This paper investigates the effectiveness of AI for fault localization using only natural-language bug reports in an industrial setting. It benchmarks traditional machine learning models against fine-tuned transformer models using proprietary data from ABB Robotics, finding that traditional models often outperform transformers in this context.

This paper proposes QAROO, an AI-driven online task offloading framework for wireless-powered mobile edge computing (MEC) networks. It integrates quantum neural networks, attention mechanisms, and recurrent neural networks to co-optimize computing and energy resources, offering an efficient and stable solution for dynamic IoT environments.

This paper introduces an autoresearch framework that uses AI coding agents to optimize ground-state preparation protocols for quantum and quantum-classical many-body systems. It demonstrates how this method can effectively tune hyperparameters for VQE, DMRG, and AFQMC protocols, leading to improved energy proxies within fixed computational budgets across various spin models and molecular Hamiltonians.

This paper introduces an AI-based Automatic Ground Collision Avoidance System (AGCAS) for advanced jet trainers. It utilizes a customized Soft Actor-Critic (SAC) algorithm, integrated with a custom Convolutional Neural Network (CNN) and hyperparameter optimization, to enhance safety and operational capabilities by effectively avoiding ground collisions within limited observation spaces.

This paper introduces a novel hardware-aware Neural Architecture Search (NAS) framework that integrates deployment-aligned low-precision training. This approach addresses the accuracy degradation caused by the mismatch between full-precision optimization and low-precision deployment on edge accelerators, particularly for spaceborne AI applications.