鲁棒基准论文趋势 2023–2026

主要结论

• 鲁棒基准论文从 2023 年的 64 篇增长到 2026 年的 69 篇，三年间扩大到约 1.1 倍。
• NeurIPS 是发鲁棒基准论文最多的会议，2023–2026 年共 80 篇，占四个会议总量的 30%。
• 交叉方向里涨得最快的是通用机器学习 · evaluation：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 8 篇。
• 在「评测与基准」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的大模型基准是 154 倍，鲁棒基准是 1.1 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026Brittlebench: Quantifying LLM robustness via prompt sensitivity
• ICML 2026Geometrically Constrained Outlier Synthesis
• ICLR 2026Pitfalls in Evaluating Language Model Forecasters
• CVPR 2026FoV-Net Rotation-Invariant CAD B-rep Learning via Field-of-View Ray Casting
• NeurIPS 2025Aggregation Hides Out-of-Distribution Generalization Failures from Spurious Correlations
• ICML 2025Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
• ICLR 2025DRoP: Distributionally Robust Data Pruning
• CVPR 2025FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering
• NeurIPS 2024An Analysis of Robustness of Non-Lipschitz Networks
• ICML 2024Controllable Prompt Tuning For Balancing Group Distributional Robustness
• ICLR 2024Realistic Evaluation of Semi-supervised Learning Algorithms in Open Environments
• CVPR 2024A Bayesian Approach to OOD Robustness in Image Classification
• NeurIPS 2023Revisiting Out-of-distribution Robustness in NLP: Benchmarks, Analysis, and LLMs Evaluations
• ICML 2023A Law of Robustness beyond Isoperimetry
• ICLR 2023A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
• CVPR 2023Improving Robustness of Vision Transformers by Reducing Sensitivity To Patch Corruptions
• NeurIPS 2026Stress-Testing Neural Network Verifiers with Provably Robust Instances
• ICML 2026Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions
• ICLR 2026CLARC: C/C++ Benchmark for Robust Code Search
• CVPR 2026LIBERO-Plus A Progressive Robustness Benchmark for Visual-Language-Action Models

全部鲁棒基准论文

共 264 篇，第 2 / 6 页

51. ICML 2026When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
52. NeurIPS 2026ActionUNet: Improving Robustness of VLA Models with Efficient Multi-scale Fine-tuning
53. NeurIPS 2026Aligning Language Model Benchmarks with Pairwise Preferences
54. NeurIPS 2026Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
55. NeurIPS 2026Benchmarks Design under Data Scarcity: From Coarse Labels to Diagnostic Evaluation of Biosynthetic Gene Cluster Models
56. NeurIPS 2026Brittlebench: Quantifying LLM robustness via prompt sensitivity
57. NeurIPS 2026Control First, Robustness Next: Decoupled Representation Learning for Visual RL Generalization
58. NeurIPS 2026CREF: Forecasting Benchmarks for the Age of Agents
59. NeurIPS 2026Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR
60. NeurIPS 2026Do Robust LVLMs Hallucinate More? Uncovering Robustness-Induced Hallucination in Large Vision-Language Models
61. NeurIPS 2026FusionAudit: Pathway-Conditioned Robustness Auditing for Native Multimodal Models
62. NeurIPS 2026Orthogonal Origin Parking: Decoupling Lorentz Manifolds for Robust OOD Generalization
63. NeurIPS 2026Recovering Clean Evaluation Metrics from Contaminated Benchmarks
64. NeurIPS 2026Strategic Evaluation: Incentivizing AI Capability Coverage with Private Benchmarks
65. NeurIPS 2026Stress-Testing Neural Network Verifiers with Provably Robust Instances
66. NeurIPS 2026SynDORBench: Evaluating LVLM Perceptual Robustness Under Physically Constrained Visibility Conditions
67. NeurIPS 2026The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation
68. NeurIPS 2026Variational Cover Modification Steganography
69. NeurIPS 2026We Need to Improve Benchmarks in AI for Mathematics
70. CVPR 2025Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF Attack
71. CVPR 2025FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering
72. CVPR 2025Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes
73. CVPR 2025R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
74. CVPR 2025Rethinking the Adversarial Robustness of Multi-Exit Neural Networks in an Attack-Defense Game
75. CVPR 2025SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers
76. CVPR 2025Tightening Robustness Verification of MaxPool-based Neural Networks via Minimizing the Over-Approximation Zone
77. CVPR 2025Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
78. ICLR 2025Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings
79. ICLR 2025Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness
80. ICLR 2025Consistency Checks for Language Model Forecasters
81. ICLR 2025Directional Gradient Projection for Robust Fine-Tuning of Foundation Models
82. ICLR 2025Do Large Language Models Truly Understand Geometric Structures?
83. ICLR 2025DRoP: Distributionally Robust Data Pruning
84. ICLR 2025In Search of Forgotten Domain Generalization
85. ICLR 2025MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures
86. ICLR 2025THE ROBUSTNESS OF DIFFERENTIABLE CAUSAL DISCOVERY IN MISSPECIFIED SCENARIOS
87. ICLR 2025Zero-cost Proxy for Adversarial Robustness Evaluation
88. ICML 2025Adversarial Robustness via Deformable Convolution with Stochasticity
89. ICML 2025Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
90. ICML 2025Explaining the role of Intrinsic Dimensionality in Adversarial Training
91. ICML 2025LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models
92. ICML 2025LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
93. ICML 2025Mahalanobis++: Improving OOD Detection via Feature Normalization
94. ICML 2025Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation
95. ICML 2025One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
96. ICML 2025Pixel-level Certified Explanations via Randomized Smoothing
97. ICML 2025Position: AI Safety Must Embrace an Antifragile Perspective
98. ICML 2025Position: AI Should Not Be An Imitation Game: Centaur Evaluations
99. ICML 2025Position: Certified Robustness Does Not (Yet) Imply Model Security
100. ICML 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

常见问题

2023–2026 年四大会议一共有多少篇鲁棒基准论文？

共 264 篇，按年份分别是 2023 年 64 篇、2024 年 59 篇、2025 年 72 篇、2026 年 69 篇。

NeurIPS 2026 有多少篇鲁棒基准论文？

NeurIPS 2026 有 18 篇鲁棒基准论文，上一届是 34 篇。

ICLR 2026 有多少篇鲁棒基准论文？

ICLR 2026 有 15 篇鲁棒基准论文，上一届是 10 篇。

ICML 2026 有多少篇鲁棒基准论文？

ICML 2026 有 24 篇鲁棒基准论文，上一届是 20 篇。

CVPR 2026 有多少篇鲁棒基准论文？

CVPR 2026 有 12 篇鲁棒基准论文，上一届是 8 篇。

哪个会议的鲁棒基准论文最多？

NeurIPS，2023–2026 年共 80 篇。