·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
The browser wars aren’t about search anymore — here are the best alternatives to Chrome and Safari56m◆Building AI infrastructure with the Effingham County community1h◆Passionfroot raises $15M to expand its B2B creator marketplace to the US1h◆3 Google updates from Galaxy Unpacked 20261h◆Meta made its own AI detection system. It should have just used Google’s3h◆Utility companies promise to spare us from AI’s energy bill4h◆Glow emerges from stealth at $1.2B valuation to challenge endpoint security in the AI era4h◆Synthesia’s AI training platform is moving beyond videos into live coaching6h◆Introducing OpenAI Presence8h◆SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval10h◆DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models10h◆HPD-Parsing: Hierarchical Parallel Document Parsing10h◆Incomplete Observations Boost Evolutionary Performance in Ocean Modeling10h◆SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling10h◆Calibrated Selective Fact-Checking via Evidence Chain Evaluation10h◆NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift10h◆SAAG: Structured Agent Assessment and Grounding10h◆Kernel Regression with Tensor Trains and Hadamard Overparameterization10h◆MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction10h◆The Information Shadow: Measuring Structural Limits on What Language Models Can Learn10h◆The browser wars aren’t about search anymore — here are the best alternatives to Chrome and Safari56m◆Building AI infrastructure with the Effingham County community1h◆Passionfroot raises $15M to expand its B2B creator marketplace to the US1h◆3 Google updates from Galaxy Unpacked 20261h◆Meta made its own AI detection system. It should have just used Google’s3h◆Utility companies promise to spare us from AI’s energy bill4h◆Glow emerges from stealth at $1.2B valuation to challenge endpoint security in the AI era4h◆Synthesia’s AI training platform is moving beyond videos into live coaching6h◆Introducing OpenAI Presence8h◆SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval10h◆DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models10h◆HPD-Parsing: Hierarchical Parallel Document Parsing10h◆Incomplete Observations Boost Evolutionary Performance in Ocean Modeling10h◆SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling10h◆Calibrated Selective Fact-Checking via Evidence Chain Evaluation10h◆NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift10h◆SAAG: Structured Agent Assessment and Grounding10h◆Kernel Regression with Tensor Trains and Hadamard Overparameterization10h◆MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction10h◆The Information Shadow: Measuring Structural Limits on What Language Models Can Learn10h◆
News/Large Language Models Explore by Latent Distilling
arxiv
PublishedJuly 22, 2026 at 4:00 AM

Large Language Models Explore by Latent Distilling

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

arXiv:2604.24927v2 Announce Type: replace-cross Abstract: Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limiting semantic exploration. In this paper, we propose Explorator

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

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

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
arxivSkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval10harxivDBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models10harxivHPD-Parsing: Hierarchical Parallel Document Parsing10harxivIncomplete Observations Boost Evolutionary Performance in Ocean Modeling10h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews