arXiv:2508.19366v2 Announce Type: replace-cross Abstract: Hallucinations in large language models (LLMs) remain a fundamental obstacle to trustworthy AI, particularly in high-stakes multimodal domains such as medicine, law, and finance. Existing evaluation techniques are largely heuristic -- anchored in qualitative benchmarking or ad-hoc empirical mitigation -- providing neither principled quantification nor actionable theoretical guarantees. This gap leaves a critical blind spot in understanding how hallucinations arise, propagate, and interact across modalities. We introduce the first (to our knowledge) rigorous information geometric framework in diffusion dynamics for quantifying hallucinations in multimodal LLMs (MLLMs), advancing the field from qualitative detection to mathematically grounded measurement. Our approach represents MLLM outputs as the spectral embeddings over multimodal graph Laplacians and characterizes the manifold gaps of truth vs inconsistencies as the semantic distortion, enabling the tight Rayleigh--Ritz bounds on the multimodal hallucination energy as a functional of time-dependent temperature profiles. By leveraging eigenmode decompositions in Reproducing Kernel Hilbert Space (RKHS) embeddings, our framework delivers modality-aware, theoretically interpretable metrics that capture the evolution of hallucinations across time and input prompts through temperature annealing. This work establishes a principled foundation for quantifying and bounding hallucinations, transforming them from a qualitative risk to a tractable, analyzable phenomenon.