Thermodynamic Compression: A Physical Metric for Generalization in AGI Architectures

Thermodynamic Compression: A Physical Metric for Generalization in AGI Architectures proposes a physics-grounded framework for understanding generalization in artificial intelligence as a thermodynamic phenomenon rather than a purely statistical property. Contemporary large language models achieve impressive surface-level performance through large-scale parameterization, massive datasets, and high energy throughput. However, such systems remain fundamentally constrained by brute-force scaling and exhibit rapidly increasing energetic cost. This work argues that true generalization corresponds to a distinct physical regime in which internal representations undergo thermodynamic compression: the replacement of large volumes of stored correlations with compact internal generative rules that reduce entropy production and energetic dissipation. Energy–Information Continuity Framework (EIK) The paper builds on the Energy–Information Continuity (EIK) framework, which treats informational reorganization as a physical process coupled to energy flow. Within this framework: Understanding is formalized as a phase transition Characterized by spontaneous symmetry breaking in representational space Accompanied by a reduction in effective dimensionality of internal manifolds Generalization is therefore interpreted as a physical reorganization of internal structure, not merely improved statistical prediction. Compression Ratio per Watt (CR/W) A central contribution of the paper is the introduction of a new evaluation metric: Compression Ratio per Watt (CR/W) CR/W quantifies: How much algorithmic structure a system extracts from data Per unit of dissipated energy This metric directly links informational compression to physical cost and provides an orthogonal evaluation axis to accuracy, enabling the distinction between: Memorization-dominated systems Structurally generative systems even when their observable performance is similar. The Thermodynamic Illusion of LLMs The framework explains why contemporary large language models exhibit a thermodynamic illusion of intelligence: High apparent coherence arises from extremely high informational throughput Sustained by large external energy input While autonomy and internal coherence remain low As a result, thermodynamic compression is negligible despite impressive surface competence. Predictions and Implications The paper derives falsifiable predictions regarding: Energetic efficiency during learning Phase-transition-like learning dynamics Transfer behavior to novel tasks Scaling limits of current architectures It further argues that artificial general intelligence should be identified not by behavioral imitation or benchmark performance, but by anomalously high thermodynamic efficiency on novel tasks. Experimental Protocol A complementary experimental protocol is available, describing a proof-of-concept methodology for testing thermodynamic compression via computational cost pressure and controlled sandboxed agents. Core Perspective This work provides a unifying physical perspective on intelligence as: The capacity of a physical system to replace data with structure under minimal entropy production. For feedback or questions, contact: k.havrankova@proton.me