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

49 papers, page 1 of 1

1. CVPR 2024SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
2. ICLR 2024Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
3. ICLR 2024Circuit Component Reuse Across Tasks in Transformer Language Models
4. ICLR 2024Feature emergence via margin maximization: case studies in algebraic tasks
5. ICLR 2024Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking
6. ICLR 2024Function Vectors in Large Language Models
7. ICLR 2024Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching
8. ICLR 2024Linearity of Relation Decoding in Transformer Language Models
9. ICLR 2024Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
10. ICLR 2024Overthinking the Truth: Understanding how Language Models Process False Demonstrations
11. ICLR 2024PRIME: Prioritizing Interpretability in Failure Mode Extraction
12. ICLR 2024Scaling Laws for Associative Memories
13. ICLR 2024Successor Heads: Recurring, Interpretable Attention Heads In The Wild
14. ICLR 2024The mechanistic basis of data dependence and abrupt learning in an in-context classification task
15. ICLR 2024Towards Best Practices of Activation Patching in Language Models: Metrics and Methods
16. ICML 2024Attention Meets Post-hoc Interpretability: A Mathematical Perspective
17. ICML 2024Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks
18. ICML 2024From Neurons to Neutrons: A Case Study in Interpretability
19. ICML 2024Interpretability Illusions in the Generalization of Simplified Models
20. ICML 2024Local vs. Global Interpretability: A Computational Complexity Perspective
21. ICML 2024Mechanistic Neural Networks for Scientific Machine Learning
22. ICML 2024Observable Propagation: Uncovering Feature Vectors in Transformers
23. ICML 2024Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience
24. ICML 2024What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
25. NeurIPS 2024Algorithmic Capabilities of Random Transformers
26. NeurIPS 2024An Analysis of Tokenization: Transformers under Markov Data
27. NeurIPS 2024B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
28. NeurIPS 2024Compact Proofs of Model Performance via Mechanistic Interpretability
29. NeurIPS 2024Confidence Regulation Neurons in Language Models
30. NeurIPS 2024CoSy: Evaluating Textual Explanations of Neurons
31. NeurIPS 2024Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space
32. NeurIPS 2024Evidence of Learned Look-Ahead in a Chess-Playing Neural Network
33. NeurIPS 2024Grokking of Implicit Reasoning in Transformers: A Mechanistic Journey to the Edge of Generalization
34. NeurIPS 2024Hypothesis Testing the Circuit Hypothesis in LLMs
35. NeurIPS 2024Improving Sparse Decomposition of Language Model Activations with Gated Sparse Autoencoders
36. NeurIPS 2024InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques
37. NeurIPS 2024InversionView: A General-Purpose Method for Reading Information from Neural Activations
38. NeurIPS 2024Iteration Head: A Mechanistic Study of Chain-of-Thought
39. NeurIPS 2024Linking In-context Learning in Transformers to Human Episodic Memory
40. NeurIPS 2024LLM Circuit Analyses Are Consistent Across Training and Scale
41. NeurIPS 2024Measuring Per-Unit Interpretability at Scale Without Humans
42. NeurIPS 2024Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
43. NeurIPS 2024Optimal ablation for interpretability
44. NeurIPS 2024Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics
45. NeurIPS 2024Refusal in Language Models Is Mediated by a Single Direction
46. NeurIPS 2024Talking Heads: Understanding Inter-Layer Communication in Transformer Language Models
47. NeurIPS 2024Transcoders find interpretable LLM feature circuits
48. NeurIPS 2024Truth is Universal: Robust Detection of Lies in LLMs
49. NeurIPS 2024What Makes and Breaks Safety Fine-tuning? A Mechanistic Study

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.