Deep research systems iteratively plan, gather evidence, synthesize, and revise, making end‑to‑end latency a key bottleneck for interactive use. Almost all production systems rely on autoregressive (AR) language models and inherit left‑to‑right decoding for planning and long‑form writing. DiffResearch is an open multi‑agent deep research framework that places a diffusion language model (dLLM) at the core of every stage. Keeping the agentic scaffold, retrieval code, prompts, and evaluation fixed, we swap the backbone between an open diffusion model and a comparable AR model to compare latency and report quality. Across 100 Deep Research Bench tasks, the diffusion backbone is roughly six times faster end‑to‑end, driven by order‑of‑magnitude reductions in planning and writing. We further introduce dFast, a decomposition mode that reads intermediate denoising states and extracts subqueries before generation finishes, delivering a 1.99× median speedup for decomposition in isolation with no loss of report quality. On the benchmark's RACE rubric, dFast achieves the best overall score among the three configurations. In a public system‑level leaderboard snapshot, an instance built on a stronger diffusion model ranks above several commercial systems. DiffResearch is released under an Apache‑2.0 license.
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