{"ID":5552795,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-03T19:41:52.190318515Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00115","arxiv_id":"2607.00115","title":"PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking","abstract":"This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leading to long, redundant trajectories. We attribute this failure to the entanglement of reasoning and perception within a single model, the MLLM reasons and localizes simultaneously, and inaccurate localization triggers additional reasoning turns that bloat the trajectory. To solve this problem, we propose PixelEyes, a multi-turn visual reasoning agent that explicitly decouples reasoning from perception, i.e., the reasoner decides what to look for, while a specialized perception tool answers where it is. Specifically, PixelEyes introduces 1) Mask-guided Visual Search. A referring segmentation model is invoked to provide mask-precise localization, freeing the reasoner from the need to compensate for imprecise grounding. 2) Semantic-region Breadth-first Search (BFS). To eliminate redundant loops caused by repeatedly cropping incorrect sub-regions, we organize exploration as a breadth-first search over semantic regions. To internalize these capabilities, we construct the PixelEyes-6K dataset by resynthesizing expert trajectories from existing data. This explicitly embeds our mask-guided search and BFS logic into the model. We further introduce Pinpoint-Bench, a zero-hint visual search benchmark, i.e., no location cues are provided in the question, with instance-level masks and bounding boxes that separate localization failures from reasoning failures, enabling fine-grained analysis of failure modes such as inattentional blindness. Recent state-of-the-art MLLMs and visual reasoning agents leave large headroom on Pinpoint-Bench, demonstrating its quality and difficulty. Code and models are open-sourced.","short_abstract":"This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leading to long, redundant trajectories. We attribute this failure to the entanglement of reasoning and perception within a single model, the MLLM reasons and localizes simultaneously, and inaccurate localiza...","url_abs":"https://arxiv.org/abs/2607.00115","url_pdf":"https://arxiv.org/pdf/2607.00115v1","authors":"[\"Dengxian Gong\",\"Yuanzheng Wu\",\"Haobo Yuan\",\"Zhengdong Hu\",\"Tao Zhang\",\"Yikang Zhou\",\"Shihao Chen\",\"Quanzhu Niu\",\"Kai Wang\",\"Jason Li\",\"Haochen Wang\",\"Lu Qi\",\"Shunping Ji\",\"Ming-Hsuan Yang\"]","published":"2026-06-30T19:51:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Large Language Model\",\"LoRA\",\"Generative Adversarial Network\"]","has_code":false}