·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
OpenAI says it will no longer train its AI models on the live internet, citing “significant risks”9h◆Hottest toy for Christmas? Nvidia CEO plays with adorable Microduck robots, 20,000 sell10h◆OpenAI now trains its frontier AI models with no internet access11h◆Jack Dorsey’s Block made an AI model that talks to you like an awkward new friend13h◆From Toast to Bessemer: Who founders will hear from at TechCrunch Founder Summit 202613h◆The iPhone Duo should have been an iPad15h◆Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)15h◆Now Google says it might use your Gmail and Google Photos data to target ads15h◆LitPivot: Developing research ideas through dynamic contextualization within the literature landscape16h◆Meta delays new business terms after publisher pushback on AI scraping18h◆Retro gaming is being overrun with AI20h◆Securing our frontier model training environment22h◆VeriEvolve: Evaluator-First Evolution for Tool-Using Agents1d◆TNODEV: Toolbox for Neural ODE Verification1d◆Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding1d◆On the Expressive Power and Limitations of Multi-Layer SSMs1d◆From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale1d◆Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression1d◆A Generalisation Signal Need Not Be a Model-Selection Signal1d◆Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering1d◆OpenAI says it will no longer train its AI models on the live internet, citing “significant risks”9h◆Hottest toy for Christmas? Nvidia CEO plays with adorable Microduck robots, 20,000 sell10h◆OpenAI now trains its frontier AI models with no internet access11h◆Jack Dorsey’s Block made an AI model that talks to you like an awkward new friend13h◆From Toast to Bessemer: Who founders will hear from at TechCrunch Founder Summit 202613h◆The iPhone Duo should have been an iPad15h◆Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)15h◆Now Google says it might use your Gmail and Google Photos data to target ads15h◆LitPivot: Developing research ideas through dynamic contextualization within the literature landscape16h◆Meta delays new business terms after publisher pushback on AI scraping18h◆Retro gaming is being overrun with AI20h◆Securing our frontier model training environment22h◆VeriEvolve: Evaluator-First Evolution for Tool-Using Agents1d◆TNODEV: Toolbox for Neural ODE Verification1d◆Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding1d◆On the Expressive Power and Limitations of Multi-Layer SSMs1d◆From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale1d◆Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression1d◆A Generalisation Signal Need Not Be a Model-Selection Signal1d◆Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering1d◆
News/Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution
arxiv
PublishedJune 6, 2026 at 4:00 AM
▲bullish

Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution

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

arXiv:2505.11766v4 Announce Type: replace-cross Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces. While most advances focus on refining kernel parameterizations over the $d$-dimensional physical domain, the evolution of lifted embeddings remai

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 →
Tags
03
#machine-learning#artificial-intelligence#quantum-physics

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#machine-learning#artificial-intelligence#quantum-physics

Related coverage

More from ARXIV
arxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents1darxivTNODEV: Toolbox for Neural ODE Verification1darxivLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding1darxivOn the Expressive Power and Limitations of Multi-Layer SSMs1d
The Bubble Brief
MON · WED · FRI

Read machine-learning 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