DIGAA: Decoupling Information Gathering and Accurate Answering in Retrieval-Augmented Diffusion Language Models

Diffusion language models (DLMs) have recently attracted attention in retrieval-augmented generation (RAG) owing to their ability to predict tokens at all masked positions in parallel at each denoising step. These parallel predictions can reveal bridge entities early (Jünger et al., 2026) and provide diverse information, thereby enabling in-depth and broad retrieval. However, this introduces noise along with additional information, making it harder for the model to generate accurate answers. The conflict here is that in-depth and broad retrieval of external knowledge introduces wide-ranging information, while answer generation requires accuracy. Therefore, we introduce Decoupled Information Gathering and Accurate Answering (DIGAA), which explicitly separates the roles of information gathering and answer generation to reduce this conflict. During information gathering, the model uses bridge entities generated during intermediate reasoning to explore external knowledge and collect relevant facts. During answer generation, the model needs to select and integrate this information to answer directly and accurately. Experiments on Dream-7B-Instruct and LLaDA-8B-Instruct across six factual QA benchmarks show that, relative to the strongest baseline for each benchmark and metric, DIGAA improves Dream's average EM and F1 by 4.10 and 5.16 percentage points, respectively, and LLaDA's by 12.39 and 15.61 percentage points. These results indicate that decoupling wide-ranging information gathering from accurate answering is a promising design principle when DLMs are augmented with dynamic retrieval.