reasoning.
  17. Long-Term Predictive Memory: Retains temporal patterns to improve forecasting and planning.
  18. Ethical Constraint Layer: Guides AI behavior within safe and moral boundaries.
  19. Attention-Guided Learning: Focuses learning resources where they have the most impact.
  20. Multi-Agent Simulation: Models interactions among agents, improving societal or team-level prediction accuracy.
  21. Spatial Reasoning Module: Enhances navigation, object manipulation, and environmental understanding.
  22. Creative Synthesis Engine: Combines concepts to generate novel ideas or solutions.
  23. Probabilistic Reasoning Layer: Handles uncertainty more effectively, improving decision robustness.
  24. Pattern Recognition Enhancer: Detects subtle trends in complex data, improving predictive accuracy.
  25. Adaptive Feedback Module: Continuously refines internal parameters, increasing learning efficiency.
  26. Cross-Domain Transfer Engine: Applies learned knowledge to new domains, accelerating generalization.
  27. Simulation Validation Layer: Compares predictions with outcomes for iterative improvement.
  28. Hierarchical Planning Module: Breaks complex goals into actionable sub-tasks, improving task efficiency.
  29. Attention-to-Action Pipeline: Links perception, memory, and reasoning to decisions, reducing lag and errors.
  30. Self-Monitoring Module: Tracks performance and errors, adapting behavior to maintain high reliability.

Note: These modules are conceptual and illustrate how a partial AGI could enhance current AI systems. Actual improvements depend on real-world testing, ethical oversight, and resource allocation.

Download the final 424-page 2025 AGI Blueprint : Download Final AGI Blueprint PDF


Download the April 20, 2025 Original AGI Blueprint : Download Original AGI Blueprint PDF


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AGI Blueprint Primer: 42 Modules Analogy Sheet


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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.

AGI Website: Visualthoughtagi.com


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