·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Anthropic joins AI’s natural gas frenzy, buys $3.4B worth of turbines29m◆Medium’s new TK app brings AI assistance directly into the writing process1h◆Trump’s 60 Minutes interview touched on AI safety, threat to arrest an AI exec1h◆Disney and Adobe are building generative AI tools together1h◆Hold up — I had a great time talking to the RIAA guy1h◆Nemotron-Personas-Finance-Japan: A Synthetic Dataset Capturing Japan's Demographics and Financial Context2h◆Jonathan Kanter is using AI to take on Big Tech2h◆Automattic debuts a platform to deploy vibe-coding apps amid a changing internet3h◆Innovating with AI in the special chemicals industry4h◆Your Experts Have Partners. There's HOPE for Keeping Them Together4h◆Is Jensen Huang’s job on the line? Yale’s Jeffrey Sonnenfeld estimates it’s “about a 50-50”12h◆Phantom Consensus: Measuring Social Reasoning in Multi-Agent LLM Decision-Making12h◆ROSE: Risk-Aware Orthogonal Subspace Navigation for Lifelong Knowledge Editing in Multimodal Large Language Models12h◆Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors12h◆Prompt Lottery in LLM-as-a-Judge: Measuring and Mitigating Sensitivity to Syntactic Rephrasing12h◆FindLink: Learning to Assess Scientific Connections from Source-Grounded Premises12h◆AlienBench: A Benchmark of Structural Novelty in Language Model Outputs12h◆Internal Safety Collapse in LLMs and Coding Agents12h◆Semantic Conditional Similarity (SCS): A Formal Mathematical Framework12h◆Steerable Multi-Objective Group-Relative Preference Optimization with Jointly Trained Low-Rank Adapters12h◆Anthropic joins AI’s natural gas frenzy, buys $3.4B worth of turbines29m◆Medium’s new TK app brings AI assistance directly into the writing process1h◆Trump’s 60 Minutes interview touched on AI safety, threat to arrest an AI exec1h◆Disney and Adobe are building generative AI tools together1h◆Hold up — I had a great time talking to the RIAA guy1h◆Nemotron-Personas-Finance-Japan: A Synthetic Dataset Capturing Japan's Demographics and Financial Context2h◆Jonathan Kanter is using AI to take on Big Tech2h◆Automattic debuts a platform to deploy vibe-coding apps amid a changing internet3h◆Innovating with AI in the special chemicals industry4h◆Your Experts Have Partners. There's HOPE for Keeping Them Together4h◆Is Jensen Huang’s job on the line? Yale’s Jeffrey Sonnenfeld estimates it’s “about a 50-50”12h◆Phantom Consensus: Measuring Social Reasoning in Multi-Agent LLM Decision-Making12h◆ROSE: Risk-Aware Orthogonal Subspace Navigation for Lifelong Knowledge Editing in Multimodal Large Language Models12h◆Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors12h◆Prompt Lottery in LLM-as-a-Judge: Measuring and Mitigating Sensitivity to Syntactic Rephrasing12h◆FindLink: Learning to Assess Scientific Connections from Source-Grounded Premises12h◆AlienBench: A Benchmark of Structural Novelty in Language Model Outputs12h◆Internal Safety Collapse in LLMs and Coding Agents12h◆Semantic Conditional Similarity (SCS): A Formal Mathematical Framework12h◆Steerable Multi-Objective Group-Relative Preference Optimization with Jointly Trained Low-Rank Adapters12h◆
News/Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors
arxiv
PublishedNovember 9, 2026 at 5:00 AM

Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors

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

arXiv:2605.07847v2 Announce Type: replace Abstract: As user simulators are increasingly used for interactive training and evaluation of AI assistants, it is essential that they represent the diverse behaviors of real users. While existing works train user simulators to generate human-like responses,

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

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

Your email is used only to send The Bubble Brief. Privacy policy

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
arxivPhantom Consensus: Measuring Social Reasoning in Multi-Agent LLM Decision-Making12harxivROSE: Risk-Aware Orthogonal Subspace Navigation for Lifelong Knowledge Editing in Multimodal Large Language Models12harxivPrompt Lottery in LLM-as-a-Judge: Measuring and Mitigating Sensitivity to Syntactic Rephrasing12harxivFindLink: Learning to Assess Scientific Connections from Source-Grounded Premises12h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

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

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews