Large Language Models (LLMs) frequently "hallucinate" plausible but incorrect assertions, a vulnerability often missed by uncertainty metrics when models are "confidently wrong." We propose DiffuTruth, an unsupervised framework that re-conceptualizes fact verification via non-equilibrium thermodynamics, positing that factual truths act as stable attractors on a generative manifold while hallucinations are unstable. We introduce the "Generative Stress Test": claims are corrupted with noise and reconstructed using a discrete text diffusion model. We define Semantic Energy, a metric measuring the semantic divergence between the original claim and its reconstruction using an NLI critic. Unlike vector-space errors, Semantic Energy isolates deep factual contradictions. We further propose a Hybrid Calibration fusing this stability signal with discriminative confidence. Extensive experiments on FEVER demonstrate DiffuTruth achieves a state-of-the-art unsupervised AUROC of 0.725, outperforming baselines by +1.5% through the correction of overconfident predictions. Furthermore, we show superior zero-shot generalization on the multi-hop HOVER dataset, outperforming baselines by over 4%, confirming the robustness of thermodynamic truth properties to distribution shifts.