标题： EndoAgent：一种用于智能内窥镜视觉到决策推理的内存引导反思代理 显示英文标题

标题： EndoAgent: A Memory-Guided Reflective Agent for Intelligent Endoscopic Vision-to-Decision Reasoning

摘要： 开发通用的人工智能（AI）系统以支持内窥镜图像诊断是一项新兴的研究重点。 基于大规模预训练的现有方法通常在任务之间缺乏统一的协调，难以处理复杂临床工作流程中所需的多步骤过程。 尽管AI代理在跨领域的灵活指令解析和工具集成方面显示出潜力，但其在内窥镜领域的潜力仍鲜有研究。 为解决这一差距，我们提出了EndoAgent，这是第一个基于记忆引导的视觉到决策的内窥镜分析代理，它结合了迭代推理与自适应工具选择和协作。 基于双记忆设计，它通过短期动作跟踪确保逻辑连贯性，并通过长期经验学习逐步提高推理敏锐度，从而实现复杂的决策。 为了支持多种临床任务，EndoAgent在一个统一的推理循环中集成了专家设计的一套工具。 我们进一步引入了EndoAgentBench，这是一个包含5,709个视觉问答对的基准，用于评估真实场景中的视觉理解和语言生成能力。 大量实验表明，EndoAgent始终优于通用和医学多模态模型，表现出其强大的灵活性和推理能力。 显示英文摘要

摘要： Developing general artificial intelligence (AI) systems to support endoscopic image diagnosis is an emerging research priority. Existing methods based on large-scale pretraining often lack unified coordination across tasks and struggle to handle the multi-step processes required in complex clinical workflows. While AI agents have shown promise in flexible instruction parsing and tool integration across domains, their potential in endoscopy remains underexplored. To address this gap, we propose EndoAgent, the first memory-guided agent for vision-to-decision endoscopic analysis that integrates iterative reasoning with adaptive tool selection and collaboration. Built on a dual-memory design, it enables sophisticated decision-making by ensuring logical coherence through short-term action tracking and progressively enhancing reasoning acuity through long-term experiential learning. To support diverse clinical tasks, EndoAgent integrates a suite of expert-designed tools within a unified reasoning loop. We further introduce EndoAgentBench, a benchmark of 5,709 visual question-answer pairs that assess visual understanding and language generation capabilities in realistic scenarios. Extensive experiments show that EndoAgent consistently outperforms both general and medical multimodal models, exhibiting its strong flexibility and reasoning capabilities.