·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods5h◆Can Graph Learning Learn Circuits?5h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery5h◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States5h◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation5h◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model5h◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents5h◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems5h◆Functional Welfare for AI Safety5h◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds5h◆Repairing LLM-Generated Specifications with Deterministic Diagnosis5h◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier5h◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents5h◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation5h◆Repair, Retry, and the Cost of Hindsight: Verifier Feedback in Formal Theorem Proving5h◆Brain-Aligned Multimodal LLMs for Social and Emotional Understanding of Cartoon Movies5h◆LEMON: Equivariant Learning over Attention Maps for Zero-Shot Misbehavior Monitoring on New LLMs5h◆R$^2$Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop5h◆When Can You Trust an Agent's Own Verifier? Stress-Testing Counterfactual Rationale Verification Under Search Pressure and Oracle Access5h◆Look Back, Correct Forward: Error Feedback Calibration for Time Series Forecasting5h◆RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods5h◆Can Graph Learning Learn Circuits?5h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery5h◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States5h◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation5h◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model5h◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents5h◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems5h◆Functional Welfare for AI Safety5h◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds5h◆Repairing LLM-Generated Specifications with Deterministic Diagnosis5h◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier5h◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents5h◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation5h◆Repair, Retry, and the Cost of Hindsight: Verifier Feedback in Formal Theorem Proving5h◆Brain-Aligned Multimodal LLMs for Social and Emotional Understanding of Cartoon Movies5h◆LEMON: Equivariant Learning over Attention Maps for Zero-Shot Misbehavior Monitoring on New LLMs5h◆R$^2$Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop5h◆When Can You Trust an Agent's Own Verifier? Stress-Testing Counterfactual Rationale Verification Under Search Pressure and Oracle Access5h◆Look Back, Correct Forward: Error Feedback Calibration for Time Series Forecasting5h◆
News/DINORANKCLIP: DINOv3 Distillation and Injection for Vision-Language Pretraining with High-Order Ranking Consistency
arxiv
PublishedMay 8, 2026 at 4:00 AM

DINORANKCLIP: DINOv3 Distillation and Injection for Vision-Language Pretraining with High-Order Ranking Consistency

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

arXiv:2605.06592v1 Announce Type: cross Abstract: Contrastive language-image pretraining (CLIP) suffers from two structural weaknesses: the symmetric InfoNCE loss discards the relative ordering among unmatched in-batch pairs, and global pooling collapses the visual representation into a semantic bot

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
arxivRCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods5harxivCan Graph Learning Learn Circuits?5harxivBeyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery5harxivFrom Events to Reasoning: Hierarchical Rule Composition over Explicit States5h
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