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arXiv · 2511.15204

Physics-Based Benchmarking Metrics for Multimodal Synthetic Images

Kishor Datta Gupta·Marufa Kamal·Md. Mahfuzur Rahman·Fahad Rahman·Mohd Ariful Haque·Sunzida Siddique
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Abstract

Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accuracy, especially for domain-specific or context-dependent scenarios. For this, this paper proposes a Physics-Constrained Multimodal Data Evaluation (PCMDE) metric combining large language models with reasoning, knowledge based mapping and vision-language models to overcome these limitations. The architecture is comprised of three main stages: (1) feature extraction of spatial and semantic information with multimodal features through object detection and VLMs; (2) Confidence-Weighted Component Fusion for adaptive component-level validation; and (3) physics-guided reasoning using large language models for structural and relational constraints (e.g., alignment, position, consistency) enforcement.

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Kishor Datta Gupta, Marufa Kamal, Md. Mahfuzur Rahman, Fahad Rahman, Mohd Ariful Haque, Sunzida Siddique. 2025-11-19. Physics-Based Benchmarking Metrics for Multimodal Synthetic Images. https://arxiv.org/abs/2511.15204

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