Beyond Bigger LLMs: Supercharging Visual Perception in Efficient Multimodal AI with Extract+Think
By Mark Endo, Serena Yeung-Levy
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CV updates on arXiv.org.
Summary
This research explores the impact of reducing Large Language Model (LLM) capacity on small multimodal models, finding that downscaling disproportionately affects visual capabilities, including both perception and reasoning. The study reveals that the loss in visual perception often matches or exceeds the impact on reasoning. To counter this, the authors propose "visual extraction tuning," a method that trains models to consistently extract instruction-relevant visual details. This, combined with step-by-step reasoning, forms their "Extract+Think" approach, aiming to enhance efficiency and performance in compact multimodal AI systems.
Why It Matters
This research holds significant weight for the future of AI, specifically in the push towards more accessible, cost-effective, and deployable intelligence. As the AI industry matures, the demand for compact, efficient models capable of running on edge devices, mobile platforms, and in environments with limited resources is skyrocketing. This work directly addresses a critical bottleneck in achieving truly intelligent small multimodal AI. The finding that visual perception is disproportionately affected by LLM downscaling, often more so than reasoning, is a profound insight. It challenges the common assumption that simply reducing the LLM's "brainpower" is the main problem. Instead, it reveals that the very foundation of visual understanding - the ability to accurately "see" and extract relevant details - crumbles significantly. For AI professionals, this means that merely shrinking an LLM and hoping for the best is insufficient. Focus must shift to sophisticated visual feature extraction and attention mechanisms, especially when resource-constrained. The "Extract+Think" approach provides a compelling blueprint: by explicitly training models to extract relevant visual information first before applying reasoning, it offers a pragmatic pathway to overcoming these limitations. This paradigm shift could unlock a new generation of high-performance, on-device multimodal applications, driving innovation in fields from robotics and autonomous systems to smart wearables and privacy-preserving AI. It's a clear signal that the future of powerful AI isn't solely about brute-force scaling but also about ingenious architectural and training optimizations for efficiency.