牛哥精选 · 三个月

📋 全部 🤖 AI·大模型 ⚡ 效率工具 📝 深度技术 🚀 产品观察 💰 商业科技 🔓 开源项目 🎨 设计创意 📖 阅读推荐 🏷 资源合集 🌱 成长效率

📝 深度技术 arXiv 机器学习 2026-06-02

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection

用多模态大语言模型加强推理能力，实现更精准的时间序列异常检测，前沿思路值得关注。

arXiv:2602.08868v2 Announce Type: replace Abstract: Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging …

时间序列异常检测多模态大模型 llm推理异常检测论文解读

🤖 AI·大模型 arXiv AI 2026-05-29

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy

无需预训练数据即可实现零样本时间序列异常检测，合成数据与相对上下文差异是关键创新。

arXiv:2509.21190v4 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is a critical task, but developing models that generali…

时间序列异常检测基础模型零样本学习合成数据相对上下文差异

📝 深度技术 arXiv AI 2026-05-20

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

提出PaAno方法，用补丁表示学习实现高效时间序列异常检测，兼顾性能与计算资源

arXiv:2602.01359v2 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larg…

时间序列异常检测补丁表示学习 paano 计算效率神经网络

📅 日期

2026-05-20 2026-05-19

🐂 牛哥精选

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

📅 日期