·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response26m◆Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training26m◆Large language model and reinforcement learning based autonomous construction and progress updates26m◆Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models26m◆MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring26m◆DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution26m◆Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing26m◆Fix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 426m◆MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning26m◆LightCRS: Lightweight Multi-Domain Conversational Recommendation System26m◆When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability26m◆SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents26m◆Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?26m◆Teaching AI Agent to Play Poker Strategically Through Preference Optimization26m◆M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement26m◆Physical AI: Concepts, Techniques, and Challenges26m◆ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems26m◆A general-purpose System One decision model for clinical records26m◆Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMs26m◆PROVISO: Provenance-Informed Action Selection for Secure LLM Agents26m◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response26m◆Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training26m◆Large language model and reinforcement learning based autonomous construction and progress updates26m◆Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models26m◆MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring26m◆DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution26m◆Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing26m◆Fix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 426m◆MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning26m◆LightCRS: Lightweight Multi-Domain Conversational Recommendation System26m◆When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability26m◆SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents26m◆Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?26m◆Teaching AI Agent to Play Poker Strategically Through Preference Optimization26m◆M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement26m◆Physical AI: Concepts, Techniques, and Challenges26m◆ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems26m◆A general-purpose System One decision model for clinical records26m◆Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMs26m◆PROVISO: Provenance-Informed Action Selection for Secure LLM Agents26m◆
News/HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures
arxiv
PublishedJuly 3, 2026 at 4:00 AM

HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures

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

arXiv:2607.02266v1 Announce Type: cross Abstract: Most data-mixing methods assume the corpus has already been partitioned into groups, and the choice of those groups determines what a mixer can express. Existing labels, including provenance, topic or format taxonomies, and flat embedding clusters, c

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
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response26marxivNavalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training26marxivLarge language model and reinforcement learning based autonomous construction and progress updates26marxivPareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models26m
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