The Subfield Collapse Hypothesis: A Unified Explanation for OOD Inversions and Syntactic Shortcuts

Modern ML and LLM reliability failures are often narrated as separate phenomena: (i) out-of-distribution (OOD) performance inversions where a “best” model becomes worst for critical slices under deployment shift, and (ii) syntactic shortcuts where models respond based on surface form rather than meaning. This paper proposes a unifying thesis: Subfield Collapse — the evaluation and deployment habit of collapsing a multi-layer operational reality into a single “domain” label and a small set of averaged metrics. When subfields are collapsed, selection pressure rewards correlates that remain stable within the pooled view, even if they are brittle across regimes. We treat latent modes — emergent shortcuts, subgroup cliffs, and template-driven behaviors — as the discovery engine: they are inevitable in large models and become visible only when testing probes beyond means. We formalize a field-stack hierarchy — Fields, Subfields, Overfields, Metafields, and Metaoverfields — and propose Subfield-Stack Evaluation (SSE), a deployment-facing protocol that detects and controls latent modes by measuring dispersion, rank instability, and shortcut reliance across declared subfields and interface conditions. Reliability is reframed as stack-aware closure: models are accepted not because their mean score is high, but because their behavior remains bounded across the declared subfield stack.