·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy1h◆Meta’s new terms may allow it to train AI on news sites for free1h◆Veritas expands its anti-counterfeit chip to jewelry, trophies, and fragrance bottles1h◆Replit’s CEO says the future of work is the ‘self-driving company’3h◆Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows11h◆Open Benchmark of AI Impact on Humans (ImpactBench): an expert-guided, open platform for the holistic evaluation of AI impact on humans11h◆WorldCast: Exploring Intelligent Visual Generation in World State Prediction11h◆Interpreting Language Model Parameters11h◆Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets11h◆Reference-Guided Turn-Taking Style Transfer for Full-Duplex Spoken Language Models11h◆Wolof-Adapted Industry-Ready Open-Weight Language Models11h◆CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation11h◆Amortizing Sequential Experimental Design in Reasoning LLMs11h◆Probing Negation Understanding in Vision-Language Models With Object Attention11h◆SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations11h◆Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL11h◆TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping11h◆ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews11h◆When Judges Agree and Are Wrong: Consistency-Accuracy Dissociation as a Blind Spot for Label-Free Evaluation11h◆Stronger Judges Do Not See More: Deployment-Integrity Blindness in LLM Evaluation11h◆Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy1h◆Meta’s new terms may allow it to train AI on news sites for free1h◆Veritas expands its anti-counterfeit chip to jewelry, trophies, and fragrance bottles1h◆Replit’s CEO says the future of work is the ‘self-driving company’3h◆Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows11h◆Open Benchmark of AI Impact on Humans (ImpactBench): an expert-guided, open platform for the holistic evaluation of AI impact on humans11h◆WorldCast: Exploring Intelligent Visual Generation in World State Prediction11h◆Interpreting Language Model Parameters11h◆Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets11h◆Reference-Guided Turn-Taking Style Transfer for Full-Duplex Spoken Language Models11h◆Wolof-Adapted Industry-Ready Open-Weight Language Models11h◆CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation11h◆Amortizing Sequential Experimental Design in Reasoning LLMs11h◆Probing Negation Understanding in Vision-Language Models With Object Attention11h◆SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations11h◆Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL11h◆TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping11h◆ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews11h◆When Judges Agree and Are Wrong: Consistency-Accuracy Dissociation as a Blind Spot for Label-Free Evaluation11h◆Stronger Judges Do Not See More: Deployment-Integrity Blindness in LLM Evaluation11h◆
News/Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection
arxiv
PublishedOctober 1, 2026 at 4:00 AM

Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection

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

arXiv:2609.18860v2 Announce Type: replace-cross Abstract: When large vision-language models misclassify harmful memes, the failure may reflect missing internal evidence or an inability to route represented evidence to their outputs. We distinguish these cases in Gemma-3 and Qwen3.5 using sparse auto

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
arxivKolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows11harxivOpen Benchmark of AI Impact on Humans (ImpactBench): an expert-guided, open platform for the holistic evaluation of AI impact on humans11harxivWorldCast: Exploring Intelligent Visual Generation in World State Prediction11harxivInterpreting Language Model Parameters11h
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