·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Neil Rimer thinks the AI money is coming back out3h◆The Steering Budget: Examples beat Knobs4h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs4h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination4h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models4h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation4h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System4h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets4h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility4h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation4h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks4h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios4h◆When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration4h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents4h◆Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors4h◆ANet Patu-1: The Value of Connection in the Agent Network4h◆Automatically Evolving Prompt Guidelines for Task-Specific Optimization4h◆MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue4h◆Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation4h◆Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions4h◆Neil Rimer thinks the AI money is coming back out3h◆The Steering Budget: Examples beat Knobs4h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs4h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination4h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models4h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation4h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System4h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets4h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility4h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation4h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks4h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios4h◆When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration4h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents4h◆Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors4h◆ANet Patu-1: The Value of Connection in the Agent Network4h◆Automatically Evolving Prompt Guidelines for Task-Specific Optimization4h◆MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue4h◆Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation4h◆Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions4h◆
News/MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs
arxiv
PublishedJuly 10, 2026 at 4:00 AM
—neutral

MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs

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

arXiv:2512.12477v2 Announce Type: replace Abstract: Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems. However, most existing methods rely on pairwise message passing mechanisms that fail to capture

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
arxivThe Steering Budget: Examples beat Knobs4harxivPolestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs4harxivRxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination4harxivWhen a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models4h
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