·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Introducing Mellum2: A 12B Mixture-of-Experts Model by JetBrains35m◆DuckDuckGo makes its ‘no-AI’ search engine easier to access as its traffic booms1h◆Microsoft to unveil new AI models and Windows improvements at Build1h◆AI is blowing up music. How should the Grammys handle it?1h◆Strava blames zero-code AI apps and scrapers as it tightens API access2h◆Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic2h◆Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action11h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI12h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability12h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents12h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning12h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI12h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes12h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies12h◆Universal Decision Learners12h◆Algorithmic Recourse of In-Context Learning for Tabular Data12h◆Graph Machine Learning in the Era of Large Language Models (LLMs)12h◆NGDBench: Towards Neural Graph Data Management12h◆End-to-End Compression for Tabular Foundation Models12h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning12h◆Introducing Mellum2: A 12B Mixture-of-Experts Model by JetBrains35m◆DuckDuckGo makes its ‘no-AI’ search engine easier to access as its traffic booms1h◆Microsoft to unveil new AI models and Windows improvements at Build1h◆AI is blowing up music. How should the Grammys handle it?1h◆Strava blames zero-code AI apps and scrapers as it tightens API access2h◆Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic2h◆Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action11h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI12h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability12h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents12h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning12h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI12h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes12h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies12h◆Universal Decision Learners12h◆Algorithmic Recourse of In-Context Learning for Tabular Data12h◆Graph Machine Learning in the Era of Large Language Models (LLMs)12h◆NGDBench: Towards Neural Graph Data Management12h◆End-to-End Compression for Tabular Foundation Models12h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning12h◆
News/PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs
arxiv
PublishedMay 26, 2026 at 4:00 AM

PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs

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

arXiv:2603.09943v2 Announce Type: replace Abstract: Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading criteria, and clinical evidence. In practice, diagnostic reasoning requires linking morphological ev

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
arxivPhysically Viable World Models: A Case for Query-Conditioned Embodied AI12harxivDiscovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability12harxivDiagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents12harxivAnswer-Set-Programming-based Abstractions for Reinforcement Learning12h
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