Induced Numerical Instability: Hidden Costs in Multimodal Large Language Models
#multimodal LLMs #numerical instability #hidden costs #computational resources #error correction #algorithmic approaches #testing frameworks
📌 Key Takeaways
- Multimodal large language models (MLLMs) can exhibit induced numerical instability, leading to unpredictable errors.
- This instability arises from the integration of diverse data types, such as text, images, and audio, which may not align numerically.
- Hidden costs include increased computational resources for error correction and reduced reliability in real-world applications.
- Addressing these instabilities requires new algorithmic approaches and rigorous testing frameworks.
📖 Full Retelling
arXiv:2603.04453v1 Announce Type: cross
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 mu
🏷️ Themes
AI Reliability, Computational Costs
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
--> Computer Science > Computation and Language arXiv:2603.04453 [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 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); Machine Learning (cs.LG) Cite as: arXiv:2603.04453 [cs.CL] (or arXiv:2603.04453v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.04453 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wai Tuck Wong [ view email ] [v1] Fri, 27 Feb 2026 18:47:36 UTC (7,605 KB) Full-text links: Access Paper: 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 TeX Source view lice...
Read full article at source