@inproceedings{liu-etal-2026-large-language,
title = "Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?",
author = "Liu, Zewen and
Ni, Juntong and
Tang, Xianfeng and
Lau, Max SY and
He, Qi and
Yin, Wenpeng and
Jin, Wei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1756/",
pages = "35201--35226",
ISBN = "979-8-89176-395-1",
abstract = "Uncovering hidden symbolic laws from time series data, as an aspiration dating back to Kepler{'}s discovery of planetary motion, remains a core challenge in scientific discovery and artificial intelligence. While Large Language Models show promise in structured reasoning tasks, their ability to infer interpretable, context-aligned symbolic structures from time series data is still underexplored. To systematically evaluate this capability, we introduce SymbolBench, a comprehensive benchmark designed to assess symbolic reasoning over real-world time series across three tasks: multivariate symbolic regression, Boolean network inference, and causal discovery. Unlike prior efforts limited to simple algebraic equations, SymbolBench spans a diverse set of symbolic forms with varying complexity. We further propose a unified framework that integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system. Our empirical results reveal key strengths and limitations of current models, highlighting the importance of combining domain knowledge, context alignment, and reasoning structure to improve LLMs in automated scientific discovery."
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<abstract>Uncovering hidden symbolic laws from time series data, as an aspiration dating back to Kepler’s discovery of planetary motion, remains a core challenge in scientific discovery and artificial intelligence. While Large Language Models show promise in structured reasoning tasks, their ability to infer interpretable, context-aligned symbolic structures from time series data is still underexplored. To systematically evaluate this capability, we introduce SymbolBench, a comprehensive benchmark designed to assess symbolic reasoning over real-world time series across three tasks: multivariate symbolic regression, Boolean network inference, and causal discovery. Unlike prior efforts limited to simple algebraic equations, SymbolBench spans a diverse set of symbolic forms with varying complexity. We further propose a unified framework that integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system. Our empirical results reveal key strengths and limitations of current models, highlighting the importance of combining domain knowledge, context alignment, and reasoning structure to improve LLMs in automated scientific discovery.</abstract>
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%0 Conference Proceedings
%T Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?
%A Liu, Zewen
%A Ni, Juntong
%A Tang, Xianfeng
%A Lau, Max SY
%A He, Qi
%A Yin, Wenpeng
%A Jin, Wei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-large-language
%X Uncovering hidden symbolic laws from time series data, as an aspiration dating back to Kepler’s discovery of planetary motion, remains a core challenge in scientific discovery and artificial intelligence. While Large Language Models show promise in structured reasoning tasks, their ability to infer interpretable, context-aligned symbolic structures from time series data is still underexplored. To systematically evaluate this capability, we introduce SymbolBench, a comprehensive benchmark designed to assess symbolic reasoning over real-world time series across three tasks: multivariate symbolic regression, Boolean network inference, and causal discovery. Unlike prior efforts limited to simple algebraic equations, SymbolBench spans a diverse set of symbolic forms with varying complexity. We further propose a unified framework that integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system. Our empirical results reveal key strengths and limitations of current models, highlighting the importance of combining domain knowledge, context alignment, and reasoning structure to improve LLMs in automated scientific discovery.
%U https://aclanthology.org/2026.findings-acl.1756/
%P 35201-35226
Markdown (Informal)
[Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?](https://aclanthology.org/2026.findings-acl.1756/) (Liu et al., Findings 2026)
ACL
- Zewen Liu, Juntong Ni, Xianfeng Tang, Max SY Lau, Qi He, Wenpeng Yin, and Wei Jin. 2026. Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?. In Findings of the Association for Computational Linguistics: ACL 2026, pages 35201–35226, San Diego, California, United States. Association for Computational Linguistics.