Neural Network Robustness

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9 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

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Latest papers 173

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HARVE: Hacking-Aware Reward-Head Vector Editing for Robust Reward Models
Jun 2, 2026Shuang Liu, Yuxuan Bo, Qiuyang Zhao +4Reward Modeling Reward Hacking
Carnegie Mellon University · University of Virginia · Harvard University +4
RRISE: Robust Radius Inference via a Surrogate Estimator
Jun 1, 2026Jong-Ik Park, Shreyas Chaudhari, Carlee Joe-Wong +1Randomized Smoothing Neural Network Robustness
Carnegie Mellon University
A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
Jun 1, 2026Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati +1Neural Network Robustness Adversarial Robustness
Institute of Neuroinformatics
Rethinking Evaluation Paradigms in IBP-based Certified Training
Jun 1, 2026Konstantin Kaulen, Hadar Shavit, Holger H. HoosNeural Network Verification Neural Network Robustness
Chair of AI Methodology, RWTH Aachen University, Aachen, Germany · LIACS, Leiden University, Leiden, The Netherlands
Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness
Jun 1, 2026Kai WangPrototype-Based Classification Neural Network Robustness
CEAR: Certified Ensemble Adversarial Robustness in DNNs
May 31, 2026Daniel Sadig, Mohammadreza Maleki, Hamed Karimi +1Neural Network Robustness Adversarial Robustness
Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada · Vector Institute, Toronto, ON, Canada
Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment
May 30, 2026Uiwon Hwang, Jaeho HwangNeural Network Robustness Physics-Informed ML
Department of Computer Science and Engineering Ewha Womans University · Human-Centered Artificial Intelligence Research Institute Ewha Womans University
SORA: Free Second-Order Attacks in Fast Adversarial Training
May 30, 2026Mazdak Teymourian, Ramtin Moslemi, Farzan Rahmani +1Adversarial Training Neural Network Generalization
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
TASER: Task-Aware Stein Regularisation for Geometry-Driven Robustness
May 28, 2026Michał Kozyra, Gesine ReinertDistribution Shift Robustness Neural Network Robustness
Department of Statistics, University of Oxford, United Kingdom
Improving Adversarial Robustness of Attribution via Implicit Regularization
May 28, 2026Amir Mehrpanah, Matteo Gamba, Hossein AzizpourTransformer Interpretability Neural Network Robustness
Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Computer Science, Brown University, USA · Science for Life Laboratory, Stockholm, Sweden
Harnessing non-adversarial robustness in large language models
May 28, 2026Qinghua Zhou, Ellina Aleshina, Andrey Lovyagin +6LLM Fine-Tuning Neural Network Robustness
International Joint Laboratory of AI for Industry, QUST, Qingdao, China · King’s College London, London, UK · Applied AI Institute, Moscow, Russia +2
TRACER: Persistent Regularization for Robust Multimodal Finetuning
May 28, 2026Hesam Asadollahzadeh, Feng Liu, Christopher Leckie +1VLM Robustness Neural Network Robustness
School of Computing and Information Systems (CIS), Faculty of Engineering and IT (FEIT), University of Melbourne, Australia.
Beyond Lipschitz: Data-Driven Robustness via Discrete Modulus of Continuity
May 27, 2026Jürgen Dölz, Michael Multerer, Michele PalmaNeural Network Robustness Adversarial Robustness
Institute for Numerical Simulation University of Bonn Germany · Dalle Molle Institute for Artificial Intelligence USI Lugano Switzerland
When Muon Optimizer Meets Adversarial Training: A Theoretical and Empirical Study
May 26, 2026Jun Yan, Weiquan Huang, Jiankai Zuo +4Adversarial Training Neural Network Optimization
IT College, Shanghai Ocean University, Shanghai, 201306 · School of Computer Science and Technology, Tongji University, Shanghai, 201804 · SEIE, Suzhou University of Science and Technology, Suzhou, 215009 +2
Capability and Robustness Cannot Both Be Free: An Information-Theoretic Bound for Vision-Language-Action Models
May 25, 2026Jianwei TaiNeural Network Robustness Adversarial Robustness
School of Internet, Anhui University
Certified Robustness from Approximate Gaussian Mixture Structures in Pretrained Latent Spaces
May 25, 2026Konstantinos Emmanouilidis, Tianjiao Ding, Nghia Nguyen +2Neural Network Robustness Adversarial Robustness
CS & MINDS Johns Hopkins University · CIS University of Pennsylvania
ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks
May 23, 2026Rowan MartnishnPolynomial Neural Networks Neural Network Robustness
Sentivity AI
Toward Understanding Adversarial Distillation: Why Robust Teachers Fail
May 21, 2026Hongsin Lee, Hye Won ChungAdversarial Training Neural Network Robustness
School of Electrical Engineering, KAIST, Daejeon, Korea.
A New Framework to Analyse the Distributional Robustness of Deep Neural Networks
May 20, 2026Divij Khaitan, Subhashis BanerjeeDistribution Shift Robustness Neural Network Generalization
Microsoft · Ashoka University
Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?
May 20, 2026Muquan Li, Yingyi Ma, Yihong Huang +5Neural Network Robustness Adversarial Robustness
The Laboratory of Intelligent Collaborative Computing of UESTC, Chengdu, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China · Monash University, Melbourne, Australia
Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels
May 19, 2026Alexandre Lemire Paquin, Brahim Chaib-Draa, Philippe GiguèreNeural Network Robustness Noisy-Label Learning
Department of Computer Science and Software Engineering Université Laval, Québec, Canada
SDM: A Powerful Tool for Evaluating Model Robustness
May 19, 2026Xinlei Liu, Tao Hu, Jichao Xie +3Adversarial Attacks Neural Network Robustness
Information Engineering University, Zhengzhou, China · Key Laboratory of Cyberspace Endogenous Safety & Security of Henan Province, Zhengzhou, China · Key Laboratory of Cyberspace Security Ministry of Education of China, Zhengzhou, China +1
Updating the standard neuron model in artificial neural networks
May 19, 2026Raul Mohedano, Thomas Batard, Erik Velasco-Salido +4Neural Network Robustness Neural Network Expressivity
1Spanish National Research Council (CSIC), Madrid, Spain. · Center for Research in Mathematics (CIMAT), Guanajuato, Mexico. · 3Universidad Aut´onoma de Madrid (UAM), Madrid, Spain. +1
MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
May 18, 2026Xiangxiang Cui, Tianjin Huang, Yifang Wang +2Multimodal Robustness Neural Network Robustness
Beijing Normal University, China · University of Exeter, United Kingdom · University College London, United Kingdom +2
UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
May 17, 2026Tongze Wang, Xiaohui Xie, Wenduo Wang +2Distribution Shift Robustness Domain Adaptation
Institute for Network Sciences and Cyberspace, Tsinghua University · Department of Computer Science and Technology, Tsinghua University
Stress-Testing Neural Network Verifiers with Provably Robust Instances
May 16, 2026David Troxell, Yulia Alexandr, Sofia Hunt +2Neural Network Verification Neural Network Robustness
Department of Statistics & Data Science, University of California, Los Angeles · Department of Mathematics, University of California, Los Angeles · Max Planck Institute for Mathematics in the Sciences, Leipzig
Semantic Smoothing via Novel View Synthesis for Robust SAR Image Classification
May 15, 2026Daniel Brignac, Fengwei Tian, Banafsheh Latibari +2Randomized Smoothing Adversarial Attacks
The University of Arizona Tucson, Arizona, United States
LPDS: Evaluating LLM Robustness Through Logic-Preserving Difficulty Scaling
May 14, 2026Philipp Mondorf, Samuel J. Bell, Jesse Dodge +1LLM Evaluation Neural Network Robustness
FAIR at Meta · LMU Munich · Munich Center for Machine Learning +1
Fast Adversarial Attacks with Gradient Prediction
May 14, 2026Kamil Ciosek, Aleksandr V. Petrov, Nicolò Felicioni +1Adversarial Attacks Neural Network Robustness
Spotify
Dual-axis attribution of zebrafish tectal microcircuits for energy-efficient and robust neurocomputing
May 13, 2026Ningping Li, Hao Zhang, Yi ZhouEfficient Neural Network Inference Neural Processes
University of Science and Technology of China, Hefei, China

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HARVE: Hacking-Aware Reward-Head Vector Editing for Robust Reward Models

Shuang Liu, Yuxuan Bo, Qiuyang Zhao, Caiyue Huang+3

Carnegie Mellon University · University of Virginia · Harvard University+4

Reward Modeling Reward Hacking Neural Network Robustness Representation Engineering Reward Model Evaluation

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Reward models are central to large language model (LLM) alignment, but they remain vulnerable to reward hacking. To evaluate reward-model robustness, we introduce RewardHackBench containing 13 reward-hacking patterns covering real life high-stakes domains and general settings, and we find severe failures on specific subcategories across eight reward models. To mitigate these failures, we propose HARVE, a training-free reward-head editing method for scalar reward models. Instead of fine-tuning the reward model, HARVE identifies a multi-directional hacking subspace from residual stream directions associated with selected hacking subcategories, and removes the component of the reward-head vector aligned with that subspace. This directly reduces the reward head's sensitivity to hacking-related features using only a small set of contrastive gold-hacked examples, without gradient updates or fine-tuning. Comprehensive experiments across eight reward models indicates that \model improves hacking robustness, outperforms fine-tuning baselines, and preserves reward-models' general capability. Further analyses suggest that reward hacking is better captured as a multidimensional residual-space structure than by isolated surface cues.

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