·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing6h◆Instruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems6h◆TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation6h◆An Empirical Study of LLM-Generated Specifications for VeriFast6h◆State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading6h◆Delegation and Verification Under AI6h◆A Concept of Possibility for Real-World Events6h◆Learning State-Tracking from Code Using Linear RNNs6h◆One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech6h◆The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms6h◆AnySimLite: A Lightweight Few-Shot Similarity Encoder for On-Device Speech-Adjacent Classification6h◆Hallucination in World Models is Predictable and Preventable6h◆Enabling self-supervised learned primal dual with Noise2Inverse6h◆Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs6h◆Nemotron-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context6h◆Assessing Post-Reform Changes in Risk Disclosure Quality with a Multidimensional Text Analysis Approach6h◆Parametric Open Source Games6h◆Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention6h◆GenRecal: Generation after Recalibration from Large to Small Vision-Language Models6h◆Semantic Early-Stopping for Iterative LLM Agent Loops6h◆NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing6h◆Instruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems6h◆TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation6h◆An Empirical Study of LLM-Generated Specifications for VeriFast6h◆State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading6h◆Delegation and Verification Under AI6h◆A Concept of Possibility for Real-World Events6h◆Learning State-Tracking from Code Using Linear RNNs6h◆One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech6h◆The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms6h◆AnySimLite: A Lightweight Few-Shot Similarity Encoder for On-Device Speech-Adjacent Classification6h◆Hallucination in World Models is Predictable and Preventable6h◆Enabling self-supervised learned primal dual with Noise2Inverse6h◆Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs6h◆Nemotron-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context6h◆Assessing Post-Reform Changes in Risk Disclosure Quality with a Multidimensional Text Analysis Approach6h◆Parametric Open Source Games6h◆Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention6h◆GenRecal: Generation after Recalibration from Large to Small Vision-Language Models6h◆Semantic Early-Stopping for Iterative LLM Agent Loops6h◆
News/Hallucination in World Models is Predictable and Preventable
arxiv
PublishedJune 26, 2026 at 4:00 AM

Hallucination in World Models is Predictable and Preventable

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

arXiv:2606.27326v1 Announce Type: new Abstract: Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics. We hypothesize that hallucination concentrates in

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
arxivNASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testing6harxivInstruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems6harxivTAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation6harxivAn Empirical Study of LLM-Generated Specifications for VeriFast6h
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