Artificial Intelligence

    

Operator-Level Prompting as Soft Behavioural Control in LLMs: Evidence from a 7.4× Manifold Compression

Authors: Claire Nicholson

Large language models often exhibit behavioural variability, adversarial drift, and structural inconsistency across repeated generations. This study presents empirical evidence that a structured prompt operator, referred to as the HelixScribe operator, can reliably stabilise these behaviours without modifying model weights. Across more than 1,100 generations spanning 120 paired business scenarios, the operator induced a compact behavioural manifold approximately 7.4 times smaller than that produced by vanilla prompting, with a centroid shift of 3.35σ in six-dimensional metric space. Outputs remained stable even under conflicting or adversarial instructions, whereas vanilla prompting showed marked degradation. These results suggest that operator-level syntax can act as a form of soft behavioural control, producing fine-tuning-like stability through prompt structure alone.

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[v1] 2025-11-24 01:45:21

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