Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning

Abstract: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 (\textsc{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 benchmark datasets demonstrate that \method{} delivers substantial and consistent performance gains across 5 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. Code is available at this https URL.

17 pages, 12 figures, 8 tables. Accepted by ACM MM 2026

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Multimedia (cs.MM)

I.2.0; I.2.4; I.5.4

arXiv:2605.09395 [cs.AI]

(or arXiv:2605.09395v3 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.09395 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3767308.3836170 Focus to learn more DOI(s) linking to related resources

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