arXiv subjects

Chaoran Wu

Publications and source records attributed to Chaoran Wu.

2 recordsLinked to original sources

arXiv2026

WordCraft: Scaffolding the Keyword Method for L2 Vocabulary Learning with Multimodal LLMs

Applying the keyword method for vocabulary memorization remains a significant challenge for L1 Chinese-L2 English learners. They frequently struggle to generate phonologically appropriate keywords, construct coherent associations, and create vivid mental imagery to aid long-term retention. Existing approaches, including fully automated keyword generation and outcome-oriented mnemonic aids, either compromise learner engagement or lack adequate process-oriented guidance. To address these limitations, we conducted a formative study with L1 Chinese-L2 English learners and educators (N=18), which revealed key difficulties and requirements in applying the keyword method to vocabulary learning. Building on these insights, we introduce WordCraft, a learner-centered interactive tool powered by Multimodal Large Language Models (MLLMs). WordCraft scaffolds the keyword method by guiding learners through keyword selection, association construction, and image formation, thereby enhancing the effectiveness of vocabulary memorization. Two user studies demonstrate that WordCraft not only preserves the generation effect but also achieves high levels of effectiveness and usability.

cs.CL↗

arXiv2025

Multi-Agent Deep Reinforcement Learning for Multiple Anesthetics Collaborative Control

Automated control of personalized multiple anesthetics in clinical Total Intravenous Anesthesia (TIVA) is crucial yet challenging. Current systems, including target-controlled infusion (TCI) and closed-loop systems, either rely on relatively static pharmacokinetic/pharmacodynamic (PK/PD) models or focus on single anesthetic control, limiting personalization and collaborative control. To address these issues, we propose a novel framework, Value Decomposition Multi-Agent Deep Reinforcement Learning (VD-MADRL). VD-MADRL optimizes the collaboration between two anesthetics propofol (Agent I) and remifentanil (Agent II). And It uses a Markov Game (MG) to identify optimal actions among heterogeneous agents. We employ various value function decomposition methods to resolve the credit allocation problem and enhance collaborative control. We also introduce a multivariate environment model based on random forest (RF) for anesthesia state simulation. Additionally, a data resampling and alignment technique ensures synchronized trajectory data. Our experiments on general and thoracic surgery datasets show that VD-MADRL performs better than human experience. It improves dose precision and keeps anesthesia states stable, providing great clinical value.

eess.SY↗