The increasing number of maritime transport vessels has elevated ship distress accidents, emphasizing the imperative for effective Search and Rescue (SAR) operations. In this study, we introduce a coverage path planning framework for multiple Unmanned Aerial Vehicles (UAVs) to optimize maritime SAR missions by explicitly accounting for regional priorities. The proposed framework hierarchically incorporates these priorities and derives coverage paths using a heuristic algorithm based on Ant Colony Optimization (ACO) for each priority region. The algorithm consists of two key phases: the Region Allocation Phase (RAP), which utilizes a greedy approach guided by regional importance, and the Dynamic Refinement Phase (DRP), which employs ACO to ensure balanced resource usage. Comprehensive numerical experiments, utilizing actual sea coordinates, are conducted to validate the effectiveness of the proposed methodology. The proposed method outperforms both a MILP model and a reinforcement learning-based approach, particularly in large-scale instances. It demonstrates computational robustness and scalability, consistently generating high-quality solutions within practical computation times, even in complex environments. The methodology is further validated through experiments on irregular environments, confirming its adaptability and effectiveness.

Masked diffusion models have demonstrated competitive results on various tasks including language generation. However, due to its iterative refinement process, the inference is often bottlenecked by slow and static sampling speed. To overcome this problem, we introduce `KL-Adaptive Stability Sampling' (KLASS), a fast yet effective sampling method that exploits token-level KL divergence to identify stable, high-confidence predictions. By unmasking multiple tokens in each iteration without any additional model training, our approach speeds up generation significantly while maintaining sample quality. On reasoning benchmarks, KLASS achieves up to $2.78\times$ wall-clock speedups while improving performance over standard greedy decoding, attaining state-of-the-art results among diffusion-based samplers. We further validate KLASS across diverse domains, including text, image, and molecular generation, showing its effectiveness as a broadly applicable sampler across different models.

Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation remains a fundamental challenge due to an intrinsic trade-off between targeting specific chemical properties and generating meaningful samples from the data distribution. In this work, we present Time-Aware Conditional Synthesis (TACS), a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties while maintaining validity and stability. A key component of our algorithm is our new type of diffusion sampler, Time Correction Sampler (TCS), which is used to control guidance and ensure that the generated molecules remain on the correct manifold at each reverse step of the diffusion process at the same time. Our proposed method demonstrates significant performance in conditional 3D molecular generation and offers a promising approach towards inverse molecular design, potentially facilitating advancements in drug discovery, materials science, and other related fields.