·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Vision Hopfield Memory Networks2h◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations2h◆Insurance of Agentic AI2h◆Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation2h◆MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery2h◆Beyond Rewards in Reinforcement Learning for Cyber Defence2h◆Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory2h◆Residual Modeling for High-Fidelity Learned Compression of Scientific Data2h◆Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution2h◆LatentWave: JEPA Pretraining for Wireless Foundation Models2h◆How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment2h◆What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems2h◆GITCO: Gated Inference-Time Context Optimization in TSFMs2h◆SentinelBench: A Benchmark for Long-Running Monitoring Agents2h◆An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)2h◆Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models2h◆SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization2h◆When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty2h◆Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity2h◆GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection2h◆Vision Hopfield Memory Networks2h◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations2h◆Insurance of Agentic AI2h◆Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation2h◆MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery2h◆Beyond Rewards in Reinforcement Learning for Cyber Defence2h◆Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory2h◆Residual Modeling for High-Fidelity Learned Compression of Scientific Data2h◆Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution2h◆LatentWave: JEPA Pretraining for Wireless Foundation Models2h◆How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment2h◆What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems2h◆GITCO: Gated Inference-Time Context Optimization in TSFMs2h◆SentinelBench: A Benchmark for Long-Running Monitoring Agents2h◆An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)2h◆Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models2h◆SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization2h◆When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty2h◆Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity2h◆GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection2h◆
News/Parabolic Position Encoding: Vision-Centric, Principled, Extrapolatable, General
arxiv
PublishedMay 13, 2026 at 4:00 AM

Parabolic Position Encoding: Vision-Centric, Principled, Extrapolatable, General

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

arXiv:2602.01418v2 Announce Type: replace-cross Abstract: We propose Parabolic Position Encoding (PaPE), a parabola-based position encoding for vision modalities in attention-based architectures. Given a set of vision tokens-such as from videos, event camera streams, images, or point clouds-our obje

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
arxivVision Hopfield Memory Networks2harxivStable Deep Reinforcement Learning via Isotropic Gaussian Representations2harxivInsurance of Agentic AI2harxivDo Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation2h
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