·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Here comes new Siri again3h◆Persona Atlas: Mapping How Famous Minds Think3h◆Vision Hopfield Memory Networks11h◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations11h◆Insurance of Agentic AI11h◆Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety11h◆Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics11h◆CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model11h◆Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads11h◆Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents11h◆MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery11h◆TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management11h◆Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement11h◆From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability11h◆Compositional Boundaries for Density Fusion11h◆Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation11h◆Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory11h◆Residual Modeling for High-Fidelity Learned Compression of Scientific Data11h◆Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution11h◆LatentWave: JEPA Pretraining for Wireless Foundation Models11h◆Here comes new Siri again3h◆Persona Atlas: Mapping How Famous Minds Think3h◆Vision Hopfield Memory Networks11h◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations11h◆Insurance of Agentic AI11h◆Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety11h◆Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics11h◆CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model11h◆Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads11h◆Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents11h◆MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery11h◆TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management11h◆Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement11h◆From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability11h◆Compositional Boundaries for Density Fusion11h◆Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation11h◆Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory11h◆Residual Modeling for High-Fidelity Learned Compression of Scientific Data11h◆Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution11h◆LatentWave: JEPA Pretraining for Wireless Foundation Models11h◆
News/Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions
arxiv
PublishedApril 29, 2026 at 4:00 AM
—neutral

Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions

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

arXiv:2604.24293v1 Announce Type: cross Abstract: Graph neural ordinary differential equations (Graph ODEs) extend graph learning from discrete message-passing layers to continuous-time representation flows. While it supports adaptive long-range propagation, we show that Graph ODEs with strictly pos

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
02
  • 01
    Graph ODEs
  • 02
    Hysteresis Graph ODE (HGODE)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#graph-learning#machine-learning#artificial-intelligence

No replies yet. Be first.

Mentioned models
02
  • 01
    Graph ODEs
  • 02
    Hysteresis Graph ODE (HGODE)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#graph-learning#machine-learning#artificial-intelligence

Related coverage

More from ARXIV
arxivVision Hopfield Memory Networks11harxivStable Deep Reinforcement Learning via Isotropic Gaussian Representations11harxivInsurance of Agentic AI11harxivOutput Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety11h
The Bubble Brief
WEEKLY

Read graph-learning insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews