NMCA · VISUAL-THOUGHT AGI
What is AGI?
From Wikipedia:
“Artificial general intelligence (AGI) is a hypothetical type of artificial intelligence that matches or surpasses human capabilities across virtually all cognitive tasks.
Unlike artificial narrow intelligence (ANI), whose competence is confined to well-defined tasks, an AGI system can generalise knowledge, transfer skills between domains, and solve novel problems without task-specific reprogramming.
Creating AGI is a stated goal of technology companies such as OpenAI, Google, SpaceXAI, and Meta.
A 2020 survey identified 72 active AGI research and development projects across 37 countries.”
Thinking Is More Than Seeing Images
NMCA is not simply designed to see images or construct visual scenes. Its central concept is that thinking itself can involve the creation, manipulation, and re-entry of internal simulations.
Language, concepts, memories, and imagined possibilities can be represented within an internal world model, where scenes, relationships, and events can be mentally explored and transformed.
The NMCA architecture models an AGI avatar in a simulated environment, not an embodied AGI.
However, avatar to embodiment transfer recommendations are discussed.
The AI depicted in the movie mentioned below are embodied AI, or ASI (Artificial Superintelligence).
Imagine the Advanced AI in A.I. Artificial Intelligence (2001)
Watch the Visual Thought analogy in the A.I. (Artificial Intelligence) movie
In the film's closing sequence, images of memories and scenes appear across the faces of the advanced AI as they explore David's memories.
As the first AI sees David's life memories, the others touch the shoulder of the next to see the same memories, which is similar to the function of "dream sharing" in my design.
Imagine extending that visual idea: what if those images represented the visible rendering of a much deeper internal process?
In a Visual-Thought AGI, thinking or communicating involves structured internal visual simulations. Rather than continuously rendering every thought as a complete image, the system maintains a more compact, structured representation in code and render it when needed for visualization, inspection, communication, or explanation.
The Representation Is Not the Rendering
The internal scene is not merely an image presented to the AI. It is a representation the AI is intended to work with as part of its thought process. A visible image would be a window into that process—not the process itself.
NMCA: A Neurosymbolic Multimodal Cognitive Architecture
A modular, control-oriented framework for visual-symbolic intelligence
128-module specification | 42-module cognitive core | October 2026 final 504-page (AI-readable edition)Independent architectural claim. Judge it by the tests below.
Interactive demo · NMCA Blueprint PDF · NMCA Addendum PDF ·
Original 46-page blueprint (20 April 2025) — DOI 10.5281/zenodo.15313197
No patent application was filed. A copy was sent to the USPTO by email.The public record is the Zenodo deposit.
Zenodo - 46-page first version What is AGI?
NMCA is a documented cognitive architecture centered on a persistent, re-enterable internal spatial scene as the working state of cognition.
What NMCA Is Designed to Do
NMCA is a multimodal cognitive system centered on an Internal 3D Scene Builder as the core substrate of thought.
The architecture is designed to maintain and operate on a persistent internal spatial world rather than treating visual information as something attached to a primarily linguistic or predictive process.
Key elements specified in the original April 20, 2025 disclosure include:
- Persistent internal visual/3D scene representation used for reasoning
- Memory encoding that stores visual scenes for future recall
- Internal visual simulation synchronized with observation
- Symbolic scene representations tied to memory and belief structures
- Contradiction detection and belief updating
- Planning inside the 3D spatial model
- Simulation of routes and actions before execution
- Feedback from real observation that triggers replanning and contradiction resolution
- Multiple simulation branches
- An explicit computational loop: Input → Internal Simulation → Action → Reflective Update
A concrete example from the original disclosure is an agent that loads a location, such as a refrigerator, from prior visual memory; internally simulates the route and interaction; executes the plan; discovers that reality differs from the simulation; and generates a new subgoal.
The central architectural idea is the combination of a persistent, updatable internal 3D/visual scene with memory, belief updating, mental simulation, planning, and action. The scene is not merely an output or visualization. It is the working state on which these processes operate.
This architecture was first publicly formalized and released on April 20, 2025, in the original 46-page NMCA blueprint.
Later technical work describing related capabilities—such as persistent 3D scene memory, multi-hypothesis imagination of unobserved regions, sequential belief updating from observations, and mental simulation for embodied planning—should cite that prior public disclosure where applicable.
Final version. No further additions will be made. The author stepped away from AI/AGI research in September 2026.
The claim
Current systems are fluent predictors. They can describe a scene and generate an image of a scene. They have not been shown to keep a private, re-enterable spatial scene that remains after the original description is gone, and to use that scene as the medium of thought.
Retrieving where an object was is not the same as returning to the place where the object is.
Humans do this routinely. A short phrase is often enough to construct an internal scene that can then be inspected, moved around in, and used. Einstein’s thought experiments and Tesla’s reported mental rehearsal of machines are familiar cases of the same capacity used as a working method.
NMCA treats that capacity as architectural, not as an emergent side-effect of more tokens.
This page is a summary. The specification is the 504-page NMCA blueprint. The WebGL demo is an illustration of the distinction, not a running implementation of the 128 modules.
How It Works
Thought is not a token stream with a picture attached. The system constructs an internal scene and keeps it. Objects in that scene have identity and location.
Symbols are attached to those objects. When the system is asked where the object is, it queries the scene, not a stored sentence about the object. Reflection, planning, and memory update operate on that same state. Language is an interface into the scene, not the place thought happens.
A human reading fiction is the same distinction. The page is text. What the human reader keeps is an inner world that updates as the sentences arrive. A system that only continues the text is not doing what the reader is doing.
The first design used an explicit rendered 3D scene. The final blueprint does not keep a continuously rendered world.
The working state is an implicit scene: a structured spatial representation in code. It can be queried, updated, and re-entered without drawing it. A render is optional and on demand — for inspection, debugging, or a human-facing view.
That is a speed and resource choice. Humans do not photorealistically render an inner room either. They keep a usable spatial model and bring parts into focus when needed. NMCA is specified the same way: implicit by default, rendered only when something has to be shown.
Why 42, then 128
- Modules 1–42 — cognitive core. Visual simulation is the substrate.
- Modules 43–119 — integration with perception, latent models, learning, embodiment, and collaboration.
- Modules 120–128 — thought throttling, drift control, oversight, alignment, containment.
The later modules do not replace that core. They extend it into areas that current systems treat as training side-effects: latent-space bridges, continual learning, causal and counterfactual reasoning, embodiment after the sensor stream changes, multi-agent coordination, robustness, and long-term stability.
Length comes from granularity. Each module is specified so a researcher can inspect the function, not infer it from a trained weight matrix.
The tests
Persistent scene test
Start from this prompt:
Construct a scene with a blue vase and a red apple on a wooden table.Remove the original description. Wait. Then require the system to re-enter the scene, report where the apple is relative to the vase, change viewpoint, move an object relative to itself, take another entity’s perspective and return, and keep object identity with no new sensory input.
A system that only retrieves the stored sentence fails. The object has to still be in a place.
Embodiment
Embodiment in this architecture starts inside the scene, not in the first robot trial.
The system is given an avatar in the internal simulation and is trained there: location, reach, hiding, return, and self-dialogue against the same scene. External cameras are perception of the world. The internal scene is the mental workspace — including the system’s own position in that workspace.
After the avatar loop is stable, the same scene operations are transferred to real-world robotics tests. Modules 43–119 are the integration layer for that transfer: perception, latent models, learning, embodiment, and collaboration.
Two cameras, two jobs. Outside: what is in front of the sensors. Inside: the place thought is happening, and the self talking to that place.
The designed default is an internal scene plus external perception. If visual construction is unavailable, the same loop should still run on a non-visual spatial or symbolic workspace. That is a backup mode, not a different architecture.
Scientist Test
Give the system an unsolved problem, not a finished analogy and not a famous discovery to try to solve again independently.
Historical cases show the method, they are not the exam: Einstein’s chasing-light scene, Tesla’s assembled machine, Newton’s mountain cannon, Darwin’s tree. Those representations made the discoveries possible. If a past solved problem is given, it will only be answered from stored training text.
The scored problem must be novel and difficult enough that search, calculation, and verbal reasoning are not sufficient by themselves. The system may try those routes first. If they fail, it must construct a new visual or spatial working model and work from that.
It is scored by whether it solves the problem.
NMCA treats both tests as requirements of general intelligence: inhabit an internal world, and use it to form new representations.
How current approaches handle this
Pure scaling + scaffolding
Strength: highest current capability.
Limit: prediction, retrieval, and tools. No native requirement for a persistent scene as the medium of thought. Identity is reconstructed from records.
World models / JEPA-style predictors
Strength: reject language-only sufficiency; build internal dynamics for planning.
Limit: latent and predictive. Re-entry, identity continuity, and scene-pegged memory are not first-class by default.
Neurosymbolic hybrids
Strength: more inspectable structure.
Limit: the predictive engine usually remains primary. Symbols help; they are not the place thought happens in.
NMCA
Visual / implicit-3D simulation is the substrate. Symbols are pegged to scene entities. Identity, reflection, contradiction handling, and re-entry sit in the same loop.
On this specific goal, NMCA is the most explicit written architecture. That is a design claim. It is not a claim of empirical superiority.
Predicted walls
Each funded trajectory can keep improving capability metrics and still fail the same probe: after context is cleared, does the system know where the object is because a scene continues, or only because a sentence was stored?
- Scaling + agents: better tools and memory lookup. Presence stays reconstructed.
- World models: better “what happens next.” Still a predictor, not a place that can be re-entered.
- Memory banks: the agent queries records about a scene. It does not inhabit one.
- Hybrids: structure remains a helper unless the scene becomes the substrate.
- Robotics / SLAM: positions are known while sensors are live. The map is not turned into the place where thought continues after the stream ends.
The apple distinction
A language model can complete patterns around the word “apple,” describe fruit, or generate a picture. That is not the same as keeping an apple in a private scene and later knowing where it is because the place remained.
When the original sentence is gone, current systems typically do one of three things:
- hold the sentence in context
- store the sentence and retrieve it
- update a short-lived latent state
Fluency conceals the difference. It does not close it.
Already Done?
A robot map of a house, a next-frame predictor, or a picture of an apple is not the tests above.
Those systems track the world in front of sensors, or they store the sentence. They do not keep a private place after the words are gone.
In the public machine-architecture literature examined for this record, no document dated before April 20, 2025 specifies those tests. 10.5281/zenodo.15313197
Downloads and records
Full NMCA Blueprint (504-page PDF)
Zenodo — NMCA Blueprint
NMCA Document Modules Addendum
Zenodo — Addendum
OSF
Academia
ORCID
Google Scholar
Final 504-page blueprint DOI: Zenodo DOI 10.5281/zenodo.23417247
Final 504-page blueprint IPFS CID: bafybeib2awby7hvgk7wqajabdt5hs7hu2g6ewz5yzriforlvysy5mpuukm
Original 46-page blueprint DOI: Zenodo DOI 10.5281/zenodo.15313197
Original 46-page blueprint IPFS CID: bafybeifjiyzm6tmk2jj27tjvbpqgdqpjm5loeon6y6ikxzeek3ff2vbdey
IPFS DownloadUSPTO prior-art copy filed. No patent pursued.
Download 46-page blueprint directlyAddendum to the 504-page blueprint DOI: Zenodo DOI 10.5281/zenodo.22352561
Addendum to the 504-page blueprint IPFS CID: bafkreibse76prz3wypp3tcveczq4iuoifmk6sp5e73cbqiz2sry6b3hmvy
IPFS DownloadMirror: visualthoughtagi.netlify.app
Publication & Attribution Record
Original public NMCA blueprint: April 20, 2025
Derek Van DervenThe original NMCA architecture was publicly disclosed on April 20, 2025 in a 46-page blueprint. Later publications expanded and formalized the architecture into the 2026 NMCA specification.
2026 attribution requirement:
All derivatives of the 2026 NMCA work must prominently cite:Cite the original disclosure:
Van Derven, D. (2025). NMCA Original Blueprint (46 pages). https://doi.org/10.5281/zenodo.15313197Cite the 2026 specification:
Van Derven, D. (2026). Neurosymbolic Multimodal Cognitive Architecture (NMCA). https://doi.org/10.5281/zenodo.23417247License (2026 specification): CC BY-NC-SA 4.0.
Theoretical research blueprint for academic discussion, sandbox simulation, and human-guided exploration. Not intended for autonomous deployment.
