AnomSeer: 增强多模态大语言模型进行时间序列异常检测的推理能力
AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
AI总结：
提出AnomSeer，通过专家思维链和基于最优传输的时间序列接地策略优化，增强多模态大语言模型在时间序列异常检测中的细粒度推理能力，统一异常分类、定位和解释。
摘要：
基于多模态大语言模型（MLLM）的时间序列异常检测（TSAD）是一个新兴领域，但一个持续存在的挑战是：MLLM依赖于粗略的时间序列启发式方法，但在多维、详细的推理方面存在困难，而这对于理解复杂的时间序列数据至关重要。我们提出AnomSeer来解决这个问题，通过增强模型将其推理基于时间序列的精确结构细节，统一异常分类、定位和解释。其核心是生成专家思维链轨迹，从经典分析（如统计度量、频率变换）中提供可验证的细粒度推理。在此基础上，我们提出了一种新颖的时间序列接地策略优化（TimerPO），它在标准强化学习之外引入了两个额外组件：基于最优传输的时间序列接地优势，以及确保这种辅助细粒度信号不干扰主要检测目标的正交投影。在各种异常场景中，使用Qwen2.5-VL-3B/7B-Instruct的AnomSeer在分类和定位准确性上优于更大的商业基线（如GPT-4o），特别是在点和频率驱动的异常上。此外，它产生了合理的时间序列推理轨迹，支持其结论。
英文摘要：
Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: MLLMs rely on coarse time-series heuristics but struggle with multi-dimensional, detailed reasoning, which is vital for understanding complex time-series data. We present AnomSeer to address this by reinforcing the model to ground its reasoning in precise, structural details of time series, unifying anomaly classification, localization, and explanation. At its core, an expert chain-of-thought trace is generated to provide a verifiable, fine-grained reasoning from classical analyses (e.g., statistical measures, frequency transforms). Building on this, we propose a novel time-series grounded policy optimization (TimerPO) that incorporates two additional components beyond standard reinforcement learning: a time-series grounded advantage based on optimal transport and an orthogonal projection to ensure this auxiliary granular signal does not interfere with the primary detection objective. Across diverse anomaly scenarios, AnomSeer, with Qwen2.5-VL-3B/7B-Instruct, outperforms larger commercial baselines (e.g., GPT-4o) in classification and localization accuracy, particularly on point- and frequency-driven exceptions. Moreover, it produces plausible time-series reasoning traces that support its conclusions.
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。
