The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two classical combinatorial optimization problems. In recent years, their drone-assisted variants, the Traveling Salesman Problem with Drones (TSP-D) and the Vehicle Routing Problem with Drones (VRP-D) have attracted growing attention. Generally, these problems are solved using exact algorithms or metaheuristic algorithms. However, as the problem complexity increases and the scale of instances grows, these approaches often become less efficient. In this paper, we propose a reinforcement learning method with a shared attention encoder and a hierarchical dual-decoder architecture, where truck–drone coordination is achieved by first decoding the truck’s next node and then conditionally decoding the drone action. To further explore the solution space of large-scale instances, the proposed method adopts a multi-rollout learning strategy. We conducted experiments on large-scale TSP-D and VRP-D instances, and the results show that this model outperforms traditional metaheuristic algorithms in terms of both solution quality and computational efficiency.
With the explosive growth of large language models (LLMs), research on machine-generated text detection (MGTD) has also proliferated. Alongside these developments, a wide range of attack algorithms targeting MGTD systems have emerged. While previous studies have surveyed detection techniques, few have examined the dynamic interplay between attack and defense. Following PRISMA 2020, this paper systematically synthesizes 27 studies of attacks against MGTD and the available evidence on corresponding defenses. We categorize existing research into four major types of evasion strategies: watermark attacks, paraphrasing attacks, prompt-based attacks, and adversarial-text attacks, and summarize the available defense evidence. Furthermore, to better understand the practical implications of these methods, we compile the reported performance results of attack and defense techniques across different detectors. Finally, we highlight the current challenges in this area and outline potential future research directions. A companion repository containing the categorized literature, paper links, and available code, data, and project repositories is provided at https://github.com/AIGC1999/A-Survey-of-Anti-Detection-Techniques-for-Machine-Generated-Texts.
De-Yu Meng, Tad Gonsalves· Neural Networks· 0 citations
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