Feature Attribution

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14 papers in the last four weeks, down 7% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 147

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GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
May 5, 2026Rishi Raj Sahoo, Subhankar MishraFeature Attribution Graph Neural Networks
National Institute of Science Education and Research (NISER), Bhubaneswar, India · Homi Bhabha National Institute, Mumbai, India
Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
May 4, 2026Jose Vinicius Ribeiro, Rafael Figueira Goncalves, Fabio Luiz Melquiades +1Feature Attribution Explainable Artificial Intelligence
Applied Nuclear Physics Laboratory · State University of Londrina · Dept. of Engineering and Architecture +2
Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution
May 4, 2026Soyeon Kim, Seongwoo Lim, Kyowoon Lee +1Gradient-Based Attribution Integrated Gradients
Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea · INEEJI, Seongnam, Korea · KAIST InnoCORE LLM, KAIST, Daejeon, Korea
How Can One Choose the Best CAM-Based Explainability Method for a CNN Model?
May 3, 2026Daniel da Silva Costa, Pedro Nuno de Souza Moura, Adriana C. F. AlvimFeature Attribution Saliency Map Evaluation
Postgraduate Program in Informatics, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, RJ, 22290-240, Brazil
CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations
Apr 30, 2026Louth Bin Rawshan, Zhuoyu Wang, Brian Y. LimFeature Attribution Explainable Artificial Intelligence
National University of Singapore, Singapore
Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution
Apr 29, 2026Karol Dobiczek, Maciej Mozolewski, Szymon Bobek +3Feature Attribution Explainable Artificial Intelligence
Department of Human-Centered Artificial Intelligence, Institute of Applied Computer Science, Jagiellonian University, Krakow, Poland · Faculty of Medicine, Jagiellonian University Medical College, Krakow, Poland
Object-Level Explanations for Image Geolocation Models: a GeoGuessr use-case
Apr 29, 2026Emilie Durrieu, Christophe Hurter, Philippe Muller +1Feature Attribution
ENAC, University of Toulouse · IRIT, University of Toulouse, ANITI · CNRS
On the explainability of max-plus neural networks
Apr 27, 2026Ikhlas Enaieh, Olivier Fercoq, García ÁngelFeature Attribution Neural Network Interpretability
S2A, LTCI · LTCI, T´el´ecom Paris · Institut Polytechnique de Paris +5
On the Properties of Feature Attribution for Supervised Contrastive Learning
Apr 24, 2026Leonardo Arrighi, Julia Eva Belloni, Aurélie Gallet +3Feature Attribution Supervised Contrastive Learning
University of Trieste, Trieste, Italy · University of Groningen, Groningen, the Netherlands · University of Amsterdam, Amsterdam, the Netherlands +3
H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers
Apr 23, 2026Ayushi Mehrotra, Dipkamal Bhusal, Michael Clifford +1Gradient-Based Attribution Feature Attribution
Rochester Institute of Technology · Toyota InfoTech Labs
TabSHAP
Apr 22, 2026Aryan Chaudhary, Prateek Agarwal, Tejasvi AlladiFeature Attribution SHAP Feature Attribution
Department of Computer Science Birla Institute of Technology and Science India
Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks
Apr 20, 2026Rongyuan Tan, Jue Zhang, Zhuozhao Li +3LLM Evaluation Feature Attribution
1Southern University of Science and Technology, China · *Work done during an internship at Microsoft. · 2Microsoft
Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Apr 19, 2026Shashank Mishra, Karan Patil, Cedric Schockaert +2Feature Attribution Interpretable Anomaly Detection
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Germany · Paul Wurth S.A, Luxembourg
Prune, Interpret, Evaluate: A Cross-Layer Transcoder-Native Framework for Efficient Circuit Discovery via Feature Attribution
Apr 18, 2026Qinhao Chen, Linyang He, Nima MesgaraniLLM Pruning Feature Attribution
Columbia University
The Query Channel: Information-Theoretic Limits of Masking-Based Explanations
Apr 17, 2026Erciyes Karakaya, Ozgur ErcetinFeature Attribution Explainable Artificial Intelligence
*Department of Electrical and Computer Engineering, University of Maryland, College Park, USA · †Faculty of Engineering and Natural Sciences, Sabanci University, Turkiye
AtManRL: Towards Faithful Reasoning via Differentiable Attention Saliency
Apr 17, 2026Max Henning Höth, Kristian Kersting, Björn Deiseroth +1CoT Faithfulness Feature Attribution
Aleph Alpha Research Lab1141 · TU Darmstadt Hessian.AI
Towards Rigorous Explainability by Feature Attribution
Apr 17, 2026Olivier Létoffé, Xuanxiang Huang, Joao Marques-SilvaFeature Attribution Explainable Artificial Intelligence
IRIT, University of Toulouse France · Nanyang Technological University, Singapore · ICREA & Univ. Lleida, Spain
LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance
Apr 16, 2026Jack Wei Lun Shi, Minghao Dang, Wawan Solihin +1Feature Attribution LLM Interpretability
Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore · School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China · Research and Innovation, NovaCITYNETS Pte.Ltd., 348615, Singapore
Feature-level Interaction Explanations in Multimodal Transformers
Mar 4, 2026Yeji Kim, Housam Khalifa Bashier Babiker, Mi-Young Kim +1Transformer Interpretability Feature Attribution
University of Alberta, Edmonton, AB, Canada · University of Waikato, Hamilton, New Zealand · University of Alberta, Camrose, AB, Canada
Motion Attribution for Video Generation
Jan 13, 2026Xindi Wu, Despoina Paschalidou, Jun Gao +5Gradient-Based Attribution Feature Attribution
NVIDIA · Princeton University · University of Michigan +3
Explainability of Complex AI Models with Correlation Impact Ratio
Jan 10, 2026Poushali Sengupta, Rabindra Khadka, Sabita Maharjan +5Feature Attribution Interpretable ML
Institute of Informatics, University of Oslo, Oslo, Norway · Department of Computer Science, Oslo Metropolitan University, Oslo, Norway · Aalborg University, Denmark
CID: Measuring Feature Importance Through Counterfactual Distributions
Nov 19, 2025Eddie Conti, Álvaro Parafita, Axel BrandoFeature Attribution Explainability Evaluation
TAIES Group, HPES Lab, Barcelona Supercomputing Center (BSC)
Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)
Oct 26, 2025Sze Chai Leung, Di Zhou, H. Jane BaeFeature Attribution
Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA 91125, USA. · Lynn Booth & Kent Kresa Department of Aerospace, California Institute of Technology, Pasadena, CA 91125, USA. · Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville, TN 37996, USA.
Evidence Recomposition and Predictive Context Residualization for Visual Attribution in Multimodal Large Language Models
Sep 26, 2025Jiawei Liang, Jianjie Huang, Ruoyu Chen +4Feature Attribution Multimodal Large Language Models
Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China · Zhongguan-cun Academy, Beijing 100094, China · University of Chinese Academy of Sciences, Beijing 100049, China +2
Explainable embeddings with Distance Explainer
May 21, 2025Christiaan Meijer, E. G. Patrick BosFeature Attribution Perturbation-Based Feature Attribution
Netherlands eScience Center, Science Park 402, 1098 XH Amsterdam, Netherlands · Center for Information Technology, University of Groningen, P.O. Box 11044, 9700 CA Groningen, Netherlands
CHAOSMINING: Benchmarking Post-Hoc Attribution with Sparse Informative Features in High Dimensions
Jun 17, 2024Ge Shi, Fangyi Liu, Ziwen Kan +1Gradient-Based Attribution Feature Attribution
Department of Computer Science University of California, Davis
Relevance Is Not Permission: Localizing and Controlling Metric-Facing Attention Contributions
Date pendingMinwoo Yu, Young-guk HaFeature Attribution Evidence Selection
Smart Computing Laboratory, Department of Computer Science and Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea

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GRAFT: Auditing Graph Neural Networks via Global Feature Attribution

Rishi Raj Sahoo, Subhankar Mishra

National Institute of Science Education and Research (NISER), Bhubaneswar, India · Homi Bhabha National Institute, Mumbai, India

Feature Attribution Graph Neural Networks Explainability Evaluation GNN Explainability

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Graph Neural Networks (GNNs) achieve strong performance on node classification tasks but remain difficult to interpret, particularly with respect to which input features drive their predictions. Existing global GNN explainers operate at the structural level identifying recurring subgraph motifs, but none explain model behaviour globally at the level of input node attributes. We propose GRAFT, a posthoc global explanation framework that identifies class-level feature importance profiles for GNNs. The method combines diversity-guided exemplar selection, Integrated Gradients-based attribution, and aggregation to construct a global view of feature influence for each class, which can be further expressed as concise natural language rules using a large language model with self-refinement. We evaluate GRAFT across multiple datasets, architectures, and experimental settings, demonstrating its effectiveness in capturing model-relevant features, supporting bias analysis, and enabling feature-efficient transfer learning. In addition, we introduce a structured human evaluation protocol to assess the interpretability of generated rules along dimensions such as accuracy and usefulness. Our results suggest that GRAFT provides a practical and interpretable approach for analysing feature-level behaviour in GNNs, bridging quantitative attribution with human-understandable explanations.

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