arxiv
PublishedOctober 16, 2026 at 4:00 AM
ManiFusion: Unlocking the High-Throughput Generation via Superposition in Manifold Space
Publisher summary· verbatim
arXiv:2610.21433v1 Announce Type: cross Abstract: Diffusion models achieve high-quality image generation but remain computationally expensive due to iterative denoising across many timesteps. Existing acceleration methods mainly reduce per-sample latency, but gains along this axis are becoming satur
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response27marxivNavalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training27marxivLarge language model and reinforcement learning based autonomous construction and progress updates27marxivPareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models27mThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗