arXiv AI

Tropical Algebraic Geometry for Neuronal Representations: An Arakelov-Green Measure Based Descriptor for Graph Learning

arXiv:2608. 04460v1 Announce Type: cross Abstract: The quantitative analysis of 3D neuronal morphologies requires capturing both graph topology and spatial geometry.

arXiv Machine Learning

Aug 7

Phylogenetic Tree Inference with Tropical Axial Attention

arXiv:2605. 13894v2 Announce Type: replace-cross Abstract: In this work, we introduce a Tropical Axial Attention neural reasoning architecture that replaces vanilla softmax dot-product attention with max-plus operators, inducing a piecewise-linear structure aligned with dynamic programming formulations.

By Chris Teska, Kurt Pasque, Ruriko Yoshida, Baran Hashemi

safety

arXiv Machine Learning

Jul 7

Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphs

arXiv:2512. 18454v3 Announce Type: replace Abstract: Predictive machine learning models generally excel on in-distribution data, but their performance degrades on out-of-distribution (OOD) inputs.

By David Graber, Victor Armegioiu, Rebecca Buller, Siddhartha Mishra

diffusion

arXiv Machine Learning

Aug 7

Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity

arXiv:2608. 05336v1 Announce Type: cross Abstract: Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships.

By Jacob W. Toney, Ayleen Y. Farnood, Samir Darouich, Heather J. Kulik

rag

arXiv Machine Learning

1d ago

Generative Learning of Separatrices

arXiv:2608. 14743v1 Announce Type: new Abstract: The identification and reconstruction of the boundaries separating basins of attraction in multistable, multidimensional dynamical systems presents a fundamental challenge in computational dynamics.

By Ellis R. Crabtree, Dimitris G. Giovanis, Anastasia Georgiou, George Datseris, Ioannis G. Kevrekidis

arXiv AI

Jun 3

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining

arXiv:2509. 22468v2 Announce Type: replace-cross Abstract: High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce.

By Boshra Ariguib, Mathias Niepert, Andrei Manolache

llms rag diffusion fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 6

Geometry-Informed Parameter-Efficient Fine-Tuning of Pre-trained Molecular GNNs for Blood-Brain Barrier Permeability Prediction

arXiv:2608. 04257v1 Announce Type: new Abstract: Blood-brain barrier permeability (BBBP) prediction is a critical screening task in central nervous system drug discovery, where candidate molecules must be assessed for whether they can cross, or should be prevented from crossing, the blood-brain barrier.

By Marco Vieto Vega, Long D. Nguyen, Binh P. Nguyen

fine-tuning

arXiv Machine Learning

Jun 16

Generative Modeling on Metric Graphs via Neural Optimal Transport

arXiv:2606. 16273v1 Announce Type: cross Abstract: We introduce, to our knowledge, the first deep generative modeling framework for probability distributions continuously supported on compact metric graphs.

By Alessandro Micheli, Yueqi Cao, Anthea Monod, Samir Bhatt

rag

arXiv AI

Jul 7

CuBAS: Information Geometric Curvature-Based Adaptive Sampling for Supervised Classification

arXiv:2607. 03145v1 Announce Type: cross Abstract: The informativeness of a training set is as consequential as its size, yet most sampling strategies remain agnostic to the intrinsic geometry of the data distribution.

By Alexandre L. M. Levada

benchmarks

arXiv Machine Learning

Jun 16

Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalization

arXiv:2510. 14217v2 Announce Type: replace Abstract: The spectral properties of feature embeddings offer critical insights into model generalization and representation quality.

By Asma Jamali, Tin Sum Cheng, Rodrigo A. Vargas-Hern\'andez

llms rag benchmarks

arXiv Machine Learning

Jul 8

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings

arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.

By Qiwei Han, Chi Zhou, Ruobing Wang, Zheng Ma

rag diffusion efficiency multimodal benchmarks safety

arXiv Machine Learning

Jul 22

Geometric Capacity of Transformers: A Tropical Geometry Perspective

arXiv:2604. 14727v2 Announce Type: replace Abstract: To quantify the geometric capacity of transformers, we develop a tropical-geometric framework for analyzing the spatial partitions induced by conditioned self-attention.

By Ye Su, Yong Liu

llms

arXiv Machine Learning

Jun 11

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction

arXiv:2606. 11382v1 Announce Type: new Abstract: Deep learning models facilitate the discovery of molecules with tailored properties among billions of candidate compounds.

By Emily Nguyen, Yongchan Hong, Harsh Toshniwal, Yan Liu, Andreas Luttens

llms rag multimodal benchmarks safety

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