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15 papers in the last four weeks, up 275% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 76

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Arrow: A Foundation Model for Causal Discovery
May 8, 2026Ryan Thompson, He Zhao, Daniel M. Steinberg +1Causal Discovery Methods Model Discovery
University of Technology Sydney · CSIRO’s Data61
Permutation-preserving Functions and Neural Vecchia Covariance Kernels
May 6, 2026Jian Cao, Nian Liu, Ying LinGaussian Process Covariance
Department of Mathematics University of Houston Houston, TX 77047 · Department of Industrial and Systems Engineering University of Houston Houston, TX 77047
Discovering Sparse Counterfactual Factors via Latent Adjustment for Survey-based Community Intervention
May 6, 2026Fatima Ashraf, Muhammad Ayub Sabir, Junbiao Pang +2Latent Feature Interventions Survey
FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection
May 5, 2026Kaixiang Zhao, Mao Ye, Lihua Zhou +4Open-Vocabulary Object Detection Test-Time Adaptation
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China · Surrey Institute for People-Centred Artificial Intelligence, CVSSP, University of Surrey, Guildford, UK.
To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems
Apr 30, 2026Shreya Chappidi, Jatinder SinghArtificial Intelligence Systems Software Engineering
University of Cambridge, United Kingdom · National Cancer Institute, National Institutes of Health, United States · Research Centre Trust, UA Ruhr, University Duisburg-Essen, Germany
Staged Factorial Screening for Budget-Constrained Micro-Pretraining
Apr 27, 2026Felipe Chavarro PolaniaEarly Layers Factor
Hewlett Packard Enterprise
SNGR: Selective Non-Gaussian Refinement for Ambiguous SLAM Factor Graphs
Apr 23, 2026Anushka Kulkarni, Sarthak DubeySimultaneous Localization And Mapping Factor
Northeastern University, Boston, MA, USA
QRAFTI: An Agentic Framework for Empirical Research in Quantitative Finance
Apr 20, 2026Terence Lim, Kumar Muthuraman, Michael SuryResearch Automation Factor
Graphen Inc. · The University of Texas at Austin
Factor Graph-Based Shape Estimation for Continuum Robots via Magnus Expansion
Apr 17, 2026Lorenzo Ticozzi, Patricio A. Vela, Panagiotis TsiotrasCosserat Rod Theory Robotic Manipulator
School of Aerospace Engineering · School of Electrical and Computer Engineering
Understanding Tool-Augmented Agents for Lean Formalization: A Factorial Analysis
Apr 16, 2026Ke Zhang, Patricio Gallardo, Maziar Raissi +1Formalization Factor
University of California, Riverside.
CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors
Apr 16, 2026Hang Su, Zequn Liu, Chen Hu +3Large Language Model Personalization Preference Alignment Learning
East China Normal University, China · Zhongguancun Academy, China
From Directions to Regions: Decomposing Activations in Language Models via Local Geometry
Feb 2, 2026Or Shafran, Shaked Ronen, Omri Fahn +3Model Activations Geometric Constraints
Blavatnik School of Computer Science and AI, Tel Aviv University, Israel · New York University, New York, NY, USA · Goodfire.
BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression
Sep 29, 2025David González-MartínezLow-Rank Structure Data Compression Methods
Department of Computer Science and Artificial Intelligence, Universidad de Sevilla · Tübingen AI Center
Probabilistic Analysis of Least Squares, Orthogonal Projection, and QR Factorization Algorithms Subject to Gaussian Noise
Sep 27, 2024Ali Lotfi, Julien Langou, Mohammad MeysamiSpectral Norm Singular Value Decomposition
Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada · Department of Applied Mathematics, University of Colorado Denver, Denver, Colorado2026 80201, USA · Department of Mathematics, The University of Tulsa, Tulsa, OK 74104, USA
Analytical Standard Errors for Exploratory Factor Solutions
Nov 12, 2018Xingwei Hu, Caihong Hu, Cheng-Kuang WuExploratory Factor Analysis Heteroskedasticity
International Monetary Fund, Washington, DC, 20431, USA · Zhangjiajie College, Zhangjiajie, Hunan, 427000, China · Zhejiang Industry and Trade Vocational College, Wenzhou, Zhejiang, 325700, China
A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books
Date pendingVarun Ghat Ravikumar, Sina Ahmadi, Lena Jäger +1Machine Translation Low-Resource Languages
Department of Informatics, University of Zurich · Department of Computational Linguistics, University of Zurich

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Arrow: A Foundation Model for Causal Discovery

Ryan Thompson, He Zhao, Daniel M. Steinberg, Edwin V. Bonilla

University of Technology Sydney · CSIRO’s Data61

Causal Discovery Methods Model Discovery Foundation Model Factor

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We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an undirected skeleton and a topological order, guaranteeing acyclicity by construction. Given a new dataset, it uses a transformer-based architecture to contextualize variables within and across observations, then predicts skeleton edge probabilities and node order scores that together define a graph. Arrow is trained in a supervised fashion on synthetic datasets with ground-truth graphs, using an end-to-end differentiable directed edge composite likelihood induced by the skeleton-order factorization. The training distribution spans diverse graph families, functional forms, noise models, and dataset shapes. Across in- and out-of-distribution synthetic, semi-synthetic, and real datasets, Arrow matches or outperforms existing causal discovery methods at substantially lower inference cost than competitive alternatives. Our results demonstrate that large-scale pretraining on diverse synthetic data can yield zero-shot causal discovery models that are fast, accurate, and reusable on new datasets.

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