Diffusion models promise efficient parallel text generation but rely on bidirectional attention, creating a structural mismatch with pre-trained Autoregressive (AR) models. This incompatibility precludes reusing robust AR priors, necessitating prohibitive pre-training from scratch. To bridge this gap, we propose FLUID, a framework that efficiently adapts AR backbones to the diffusion paradigm. By enforcing Strictly Causal Alignment, FLUID enables seamless initialization from standard GPT-style checkpoints, circumventing the need for massive pre-training. Furthermore, we introduce Elastic Horizons, an entropy-driven mechanism that dynamically modulates denoising strides based on local information density rather than fixed schedules. Experiments demonstrate that FLUID achieves state-of-the-art performance while reducing training costs by orders of magnitude, effectively reconciling established AR foundations with efficient parallel generation. Our code is available at https://huggingface.co/MYTH-Lab/FLUID.

Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) methods have demonstrated significant potential on tasks across multiple domains. However, ellipses and coreferences, as common phenomena in dialogue scenes, pose challenges to LLMs’ understanding and RAG’s retrieval accuracy. The previous works ignore the negative impact of this fuzzy data on RAG system.We explore the capabilities of LLMs and RAG systems in dialogue scenarios and use Incomplete Utterance Rewriting (IUR) to complete the key information in dialogue to enhance retrieval.Besides, we propose a lightweight IUR model for query rewriting. It is an end-to-end framework for node linking and iterative inference, incorporating two newly proposed probing semantic features derived from generative pre-training. This framework treats IUR as a series of link decisions on the input sequence and the incrementally constructed rewriting outputs.To test the performance of RAG system in the model multi-round dialogue scenario, we construct an RAG dialogue dataset on English and Chinese, Dialogue-RAG-MULTI-v1.0.Experiment results show that utterance rewriting can effectively improve the retrieval and generation ability of RAG system in dialogue scenes. Experiments on IUR tasks demonstrate the excellent performance of our lightweight IUR method.