Recently, a research team from the Institute of Software, Chinese Academy of Sciences, proposed a novel framework for time series forecasting aimed at enhancing the performance of large language models (LLMs). The new approach, called Vector-Injected Contextual Learning (LVICL), effectively improves prediction accuracy while significantly reducing computational costs. A major challenge in applying LLMs to time series forecasting lies in the fundamental mismatch between the text-based pre-training data and the structured, sequential nature of time series data. Traditional methods attempt to bridge this gap through full fine-tuning, but this approach demands substantial computational resources and high memory usage, limiting its practical deployment. To address this, the team introduced LVICL, a context learning method that enables LLMs to adapt to time series tasks without updating any model parameters. Instead, the framework incorporates task examples directly into the input prompt, allowing the model to achieve performance akin to fine-tuning through inference alone. To overcome the instability of conventional context learning—where prediction results are sensitive to the order and selection of examples—LVICL extracts vector representations of the examples and aggregates them using a permutation-invariant method. This eliminates order dependence and enhances robustness. A lightweight adapter is then applied to refine the aggregated context vectors, suppressing irrelevant or noisy components and improving the quality of the injected information. The optimized context vectors are injected into the residual streams of the LLM at multiple layers, enabling controlled and effective guidance during inference. This design allows the model to leverage contextual knowledge without compromising its original capabilities. The researchers conducted extensive evaluations of LVICL across multiple benchmark time series datasets. Results show that LVICL achieves stable and consistent improvements in forecasting accuracy while keeping the LLM fully frozen and avoiding any training overhead. Compared to lightweight fine-tuning methods, LVICL demonstrates superior predictive performance across diverse datasets and experimental settings. It also offers a better balance between efficiency and accuracy, making it more practical for real-world applications. The findings have been accepted for presentation at The Web Conference 2026 (WWW-26), one of the top-tier international academic conferences in the field of internet technologies. The paper presents the overall framework and experimental validation of LVICL, marking a significant advancement in the application of LLMs to time series analysis.