| title | DiffResearch: Accelerating Deep Research with Diffusion Language Models |
|---|---|
| authors | Alan-Barsag Gazzaev, Alexey Gavrilov, Mikhail Mozikov, Sergey Muravyov |
| date | 2026-10-20 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.28909 |
| affiliation | ITMO University |
| upvotes | 8 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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 are built on autoregressive (AR) language models and inherit left-to-right decoding in planning and long-form writing. We present DiffResearch, an open multi-agent deep research framework that places a diffusion language model (dLLM) at the core of every stage, and measure how a diffusion backbone changes latency and report quality. Holding the agentic scaffold, retrieval code, prompts, and evaluation fixed, we swap the backbone between an open diffusion model and a comparable AR model. Across 100 Deep Research Bench tasks, the diffusion backbone is roughly 6 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, providing a 1.99 times median speedup for decomposition in isolation with no loss of report quality. On the benchmark's RACE rubric, dFast obtains the best overall score among the three configurations. Finally, in a public system-level leaderboard snapshot, an instance built on a stronger diffusion model ranks above several commercial systems. We release DiffResearch under an Apache-2.0 license. |
cite
@misc{gazzaev2026diffresearch,
title={DiffResearch: Accelerating Deep Research with Diffusion Language Models},
author={Alan-Barsag Gazzaev and Alexey Gavrilov and Mikhail Mozikov and Sergey Muravyov},
year={2026},
eprint={2610.28909},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.28909}
}