让视觉语言模型更懂时间序列，少样本下也能精准分类。
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
- 用三个角色协作：生成、反思、修正，动态优化推理知识库。
- 12个基准测试中，6种模型都显著提升，少样本效果更好。
- 能解释每步判断依据，适合需要可解释性的实际场景。
本文提出首个面向少样本多模态时间序列分类（MarsTSC）的视觉语言模型自进化代理推理框架。该框架引入动态上下文知识库，通过反思式代理推理持续优化。包含三个协同角色：生成器基于推理完成可靠分类；反思者诊断推理错误根源，提取被忽略的时间特征洞察；修正者将验证后的更新注入知识库，防止上下文退化。此外，引入测试时更新策略，实现谨慎、连续的知识库优化，缓解少样本偏差与分布偏移。在12个主流时间序列基准上实验表明，MarsTSC在6种VLM骨干模型上均取得显著且一致的性能提升，优于经典与基础模型方法，在少样本条件下表现突出，同时生成可解释的推理过程，使每个分类决策基于人类可读的特征证据。
原文摘要 · Abstract (English)
In this paper, we propose the first VL$\underline{\textbf{M}}$ $\underline{\textbf{a}}$gentic $\underline{\textbf{r}}$easoning framework for few-$\underline{\textbf{s}}$hot multimodal $\underline{\textbf{T}}$ime $\underline{\textbf{S}}$eries $\underline{\textbf{C}}$lassification ($\textbf{MarsTSC}$), which introduces a self-evolving knowledge bank as a dynamic context iteratively refined via reflective agentic reasoning. The framework comprises three collaborative roles: i) Generator conducts reliable classification via reasoning; ii) Reflector diagnoses the root causes of reasoning errors to yield discriminative insights targeting the temporal features overlooked by Generator; iii) Modifier applies verified updates to the knowledge bank to prevent context collapse. We further introduce a test-time update strategy to enable cautious, continuous knowledge bank refinement to mitigate few-shot bias and distribution shift. Extensive experiments across 12 mainstream time series benchmarks demonstrate that $\textbf{MarsTSC}$ delivers substantial and consistent performance gains across 6 VLM backbones, outperforming both classical and foundation model-based time series baselines under few-shot conditions, while producing interpretable rationales that ground each classification decision in human-readable feature evidence.
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