Structural Rigidity and the 57-Token Predictive Window: A Physical Framework for Inference-Layer Governability in Large Language Models

Pre-commitment signals are rare, not universal: Only one out of seven models tested showed detectable signals before committing to outputs, meaning current safety monitoring approaches may fail to catch problems before they occur in most instruction-tuned models.

Different models fail in different ways: The research identified five distinct failure patterns (Authority Band, Late Signal, Inverted, Flat, and Scaffold-Selective), suggesting that a one-size-fits-all governance approach won't work—safety measures need to be tailored to how specific models behave.

Hallucination and rule-breaking are separate problems: The study found that factual hallucinations produce no predictive warning signals across 72 test conditions, indicating they stem from a different mechanism than rule violations and may require different safety interventions.

Current behavioral monitoring has blind spots: The paper's core finding is that post-training alignment and behavioral monitoring produce "no detectable pre-commitment signal in a majority" of models tested, suggesting these widely-used safety approaches may not catch problems early enough.

Timing matters for AI safety: The identification of a specific 57-token window where signals can appear (in at least one model) suggests that the opportunity to intervene in model reasoning may be narrow and model-dependent, requiring more precise safety mechanisms than currently exist.