Induced Numerical Instability: Hidden Costs in Multimodal Large Language Models
arXiv:2603.04453v1 Announce Type: new 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
arXiv:2603.04453v1 Announce Type: new 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.
Executive Summary
This article introduces a novel mode of failure in multimodal large language models, termed induced numerical instability, which causes performance degradation by optimizing a loss term that maximizes numerical instability during inference. The authors demonstrate the effectiveness of this approach on state-of-the-art models, showing significant degradation in performance with even minor changes to input images. This highlights a previously unexplored failure mode that differs from traditional adversarial perturbations.
Key Points
- ▸ Introduction of induced numerical instability as a novel mode of failure in multimodal large language models
- ▸ Demonstration of significant performance degradation with minor input image changes
- ▸ Distinction from traditional adversarial perturbations as a failure mode
Merits
Novel Contribution
The article contributes a new perspective on the vulnerabilities of multimodal large language models, expanding the understanding of their potential failure points.
Demerits
Limited Scope
The study focuses primarily on the technical demonstration of induced numerical instability, with less emphasis on the broader implications or potential mitigation strategies.
Expert Commentary
The findings of this article underscore the complex nature of vulnerabilities in multimodal large language models. By introducing the concept of induced numerical instability, the authors shed light on a critical oversight in current models, highlighting the need for a more comprehensive approach to model robustness. This not only affects the technical development of these models but also has broader implications for their safe and reliable deployment in real-world applications. As such, it is imperative for both researchers and policymakers to engage with these findings to foster more resilient AI systems.
Recommendations
- ✓ Further research into the mechanisms underlying induced numerical instability to develop targeted mitigation strategies
- ✓ Incorporation of tests for numerical instability into the standard evaluation protocols for multimodal large language models