Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs

Vision-language models (VLMs) typically process images at a native high-resolution, forcing a trade-off between accuracy and computational efficiency: high-resolution inputs capture fine details but incur significant computational costs, while low-resolution inputs advocate for efficiency, they potentially miss critical visual information, specifically OCR related. We present AwaRes, a spatial-on-demand framework that resolves this accuracy--efficiency trade-off by operating on a low-resolution global view and using tool-calling to retrieve only high-resolution segment needed for a given query, rather than escalating to the full image. We construct this supervision automatically: a judge compares low- vs. high-resolution answers to label whether cropping is needed, and an oracle grounding model localizes the evidence for the correct answer, which we map to a discrete crop set to form multi-turn tool-use trajectories. We train our framework with SFT followed by multi-turn GRPO with a composite reward that combines semantic answer correctness with explicit crop-cost penalties. AwaRes provides a practical, deployment-friendly path to high-detail VLM reasoning under tight compute budgets.