Geometric

Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
https://arxiv.org/abs/2603.23398

The Origins of Representation Manifolds in Large Language Models
https://arxiv.org/abs/2505.18235

Representational Curvature Modulates Behavioral Uncertainty in Large Language Models
https://arxiv.org/abs/2604.23985v1

Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost
https://arxiv.org/abs/2412.08541

PolyGraph Discrepancy: a classifier-based metric for graph generation
https://arxiv.org/abs/2510.06122

J-NLR is a Python library for non-linear reconciliation, learning, and geometric analysis on constraint manifolds
https://github.com/supsi-dacd-isaac/JNLR

The Information Geometry of Softmax: Probing and Steering
https://arxiv.org/abs/2602.15293

k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS
https://openreview.net/forum?id=4Y5kxbH2fI#discussion

Pytorch Volumetric
https://github.com/UM-ARM-Lab/pytorch_volumetric

Geometric kernels on manifolds, meshes and graphs
https://github.com/geometric-kernels/GeometricKernels

The Spacetime of Diffusion Models: An Information Geometry Perspective
https://arxiv.org/abs/2505.17517

An end-to-end attention-based approach for learning on graphs
https://www.nature.com/articles/s41467-025-60252-z

Is All Learning (Natural) Gradient Descent?
https://arxiv.org/abs/2409.16422
https://pubmed.ncbi.nlm.nih.gov/41237386/

An Equivariance Toolbox for Learning Dynamics
https://www.arxiv.org/abs/2512.21447

Graph Embeddings at scale with Spark and GraphFrames
https://semyonsinchenko.github.io/ssinchenko/post/graphframes-embeddings

GraphBench: Next-generation graph learning benchmarking
https://arxiv.org/abs/2512.04475

Diffusion Generative Modeling on Lie Group Representations
https://arxiv.org/pdf/2502.02513

Geodesic Calculus on Latent Space
https://sacha-wendling.vercel.app/posts/geodesic-calculus-on-latent-space

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
https://arxiv.org/abs/2408.16115

A geometric framework for momentum-based optimizers for low-rank training
https://arxiv.org/pdf/2506.17475