·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
From Noise to Control: Parameterized Diffusion Policies1h◆Mesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics1h◆Drifting Preference Optimization for One-Step Generative Models1h◆Value-Free Policy Optimization via Reward Partitioning1h◆Semantic Retrieval for Product Search in E-Commerce1h◆Efficient Test-time Inference for Generative Planning Models1h◆ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting1h◆Disentanglement-Based Equivariant Learning for Compositional VQA1h◆Large Electron Model: A Universal Ground State Predictor1h◆Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning1h◆Geometry-Aware Probabilistic Circuits via Voronoi Tessellations1h◆Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs1h◆Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective1h◆Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs1h◆ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment1h◆"Skill issues'': data-centric optimization of lakehouse agents1h◆The Case for Model Science: Verify, Explore, Steer, Refine1h◆Can LLM Agents Sustain Long-Horizon Organizational Dynamics?1h◆The Shape of Wisdom: Decision Trajectories in Language Models1h◆TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment1h◆From Noise to Control: Parameterized Diffusion Policies1h◆Mesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics1h◆Drifting Preference Optimization for One-Step Generative Models1h◆Value-Free Policy Optimization via Reward Partitioning1h◆Semantic Retrieval for Product Search in E-Commerce1h◆Efficient Test-time Inference for Generative Planning Models1h◆ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting1h◆Disentanglement-Based Equivariant Learning for Compositional VQA1h◆Large Electron Model: A Universal Ground State Predictor1h◆Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning1h◆Geometry-Aware Probabilistic Circuits via Voronoi Tessellations1h◆Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs1h◆Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective1h◆Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs1h◆ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment1h◆"Skill issues'': data-centric optimization of lakehouse agents1h◆The Case for Model Science: Verify, Explore, Steer, Refine1h◆Can LLM Agents Sustain Long-Horizon Organizational Dynamics?1h◆The Shape of Wisdom: Decision Trajectories in Language Models1h◆TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment1h◆
News/HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs
arxiv
PublishedJune 1, 2026 at 4:00 AM

HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs

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

arXiv:2605.31370v1 Announce Type: new Abstract: Abductive reasoning over knowledge graphs aims to generate logical hypotheses that explain observed entities or facts. Existing controllable hypothesis generation methods allow users to guide this process with explicit conditions, but they remain limit

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
arxivFrom Noise to Control: Parameterized Diffusion Policies1harxivMesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics1harxivDrifting Preference Optimization for One-Step Generative Models1harxivValue-Free Policy Optimization via Reward Partitioning1h
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