·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AI researchers stranded Claude on a virtual desert island. It started to “play.”2h◆RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods7h◆Can Graph Learning Learn Circuits?7h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery7h◆Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification7h◆Sentence-Level Topic Modeling with Lexical and Contextual Evidence7h◆Referring Multi-Object Tracking with Diverse Dynamic Expressions7h◆R$^2$ Flow: Recursive Self-Improvement via Recursive Skill Evolution7h◆Logic-Guided Graph Denoising Diffusion Models7h◆Unit-level aggregation breaks coverage in causal foundation models7h◆Mask-Based Deep Symbolic Regression7h◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States7h◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation7h◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model7h◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents7h◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems7h◆Functional Welfare for AI Safety7h◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds7h◆Repairing LLM-Generated Specifications with Deterministic Diagnosis7h◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier7h◆AI researchers stranded Claude on a virtual desert island. It started to “play.”2h◆RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods7h◆Can Graph Learning Learn Circuits?7h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery7h◆Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification7h◆Sentence-Level Topic Modeling with Lexical and Contextual Evidence7h◆Referring Multi-Object Tracking with Diverse Dynamic Expressions7h◆R$^2$ Flow: Recursive Self-Improvement via Recursive Skill Evolution7h◆Logic-Guided Graph Denoising Diffusion Models7h◆Unit-level aggregation breaks coverage in causal foundation models7h◆Mask-Based Deep Symbolic Regression7h◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States7h◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation7h◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model7h◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents7h◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems7h◆Functional Welfare for AI Safety7h◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds7h◆Repairing LLM-Generated Specifications with Deterministic Diagnosis7h◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier7h◆
News/ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation
arxiv
PublishedJuly 10, 2026 at 4:00 AM
—neutral

ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation

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

arXiv:2607.08741v1 Announce Type: cross Abstract: Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, th

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 neighborhoods7harxivCan Graph Learning Learn Circuits?7harxivBeyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery7harxivTrajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification7h
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