OOD Generalization

OOD: Out-of-Distribution

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29 papers in the last four weeks, up 263% on the four weeks before. 0.3% of all new papers.

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Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
Mar 25, 2026Weixin Chen, Antonio Vergari, Han ZhaoVLM Robustness Visual Reasoning
University of Illinois Urbana-Champaign · University of Edinburgh
GenHOI: Generalized Hand-Object Pose Estimation with Occlusion Awareness
Mar 19, 2026Hui Yang, Wei Sun, Jian Liu +63D Hand Pose Estimation OOD Generalization
NERC-RVC, Hunan University · National University of Singapore · Lancaster University +2
Rethinking Role-Playing Evaluation: Anonymous Benchmarking and a Systematic Study of Personality Effects
Mar 4, 2026Ji-Lun Peng, Yun-Nung ChenPersonality Modeling in Language Models OOD Generalization
National Taiwan University, Taipei, Taiwan · Academia Sinica, Taipei, Taiwan
Tool Use Reduces Depth-Induced Collapse in OOD Reasoning
Feb 24, 2026David Koplow, Tomer Galanti, Tomaso PoggioOOD Generalization LLM Tool Use
Massachusetts Institute of Technology · Texas A&M University
A Constrained Optimization Perspective of Unrolled Transformers
Jan 24, 2026Javier Porras-Valenzuela, Samar Hadou, Alejandro RibeiroDeep Learning Optimization Transformer
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States of America.
6 Fingers, 1 Kidney: Natural Adversarial Medical Images Reveal Critical Weaknesses of Vision-Language Models
Dec 3, 2025Leon Mayer, Piotr Kalinowski, Caroline Ebersbach +6VLM Evaluation VLM Robustness
German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Germany. · Medical Faculty, Heidelberg University, Germany. · Faculty of Mathematics and Computer Science, Heidelberg University, Germany. +9
Did Models Learn Sufficiently? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation
Nov 15, 2025Yannan Chen, Ruoyu Chen, Wei Wang +6Adversarial Robustness OOD Generalization
School of Cyber Science and Technology, Sun Yat-Sen University, Guangdong Province, China · Institute of Perception, PC Laboratory, Guangdong Province, China · University of Chinese Academy of Sciences, Beijing, China +3
D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces
Nov 14, 2025Ruoqi Wang, Haitao Wang, Shaojie Guo +1Unsupervised Domain Adaptation Data Augmentation
HKUST(GZ) · SYSU · ECNU +1
Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC Codes
Oct 5, 2025Xiangjun Mi, Frank MuellerOOD Generalization Uncertainty Quantification
North Carolina State University, Raleigh, NC 27695, USA
How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
Oct 1, 2025Waïss Azizian, Ali HasanLanguage Model Pretraining OOD Generalization
Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France · Work done during an internship at Morgan Stanley Machine Learning Research. · Machine Learning Research, Morgan Stanley, New York, USA
Unveiling the Mechanisms of Multi-Hop Reasoning in Transformers via Identity Bridge
Sep 29, 2025Pengxiao Lin, Zheng-An Chen, Zhi-Qin John XuTransformer Interpretability OOD Generalization
School of Mathematical Sciences, Shanghai Jiao Tong University · Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University · Shanghai Seres Information Technology Co., Ltd, Shanghai 200040, China.
CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization
Sep 18, 2025Min Zhang, Yuyin Wang, Zhongxiang Dai +4VLM Adaptation Domain Generalization
East China Normal University · Xidian University · Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security +3
DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy
Aug 16, 2025Frederik L. Dennig, Daniel A. KeimVariational Autoencoders Uncertainty Quantification
University of Konstanz
Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark
Aug 8, 2025Ngoc N. Tran, Anwar Said, Waseem Abbas +2Distribution Shift Robustness Graph Representation Learning
Vanderbilt University, USA · Highmark Health, USA · University of Texas at Dallas, USA
Disassociating performance from compositional feature learning
May 14, 2025George Dimitriadis, Spyridon SamothrakisRepresentation Learning OOD Generalization
Computer Science and Electronic Engineering Department of the University of Essex
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
May 3, 2025Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun +10OOD Generalization Molecular Property Prediction
Lawrence Livermore National Laboratory, Livermore, CA · Binghamton University, Binghamton, NY
Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
Jan 3, 2025Evan R. Antoniuk, Peggy Li, Nathan Keilbart +3Molecular Optimization OOD Generalization
Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
Oct 28, 2024Jaedong Hwang, Brian Cheung, Zhang-Wei Hong +3Neural Network Robustness OOD Generalization
Massachusetts Institute of Technology
Generalization error of min-norm interpolators in transfer learning
Jun 20, 2024Yanke Song, Kenneth Gu, Sohom Bhattacharya +1OOD Generalization Cross-Domain Transfer Learning
Department of Statistics, Harvard University · Department of Statistics, Stanford University · Department of Statistics, University of Florida
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
May 29, 2024Lanting Fang, Yulian Yang, Yawei Zhang +3Causal Representation Learning Structural Causal Models
1Beijing Institue of Technology · Southeast University · Wuhan University
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization
Apr 25, 2022Minghui Chen, Cheng Wen, Feng Zheng +2Synthetic Data Augmentation Image Corruption Robustness
Department of Computer Science and Engineering, Southern University of Science and Technology · The University of Sydney · JD Explore Academy, JD.com Inc +1

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Can VLMs Reason Robustly? A Neuro-Symbolic Investigation

Weixin Chen, Antonio Vergari, Han Zhao

University of Illinois Urbana-Champaign · University of Edinburgh

VLM Robustness Visual Reasoning OOD Generalization VLM Reasoning Visually Grounded Reasoning Neuro-Symbolic Reasoning

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Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts. In this paper, we study covariate shifts in which the perceptual input distribution changes while the underlying prediction rules do not. To investigate this question, we consider visual deductive reasoning tasks, where a model is required to answer a query given an image and logical rules defined over the object concepts in the image. Empirically, we find that VLMs fine-tuned through gradient-based end-to-end training can achieve high in-distribution accuracy but fail to generalize under such shifts, suggesting that fine-tuning does not reliably induce the underlying reasoning function. This motivates a neuro-symbolic perspective that decouples perception from reasoning. However, we further observe that recent neuro-symbolic approaches that rely on black-box components for reasoning can still exhibit inconsistent robustness across tasks. To address this issue, we propose VLC, a neuro-symbolic method that combines VLM-based concept recognition with circuit-based symbolic reasoning. In particular, task rules are compiled into a symbolic program, specifically a circuit, which executes the rules exactly over the object concepts recognized by the VLM. Experiments on three simple visual deductive reasoning tasks with distinct rule sets show that VLC consistently achieves higher task accuracy on out-of-distribution data than other reasoning paradigms. Code is available at https://github.com/uiuctml/VLC.

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