·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Why China is giving away its best AI models29m◆Threads users can now chat with Meta AI in their DMs35m◆Google’s AI search is rapidly becoming the default, new data shows1h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 20261h◆This $9 key physically locks your most addictive apps1h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research2h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume4h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic5h◆The path to artificial superintelligence5h◆Closing the data loop in AI-driven drug discovery5h◆Building the enterprise environment for agentic AI5h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics7h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models13h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders13h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA13h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging13h◆Multi-Mask Diffusion Language Models for Few-Step Generation13h◆Solar Open 2 Technical Report13h◆The Geometry of Personality: Activation Steering with Jungian Cognitive Functions13h◆Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning13h◆Why China is giving away its best AI models29m◆Threads users can now chat with Meta AI in their DMs35m◆Google’s AI search is rapidly becoming the default, new data shows1h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 20261h◆This $9 key physically locks your most addictive apps1h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research2h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume4h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic5h◆The path to artificial superintelligence5h◆Closing the data loop in AI-driven drug discovery5h◆Building the enterprise environment for agentic AI5h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics7h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models13h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders13h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA13h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging13h◆Multi-Mask Diffusion Language Models for Few-Step Generation13h◆Solar Open 2 Technical Report13h◆The Geometry of Personality: Activation Steering with Jungian Cognitive Functions13h◆Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning13h◆
News/DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems
arxiv
PublishedJune 12, 2026 at 4:00 AM
—neutral

DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems

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

arXiv:2601.13591v2 Announce Type: replace Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, which often span multiple taxonomies and lack standard answers, poses a

Stay posted· Newsletter

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

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

Discussion
Mentioned models
04
  • 01
    Claude-Sonnet-4.5
  • 02
    MiMo-V2-Pro
  • 03
    GPT-5.2
  • 04
    MiMo-V2-Flash
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#benchmark#evaluation#data science#llms

No replies yet. Be first.

Mentioned models
04
  • 01
    Claude-Sonnet-4.5
  • 02
    MiMo-V2-Pro
  • 03
    GPT-5.2
  • 04
    MiMo-V2-Flash
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#benchmark#evaluation#data science#llms

Related coverage

More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models13harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders13harxivData Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA13harxivEnjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging13h
The Bubble Brief
WEEKLY

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

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

Originally published on arxiv ↗
HomeModelsNews