Technology Category
Computer Vision: Multi-modal VisionMultiagent Systems: TeamworkKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis
🤖 AI Summary
Astronomical image quality diagnosis faces significant challenges due to the complexity of the imaging process, its inherently interdisciplinary nature, and the strong coupling among subtasks. This work proposes the first vision-language model system based on multi-agent collaborative reasoning, integrating domain expert knowledge into a multi-agent cooperative architecture to enable automatic diagnosis and precise localization of image quality issues. By deeply fusing multi-agent reasoning with vision-language models, the method effectively addresses the coupling inherent in complex, multi-stage tasks. Evaluated on real astronomical image data, the approach substantially outperforms existing baselines, offering a novel paradigm for tackling high-dimensional, complex procedural tasks in scientific domains.
Technology Category
Application Category
📝 Abstract
Vision Language Models (VLMs) have been applied to several specific domains and have shown strong problem-solving capabilities. However, astronomical imaging, a quite complex problem involving multidisciplinary knowledge and several subtasks, has not been adequately studied. Due to the complexity of the astronomical imaging process, both world-class astronomical organizations, such as NASA, and expert enthusiasts devote a great deal of time and effort. This is because the processes in astronomical imaging have complex underlying correlations that significantly influence one another, making the quality diagnosis and error localization of astronomical images challenging. To address this problem, we propose AstroVLM, a collaborative multi-agent system for diagnosing the quality of astronomical images. Experiment results show that AstroVLM outperforms all baselines on real-world astronomical imaging quality diagnosis tasks, providing a reference for language models to handle complicated multi-process tasks.
Problem
Research questions and friction points this paper is trying to address.
astronomical imaging
quality diagnosis
error localization
multi-agent reasoning
vision language models
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-agent Collaboration
Astronomical Imaging
Vision Language Models
Quality Diagnosis
Expert Reasoning
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