Mechanistic Interpretability papers, 2023–2026

Key findings

• Mechanistic Interpretability went from 17 papers in 2023 to 138 in 2026, roughly 8.1x in three years.
• ICML publishes the most Mechanistic Interpretability work: 119 papers from 2023 to 2026, 38% of the four-venue total.
• The fastest-growing overlap is with Deep learning · large language models: papers tagged with both rose from 1 in 2023 to 23 in 2026.
• Among the 5 topics under Interpretability, it ranks #2 by 2026 paper count; the fastest grower in the group, Feature Attribution, is up 1.9x versus 8.1x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Mechanistic Interpretability papers

102 papers, page 2 of 3

51. ICLR 2025Controllable Context Sensitivity and the Knob Behind It
52. ICLR 2025Density estimation with LLMs: a geometric investigation of in-context learning trajectories
53. ICLR 2025Differential learning kinetics govern the transition from memorization to generalization during in-context learning
54. ICLR 2025Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient
55. ICLR 2025Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
56. ICLR 2025Eliminating Position Bias of Language Models: A Mechanistic Approach
57. ICLR 2025Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
58. ICLR 2025Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation
59. ICLR 2025HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
60. ICLR 2025Improving Instruction-Following in Language Models through Activation Steering
61. ICLR 2025Interpreting Emergent Planning in Model-Free Reinforcement Learning
62. ICLR 2025Interpreting the Second-Order Effects of Neurons in CLIP
63. ICLR 2025Linear Representations of Political Perspective Emerge in Large Language Models
64. ICLR 2025Mechanistic Permutability: Match Features Across Layers
65. ICLR 2025Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
66. ICLR 2025Monet: Mixture of Monosemantic Experts for Transformers
67. ICLR 2025Not All Language Model Features Are One-Dimensionally Linear
68. ICLR 2025ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
69. ICLR 2025Residual Stream Analysis with Multi-Layer SAEs
70. ICLR 2025Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words
71. ICLR 2025Revealing and Mitigating Over-Attention in Knowledge Editing
72. ICLR 2025Revisiting In-context Learning Inference Circuit in Large Language Models
73. ICLR 2025Robust LLM safeguarding via refusal feature adversarial training
74. ICLR 2025Scalable Mechanistic Neural Networks
75. ICLR 2025Sparse Autoencoders Do Not Find Canonical Units of Analysis
76. ICLR 2025Systematic Outliers in Large Language Models
77. ICLR 2025The Computational Complexity of Circuit Discovery for Inner Interpretability
78. ICLR 2025Towards a Unified and Verified Understanding of Group-Operation Networks
79. ICLR 2025Towards Interpreting Visual Information Processing in Vision-Language Models
80. ICLR 2025Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control
81. ICLR 2025Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures
82. ICLR 2025Transformers Struggle to Learn to Search
83. ICLR 2025Uncovering Latent Memories in Large Language Models
84. ICLR 2025Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers
85. ICLR 2024Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
86. ICLR 2024Circuit Component Reuse Across Tasks in Transformer Language Models
87. ICLR 2024Feature emergence via margin maximization: case studies in algebraic tasks
88. ICLR 2024Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking
89. ICLR 2024Function Vectors in Large Language Models
90. ICLR 2024Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching
91. ICLR 2024Linearity of Relation Decoding in Transformer Language Models
92. ICLR 2024Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
93. ICLR 2024Overthinking the Truth: Understanding how Language Models Process False Demonstrations
94. ICLR 2024PRIME: Prioritizing Interpretability in Failure Mode Extraction
95. ICLR 2024Scaling Laws for Associative Memories
96. ICLR 2024Successor Heads: Recurring, Interpretable Attention Heads In The Wild
97. ICLR 2024The mechanistic basis of data dependence and abrupt learning in an in-context classification task
98. ICLR 2024Towards Best Practices of Activation Patching in Language Models: Metrics and Methods
99. ICLR 2023Concept Gradient: Concept-based Interpretation Without Linear Assumption
100. ICLR 2023Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small

FAQ

How many Mechanistic Interpretability papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

311 in total: 17 in 2023, 49 in 2024, 107 in 2025, 138 in 2026.

How many Mechanistic Interpretability papers are at NeurIPS 2026?

NeurIPS 2026 has 16 Mechanistic Interpretability papers, versus 34 the year before.

How many Mechanistic Interpretability papers are at ICLR 2026?

ICLR 2026 has 46 Mechanistic Interpretability papers, versus 38 the year before.

How many Mechanistic Interpretability papers are at ICML 2026?

ICML 2026 has 74 Mechanistic Interpretability papers, versus 33 the year before.

How many Mechanistic Interpretability papers are at CVPR 2026?

CVPR 2026 has 2 Mechanistic Interpretability papers, versus 2 the year before.

Which conference publishes the most Mechanistic Interpretability papers?

ICML, with 119 papers from 2023 to 2026.