Dev reproduces Google query fanout paper: diffusion model outputs vectors, skipping text
dejanseo · x · 2026-09-24
dejanseo reproduced Google's new query fanout research, trained the model himself, and argues that if it ships, text fanouts are gone.
- Key insight: the research skips text generation entirely—a small diffusion model is trained on vectors and outputs vectors, enabling direct semantic document retrieval.
- He simplified the pipeline by skipping RL, using teacher models to generate quality synthetic fanout data.
- The trained model is open-sourced on Hugging Face (dejanseo/fanout-diffusion) with a live Space demo that uses embeddings instead of text queries, trained on Google's e-commerce taxonomy fanouts.
- He reports blazing speeds using a 1-bit architecture with custom CUDA kernels.
Related event: Developer Replicates Google Query Fanout Research with Working Model(2 posts)→
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