LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
arXiv:2605.20449v1 Announce Type: new Abstract: Can language-pretrained transformers become effective time-series forecasters, and why? In this paper, we show that cross-modal transfer arises because language pretraining preconditions time series training with a reusable manifold. A linear probe on frozen LLM states decodes realistic time-series trajectories without paired supervision, and retrieval in this projected space yields competitive forecasts, showing that structure and dynamics exist before finetuning. Pretrained initialization also improves optimization, producing coherent gradients
This research builds on recent advancements in large language models and their unexpected emergent capabilities, exploring their generalization beyond text.
It suggests that LLMs could become versatile foundational models for diverse data types, significantly expanding their application domains and market reach beyond traditional NLP tasks.
The understanding that LLMs might possess a generalizable 'manifold' capable of encoding complex patterns across modalities changes their perceived utility from language-specific tools to potentially general-purpose intelligence architectures.
- · AI researchers
- · Time series data industries
- · Developers of foundational AI models
- · Specialized time series forecasting models
Cross-modal transfer research accelerates, leading to more efficient development of AI systems capable of handling diverse data types.
New AI services and products emerge that leverage LLMs for tasks previously requiring bespoke models, such as predictive maintenance, financial forecasting, and climate modeling.
The definition of 'intelligence' in AI expands to encompass multimodal pattern recognition and generalization, potentially shifting investment and research priorities towards more unified AI architectures.
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Read at arXiv cs.LG