基于编排器-智能体信任的智能体人工智能：一种具有信任感知编排和基于RAG推理的模块化视觉分类框架
Agentic AI with Orchestrator-Agent Trust: A Modular Visual Classification Framework with Trust-Aware Orchestration and RAG-Based Reasoning
AI总结：
本文提出一种模块化智能体AI视觉分类框架，通过信任感知编排器与RAG推理在零样本苹果叶片病害诊断中实现85.63%准确率，较基线提升77.94%，展示了可信多智能体AI的构建范式。
摘要：
现代人工智能（AI）越来越依赖融合视觉与语言理解的多智能体架构。然而，一个紧迫的挑战依然存在：我们如何信任这些智能体，尤其是在无需微调的零样本场景下？我们提出了一种新颖的模块化智能体人工智能视觉分类框架，该框架将通用多模态智能体与非视觉推理编排器以及检索增强生成（RAG）模块集成在一起。将该框架应用于苹果叶片病害诊断，我们对三种配置进行了基准测试：（I）基于置信度的零样本编排，（II）性能提升的微调智能体，以及（III）通过基于CLIP的图像检索和重新评估循环增强的信任校准编排。利用置信度校准指标（ECE、OCR、CCC），编排器在智能体之间调节信任。我们的结果表明，使用信任感知编排和RAG，零样本设置下的准确率提高了77.94%，总体达到85.63%。GPT-4o表现出更好的校准能力，而Qwen-2.5-VL则显示出过度自信。此外，图像RAG利用视觉上相似的案例对预测进行锚定，通过迭代重新评估纠正了智能体的过度自信。所提出的系统将感知（视觉智能体）与元推理（编排器）分离，实现了可扩展且可解释的多智能体AI。这一蓝图展示了智能体人工智能如何提供可信、模块化且透明的推理，并可扩展到诊断、生物学及其他信任关键型领域。在此过程中，我们强调智能体人工智能不仅是一种架构，更是一种构建可靠多智能体智能的范式。
英文摘要：
Modern Artificial Intelligence (AI) increasingly relies on multi-agent architectures that blend visual and language understanding. Yet, a pressing challenge remains: How can we trust these agents especially in zero-shot settings with no fine-tuning? We introduce a novel modular Agentic AI visual classification framework that integrates generalist multimodal agents with a non-visual reasoning orchestrator and a Retrieval-Augmented Generation (RAG) module. Applied to apple leaf disease diagnosis, we benchmark three configurations: (I) zero-shot with confidence-based orchestration, (II) fine-tuned agents with improved performance, and (III) trust-calibrated orchestration enhanced by CLIP-based image retrieval and re-evaluation loops. Using confidence calibration metrics (ECE, OCR, CCC), the orchestrator modulates trust across agents. Our results demonstrate a 77.94\% accuracy improvement in the zero-shot setting using trust-aware orchestration and RAG, achieving 85.63\% overall. GPT-4o showed better calibration, while Qwen-2.5-VL displayed overconfidence. Furthermore, image-RAG grounded predictions with visually similar cases, enabling correction of agent overconfidence via iterative re-evaluation. The proposed system separates perception (vision agents) from meta-reasoning (orchestrator), enabling scalable and interpretable multi-agent AI. This blueprint illustrates how Agentic AI can deliver trustworthy, modular, and transparent reasoning, and is extensible to diagnostics, biology, and other trust-critical domains. In doing so, we highlight Agentic AI not just as an architecture but as a paradigm for building reliable multi-agent intelligence. agentic ai, orchestrator agent trust, trust orchestration, visual classification, retrieval augmented reasoning
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。
