·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AutoSynthData: Generating Training Data for Enterprise Agents3h◆On the (In)effectiveness of AMR Augmentation for Large Language Models3h◆cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents3h◆Mitigating Memorization In Language Models3h◆MoEless: Efficient MoE LLM Serving with Serverless Experts3h◆Fork-Think with Confidence3h◆A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees3h◆Bongard: Training Machine Intuition3h◆Inference Auctions3h◆DEdit: Iterative Draft Editing for Speculative Decoding3h◆4MT-VLM: How Coarse Is a VLMs Cognitive Map?3h◆JuryFlow: Disagreement-Guided Human-in-the-Loop Multi-Agent Evaluation3h◆OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction3h◆Values as Style: Disentangling Values from Semantics with One-Way Mixing for Low-Damage LLM Steering3h◆OverForge: Reasoning Through Strategies and Tactics Helps Cooperative Lifelong Adaptation3h◆Superficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models3h◆Evaluating Persistent Calibration under Evolving Model Knowledge3h◆RAZOR: Pruning Replaceable Experts in LLMs3h◆Coding Agents for Coding Theory3h◆ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation3h◆AutoSynthData: Generating Training Data for Enterprise Agents3h◆On the (In)effectiveness of AMR Augmentation for Large Language Models3h◆cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents3h◆Mitigating Memorization In Language Models3h◆MoEless: Efficient MoE LLM Serving with Serverless Experts3h◆Fork-Think with Confidence3h◆A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees3h◆Bongard: Training Machine Intuition3h◆Inference Auctions3h◆DEdit: Iterative Draft Editing for Speculative Decoding3h◆4MT-VLM: How Coarse Is a VLMs Cognitive Map?3h◆JuryFlow: Disagreement-Guided Human-in-the-Loop Multi-Agent Evaluation3h◆OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction3h◆Values as Style: Disentangling Values from Semantics with One-Way Mixing for Low-Damage LLM Steering3h◆OverForge: Reasoning Through Strategies and Tactics Helps Cooperative Lifelong Adaptation3h◆Superficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models3h◆Evaluating Persistent Calibration under Evolving Model Knowledge3h◆RAZOR: Pruning Replaceable Experts in LLMs3h◆Coding Agents for Coding Theory3h◆ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation3h◆
News/Structure of Basic Human Values in Russian Social Media
arxiv
PublishedOctober 2, 2026 at 4:00 AM
—neutral

Structure of Basic Human Values in Russian Social Media

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

arXiv:2603.18822v2 Announce Type: replace Abstract: What is known about the values of national populations rests almost entirely on questionnaires, which prompt respondents to rate researcher-supplied descriptions of each motivation. Social media instead records values as they are invoked spontaneou

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
arxivOn the (In)effectiveness of AMR Augmentation for Large Language Models3harxivcua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents3harxivMitigating Memorization In Language Models3harxivMoEless: Efficient MoE LLM Serving with Serverless Experts3h
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 ↗
Built by Marouane Gazouzi
HomeModelsNews