PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking
This paper presents PixelEyes, a multi-turn visual reasoning agent that decouples reasoning from perception to address the repeated localization failures of multimodal LLMs. It introduces mask-guided visual search and semantic-region breadth-first search, constructs the PixelEyes-6K dataset and Pinpoint-Bench benchmark, and demonstrates significant headroom for existing models.
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[Submitted on 30 Jun 2026]
Title:PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking
View a PDF of the paper titled PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking, by Dengxian Gong and 13 other authors
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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.
Comments: 22pages, 10 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.00115 [cs.CV]
(or arXiv:2607.00115v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.00115
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dengxian Gong [view email] [v1] Tue, 30 Jun 2026 19:51:54 UTC (35,369 KB)
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