Structural Rigidity and the 57-Token Predictive Window: A Physical Framework for Inference-Layer Governability in Large Language Models
Summary: arXiv:2604.03524v1 Announce Type: new
Abstract: Current AI safety relies on behavioral monitoring and post-training alignment, yet empirical measurement shows these approaches produce no detectable pre-commitment signal in a majority of instruction-tuned models tested. We present an energy-based governance framework connecting transformer inference dynamics to constraint-satisfaction models of neural computation, and apply it to a seven-model cohort across five geometric regimes.
Introduction
As artificial intelligence (AI) systems become increasingly integrated into various sectors, ensuring their safety and reliability has become paramount. Traditional methods of AI safety, including behavioral monitoring and post-training alignment, have limitations. Recent empirical studies indicate that most instruction-tuned models fail to exhibit detectable pre-commitment signals, raising significant questions about their governability.
Energy-Based Governance Framework
This article introduces an energy-based governance framework that connects the dynamics of transformer inference to established models of neural computation. Our study examined a cohort of seven different models across five distinct geometric regimes, aiming to uncover the underlying mechanics of their inference behavior.
Key Findings
- Trajectory Tension Analysis: By analyzing trajectory tension, defined as ρ = ||a|| / ||v||, we identified a crucial 57-token pre-commitment window in the Phi-3-mini-4k-instruct model when using greedy decoding on arithmetic constraint probes.
- Model-Specific Dynamics: The pre-commitment signals observed are inherently model-specific, task-specific, and configuration-specific, indicating that while such signals can exist, they are not universally applicable across all AI systems.
- Taxonomy of Inference Behavior: Our research led to the development of a five-regime taxonomy of inference behavior, which includes Authority Band, Late Signal, Inverted, Flat, and Scaffold-Selective. This classification helps in understanding the structural rigidity of different models.
- Predictive Signals: Among the seven models studied, only one configuration displayed a predictive signal before commitment. The remaining models exhibited various failure modes, such as silent failures, late detections, inverted dynamics, or flat geometries.
- Factual Hallucination Insights: We found that factual hallucination resulted in no predictive signal across 72 test conditions, which aligns with the notion of spurious attractor settling when a trained world-model constraint is absent.
Conclusions and Implications
The findings of this study delineate the distinctions between rule violation and hallucination as different failure modes, each requiring unique detection strategies. Internal geometry monitoring proves effective only in scenarios where resistance exists, while the detection of factual confabulation necessitates external verification mechanisms.
This work not only establishes a measurable framework for inference-layer governability but also introduces a taxonomy that can be utilized to evaluate deployment risk in autonomous AI systems. As we move toward more complex AI applications, understanding these nuances becomes essential for developing reliable and safe systems.