·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Anthropic’s Dario Amodei responds: doesn’t oppose open-weight models, but fears Chinese AI3h◆Satya Nadella says companies that trust one AI for everything may not survive6h◆PSA: Your Claude shared chats and Artifacts may have ended up on Google7h◆Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system9h◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.9h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control10h◆Why China is giving away its best AI models10h◆Threads users can now chat with Meta AI in their DMs10h◆Google’s AI search is rapidly becoming the default, new data shows11h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 202612h◆This $9 key physically locks your most addictive apps12h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research12h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume14h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic15h◆The path to artificial superintelligence15h◆Closing the data loop in AI-driven drug discovery15h◆Building the enterprise environment for agentic AI16h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics18h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models23h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders23h◆Anthropic’s Dario Amodei responds: doesn’t oppose open-weight models, but fears Chinese AI3h◆Satya Nadella says companies that trust one AI for everything may not survive6h◆PSA: Your Claude shared chats and Artifacts may have ended up on Google7h◆Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system9h◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.9h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control10h◆Why China is giving away its best AI models10h◆Threads users can now chat with Meta AI in their DMs10h◆Google’s AI search is rapidly becoming the default, new data shows11h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 202612h◆This $9 key physically locks your most addictive apps12h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research12h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume14h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic15h◆The path to artificial superintelligence15h◆Closing the data loop in AI-driven drug discovery15h◆Building the enterprise environment for agentic AI16h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics18h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models23h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders23h◆
News/Quantifying and Mitigating Premature Closure in Frontier LLMs
arxiv
PublishedMay 16, 2026 at 4:00 AM
▼bearish

Quantifying and Mitigating Premature Closure in Frontier LLMs

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

arXiv:2605.15000v1 Announce Type: cross Abstract: Premature closure, or committing to a conclusion before sufficient information is available, is a recognized contributor to diagnostic error but remains underexamined in large language models (LLMs). We define LLM premature closure as inappropriate c

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
01
  • 01
    LLMs
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#safety#evaluation#language-models#medical-ai

No replies yet. Be first.

Mentioned models
01
  • 01
    LLMs
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#safety#evaluation#language-models#medical-ai

Related coverage

More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models23harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders23h
The Bubble Brief
WEEKLY

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

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

Originally published on arxiv ↗
HomeModelsNews