·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Replit is no longer working with Anthropic, CEO says7h◆How Itaú is building generative AI for its entire customer base9h◆Roundtables: A Conversation With the Creator of AI-Designed Viruses10h◆India becomes the latest country to report rogue AI agent activity10h◆An Nvidia insider-turned-VC on why robotics hasn’t hit its ChatGPT moment, yet12h◆Physical AI is more than robots, says the Nvidia veteran now investing in it12h◆Elorian’s first AI model wants to reason with images, not just about them12h◆Jev is Vercel’s fastest-adopted AI model ever13h◆A week with RTX Spark: everything we’ve learned using Nvidia’s new laptops13h◆Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy14h◆Meta’s new terms may allow it to train AI on news sites for free14h◆Veritas expands its anti-counterfeit chip to jewelry, trophies, and fragrance bottles14h◆Replit’s CEO says the future of work is the ‘self-driving company’15h◆Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets1d◆Judging the Judges: Confidence and Disagreement in Rubrics as Rewards1d◆Content associations with answer selection and engagement in supply chain community question answering1d◆ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents1d◆REKI: Registering Knowledge in VLAs1d◆Multimodal Remote Inference1d◆HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding1d◆Replit is no longer working with Anthropic, CEO says7h◆How Itaú is building generative AI for its entire customer base9h◆Roundtables: A Conversation With the Creator of AI-Designed Viruses10h◆India becomes the latest country to report rogue AI agent activity10h◆An Nvidia insider-turned-VC on why robotics hasn’t hit its ChatGPT moment, yet12h◆Physical AI is more than robots, says the Nvidia veteran now investing in it12h◆Elorian’s first AI model wants to reason with images, not just about them12h◆Jev is Vercel’s fastest-adopted AI model ever13h◆A week with RTX Spark: everything we’ve learned using Nvidia’s new laptops13h◆Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy14h◆Meta’s new terms may allow it to train AI on news sites for free14h◆Veritas expands its anti-counterfeit chip to jewelry, trophies, and fragrance bottles14h◆Replit’s CEO says the future of work is the ‘self-driving company’15h◆Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets1d◆Judging the Judges: Confidence and Disagreement in Rubrics as Rewards1d◆Content associations with answer selection and engagement in supply chain community question answering1d◆ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents1d◆REKI: Registering Knowledge in VLAs1d◆Multimodal Remote Inference1d◆HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding1d◆
News/Universal Local Error and Realized Amplification for the First-Order EDM Predictor
arxiv
PublishedOctober 8, 2026 at 4:00 AM

Universal Local Error and Realized Amplification for the First-Order EDM Predictor

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2610.10190v1 Announce Type: cross Abstract: We analyze the first-order deterministic diffusion sampler of Karras et al. (2022), termed EDM, in 2-Wasserstein distance by separating two sources of error: local discretization error and its amplification by subsequent learned steps. We prove that

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets1darxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards1darxivContent associations with answer selection and engagement in supply chain community question answering1darxivORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents1d
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews