用多智能体协作诊断天文成像质量，效果优于现有方法。
AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis
- 设计多智能体协同系统，分工处理天文成像复杂流程。
- 在真实数据上超越所有基线模型，显著提升诊断准确率。
- 适合需要跨领域分析的科研人员和天文爱好者参考。
视觉语言模型（VLMs）已在多个特定领域应用并展现出强大的问题求解能力。然而，天文成像作为涉及多学科知识与多个子任务的复杂问题，尚未得到充分研究。由于天文成像过程存在复杂的内在关联，相互影响显著，导致成像质量诊断与错误定位极具挑战性。无论是世界级天文机构（如NASA）还是专业爱好者，都需投入大量时间与精力。为此，我们提出AstroVLM，一个用于诊断天文成像质量的协作式多智能体系统。实验结果表明，AstroVLM在真实世界天文成像质量诊断任务中优于所有基线模型，为语言模型处理复杂多流程任务提供了参考。
原文摘要 · Abstract (English)
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.
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