Diffusion language models (DLMs) have emerged as a powerful non-autoregressive alternative to GPT-style sequential generation, but suffer from substantial computational overhead due to their iterative parallel denoising.Existing acceleration works cannot accurately detect semantically stabilized tokens and then skip computation, leading to sub-optimal speedup in practice.This paper presents the first systematic study of convergence dynamics in DLMs.Innovative observations include the misalignment between traditionally used scalar detection criterion and the semantic convergence, and the post-peak confidence score, that wastes denoising computation and degrades inference quality.To address these limitations, we propose Ada-DLM, a semantic-aware adaptive denoising framework that encodes the trajectory of scalar confidence scores into an evolutionaware feature vector and then clusters vectors proactively to adaptively identify semantically converged tokens.Furthermore, we incorporate system-level optimizations to maximize runtime efficiency.Experiments show that Ada-DLM consistently outperforms the SOTA competitor, achieving up to 2× speedup and 19% quality improvement.That offers a practical path toward efficient high-quality DLM deployment.

Traditional Web crawlers use one or more URLs of the initial Webpages to extract new URLs continuously, and then access data of the pages. AJAX, as one of the core technologies of Web2.0, greatly enhances the response efficiency of Web applications, brings good user experience, and therefore has been widely used. However, due to the use of AJAX techniques shatters the architecture of traditional Web pages which is based on static pages, the traditional Web crawlers cannot meet the challenges of dynamic partial refresh and asynchronous loading. In this paper, we propose an efficient approach for the information in dynamic pages by analyzing script language, and use path repository and judge the page refreshing state to improve the accuracy and efficiency of the algorithm. Experimental evaluation shows the efficiency and effectiveness of our approach.