·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action7h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI8h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability8h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents8h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning8h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI8h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes8h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies8h◆Universal Decision Learners8h◆Algorithmic Recourse of In-Context Learning for Tabular Data8h◆Graph Machine Learning in the Era of Large Language Models (LLMs)8h◆NGDBench: Towards Neural Graph Data Management8h◆End-to-End Compression for Tabular Foundation Models8h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning8h◆A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models8h◆Counterfactual Trace Auditing of LLM Agent Skills8h◆GETA: Generalized Encrypted Traffic Analysis8h◆SERA: Soft-Verified Efficient Repository Agents8h◆LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis8h◆EchoRL: Reinforcement Learning via Rollout Echoing8h◆Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action7h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI8h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability8h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents8h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning8h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI8h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes8h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies8h◆Universal Decision Learners8h◆Algorithmic Recourse of In-Context Learning for Tabular Data8h◆Graph Machine Learning in the Era of Large Language Models (LLMs)8h◆NGDBench: Towards Neural Graph Data Management8h◆End-to-End Compression for Tabular Foundation Models8h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning8h◆A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models8h◆Counterfactual Trace Auditing of LLM Agent Skills8h◆GETA: Generalized Encrypted Traffic Analysis8h◆SERA: Soft-Verified Efficient Repository Agents8h◆LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis8h◆EchoRL: Reinforcement Learning via Rollout Echoing8h◆
News/Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery
arxiv
PublishedMay 22, 2026 at 4:00 AM
—neutral

Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery

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

arXiv:2605.20440v1 Announce Type: cross Abstract: We introduce the $\star_G$ tensor algebra, in which any finite group $G$ defines the multiplication rule, making equivariance an intrinsic algebraic property rather than an architectural constraint. The framework rests on three machine-verified theor

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 AI8harxivDiscovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability8harxivDiagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents8harxivAnswer-Set-Programming-based Abstractions for Reinforcement Learning8h
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