鲁棒基准论文趋势 2023–2026
分布外评测
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 264 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 12 | 29 | 14 | 9 | 64 |
| 2024 | 16 | 18 | 16 | 9 | 59 |
| 2025 | 34 | 10 | 20 | 8 | 72 |
| 2026 | 18 | 15 | 24 | 12 | 69 |
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 69 篇，第 2 / 2 页
- ICML 2026When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
- NeurIPS 2026ActionUNet: Improving Robustness of VLA Models with Efficient Multi-scale Fine-tuning
- NeurIPS 2026Aligning Language Model Benchmarks with Pairwise Preferences
- NeurIPS 2026Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
- NeurIPS 2026Benchmarks Design under Data Scarcity: From Coarse Labels to Diagnostic Evaluation of Biosynthetic Gene Cluster Models
- NeurIPS 2026Brittlebench: Quantifying LLM robustness via prompt sensitivity
- NeurIPS 2026Control First, Robustness Next: Decoupled Representation Learning for Visual RL Generalization
- NeurIPS 2026CREF: Forecasting Benchmarks for the Age of Agents
- NeurIPS 2026Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR
- NeurIPS 2026Do Robust LVLMs Hallucinate More? Uncovering Robustness-Induced Hallucination in Large Vision-Language Models
- NeurIPS 2026FusionAudit: Pathway-Conditioned Robustness Auditing for Native Multimodal Models
- NeurIPS 2026Orthogonal Origin Parking: Decoupling Lorentz Manifolds for Robust OOD Generalization
- NeurIPS 2026Recovering Clean Evaluation Metrics from Contaminated Benchmarks
- NeurIPS 2026Strategic Evaluation: Incentivizing AI Capability Coverage with Private Benchmarks
- NeurIPS 2026Stress-Testing Neural Network Verifiers with Provably Robust Instances
- NeurIPS 2026SynDORBench: Evaluating LVLM Perceptual Robustness Under Physically Constrained Visibility Conditions
- NeurIPS 2026The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation
- NeurIPS 2026Variational Cover Modification Steganography
- NeurIPS 2026We Need to Improve Benchmarks in AI for Mathematics
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查鲁棒基准在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "robustness-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。