The rapid advancement of large language models (LLMs) has intensified the need for effective mechanisms to transform continuous multimodal data into discrete representations suitable for language-based processing. Discrete tokenization, with vector quantization (VQ) as a central approach, offers both computational efficiency and compatibility with LLM architectures. Despite its growing importance, there is a lack of a comprehensive survey that systematically examines VQ techniques in the context of LLM-based systems. This work fills this gap by presenting the first structured taxonomy and analysis of discrete tokenization methods designed for LLMs. We categorize 8 representative VQ variants that span classical and modern paradigms and analyze their algorithmic principles, training dynamics, and integration challenges with LLM pipelines. Beyond algorithm-level investigation, we discuss existing research in terms of classical applications without LLMs, LLM-based single-modality systems, and LLM-based multimodal systems, highlighting how quantization strategies influence alignment, reasoning, and generation performance. In addition, we identify key challenges including codebook collapse, unstable gradient estimation, and modality-specific encoding constraints. Finally, we discuss emerging research directions such as dynamic and task-adaptive quantization, unified tokenization frameworks, and biologically inspired codebook learning. This survey bridges the gap between traditional vector quantization and modern LLM applications, serving as a foundational reference for the development of efficient and generalizable multimodal systems.

The proliferation of healthcare IoT devices and the resulting rich healthcare data sprout new possibilities for intelligent healthcare applications. Patients in intensive care units (ICUs) rely on various networked gadgets to continuously monitor their health and manage critical situations. Among the common therapeutic interventions in ICUs, invasive mechanical ventilation and injecting sedatives during ventilation play crucial roles in maintaining respiratory function and enhancing patient care. While existing therapeutic interventions largely depend on experience and intuition, we propose a federated inverse reinforcement learning framework, termed FERRY, which automatically and intelligently learns optimal therapeutic intervention policies across networked ICUs while keeping raw data local. Specifically, our federated approach overcomes limitations in medical data privacy and facilitates collaboration; our proposed inverse reinforcement learning framework learns the variational posterior distribution from historical trajectories to handle the unknown reward. Additionally, we enhance our framework with distributionally robust optimization to ensure worst-case performance and adaptively filter out noisy data through joint loss learning. Extensive experiments on the real-world dataset demonstrate that FERRY improves the overall ventilation and sedation decision-making accuracy by 36.75% compared to other state-of-the-art baselines.

Clinical decision-making models have been developed to support therapeutic interventions based on medical data from either a single hospital or multiple hospitals. However, models based on multihospital data require collaboration among hospitals to integrate local data, which can result in information leakage and violate patient privacy. To address this challenge, we propose a novel approach that combines federated learning (FL) with inverse reinforcement learning (IRL) to create an efficient medical decision-making support tool while preserving patient privacy. Our approach uses an IRL algorithm with differential privacy to train a neural network-based agent on local data containing clinician trajectories, which learns a private treatment policy by observing patients’ conditions. Additionally, we integrate FL into the proposed algorithm to learn a global optimal action policy collaboratively among various smart intensive care units, overcoming data limitations at each hospital. We evaluate our approach using real-world medical data and demonstrate that it achieves superior performance in a distributed manner.