Neuromantix: A Spiking Neuro-Symbolic Cognitive Architecture for Autonomous General Intelligence

Contemporary artificial intelligence is dominated by dense, autoregressive Transformer Large Language Models (LLMs). While effective for surface-level statistical interpolation, Transformers suffer from three foundational trilemmas: (1) Unsoundness & Confabulation (lack of formal truth guarantees), (2) Plasticity Freeze & Catastrophic Forgetting (static parameters during inference), and (3) Quadratic Energy Scaling (O(N^2) dense attention). Neuromantix is an open, bio-plausible, neuro-symbolic cognitive architecture implemented natively in Rust (v1.85) and GPU-accelerated WebGPU/WGSL compute shaders. This paper provides a complete, mathematically verified specification of the architecture across seven core subsystems:1. GPU Spiking Neural Substrate: 6.6M multi-compartment Leaky Integrate-and-Fire (LIF) neurons and 33M synapses with real-time Spike-Timing-Dependent Plasticity (STDP) and Tsodyks-Markram Short-Term Plasticity (STP).2. Global Workspace Theory (GWT): Coordinated via non-linear Kuramoto phase synchronization (r(t) >= 0.10).3. Integrated Information Theory (Phi): Minimum Information Partition (MIP) mutual information engine (Phi in [4.07, 4.96]).4. JEPA Latent World Model: Joint-Embedding Predictive Architecture with VICReg loss objective and 12-depth latent Monte Carlo Tree Search (MCTS) rollouts.5. Calculus of Inductive Constructions (CIC) Proof Engine: A trusted micro-kernel guaranteeing 100% formal deduction soundness via the Curry-Howard isomorphism.6. CEGIS Program Synthesis: Compile-verified Counter-Example Guided Inductive Synthesis.7. Lifelong Concept Formation & Graph Isomorphism: Semantic centroid clustering and cross-domain relational transfer across 8 scientific ontologies. We formally prove: (1) Dynamical Non-Equivalence to LLMs, (2) Curry-Howard Soundness Guarantee (Zero Hallucination), and (3) The Architectural Completeness & Linear Scaling Theorem.