·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models6h◆Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling6h◆Evidence-Aligned Local Composition of Discrete Experts for Sequence Restoration6h◆Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model6h◆FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data6h◆A Multi-Vehicle Dataset with Camera, LiDAR, and Radar Sensors and Scanned 3D Models for Custom Auto-Annotation using RTK-GNSS6h◆Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval6h◆Physics-enriched neural solvers for transient ice-flow simulation6h◆Dissecting GPU Utilization for LLM Inference on Nvidia Hopper6h◆Scaling Online Complex Event Detection with Synthetic Supervision and Mamba-Based Neural Algorithmic Reasoning6h◆Space as an Interventional Invariant: Cross-Modal Predictive Geometry for Stratified Cities and Em-Spaced Intelligence6h◆Cortex: Content Analysis Support Software, a Resource for Qualitative Research6h◆Is Bash All You Need? An Empirical Study of Tool Interfaces for Enterprise Digital Worker Agents6h◆KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs6h◆Translationese as a Rational Response to Translation Task Difficulty6h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization6h◆Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks6h◆Classical and quantum kernel fusion for two-sample testing6h◆Granularity-Adaptive Credit Assignment for Long-Horizon LLM Agent Reinforcement Learning6h◆One Simple Trick for Improving the Performance of Energy-Limited Local Inference and Training6h◆Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models6h◆Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling6h◆Evidence-Aligned Local Composition of Discrete Experts for Sequence Restoration6h◆Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model6h◆FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data6h◆A Multi-Vehicle Dataset with Camera, LiDAR, and Radar Sensors and Scanned 3D Models for Custom Auto-Annotation using RTK-GNSS6h◆Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval6h◆Physics-enriched neural solvers for transient ice-flow simulation6h◆Dissecting GPU Utilization for LLM Inference on Nvidia Hopper6h◆Scaling Online Complex Event Detection with Synthetic Supervision and Mamba-Based Neural Algorithmic Reasoning6h◆Space as an Interventional Invariant: Cross-Modal Predictive Geometry for Stratified Cities and Em-Spaced Intelligence6h◆Cortex: Content Analysis Support Software, a Resource for Qualitative Research6h◆Is Bash All You Need? An Empirical Study of Tool Interfaces for Enterprise Digital Worker Agents6h◆KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs6h◆Translationese as a Rational Response to Translation Task Difficulty6h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization6h◆Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks6h◆Classical and quantum kernel fusion for two-sample testing6h◆Granularity-Adaptive Credit Assignment for Long-Horizon LLM Agent Reinforcement Learning6h◆One Simple Trick for Improving the Performance of Energy-Limited Local Inference and Training6h◆
News/KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs
arxiv
PublishedSeptember 14, 2026 at 4:00 AM
—neutral

KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs

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

arXiv:2510.18368v2 Announce Type: replace Abstract: We present $\textbf{Korean SimpleQA (KoSimpleQA)}$, a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is designed to be challenging yet easy to grade, consisting of 938 short

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
arxivBenford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models6harxivTransformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling6harxivEvidence-Aligned Local Composition of Discrete Experts for Sequence Restoration6harxivDynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model6h
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