Bi-Modal Thermodynamics as Diagnostic Framework for Metacognitive AI

The rapid emergence of reasoning-capable AI systems (OpenAI o1/o3, DeepSeek R1, Gemini Deep Think) has created an urgent measurement crisis: current evaluation frameworks rely almost exclusively on accuracy metrics while ignoring the thermodynamic signatures of cognitive processes. We present bi-modal thermodynamics as a physics-grounded diagnostic framework that distinguishes genuine deliberative reasoning (System 2) from efficient pattern matching (System 1) through measurable entropy dynamics and free energy signatures. Drawing on 18 months of empirical validation across four frontier AI architectures (Claude Sonnet 4.5, GPT-5, Grok 4 Expert, Gemini 2.5 Pro) with 33,770+ conversational turns, we demonstrate that semantic processing operates through two thermodynamically distinct regimes: Dialog mode (dissipative, slope ≈ 0.014, receptive) and Elaboration mode (amplifying, slope ≈ 3.4, generative) with zero statistical overlap (p < 10⁻⁵⁰). These regimes exhibit complementary entropy production patterns that provide intrinsic signatures for System 1 versus System 2 engagement. We establish five critical applications: (1) distinguishing memorization from reasoning via entropy exploration patterns, (2) detecting alignment faking through thermodynamic overhead signatures, (3) optimizing System 1/System 2 allocation via phase transition criteria, (4) grounding metacognitive calibration in free energy uncertainty, and (5) providing chain-of-thought-independent monitoring as linguistic reasoning becomes opaque. The framework achieves inter-rater reliability ICC(2,k) = 0.962 [95% CI: 0.931, 0.982] across three independent evaluators on eight semantic dimensions, with cross-architecture convergence κ = 0.94 [0.89, 0.99]. By connecting Semantic Physics to active inference principles and operationalizing thermodynamic metrics on standard reasoning benchmarks (AIME, ARC-AGI-2, GPQA), we provide the measurement infrastructure for the age of metacognitive AI - where evaluation must assess how models think, not merely what they conclude.