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28 papers in the last four weeks, up 56% on the four weeks before. 0.3% of all new papers.

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Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Apr 28, 2026Pahal D. Patel, Sanmay GangulyExplainability Explainable Artificial Intelligence
Department of Physics, Indian Institute of Technology, Kanpur Uttar-Pradesh-208016, India
Towards interpretable AI with quantum annealing feature selection
Apr 28, 2026Francesco Aldo Venturelli, Emanuele Costa, Sikha O K +3Quantum Annealing Deep Learning
1BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain · 2Barcelona Supercomputing Center (BSC) · 3Departament de F´ısica Qu`antica i Astrof´ısica, Facultat de F´ısica, Universitat de Barcelona, E-08028 Barcelona, Spain +2
Adversarial Robustness of NTK Neural Networks
Apr 28, 2026Yuxuan HouNeural Tangent Kernel Neural Network
1Qiuzhen College, Tsinghua University
FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
Apr 27, 2026Yutong He, Zhengyang Huang, Jiahe Geng +1Federated Learning Fedavg
Peking University · Beihang University
Sliced-Regularized Optimal Transport
Apr 27, 2026Khai NguyenGradient
The University of Texas at Austin
Quasi-Quadratic Gradient: A New Direction for Accelerating the BFGS Method in Quasi-Newton Optimization
Apr 27, 2026John ChiangNewton Hessian
Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection
Apr 25, 2026Prajwal Panth, Nishant NigamSecure Aggregation Federated Learning
School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India · School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar, India
A Topology fixated Shape Gradient Framework for Non Simple Boundary Extraction for CIE Lab color images with Repulsive Energy
Apr 25, 2026Shafeequdheen Palengara, Jyotiranjan Nayak, Vijayakrishna RowthuDelineation Shaping
Tessellab, Department of Mathematics, SRM University-AP, India
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces
Apr 24, 2026Natanael Alpay, Emeric BattagliaReproducing Kernel Hilbert Spaces Stochastic Gradient Descent
Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy
Apr 24, 2026Asim Ukaye, Mubarak Abdu-Aguye, Nurbek Tastan +1Federated Learning Contributions
MBZUAI, UAE · Michigan State University, USA
An Efficient Multilevel Preconditioned Nonlinear Conjugate Gradient Method for Incremental Potential Contact
Apr 21, 2026Yu Zhang, Xing Shen, Kemeng Huang +5Solver Iterations Spectral Preconditioning
S-Lab, Nanyang Technological University, Singapore · Shanghai AI Laboratory, China · University of Hong Kong, China +3
ParamBoost: Gradient Boosted Piecewise Cubic Polynomials
Apr 20, 2026Nicolas Salvadé, Tim HillelGeneralized Linear Model Boosting
Department of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT,, United Kingdom
Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario
Apr 20, 2026Florentin Coeurdoux, Grégoire Ferré, Jean-Philippe BouchaudGradient Descent Gradient
1Capital Fund Management, Paris, France · 2X-CFM Chair of EconophysiX, Ecole polytechnique, Palaiseau, France · 3Académie des Sciences, Paris, France
Decomposing the Depth Profile of Fine-Tuning
Apr 19, 2026Jayadev BillaLayer-Wise Model Fine-Tuning
Detecting and Suppressing Reward Hacking with Gradient Fingerprints
Apr 17, 2026Songtao Wang, Quang Hieu Pham, Fangcong Yin +4Reinforcement Learning With Verifiable Reward Reward Functions
AUniversity of Alberta · N New York University · LLMU Munich +1
Natural gradient descent with momentum
Apr 16, 2026Anthony Nouy, Agustín SomacalGradient Descent Gradient
Centrale Nantes, Nantes Universit´e, Laboratoire de Math´ematiques Jean Leray UMR CNRS 6629
Wasserstein Formulation of Reinforcement Learning. An Optimal Transport Perspective on Policy Optimization
Apr 16, 2026Mathias DusRiemannian Optimization Wasserstein Distance
IRMA
Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations
Apr 16, 2026Adrian Edin, Michel Kieffer, Mikael Johansson +1Federated Learning Generative Image Compression
Department of Electrical Engineering (ISY), Linköping University, SE-581 83 Linköping, Sweden · CentraleSupélec, CNRS, L2S, Université Paris-Saclay, Gif-sur-Yvette, FR-91192, France · Division of Decision and Control Systems, School of EECS, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
GRADE: Probing Knowledge Gaps in LLMs through Gradient Subspace Dynamics
Apr 3, 2026Yujing Wang, Yuanbang Liang, Yukun Lai +2Reasoning Gaps Gradient
Beihang University · Cardiff University · King’s College London
Non-Euclidean Gradient Descent Operates at the Edge of Stability
Mar 5, 2026Rustem Islamov, Michael Crawshaw, Jeremy Cohen +1Gradient Descent Gradient
University of Basel, Switzerland · George Mason University, USA · Flatiron Institute, USA
The GRADIEND Python Package: An End-to-End System for Gradient-Based Feature Learning
Feb 27, 2026Jonathan Drechsel, Steffen HerboldGradient Feature Learning
Faculty of Computer Science and Mathematics University of Passau
Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions
Feb 4, 2026Dmitry Yarotsky, Eugene Golikov, Yaroslav GusevWasserstein Gradient Flows Gradient
Applied AI Institute, Moscow, Russia · Steklov Mathematical Institute of Russian Academy of Sciences, Moscow, Russia.
Not All Preferences Deserve Gradients: Understanding Gradient Utility in Offline Reasoning Alignment
Feb 1, 2026Hui Wu, Hengyi Cai, Jinman Zhao +6Gradient Preference Alignment Learning
Baidu Inc. · University of Toronto · School of Intelligence Science and Technology, Peking University +1
Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates
Jan 26, 2026Yibo Li, Zijie Lin, Ailin Deng +5Large Language Model Reinforcement Learning Offline Reinforcement Learning
National University of Singapore, Singapore.
Test-Time Detoxification without Training or Learning Anything
Jan 14, 2026Baturay Saglam, Dionysis KalogeriasToxicity Detoxification
Department of Electrical and Computer Engineering, Yale University, New Haven, CT.
Gradient Descent as Implicit EM in Distance-Based Neural Models
Dec 31, 2025Alan OurslandExpectation-Maximization Gradient Descent
An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows
Nov 22, 2025Francesca Romana Crucinio, Sahani PathirajaWasserstein Gradient Flows Wasserstein Distance
ESOMAS, University of Turin, Italy & Collegio Carlo Alberto, Turin, Italy · School of Mathematics & Statistics, UNSW Sydney, Australia
Auditing Information Disclosure During Large-Scale Gradient-Based Training via Gradient Uniqueness
Oct 13, 2025Sleem Abdelghafar, Maryam Aliakbarpour, Christopher JermaineGradient-Based Attacks Membership Inference Attacks
Rice University Houston, USA
Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning
Sep 26, 2025Li Xia, Jing Yu, Zheng Liu +3Federated Learning Diffusion Inversion
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China
A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models
Sep 23, 2025Tim Y. J. Wang, O. Deniz AkyildizDiffusion Models Latent Diffusion Model
Department of Mathematics Imperial College London
Negative Stepsizes Make Gradient-Descent-Ascent Converge
May 2, 2025Henry Shugart, Jason M. AltschulerStep Accuracy Gradient Descent
UPenn
Preconditioned Inexact Stochastic ADMM for Deep Model
Feb 15, 2025Shenglong Zhou, Ouya Wang, Ziyan Luo +2Stochastic Gradient Descent Alternating Direction Method Of Multiplier
School of Mathematics and Statistics, Beijing Jiaotong University, China · ITP Lab, Department of EEE, Imperial College London, UK · National Mobile Communications Research Laboratory, Southeast University, China
Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamics
Oct 31, 2024Jia-Jie ZhuWasserstein Gradient Flows Variational Inference
Department of Mathematics, KTH Royal Institute of Technology, Lindstedtsvägen 25, 114 28, Stockholm, Sweden
Local Flow Matching Generative Models
Oct 3, 2024Chen Xu, Xiuyuan Cheng, Yao XieGenerative Flow Networks Conditional Flow Matching
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. · Department of Mathematics, Duke University
On the Approximation and Convergence of Distributional Policy Gradient Algorithms for Risk-Sensitive Reinforcement Learning
May 23, 2024Xian Yu, Minheng Xiao, Lei YingDistributional Reinforcement Learning Gradient
Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA · Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients
Mar 28, 2024Johannes Müller, Semih Çaycı, Guido MontúfarPolicy Gradient Fisher Information Matrix
Department of Mathematics, RWTH Aachen University, Aachen, 52062, Germany · Departments of Mathematics and Statistics & Data Science, University of California, Los Angeles, 90095, USA · Max Planck Institute for Mathematics in the Sciences, Leipzig, 04103, Germany
Attention by Synchronization in Coupled Oscillator Networks
Date pendingFabio Pasqualetti, Taosha GuoOscillatory Dynamics Activation Functions
Electrical Engineering and Computer Science University of California, Irvine

Apr 28, 2026, hep-phJ/K move · Enter open · S save

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane

Pahal D. Patel, Sanmay Ganguly

Department of Physics, Indian Institute of Technology, Kanpur Uttar-Pradesh-208016, India

Explainability Explainable Artificial Intelligence Graph Neural Networks Gradient-Weighted Class Activation Mapping Gradient Reproducibility+1

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Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performance on jet tagging benchmarks at the Large Hadron Collider, yet the physical reasoning behind their predictions remains opaque. We present different methods, i.e. perturbation-based (GNNExplainer), Shapley-value-based (GNNShap), and gradient-based (GRADCam); adapted to operate on LundNet's Lund-plane graph representation. Leveraging the fact that each node in the Lund plane corresponds to a physically meaningful parton splitting, we construct Monte Carlo truth explanation masks and introduce a physics-informed evaluation framework that goes beyond standard fidelity metrics. We perform the analysis in three transverse-momentum bins (

\mathrm{p_T} \in [500,700]

[800,1000]

, and the inclusive region

[500,1000]

GeV), revealing how explanation quality and focus shift between non-perturbative and perturbative regimes. We further quantify the correlation between explainer-assigned node importance and classical jet substructure observables --

N

-subjettiness ratios

τ_{21}

and

τ_{32}

and the energy correlation functions -- establishing the degree to which the model has learned known QCD features. We find that overall the weight assigned by explainability methods has a correlation with analytic observables, with expected shift across different phase space regimes, indicating that a trained neural network indeed learns some aspects of jet-substructure moments. Our open-source implementation enables reproducible explainability studies for graph-based jet taggers.

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Latest papers 217