A Unified Field Theory (UFT) for SLMs and LLMs: From Latent Capability to Governed Subfields

As Small Language Models (SLMs) and Large Language Models (LLMs) move from demonstration to production, the dominant source of risk shifts from isolated model mistakes to system-level closure failures. Real deployments are composite stacks: requests are routed, external knowledge is retrieved, tools are invoked, policies are enforced, and budgets constrain what the system may do under latency, cost, privacy, and safety requirements. In such settings, a response cannot be characterized by generated tokens alone; it must be characterized by the regime that governed admissible actions, admissible data, refusal and escalation semantics, rollback capability, and the evidence produced to justify acceptance. This paper proposes a Unified Field Theory (UFT) for SLMs and LLMs in which fields—not prompts and not weights—are the primary unit of governance, specialization, and evaluation. A field is formalized as a versioned contract that defines jurisdictional scope, tool and data admissibility, budget constraints, policy gates, rollback semantics, receipt requirements, and evaluation obligations. UFT further introduces a lifecycle for capability growth: latent behaviors are detected, captured as subfield patches with bounded constraints, and promoted into governed subfields only when evidence demonstrates boundary integrity, budget compliance, and policy-consistent refusal correctness. Finally, UFT treats receipts as transportable evidence for closure, enabling third parties to verify what regime actually ran and to localize disputes to concrete mismatches in identity, constraints, or artifacts. The result is an engineering substrate for portable trust: specialization becomes controlled evolution, evaluation becomes stack-aware rather than output-only, and operational closure becomes a checkable protocol outcome rather than a narrative conclusion.