机制解释论文趋势 2023–2026

主要结论

• 机制解释论文从 2023 年的 17 篇增长到 2026 年的 138 篇，三年间扩大到约 8.1 倍。
• ICML 是发机制解释论文最多的会议，2023–2026 年共 119 篇，占四个会议总量的 38%。
• 交叉方向里涨得最快的是深度学习 · large language models：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 23 篇。
• 在「可解释性」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的特征归因是 1.9 倍，机制解释是 8.1 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 2026A Mechanistic Interpretability Study of an Astronomical Foundation Model
• ICML 2026Contribution Weights: A Geometrical Analysis of Self-Attention Transformers
• ICLR 2026Circuit Insights: Towards Interpretability Beyond Activations
• CVPR 2026CIGMA Causal Information-Gain Mechanistic Attribution of Attention Heads in Vision
• NeurIPS 2025Beyond Components: Singular Vector-Based Interpretability of Transformer Circuits
• ICML 2025Algorithm Development in Neural Networks: Insights from the Streaming Parity Task
• ICLR 2025Arithmetic Without Algorithms: Language Models Solve Math with a Bag of Heuristics
• CVPR 2025Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability
• NeurIPS 2024InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques
• ICML 2024Mechanistic Neural Networks for Scientific Machine Learning
• ICLR 2024Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking
• CVPR 2024SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
• NeurIPS 2023A Logic for Expressing Log-Precision Transformers
• ICML 2023Mechanistic Mode Connectivity
• ICLR 2023Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small
• NeurIPS 2026Discovering Mechanistic Models of Neural Activity: System Identification in an in Silico Zebrafish
• ICML 2026CorrSteer: Generation-Time LLM Steering via Correlated Sparse Autoencoder Features
• ICLR 2026CLUE: Conflict-guided Localization for LLM Unlearning Framework
• CVPR 2026From Weights to Concepts Data-Free Interpretability of CLIP via Singular
• NeurIPS 2025Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models

全部机制解释论文

共 311 篇，第 4 / 7 页

151. ICLR 2025Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
152. ICLR 2025Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation
153. ICLR 2025HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
154. ICLR 2025Improving Instruction-Following in Language Models through Activation Steering
155. ICLR 2025Interpreting Emergent Planning in Model-Free Reinforcement Learning
156. ICLR 2025Interpreting the Second-Order Effects of Neurons in CLIP
157. ICLR 2025Linear Representations of Political Perspective Emerge in Large Language Models
158. ICLR 2025Mechanistic Permutability: Match Features Across Layers
159. ICLR 2025Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
160. ICLR 2025Monet: Mixture of Monosemantic Experts for Transformers
161. ICLR 2025Not All Language Model Features Are One-Dimensionally Linear
162. ICLR 2025ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
163. ICLR 2025Residual Stream Analysis with Multi-Layer SAEs
164. ICLR 2025Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words
165. ICLR 2025Revealing and Mitigating Over-Attention in Knowledge Editing
166. ICLR 2025Revisiting In-context Learning Inference Circuit in Large Language Models
167. ICLR 2025Robust LLM safeguarding via refusal feature adversarial training
168. ICLR 2025Scalable Mechanistic Neural Networks
169. ICLR 2025Sparse Autoencoders Do Not Find Canonical Units of Analysis
170. ICLR 2025Systematic Outliers in Large Language Models
171. ICLR 2025The Computational Complexity of Circuit Discovery for Inner Interpretability
172. ICLR 2025Towards a Unified and Verified Understanding of Group-Operation Networks
173. ICLR 2025Towards Interpreting Visual Information Processing in Vision-Language Models
174. ICLR 2025Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control
175. ICLR 2025Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures
176. ICLR 2025Transformers Struggle to Learn to Search
177. ICLR 2025Uncovering Latent Memories in Large Language Models
178. ICLR 2025Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers
179. ICML 2025Algorithm Development in Neural Networks: Insights from the Streaming Parity Task
180. ICML 2025Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
181. ICML 2025Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence
182. ICML 2025ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features
183. ICML 2025Constrained Belief Updates Explain Geometric Structures in Transformer Representations
184. ICML 2025Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective
185. ICML 2025Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
186. ICML 2025Evaluating Neuron Explanations: A Unified Framework with Sanity Checks
187. ICML 2025Geometric Hyena Networks for Large-scale Equivariant Learning
188. ICML 2025How Do Transformers Learn Variable Binding in Symbolic Programs?
189. ICML 2025In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention
190. ICML 2025Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models
191. ICML 2025Learning Multi-Level Features with Matryoshka Sparse Autoencoders
192. ICML 2025Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
193. ICML 2025Looking Beyond the Top-1: Transformers Determine Top Tokens in Order
194. ICML 2025Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization
195. ICML 2025MIB: A Mechanistic Interpretability Benchmark
196. ICML 2025Models of Heavy-Tailed Mechanistic Universality
197. ICML 2025Position: Explainable AI Cannot Advance Without Better User Studies
198. ICML 2025Position: Rethinking Explainable Machine Learning as Applied Statistics
199. ICML 2025Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing
200. ICML 2025SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability

常见问题

2023–2026 年四大会议一共有多少篇机制解释论文？

共 311 篇，按年份分别是 2023 年 17 篇、2024 年 49 篇、2025 年 107 篇、2026 年 138 篇。

NeurIPS 2026 有多少篇机制解释论文？

NeurIPS 2026 有 16 篇机制解释论文，上一届是 34 篇。

ICLR 2026 有多少篇机制解释论文？

ICLR 2026 有 46 篇机制解释论文，上一届是 38 篇。

ICML 2026 有多少篇机制解释论文？

ICML 2026 有 74 篇机制解释论文，上一届是 33 篇。

CVPR 2026 有多少篇机制解释论文？

CVPR 2026 有 2 篇机制解释论文，上一届是 2 篇。

哪个会议的机制解释论文最多？

ICML，2023–2026 年共 119 篇。