·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
China delivers a one-two punch to America’s AI dominance4h◆AI is more likely than humans to form biases when hiring6h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal10h◆SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents10h◆PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment10h◆Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length10h◆Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications10h◆DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales10h◆Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors10h◆CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data10h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?10h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes10h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems10h◆Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI10h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents10h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections10h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery10h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI10h◆Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory10h◆Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung10h◆China delivers a one-two punch to America’s AI dominance4h◆AI is more likely than humans to form biases when hiring6h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal10h◆SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents10h◆PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment10h◆Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length10h◆Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications10h◆DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales10h◆Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors10h◆CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data10h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?10h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes10h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems10h◆Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI10h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents10h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections10h◆Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery10h◆How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI10h◆Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory10h◆Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung10h◆
News/Qwen-AgentWorld: Language World Models for General Agents
arxiv
PublishedJune 24, 2026 at 4:00 AM
—neutral

Qwen-AgentWorld: Language World Models for General Agents

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

arXiv:2606.24597v1 Announce Type: new Abstract: A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigate how world modeling based on language models can further push the bound

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
arxivBeyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal10harxivSkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents10harxivPolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment10harxivLatency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length10h
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