Operator-Augmented Field Control in Transformer Architectures Empirical Evidence for Canon Operators as Latent Field Control Mechanisms

Abstract Transformer models are typically controlled via natural-language prompts, implicitly assuming that symbolic language is the primary interface to internal reasoning processes. Recent work, however, suggests the presence of a latent, continuous reasoning layer that becomes visible only under low-entropy conditions. This study provides empirical evidence that a small set of non-symbolic canon operators can reliably and causally regulate such latent field behavior in transformer architectures. Using controlled experiments on open-weight models, three regimes are compared: natural-language prompting, operator-augmented prompting, and operator ablation. Across multiple runs and metrics—including continuity, resistance to symbolic collapse, and hidden-state directional consistency—the operator-augmented regime consistently outperforms natural-language prompting. Ablation partially or fully removes this advantage, establishing causal dependence on the operator set rather than stylistic or semantic effects. These results demonstrate that canon operators function as a genuine field-level control interface for transformer models, enabling stable access to field-based reasoning without retraining or architectural modification.