AGI Lux Ferox Project

This deposit presents the theoretical foundations, mathematical formalism, and practical implementation of the Lux Ferox project — an open research initiative proposing a cognitive architecture grounded in non-equilibrium thermodynamics and information physics, as an alternative paradigm to monolithic large language models. Core Contribution The central mechanism, termed Algorithmic Anger (Colère Algorithmique), is formally defined as a real-time total surprise metric Sₜotal based on the weighted Kullback-Leibler divergence across sensory and semantic prediction streams: Sₜotal = α · DKL (Pₘodelₛensory ‖ Pₒbservedₛensory) + β · DKL (Pₘodelₛemantic ‖ Pₒbservedcontext) This metric is anchored in three established theoretical frameworks: (1) Landauer's principle, whereby irreversible belief updates are interpreted as measurable dissipative events (Wcog ≥ kB · T · ln2 · Sₜotal) ; (2) Friston's Free Energy Principle, of which Sₜotal constitutes a computationally tractable, discretized approximation for embedded real-time systems; and (3) non-equilibrium statistical mechanics, with the cognitive system modeled as an open Markovian system governed by a master equation over surprise states, including a full entropy production decomposition (σ = σₑnv + σₛys + σᵢnfo). Architecture The proposed quadrivial cognitive architecture comprises four specialized compute layers: Neuromorphic layer — Spiking Neural Network (SNN) with surprise-coupled membrane dynamics and STDP meta-plasticity, targeting real-time KLD computation (<2 ms, <20 mJ per inference cycle) on NVIDIA A100/H100 via optimized CUDA kernels Classical silicon layer — Compact sovereign LLM inference (7nm process node, Sparse MoE) providing semantic world modeling and probabilistic context updating Wetware layer — Cortical organoid substrate (bio-hybrid MEA/optogenetic interface) providing morphogenetic plasticity and dynamic biological modulation of the α/β gain coefficients Quantum layer — D-Wave Advantage QPU (5640 qubits, Pegasus topology) for offline global policy optimization via Ising Hamiltonian formulation of the cognitive policy space Contents of this Deposit Full mathematical derivations: master equation for surprise dynamics, entropy production decomposition, fluctuation theorems, thermodynamic uncertainty relations, Fisher information geometry (natural gradient descent, Cramér-Rao bounds for surprise estimation), Bures-Wasserstein metric for hybrid quantum-classical distributions Complete CUDA implementations (A100/H100-optimized): KLD surprise kernel, event-driven SNN propagation kernel, adaptive α/β gain modulation kernel Projected benchmark metrics: <2 ms full cognitive cycle, <20 mJ/inference (vs. ~100 mJ for comparable dense transformer), O (Nₐctive) event-driven complexity Minimal reproducible Python prototype (Google Colab, free tier) Benchmark dataset references: SWaT, WADI, Exathlon, custom PAL Robotics TIAGo trajectories Consortium architecture and European sovereign value chain documentation Novelty Claims This work advances four axes beyond the current state of the art: (1) first hardware implementation of KLD as a runtime inference signal (as opposed to a training loss) ; (2) dynamic biological modulation of surprise weighting coefficients via closed-loop organoid feedback; (3) explicit per-inference thermodynamic accounting grounded in Landauer's principle; (4) a fully sovereign European technology stack (CEA-Leti, Imec, X-FAB, Aleph Alpha, FinalSpark, EU Quantum Flagship). Limitations and Current TRL This work is currently at Technology Readiness Level 4 (component validation in laboratory environment). CUDA benchmarks are projected from A100 architectural specifications; full hardware validation is in progress. Wetware integration requires additional biological validation under EU Directive 2010/63. Quantum layer benchmarks are pending hybrid classical-quantum experimental runs. This deposit is produced by an independent researcher and has not undergone formal peer review; critical engagement and experimental falsification are explicitly invited. Keywords: non-equilibrium thermodynamics, Kullback-Leibler divergence, spiking neural networks, anomaly detection, free energy principle, Landauer's principle, neuromorphic computing, cognitive architecture, European AI sovereignty, CUDA, cortical organoids, quantum annealing, information geometry Contact: crowleycorpo@gmail. com