Structural Rigidity and the 57-Token Predictive Window: A Physical Framework for Inference-Layer Governability in Large Language Models
Researchers propose a physical framework for inference-layer governability in large language models, connecting transformer inference dynamics to constraint-satisfaction models of neural computation
- Identify the geometric regimes of transformer models
- Apply the energy-based governance framework to analyze inference dynamics
- Evaluate the predictive window of 57 tokens for governability
- Analyze the structural rigidity of models for improved safety
AI researchers and engineers working on large language models can benefit from this framework to improve model safety and governability, while ML researchers can apply the findings to develop more robust models
💡 A physical framework can be used to connect transformer inference dynamics to constraint-satisfaction models of neural computation, improving model safety and governability
🚀 New framework for inference-layer governability in large language models! 🤖
Key Takeaways
Researchers propose a physical framework for inference-layer governability in large language models, connecting transformer inference dynamics to constraint-satisfaction models of neural computation
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Abstract:
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. Using trajector
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