@inproceedings{zhang-etal-2026-timesense,
title = "{T}ime{S}ense: Decoupling Modality Learning for Robust Time Series Reasoning",
author = "Zhang, Zhirui and
Pei, Changhua and
Gao, Tianyi. and
Xie, Zhe and
Hao, Yibo and
Yu, Zhaoyang and
Xu, Longlong and
Xiao, Tong and
Han, Jing and
Xie, Gaogang and
Pei, Dan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1216/",
pages = "27772--27789",
ISBN = "979-8-89176-459-0",
abstract = "In the time series domain, increasing efforts extend large language models (LLMs) to multimodal large language models (MLLMs) by incorporating temporal data for downstream temporal understanding tasks. In conventional MLLM training, models primarily rely on language signals, while temporal information provides substantially weaker supervision. This imbalance leads to insufficient learning of temporal patterns. To address this issue, we propose TimeSense, a framework with a disentangled reconstruction structure that introduces explicit temporal supervision and encourages the learning of both global trends and local temporal variations that are often underrepresented in text-dominated multimodal training. Existing QA benchmarks provide limited coverage of temporal variations and compositional temporal dependencies, making it difficult to systematically diagnose these MLLMs capabilities across diverse temporal understanding tasks. To facilitate a more fine-grained evaluation, we introduce EvalTS, a comprehensive benchmark spanning three difficulty levels (Atomic, Molecular, and Compositional) with diverse high-quality queries for evaluating temporal understanding abilities in large language and multimodal language models. Experiments on EvalTS and existing benchmarks demonstrate that TimeSense consistently outperforms prior methods and substantially improves temporal understanding performance."
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<abstract>In the time series domain, increasing efforts extend large language models (LLMs) to multimodal large language models (MLLMs) by incorporating temporal data for downstream temporal understanding tasks. In conventional MLLM training, models primarily rely on language signals, while temporal information provides substantially weaker supervision. This imbalance leads to insufficient learning of temporal patterns. To address this issue, we propose TimeSense, a framework with a disentangled reconstruction structure that introduces explicit temporal supervision and encourages the learning of both global trends and local temporal variations that are often underrepresented in text-dominated multimodal training. Existing QA benchmarks provide limited coverage of temporal variations and compositional temporal dependencies, making it difficult to systematically diagnose these MLLMs capabilities across diverse temporal understanding tasks. To facilitate a more fine-grained evaluation, we introduce EvalTS, a comprehensive benchmark spanning three difficulty levels (Atomic, Molecular, and Compositional) with diverse high-quality queries for evaluating temporal understanding abilities in large language and multimodal language models. Experiments on EvalTS and existing benchmarks demonstrate that TimeSense consistently outperforms prior methods and substantially improves temporal understanding performance.</abstract>
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%0 Conference Proceedings
%T TimeSense: Decoupling Modality Learning for Robust Time Series Reasoning
%A Zhang, Zhirui
%A Pei, Changhua
%A Gao, Tianyi.
%A Xie, Zhe
%A Hao, Yibo
%A Yu, Zhaoyang
%A Xu, Longlong
%A Xiao, Tong
%A Han, Jing
%A Xie, Gaogang
%A Pei, Dan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-timesense
%X In the time series domain, increasing efforts extend large language models (LLMs) to multimodal large language models (MLLMs) by incorporating temporal data for downstream temporal understanding tasks. In conventional MLLM training, models primarily rely on language signals, while temporal information provides substantially weaker supervision. This imbalance leads to insufficient learning of temporal patterns. To address this issue, we propose TimeSense, a framework with a disentangled reconstruction structure that introduces explicit temporal supervision and encourages the learning of both global trends and local temporal variations that are often underrepresented in text-dominated multimodal training. Existing QA benchmarks provide limited coverage of temporal variations and compositional temporal dependencies, making it difficult to systematically diagnose these MLLMs capabilities across diverse temporal understanding tasks. To facilitate a more fine-grained evaluation, we introduce EvalTS, a comprehensive benchmark spanning three difficulty levels (Atomic, Molecular, and Compositional) with diverse high-quality queries for evaluating temporal understanding abilities in large language and multimodal language models. Experiments on EvalTS and existing benchmarks demonstrate that TimeSense consistently outperforms prior methods and substantially improves temporal understanding performance.
%U https://aclanthology.org/2026.emnlp-main.1216/
%P 27772-27789
Markdown (Informal)
[TimeSense: Decoupling Modality Learning for Robust Time Series Reasoning](https://aclanthology.org/2026.emnlp-main.1216/) (Zhang et al., EMNLP 2026)
ACL
- Zhirui Zhang, Changhua Pei, Tianyi. Gao, Zhe Xie, Yibo Hao, Zhaoyang Yu, Longlong Xu, Tong Xiao, Jing Han, Gaogang Xie, and Dan Pei. 2026. TimeSense: Decoupling Modality Learning for Robust Time Series Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 27772–27789, Budapest, Hungary. Association for Computational Linguistics.