Title: Revisiting Visual Understanding in Multimodal Reasoning through a Lens of Image Perturbation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Multimodal LLM, Multimodal reasoning, Image perturbation

Abstract: Despite the rapid progress of multimodal large language models (MLLMs), the role of visual processing in multimodal reasoning remains underexplored. In a simple yet revealing experiment, we find that language-only models, when augmented with image captions, can sometimes outperform multimodal counterparts consuming raw visual inputs. This indicates that current MLLMs may perceive visual content but fail to effectively integrate it during reasoning. Moreover, even minimal visual perturbations such as small rotations lead to severe performance drops, exposing a fragility in their visual understanding. To address this overlooked bottleneck, we propose a lightweight visual perturbation (VP) framework that strengthens perceptual robustness without architectural changes or additional data. VP introduces three targeted strategies—distractor concatenation, dominance-preserving mixup, and random rotation—that can be seamlessly integrated into post-training pipelines including SFT, DPO, and GRPO. Extensive experiments across four multimodal reasoning benchmarks show consistent absolute gains of 1–2 points, with improvements holding across datasets, training pipelines, and even advanced RL-tuned models. Ablation and task-level analyses further reveal how different perturbations uniquely benefit geometry, algebra, OCR, and chart reasoning. These findings underscore a central insight: better reasoning begins with better seeing.

TL;DR: We show that MLLMs often fail to leverage visual inputs effectively. We propose a lightweight VP framework that boosts reasoning robustness across training methods and benchmarks, demonstrating that better reasoning begins with better seeing.