A Neurosymbolic Multimodal Cognitive Architecture (NMCA): A Modular, Control-Oriented Framework for Visual-Symbolic Intelligence (128 Modules, May 2026 Edition)

Description Updated May 2026 New in this version: New Executive Summary (for easier onboarding) Core Cognitive Loop Diagram Improved introduction and framing 471 Pages This work presents the Neurosymbolic Multimodal Cognitive Architecture (NMCA), a conceptual framework for exploring visual-symbolic reasoning in artificial general intelligence (AGI). Executive Summary Neurosymbolic Multimodal Cognitive Architecture (NMCA) A Modular, Visual-Symbolic Framework for Grounded Intelligence Derek Van Derven May 2026 Full 128-Module Version | DOI: 10.5281/zenodo.18784126 The Core Problem with Current AI Large Language Models are incredibly fluent, yet they remain fundamentally blind. A human child reading Lord of the Rings internally sees the scenes. A person says “I left my keys in the car” and instantly forms a grounded mental picture. Humans effortlessly imagine “a pink elephant wearing sunglasses riding a unicycle.” LLMs can generate text about these things but lack genuine scene-based understanding. This creates critical gaps in commonsense reasoning, causal grounding, long-term coherence, and safe agency. Scaling alone is unlikely to solve them. NMCA’s Breakthrough Approach NMCA proposes a fundamentally different foundation: visual simulation as the core substrate of thought, tightly integrated with explicit symbolic mechanisms and human-like cognitive controls. Standout breakthrough-potential modules include: Module 1 – Visual Simulation as Core (Implicit Visual Thought): The system thinks primarily through rich, internal lifelike scene simulations rather than token streams. This enables genuine commonsense reasoning by letting the AGI see and manipulate mental scenes the way humans do. Module 2 – Symbolic Memory & Pegging: A highly scalable, human-inspired mnemonic system designed for near-infinite compositional memory with proper hardware. Thought Throttling (Module 120+): A novel stability mechanism. An autonomous AI running at extremely high speeds with even a tiny nonzero error rate will rapidly accumulate and compound catastrophic errors. By throttling thought to more human-like speeds, the system gains stability and safety. This reduction in speed is compensated by much deeper, higher-quality processing per cycle — richer visual simulations, stronger symbolic grounding, more thorough reflection, and better memory composability. The full 471-page document provides comprehensive detail onthe original first 42 modules, including function, analogies, integration notes, philosophical case studies (regret, forgiveness, symbolic death), safety systems, and realistic 2026 tool mappings. The length comes from the deliberate granularity — each module is described thoroughly to support serious research and sandbox implementation. This architecture started as a 42-module core focused on human-like cognition and was later expanded to 128 modules for greater theoretical completeness, particularly in safety, stability, advanced reasoning, and hybrid integration. The architecture proposes that low-resolution internal visual simulations can function as a substrate for reasoning, enabling systems to manipulate imagined scenes while integrating structured symbolic knowledge. Simulation and Avatar Interpretation Layer A useful interpretation of this architecture is to consider contemporary AI systems as collections of interacting subsystems that collectively support perception, reasoning, and action. Modern systems already include perception modules (vision and audio models), language-based reasoning systems, and environment-based agents operating within structured virtual worlds such as game engines and robotics simulators. From this perspective, embodied environments—including large-scale 3D simulation platforms and game-like worlds with policy-driven agents—can be viewed as partial implementations of broader cognitive system components. Within NMCA, such environments are treated as externalized “scene spaces” in which agents can perceive, act, and update state over time. This aligns with the concept of internal visual simulation, where cognition is supported by dynamically constructed scene representations that can be inspected, manipulated, and stored as memory. In this framing, avatar-based systems in interactive 3D environments may be interpreted as early-stage embodiments of perception-action-memory loops distributed across simulated worlds rather than unified within a single architecture. This hybrid approach aims to support commonsense reasoning, causal inference, and grounded abstraction while maintaining transparency and modular control. The design expands an initial prototype architecture into a 128-module framework, addressing several open challenges in AI research, including: continual learning stability causal and commonsense reasoning uncertainty handling and error monitoring symbolic–latent integration interpretability and verification adversarial robustness Key architectural components include: Visual simulation layer for internal scene generation and reasoning Composable symbolic memory enabling scalable structured knowledge Reflective meta-cognition supporting monitoring and adaptive reasoning Perpetual symbolic processes for ongoing cognitive maintenance Multi-agent symbolic culture modeling Safety and governance mechanisms, including drift monitoring, deception detection, role constraints, and containment safeguards Rather than relying solely on emergent capabilities from scale, NMCA investigates whether explicit modular cognitive architectures may offer greater transparency, controllability, and long-term alignment properties. This document presents a theoretical research blueprint intended for academic exploration, conceptual modeling, and sandbox experimentation. It does not propose autonomous deployment. Any future implementations must maintain strong safety, governance, and human-oversight mechanisms. This is a conceptual design only—not intended for autonomous deployment or operational use. All implementations must preserve ethical safeguards, alignment mechanisms, and narrative integrity. Use is limited to research, sandbox reflection, academic analysis, and human-guided simulation. Version: 2026.02.25 (or v1.0 for the 128-module expansion) License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Additional conditions: All derivatives must prominently cite: "Neurosymbolic Multimodal Cognitive Architecture (NMCA) - by Derek Van Derven (2026)." Permanent link. https://ipfs.io/ipfs/bafybeic4bng6rjof5u2tk5ve5y53rlnopgrqrn3odwdbkk4uzw5wa47h6i Pin and Share. CID: bafybeic4bng6rjof5u2tk5ve5y53rlnopgrqrn3odwdbkk4uzw5wa47h6i Permanent Link 2 https://perma.cc/52EB-2WNQ My ORCID Page: https://orcid.org/0009-0008-4149-5384