Description This work presents the Neurosymbolic Multimodal Cognitive Architecture (NMCA), a conceptual framework for exploring visual-symbolic reasoning in artificial general intelligence (AGI). Many contemporary AI systems rely primarily on large-scale neural training, where reasoning abilities emerge implicitly from data. While powerful, these approaches can limit interpretability, controllability, and long-term reasoning stability. NMCA explores an alternative design strategy based on explicit cognitive structure, combining visual simulation with symbolic reasoning and reflective meta-cognitive processes. 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/bafybeifumtekbpnbli67thlxlxy5k2xnl2frwp7gpnyt427ymntcny3owu Pin and Share. CID: bafybeifumtekbpnbli67thlxlxy5k2xnl2frwp7gpnyt427ymntcny3owu Permanent Link 2 https://perma.cc/93KP-X2FB My ORCID Page: https://orcid.org/0009-0008-4149-5384