Corner cases are rare or extreme scenarios that drive real-world failures, but they are difficult to curate at scale: web data are noisy, labels are brittle, and edge deployments preclude large retraining. We present ReCCur (Recursive Corner-Case Curation), a low-compute framework that converts noisy web imagery into auditable fine-grained labels via a multi-agent recursive pipeline. First, large-scale data acquisition and filtering expands a domain vocabulary with a vision-language model (VLM), crawls the web, and enforces tri-modal (image, description, keyword) consistency with light human spot checks to yield refined candidates. Next, mixture-of-experts knowledge distillation uses complementary encoders (e.g., CLIP, DINOv2, BEiT) for kNN voting with dual-confidence activation and uncertainty sampling, converging to a high-precision set. Finally, region-evidence VLM adversarial labeling pairs a proposer (multi-granularity regions and semantic cues) with a validator (global and local chained consistency) to produce explainable labels and close the loop. On realistic corner-case scenarios (e.g., flooded-car inspection), ReCCur runs on consumer-grade GPUs, steadily improves purity and separability, and requires minimal human supervision, providing a practical substrate for downstream training and evaluation under resource constraints. Code and dataset will be released.

翻译：角点案例是导致现实世界系统失效的罕见或极端场景，但其大规模筛选面临诸多挑战：网络数据存在噪声、标注标签脆弱易错，且边缘部署环境难以支持大规模重训练。本文提出ReCCur（递归角点案例筛选框架），这是一个低计算成本的框架，通过多智能体递归流程将含噪网络图像转化为可审计的细粒度标注。首先，通过大规模数据采集与过滤流程，利用视觉-语言模型扩展领域词汇表，进行网络爬取，并通过轻量级人工抽查确保图像、描述、关键词的三模态一致性，从而获得精炼候选集。其次，采用专家混合知识蒸馏方法，利用互补编码器（如CLIP、DINOv2、BEiT）进行k近邻投票，结合双置信度激活与不确定性采样，迭代收敛至高精度数据集。最后，区域证据视觉-语言模型对抗标注机制将提议模块（多粒度区域与语义线索）与验证模块（全局与局部链式一致性校验）相结合，生成可解释的标注并形成闭环。在现实角点案例场景（如涉水车辆检测）中，ReCCur可在消费级GPU上运行，持续提升数据纯度与可分离性，且仅需极少人工监督，为资源受限环境下的下游训练与评估提供了实用基础。代码与数据集将公开发布。