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 evolution-aware feature vector and then clusters vectors proactively and 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 2x speedup and 19% quality improvement. That offers a practical path toward efficient high-quality DLM deployment.

This paper focuses on a new task of answering geographic reasoning questions based on the given image (called GeoVQA). Unlike traditional VQA tasks, GeoVQA asks for details about the image-related culture, landscape, etc. This requires not only the identification of the objects in the image, their properties and relations, but also the understanding of the geographic knowledge of the objects, such as location, transportation, landmark, cuisine, etc. This background knowledge does not explicitly appear in the image, nor is there an extra-textual description. Without this missing but necessary knowledge, it is difficult for existing matching-based methods to infer the correct answer. To tackle these challenges, we propose a new geographic reasoning framework for our task. We first analyze the image and describe its fine-grained content by text and keywords using a multi-modal retrieval augmented technique, so as to deduce an answer in a unified textual modality. Next, we retrieve the crucial geographic commonsense knowledge. To reduce the retrieval complexity, we design a dynamic method that can adaptively collect the relevant clues for each reasoning step. The step in the incorrect direction will be pruned according to some judgment criteria. The remaining steps can help us form a reasoning chain to derive a correct answer. Moreover, we create a large-scale dataset GVQA with 41,329 samples to conduct the evaluation. The results demonstrate the effectiveness of our approach.