Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs

• URL: http://arxiv.org/abs/2506.11512v1
• Date: Fri, 13 Jun 2025 07:13:05 GMT
• Title: Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs
• Authors: Wei Li, Yunyao Cheng, Xinli Hao, Chaohong Ma, Yuxuan Liang, Bin Yang, Christian S. Jensen, Xiaofeng Meng,
• Abstract summary: This paper advocates a shift in approaching time-series reasoning with Large Language Models (LLMs)<n>It addresses the core limitations of current time-series reasoning approaches, which are often costly, inflexible, and inefficient.<n>We propose three alignment paradigms: Injective Alignment, Bridging Alignment and Internal Alignment.
• Score: 24.25288301208334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Large Language Models (LLMs) have enabled unprecedented capabilities for time-series reasoning in diverse real-world applications, including medical, financial, and spatio-temporal domains. However, existing approaches typically focus on task-specific model customization, such as forecasting and anomaly detection, while overlooking the data itself, referred to as time-series primitives, which are essential for in-depth reasoning. This position paper advocates a fundamental shift in approaching time-series reasoning with LLMs: prioritizing alignment paradigms grounded in the intrinsic primitives of time series data over task-specific model customization. This realignment addresses the core limitations of current time-series reasoning approaches, which are often costly, inflexible, and inefficient, by systematically accounting for intrinsic structure of data before task engineering. To this end, we propose three alignment paradigms: Injective Alignment, Bridging Alignment, and Internal Alignment, which are emphasized by prioritizing different aspects of time-series primitives: domain, characteristic, and representation, respectively, to activate time-series reasoning capabilities of LLMs to enable economical, flexible, and efficient reasoning. We further recommend that practitioners adopt an alignment-oriented method to avail this instruction to select an appropriate alignment paradigm. Additionally, we categorize relevant literature into these alignment paradigms and outline promising research directions.

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