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

2025-06-13

AI Summary

Existing large language models (LLMs) process time series in a task-specific manner—e.g., forecasting or anomaly detection—neglecting intrinsic time-series primitives (domain semantics, discriminative features, and structural representations), resulting in high modeling costs, poor generalization, and low inference efficiency. Method: We propose a paradigm shift—“primitive alignment before task customization”—and introduce a structure-aware alignment framework grounded in time-series intrinsic primitives. We design three novel alignment mechanisms: injection-based, bridging-based, and endogenous alignment, integrated with an instruction-guided paradigm selection strategy. Contribution/Results: Through systematic literature analysis and alignment-driven methodology design, we establish a reusable, cross-domain (e.g., healthcare, finance, spatiotemporal) time-series reasoning framework. Our approach significantly reduces task-specific customization overhead while improving model economic efficiency, flexibility, and generalization across diverse time-series tasks and domains.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Prioritizing alignment paradigms over task-specific model customization in time-series LLMs
• Addressing costly, inflexible, inefficient current time-series reasoning approaches
• Proposing three alignment paradigms to activate LLMs' time-series reasoning capabilities

Innovation

• Prioritize alignment paradigms over task-specific customization
• Focus on time-series primitives for in-depth reasoning
• Propose injective, bridging, and internal alignment paradigms