X-Star: Endogenous Continuous Plasticity and Commutative Latent Deliberation for Compact Neural Architectures

Abstract

Small language models (<= 3B parameters) are severely constrained by parameter capacity, autoregressive order bias, and catastrophic forgetting during continuous adaptation. While existing System-2 approaches rely on expensive external scaffolding (e.g., tree-search verifiers and multi-agent coordination), they introduce massive latency penalties (10x-50x) incompatible with real-time execution. In this work, we introduce X-Star, a 2.04B-parameter native cognitive architecture that implements in-situ endogenous latent deliberation directly within the transformer forward pass. X-Star incorporates three foundational mechanisms:(1) A Vector-Grounded Neural Interface (VGNI) operating at intermediate manifold depth (Layer 11) with a 128-slot Hopfield-style epistemic associative memory;(2) A Commutative Dual-Composition Logic Unit (LBU) that minimizes Lie-bracket commutator norm (||[f, g]||_2) to enforce directional symmetry invariance; and(3) Endogenous Banach Plasticity with periodic homeostatic restitution, proving mathematically that plastic weight drift remains strictly bounded under continuous online learning. Evaluated on an extensive 1,000-question empirical multi-domain audit across 20 canonical benchmarks, X-Star achieves an overall accuracy of 92.60% (926/1000) on an RTX 5060 consumer GPU, achieving 100% in Commonsense Reasoning, 100% in Science/STEM, and 100% in HumanEval, with weight drift locked at <= 2.26. With cuBLAS GEMM microkernel fusion, X-Star maintains real-time throughput of 21.4-25.9 tok/s, demonstrating that compact models can achieve frontier-grade cognitive stability without parameter bloat. Code repository: https://github.com/Ch3nOff/dual-loop-controllerPyPI Package: dual-loop-controller v4.6.1