@inproceedings{qiu-jialingan-2026-reasoning,
title = "When Reasoning Meets Structure: Understanding Reasoning Strategies for Structured Analysis",
author = "Qiu, Xin and
Jialingan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1423/",
pages = "30315--30349",
ISBN = "979-8-89176-462-0",
abstract = "Recent studies have explored adapting LLMs to time series tasks by leveraging language reasoning paradigms. However, it remains unclear whether reasoning strategies are effective across time series tasks with diverse temporal structures. In this work, we systematically investigate this practice using five general reasoning strategies across 12 LLMs and 20 public datasets, together with a self-constructed benchmark of approximately 18k QA pairs. Our results show that general reasoning strategies are not universally beneficial: their effectiveness depends on alignment with task-specific temporal decision structures and output spaces, rather than on model scale or complexity. We further observe that prior information and common time series pre-processing techniques exhibit strong task-dependent effects. Based on these findings, we propose three time-aware reasoning strategies that explicitly model temporal regimes, latent dynamics, and output uncertainty. Across four tasks, they consistently outperform generic reasoning strategies, and enable reasoning to better benefit from larger model scales and multi-sampling. Our study highlights the importance of temporal structure awareness in designing reliable reasoning strategies for LLM-based time series analysis."
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%0 Conference Proceedings
%T When Reasoning Meets Structure: Understanding Reasoning Strategies for Structured Analysis
%A Qiu, Xin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Jialingan
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F qiu-jialingan-2026-reasoning
%X Recent studies have explored adapting LLMs to time series tasks by leveraging language reasoning paradigms. However, it remains unclear whether reasoning strategies are effective across time series tasks with diverse temporal structures. In this work, we systematically investigate this practice using five general reasoning strategies across 12 LLMs and 20 public datasets, together with a self-constructed benchmark of approximately 18k QA pairs. Our results show that general reasoning strategies are not universally beneficial: their effectiveness depends on alignment with task-specific temporal decision structures and output spaces, rather than on model scale or complexity. We further observe that prior information and common time series pre-processing techniques exhibit strong task-dependent effects. Based on these findings, we propose three time-aware reasoning strategies that explicitly model temporal regimes, latent dynamics, and output uncertainty. Across four tasks, they consistently outperform generic reasoning strategies, and enable reasoning to better benefit from larger model scales and multi-sampling. Our study highlights the importance of temporal structure awareness in designing reliable reasoning strategies for LLM-based time series analysis.
%U https://aclanthology.org/2026.findings-emnlp.1423/
%P 30315-30349
Markdown (Informal)
[When Reasoning Meets Structure: Understanding Reasoning Strategies for Structured Analysis](https://aclanthology.org/2026.findings-emnlp.1423/) (Qiu & Jialingan, Findings 2026)
ACL