Wang Cai, Yilin Wen, Jinchang Hou, Du Su, Guoqiu Wang, Zhonghou Lv, Chenfu Bao, Yunfang Wu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Smart transportation networks face increasing demands for efficiency and sustainability. This study presents a reinforcement learning approach that optimizes eco-driving strategies for connected and automated vehicles (CAVs) in urban environments, with a particular application to healthcare logistics. Specifically, we propose a novel approach using reinforcement learning, specifically a twin delayed deep deterministic policy gradient (TD3) algorithm, to dynamically optimize CAV trajectories at signalized intersections. The proposed healthcare eco-driving trajectory optimization (TD3-HETO) model incorporates real-time traffic conditions, signal timing information, and healthcare urgency levels to generate optimal acceleration profiles. The reward function is designed to balance energy efficiency, traffic flow, safety, comfort, and healthcare delivery timeliness. Additionally, the model introduces a dynamic exploration strategy that adapts to healthcare task urgency, enabling efficient balancing between energy consumption and delivery timelines. Experimental results show that TD3-HETO reduces energy consumption by up to 28.7% compared to baseline methods while improving average speeds by 3.7% for urgent healthcare deliveries. The model achieves superior safety performance with 98.7% of time steps showing zero conflicts, compared to 95.3% for the best baseline. TD3-HETO also demonstrates remarkable adaptability to varying traffic demands and signal timings, maintaining consistent performance even at high traffic volumes. This research contributes to developing intelligent transportation systems to enhance environmental sustainability and healthcare accessibility in smart cities, potentially improving patient outcomes and operational efficiency in urban healthcare logistics.

In-Context derived Vector (ICV) methods extract task-relevant representations from large language models (LLMs) and reinject them during inference, achieving comparable performance to few-shot In-Context Learning (ICL) without repeated demonstration processing.However, existing ICV methods remain sensitive to ICL-specific factors, often use coarse or semantically fragmented representations as the source of the vector, and rely on heuristicbased injection positions, limiting their applicability.To address these issues, we propose Dynamic Vector (DyVec), which incorporates an Exhaustive Query Rotation (EQR) strategy to extract robust semantically aggregated latent representations by mitigating variance introduced by ICL.It then applies Dynamic Latent Segmentation and Injection to adaptively partition representations based on task complexity and leverages REINFORCE-based optimization to learn optimal injection positions for each segment.Experiments results show that DyVec outperforms few-shot ICL, LoRA, and prior ICV baselines.Further analysis highlights the effectiveness of dynamically segmenting and injecting semantically aggregated latent representations.DyVec provides a lightweight and data-efficient solution for inference-time task adaptation.

The rapid proliferation of Internet of Things (IoT) devices in healthcare has posed considerable hurdles in data processing, energy efficiency, and real-time response demands. This study presents a novel framework for cognitive energy-Efficient healthcare-enabled aerial computing and trajectory optimization (CEHEAT) to solve these difficulties. CEHEAT enhances energy efficiency by merging cognitive IoT (CIoT) capabilities with autonomous aerial vehicles (AAV) aerial computing to address the varied needs of healthcare applications. The system guarantees efficient data collecting and processing using cognitive skills, such as adaptive sensing and dynamic resource allocation while ensuring trustworthy healthcare monitoring. Simulation results demonstrate that CEHEAT significantly outperforms existing approaches across multiple performance metrics. The framework achieves 25%–35% higher energy efficiency, 3x faster convergence, and improved scalability compared to baseline methods. Moreover, CEHEAT’s adaptive sensing and speed control mechanisms contribute to overall energy savings while maintaining effective coverage of the healthcare IoT network. The findings indicate CEHEAT’s ability to improve the efficiency and effectiveness of smart healthcare systems, especially in situations that demand real-time monitoring, faster response as well as large quantities of data processing. This work gives useful guidelines for deploying the AAV-assisted aerial computing system in health facilities that extend the scope of healthcare delivery systems.