Architectural Proprioception in State Space Models: Thermodynamic Training Induces Anticipatory Halt Detection

Researchers introduce the Probability Navigation Architecture (PNA) framework that trains State Space Models with thermodynamic principles, discovering that SSMs develop 'architectural proprioception' - the ability to predict when to stop computation based on internal state entropy. This breakthrough shows SSMs can achieve computational self-awareness while Transformers cannot, with significant implications for efficient AI inference systems.

• →SSMs trained with thermodynamic loss functions develop anticipatory halt detection with 83.6% correlation between state entropy and halt confidence.
• →The Universal Stopping Signature reproduces consistently across random seeds and generalizes to different tasks, suggesting genuine meta-cognitive abilities.
• →Transformers trained identically show no such coupling, indicating this is an architecture-specific phenomenon unique to SSMs.
• →SSMs demonstrate superior zero-shot transfer capabilities (94.5% vs 86.4% F1 score post-adaptation) compared to Transformers in halt detection tasks.
• →The discovery has practical implications for cost-aware inference, dynamic token budgets, and confidence-based routing in production AI systems.