Beyond Uniform Tokens: Adaptive Compression for Time Series Language Models

A new adaptive token budgeting framework promises to accelerate large language models applied to time series analysis by up to 7.68 times, according to research submitted to arXiv on 11 Jun 2026 [1][2]. The method compresses redundant numerical tokens and progressively discards prompt tokens across network layers, achieving performance gains in 78% of evaluated settings [2]. Large language models, or LLMs, are neural networks trained on vast text corpora and are typically based on transformer architectures [11]. Researchers have extended these models to time series analysis by jointly modeling numerical observations and textual context through a shared token interface [2]. However, the authors of the new paper argue that time series tokens and prompt tokens carry fundamentally different information structures, making uniform processing inefficient [2]. The study, posted on the open-access repository arXiv, examines token efficiency from what it calls an asymmetric-token perspective [2]. arXiv, which began in 1991, hosts preprints across physics, computer science, and related fields and does not conduct peer review [9]. The researchers found that time series tokens exhibit highly uneven spectral contributions: many tokens share redundant frequency patterns while a small subset preserves critical temporal evidence [2]. They also observed that the influence of prompt tokens attenuates with model depth, suggesting that retaining full prompts across every layer is unnecessary [2]. Building on these observations, the team developed an adaptive token budgeting framework. It compresses time series tokens by exploiting frequency-domain structure and progressively reduces prompt tokens as data moves through the model's layers [2]. Deep learning architectures commonly stack anywhere from three to thousands of layers to process data [4]. By trimming tokens that contribute little information, the framework lowers the computational load without discarding essential signals. Experiments across forecasting, classification, imputation, and anomaly detection tasks demonstrated up to 7.68× inference acceleration and performance gains in 78% of evaluated settings [2]. The paper concludes that asymmetric token compression can make time series foundation models more scalable [2]. The work appears under arXiv's Computation and Language category and is accessible through the repository's abstract page, where community-built tools such as the Bibliographic Explorer and CORE Recommender are available via the arXivLabs framework [1][7][8].