社会层面 · accountability transparency and interpretability论文趋势 2023–2026

主要结论

• 社会层面 · accountability transparency and interpretability论文从 2023 年的 301 篇增长到 2026 年的 1,357 篇，三年间扩大到约 4.5 倍。
• NeurIPS 是发社会层面 · accountability transparency and interpretability论文最多的会议，2023–2026 年共 1,275 篇，占四个会议总量的 37%。
• 交叉方向里涨得最快的是社会层面 · privacy：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 93 篇。
• 在「对齐与负责任 AI」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的社会层面 · safety是 93 倍，社会层面 · accountability transparency and interpretability是 4.5 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 2026A Unifying Perspective on Language Model Interpretability
• ICML 2026$\texttt{PRISM}$:A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
• ICLR 2026Automated Interpretability Metrics Do Not Distinguish Trained and Random Transformers
• CVPR 2026Beyond Matching to Tiles Bridging Unaligned Aerial and Satellite Views
• NeurIPS 2025$\mathcal{X}^2$-DFD: A framework for e$\mathcal{X}$plainable and e$\mathcal{X}$tendable Deepfake Detection
• ICML 2025(How) Can Transformers Predict Pseudo-Random Numbers?
• ICLR 2025Adaptive Transformer Programs: Bridging the Gap Between Performance and Interpretability in Transformers
• CVPR 2025Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability
• NeurIPS 20242D-OOB: Attributing Data Contribution Through Joint Valuation Framework
• ICML 2024Attention Meets Post-hoc Interpretability: A Mathematical Perspective
• ICLR 2024"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection
• CVPR 2024Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
• NeurIPS 2023(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
• ICML 2023A Modern Look at the Relationship between Sharpness and Generalization
• ICLR 2023Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization
• CVPR 2023Analyzing and Diagnosing Pose Estimation With Attributions
• NeurIPS 2026AdKnob: Ad Intensity Control and Labeling for LLM-Native Advertising
• ICML 2026$\texttt{ShaplEIG}$: Bayesian Experimental Design for Shapley Value Estimation
• ICLR 2026Bandits with Single-Peaked Preferences and Limited Resources
• CVPR 2026IEBGLAn Interpretability-Enhanced Brain Graph Learning Framework with LLM-Instructed Topology and

全部社会层面 · accountability transparency and interpretability论文

共 3,411 篇，第 7 / 69 页

301. ICLR 2026Oversmoothing, "Oversquashing'', Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
302. ICLR 2026Paradigm Shift of GNN Explainer from Label Space to Prototypical Representation Space
303. ICLR 2026Path Channels and Plan Extension Kernels: a Mechanistic Description of Planning in a Sokoban RNN
304. ICLR 2026Patronus: Interpretable Diffusion Models with Prototypes
305. ICLR 2026PerFit: Exploring Personalization Shifts in Representation Space of LLMs
306. ICLR 2026PlantRSR: A New Plant Dataset and Method for Reference-based Super-Resolution
307. ICLR 2026Polynomial, trigonometric, and tropical activations
308. ICLR 2026PolySHAP: Extending KernelSHAP with Interaction-Informed Polynomial Regression
309. ICLR 2026Practical estimation of the optimal classification error with soft labels and calibration
310. ICLR 2026Precise and Interpretable Editing of Code Knowledge in Large Language Models
311. ICLR 2026Priors in time: Missing inductive biases for language model interpretability
312. ICLR 2026PRISM: Partial-label Relational Inference with Spatial and Spectral Cues
313. ICLR 2026PRISON: Unmasking the Criminal Potential of Large Language Models
314. ICLR 2026Probability Distributions Computed by Autoregressive Transformers
315. ICLR 2026ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting
316. ICLR 2026Provable Separations between Memorization and Generalization in Diffusion Models
317. ICLR 2026Provably Explaining Neural Additive Models
318. ICLR 2026Provably Tracking Equivalent Mechanistic Interpretations Across Neural Networks
319. ICLR 2026Random Spiking Neural Networks are Stable and Spectrally Simple
320. ICLR 2026Random-projection ensemble dimension reduction
321. ICLR 2026Reinforcement Learning Fine-Tuning Enhances Activation Intensity and Diversity in the Internal Circuitry of LLMs
322. ICLR 2026RepIt: Steering Language Models with Concept-Specific Refusal Vectors
323. ICLR 2026Rethinking Continual Learning with Progressive Neural Collapse
324. ICLR 2026Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity
325. ICLR 2026Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
326. ICLR 2026Rethinking the Gold Standard: Why Discrete Curvature Fails to Fully Capture Over-squashing in GNNs?
327. ICLR 2026Reverse Distillation: Disentangling and Scaling Protein Language Model Representations
328. ICLR 2026Revisiting [CLS] and Patch Token Interaction in Vision Transformers
329. ICLR 2026Revisiting Confidence Calibration for Misclassification Detection in VLMs
330. ICLR 2026Risk Phase Transitions in Spiked Regression: Alignment Driven Benign and Catastrophic Overfitting
331. ICLR 2026Robust Training of Neural Networks at Arbitrary Precision and Sparsity
332. ICLR 2026SASFT: Sparse Autoencoder-guided Supervised Finetuning to Mitigate Unexpected Code-Switching in LLMs
333. ICLR 2026Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models
334. ICLR 2026Seeing but Not Believing: Probing the Disconnect Between Visual Attention and Answer Correctness in VLMs
335. ICLR 2026Seesaw: Accelerating Training by Balancing Batch Size and Learning Rate Scheduling
336. ICLR 2026Self-Speculative Decoding Accelerates Lossless Inference in Any-Order and Any-Subset Autoregressive Models
337. ICLR 2026Semantic Regexes: Auto-Interpreting LLM Features with a Structured Language
338. ICLR 2026Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence
339. ICLR 2026SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
340. ICLR 2026SNAPHARD CONTRAST LEARNING
341. ICLR 2026Social Agents: Collective Intelligence Improves LLM Predictions
342. ICLR 2026Sparling: End-to-End Spatial Concept Learning via Extremely Sparse Activations
343. ICLR 2026Sparse Autoencoders Trained on the Same Data Learn Different Features
344. ICLR 2026Sparse CLIP: Co-Optimizing Interpretability and Performance in Contrastive Learning
345. ICLR 2026Spectral Bellman Method: Unifying Representation and Exploration in RL
346. ICLR 2026Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability
347. ICLR 2026Stackelberg Coupling of Online Representation Learning and Reinforcement Learning
348. ICLR 2026Steering Embedding Models with Geometric Rotation: Mapping Semantic Relationships Across Languages and Models
349. ICLR 2026Steering Evaluation-Aware Language Models To Act Like They Are Deployed
350. ICLR 2026Steering the Herd: A Framework for LLM-based Control of Social Learning

常见问题

2023–2026 年四大会议一共有多少篇社会层面 · accountability transparency and interpretability论文？

共 3,411 篇，按年份分别是 2023 年 301 篇、2024 年 589 篇、2025 年 1,164 篇、2026 年 1,357 篇。

NeurIPS 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

NeurIPS 2026 有 416 篇社会层面 · accountability transparency and interpretability论文，上一届是 522 篇。

ICLR 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

ICLR 2026 有 292 篇社会层面 · accountability transparency and interpretability论文，上一届是 211 篇。

ICML 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

ICML 2026 有 541 篇社会层面 · accountability transparency and interpretability论文，上一届是 337 篇。

CVPR 2026 有多少篇社会层面 · accountability transparency and interpretability论文？

CVPR 2026 有 108 篇社会层面 · accountability transparency and interpretability论文，上一届是 94 篇。

哪个会议的社会层面 · accountability transparency and interpretability论文最多？

NeurIPS，2023–2026 年共 1,275 篇。