[2603.04453] Induced Numerical Instability: Hidden Costs in Multimodal Large Language Models

Computer Science > Computation and Language arXiv:2603.04453 (cs) [Submitted on 27 Feb 2026] Title:Induced Numerical Instability: Hidden Costs in Multimodal Large Language Models Authors:Wai Tuck Wong, Jun Sun, Arunesh Sinha View a PDF of the paper titled Induced Numerical Instability: Hidden Costs in Multimodal Large Language Models, by Wai Tuck Wong and 2 other authors View PDF HTML (experimental) Abstract:The use of multimodal large language models has become widespread, and as such the study of these models and their failure points has become of utmost importance. We study a novel mode of failure that causes degradation in performance indirectly by optimizing a loss term that seeks to maximize numerical instability in the inference stage of these models. We apply this loss term as the optimization target to construct images that, when used on multimodal large language models, cause significant degradation in the output. We validate our hypothesis on state of the art models large vision language models (LLaVa-v1.5-7B, Idefics3-8B, SmolVLM-2B-Instruct) against standard datasets (Flickr30k, MMVet, TextVQA, VQAv2, POPE, COCO) and show that performance degrades significantly, even with a very small change to the input image, compared to baselines. Our results uncover a fundamentally different vector of performance degradation, highlighting a failure mode not captured by adversarial perturbations. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); M...