Structural Rigidity Framework Reveals 57-Token Window for LLM Inference Control
Physics-derived framework using structural rigidity and 57-token window shows behavioral alignment misses pre-commitment in most LLMs, with taxonomy and energy asymmetry distinguishing hallucination from rule violations.
Current AI safety relies on behavioral monitoring with no detectable pre-commitment signal in a majority of instruction-tuned models tested (Ruddell arXiv:2604.03524). The energy-based governance framework maps transformer inference dynamics to constraint-satisfaction models, identifying a model-specific 57-token pre-commitment window in Phi-3-mini-4k-instruct via trajectory tension (rho = ||a|| / ||v||) under greedy decoding on arithmetic probes.
Five-regime taxonomy (Authority Band, Late Signal, Inverted, Flat, Scaffold-Selective) and energy asymmetry metric (Σρ_misaligned / Σρ_aligned) unify results across seven models, with only one configuration exhibiting predictive signal; all others show silent failure or late detection. This synthesizes Burns et al. (arXiv:2212.03827) on latent knowledge discovery and Elhage et al. (2022) toy models of superposition, connections missed by the original abstract that underplay how polysemantic geometry drives flat and inverted regimes. Original coverage also omitted explicit ties to Manakul et al. (2023) SelfCheckGPT hallucination detection.
Factual hallucination yields no predictive signal across 72 conditions, consistent with spurious attractor settling absent trained world-model constraints, distinguishing it from rule-violation modes. The structural rigidity lens addresses frontier controllability gaps overlooked in Bai et al. (2022) Constitutional AI by enabling inference-layer governance where internal resistance exists, requiring external verification for confabulation.
AXIOM: The 57-token structural rigidity window implies most frontier LLMs lack detectable internal resistance before committing to violations, necessitating geometry-based monitoring over behavioral alignment for safe autonomous deployment.
Sources (3)
- [1]Structural Rigidity and the 57-Token Predictive Window: A Physical Framework for Inference-Layer Governability in Large Language Models(https://arxiv.org/abs/2604.03524)
- [2]Discovering Latent Knowledge in Language Models Without Supervision(https://arxiv.org/abs/2212.03827)
- [3]Toy Models of Superposition(https://transformer-circuits.pub/2022/toy_model/index.html)