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

This paper introduces the Symbolic Control Runtime (SCR), a system-level architecture for deploying
consistency-aware reasoning mechanisms alongside standard transformer language models.

SCR does not propose a new neural architecture or training objective. Instead, it provides a
lightweight 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, latent
control fields, and risk-shaped redirection—into a unified execution environment that reduces
semantic drift, mitigates self-referential collapse, and improves stability in long-context
summarization and dialogue.

By separating symbolic state management from neural generation, SCR enables more efficient use of
transformers, avoids unbounded context growth, and converts catastrophic failure modes into
detectable 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 is
intended as a practical foundation for experimentation, evaluation, and deployment of robust
long-horizon reasoning systems.