·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Deezer launches an AI music detector for other streaming services3h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing7h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning7h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!7h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions7h◆The Impossibility of Eliciting Latent Knowledge7h◆Mapping Scientific Literature with Large Language Models and Topic Modeling7h◆Grounding Computer Use Agents on Human Demonstrations7h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models7h◆LSTM based IoT Device Identification7h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse7h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM7h◆Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models7h◆DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?7h◆Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation7h◆CoVEBench: Can Video Editing Models Handle Complex Instructions?7h◆Minimal surfaces, Knots, and Neural Networks7h◆CCKS: Consensus-based Communication and Knowledge Sharing7h◆APPO: Agentic Procedural Policy Optimization7h◆TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation7h◆Deezer launches an AI music detector for other streaming services3h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing7h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning7h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!7h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions7h◆The Impossibility of Eliciting Latent Knowledge7h◆Mapping Scientific Literature with Large Language Models and Topic Modeling7h◆Grounding Computer Use Agents on Human Demonstrations7h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models7h◆LSTM based IoT Device Identification7h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse7h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM7h◆Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models7h◆DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?7h◆Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation7h◆CoVEBench: Can Video Editing Models Handle Complex Instructions?7h◆Minimal surfaces, Knots, and Neural Networks7h◆CCKS: Consensus-based Communication and Knowledge Sharing7h◆APPO: Agentic Procedural Policy Optimization7h◆TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation7h◆
News/Modality Alignment across Trees on Heterogeneous Hyperbolic Manifolds
arxiv
PublishedMay 29, 2026 at 4:00 AM
—neutral

Modality Alignment across Trees on Heterogeneous Hyperbolic Manifolds

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

arXiv:2510.27391v2 Announce Type: replace-cross Abstract: Modality alignment is critical for vision-language models (VLMs) to effectively integrate information across modalities. However, existing methods extract hierarchical features from text while representing each image with a single feature, le

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
arxivMODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning7harxivPosition: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!7harxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions7harxivThe Impossibility of Eliciting Latent Knowledge7h
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