Operational Self-Improvement in a Frozen 14B Language Model on Consumer Hardware: Autonomous Reasoning Constraint Generation, Architectural Diagnosis, and the MERRCURR Pipeline

We present evidence that a frozen 14-billion parameter language model on consumer hardware (Apple Mac Mini M4, 24GB, under $800) can autonomously identify its own reasoning failures, draft corrective constraints, validate them against a regression battery, and deploy them with principal approval. The system produced two autonomously promoted reasoning constraints through the full MERRCURR pipeline, across two independent error classes: dependency assumptions (error recurrence 3→0, Poisson p=0.0498) and meeting occurrence assumptions (clean validation, delta +2, no regressions). A statistically significant discovery emerged: the frozen model responds to reasoning constraints but not to formatting instructions (within-probe dissociation: 88–100% reasoning accuracy vs 0% labelling compliance, Fisher p<0.001). Bare model ablation establishes a 66 percentage point architecture contribution (28%→94%, Fisher p<0.001). Fourth paper in the ATLAS research programme on sovereign AI self-improvement without fine-tuning, weight modification, or cloud dependency.