Ya Zhang

SARAD proposes a novel safety-aware hybrid framework that combines Large Language Models (LLMs) and Deep Reinforcement Learning (DRL) to improve autonomous driving decision-making by replacing random exploration with expert-guided decisions and adding collision prediction.

The paper reframes Parameter-Efficient Fine-Tuning (PEFT) from a mere cost-saving alternative to a robust architecture for creating persistent, personalized models that layer specific behaviors onto large shared foundation models.

This paper introduces MedReCo and MedReCo-VLM, a framework that enables entity-aware cross-image reasoning for medical imaging, allowing AI to compare current scans with prior studies and analogous cases based on structured clinical reports.

CLIFE is an edge-native camera-LiDAR fusion framework that enhances perception of vulnerable road users under varied environmental and traffic conditions, with adaptive calibration, O(N log N) per-frame cost, and high throughput.

This paper introduces CW-Ghost, a method for estimating cache line fill volume and determining helper-thread prefetching granularity based on cache capacity constraints.