像植物学家一样思考：用意图驱动的连续询问挑战多模态语言模型
Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry
AI总结：
本文提出PlantInquiryVQA基准，通过结构化询问提升植物病害诊断准确性，揭示多步意图驱动的视觉推理方法，改进多模态模型的临床推理能力。
摘要：
视觉评估通常通过多步骤过程完成。在大多数现代领域，专家通过结构化、基于证据的适应性提问分析图像。在植物病理学中，植物学家检查叶片图像，识别视觉线索，推断诊断意图，并通过针对性问题进一步探索，这些问题根据物种、症状和严重程度进行调整。这种结构化探索对准确的疾病诊断和治疗方案制定至关重要。然而，当前的视觉-语言模型在单轮问答上进行评估。为填补这一空白，我们引入PlantInquiryVQA，一个研究多步骤、意图驱动的视觉推理在植物诊断中的基准。我们正式化了一个询问链框架，将诊断轨迹建模为由地面视觉线索和明确的认知意图条件的有序问答序列。我们发布了一个包含24,950个专家整理的植物图像和138,068个标注有视觉定位、严重程度标签和领域特定推理模板的问答对的数据集。在顶级多模态大语言模型上的评估表明，尽管它们能够充分描述视觉症状，但它们在安全的临床推理和准确诊断方面存在困难。重要的是，结构化问题引导的探索显著提高了诊断正确性，减少了幻觉，并增加了推理效率。我们希望PlantInquiryVQA能作为基础基准，推动研究以训练诊断代理像专家植物学家一样推理，而不是静态分类器。
英文摘要：
Vision evaluations are typically done through multi-step processes. In most contemporary fields, experts analyze images using structured, evidence-based adaptive questioning. In plant pathology, botanists inspect leaf images, identify visual cues, infer diagnostic intent, and probe further with targeted questions that adapt to species, symptoms, and severity. This structured probing is crucial for accurate disease diagnosis and treatment formulation. Yet current vision-language models are evaluated on single-turn question answering. To address this gap, we introduce PlantInquiryVQA, a benchmark for studying multi-step, intent-driven visual reasoning in botanical diagnosis. We formalize a Chain of Inquiry framework modeling diagnostic trajectories as ordered question-answer sequences conditioned on grounded visual cues and explicit epistemic intent. We release a dataset of 24,950 expert-curated plant images and 138,068 question-answer pairs annotated with visual grounding, severity labels, and domain-specific reasoning templates. Evaluations on top-tier Multimodal Large Language Models reveal that while they describe visual symptoms adequately, they struggle with safe clinical reasoning and accurate diagnosis. Importantly, structured question-guided inquiry significantly improves diagnostic correctness, reduces hallucination, and increases reasoning efficiency. We hope PlantInquiryVQA serves as a foundational benchmark in advancing research to train diagnostic agents to reason like expert botanists rather than static classifiers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
