A Symbolic Control Runtime for Consistency-Aware Reasoning with Transformer Backends

This paper introduces the Symbolic Control Runtime (SCR), a system-level architecture for deployingconsistency-aware reasoning mechanisms alongside standard transformer language models. SCR does not propose a new neural architecture or training objective. Instead, it provides alightweight symbolic runtime that externalizes long-horizon state, constraints, and risk estimation,treating transformer models as stateless generative backends rather than global memory systems. The runtime integrates previously proposed mechanisms—holonomy-based consistency tracking, latentcontrol fields, and risk-shaped redirection—into a unified execution environment that reducessemantic drift, mitigates self-referential collapse, and improves stability in long-contextsummarization and dialogue. By separating symbolic state management from neural generation, SCR enables more efficient use oftransformers, avoids unbounded context growth, and converts catastrophic failure modes intodetectable and controllable degradation. The design is compatible with existing transformer models,KV caching, and standard inference workflows. This work serves as a systems unification of prior consistency-aware transformer research and isintended as a practical foundation for experimentation, evaluation, and deployment of robustlong-horizon reasoning systems.