DiffResearch: Accelerating Deep Research with Diffusion Language Models
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.
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Key points
- Deep research systems iteratively plan, gather evidence, synthesize, and revise, making end-to-end latency a key bottleneck for interactive use.
- Holding the agentic scaffold, retrieval code, prompts, and evaluation fixed, we swap the backbone between an open diffusion model and a comparable AR model.
- 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.
- We release DiffResearch under an Apache-2.0 license.
Sources (2)
- [1]DiffResearch: Accelerating Deep Research with Diffusion Language ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 08:40 PM
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.
Deep research systems iteratively plan, gather evidence, synthesize, and revise, making end-to-end latency a key bottleneck for interactive use.
- [2]DiffResearch: Accelerating Deep Research with Diffusion Language ModelsHugging Face Daily Papers · Oct 20, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
Hype check
Independently evaluated11 independent looks since
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