Interpretability

ICML 2025arXiv:2506.08216

#2

Decision Information Meets Large Language Models: The Future of Explainable Operations Research

Yansen Zhang, Qingcan Kang, Wing Yin YU et al.

Shedding Light on Time Series Classification using Interpretability Gated Networks

Yunshi Wen, Tengfei Ma, Ronny Luss et al.

What makes an Ensemble (Un) Interpretable?

Shahaf Bassan, Guy Amir, Meirav Zehavi et al.

5

ICLR 2025arXiv:2410.21331

#5

A General Theoretical Framework for Learning Smallest Interpretable Models

Sebastian Ordyniak, Giacomo Paesani, Mateusz Banany et al.

Neural Reasoning for Sure Through Constructing Explainable Models

Tiansi Dong, Mateja Jamnik, Pietro Liò

Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness

Qi Zhang, Yifei Wang, Jingyi Cui et al.

4

AAAI 2025arXiv:2502.05275

#8

Interpretable Failure Detection with Human-Level Concepts

Kien X. Nguyen, Tang Li, Xi Peng

2

ICML 2025arXiv:2501.17077

#9

Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Transformers

Shaobo Wang, Hongxuan Tang, Mingyang Wang et al.

Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learning

Anna Soligo, Pietro Ferraro, David Boyle

2

ICML 2025arXiv:2407.13594

#11

From Black-box to Causal-box: Towards Building More Interpretable Models

Inwoo Hwang, Yushu Pan, Elias Bareinboim

Validating Mechanistic Interpretations: An Axiomatic Approach

Nils Palumbo, Ravi Mangal, Zifan Wang et al.

1

ICLR 2024arXiv:2310.00164

#13

SOInter: A Novel Deep Energy-Based Interpretation Method for Explaining Structured Output Models

S. Fatemeh Seyyedsalehi, Mahdieh Baghshah, Hamid Rabiee

Enhancing Performance of Explainable AI Models with Constrained Concept Refinement

Geyu Liang, Senne Michielssen, Salar Fattahi

Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition

Sangyu Han, Yearim Kim, Nojun Kwak

PRIME: Prioritizing Interpretability in Failure Mode Extraction

Keivan Rezaei, Mehrdad Saberi, Mazda Moayeri et al.

interpretable policy learningmedical decision modelingadaptive imitation learningcontextualized policy recovery+4

#17

Towards Global-level Mechanistic Interpretability: A Perspective of Modular Circuits of Large Language Models

Yinhan He, Wendy Zheng, Yushun Dong et al.

Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning

Jannik Deuschel, Caleb Ellington, Yingtao Luo et al.

ICML 2024

attention mechanismcategorical reasoningnumerical reasoningin-context learning+4

#19

Adaptive Transformer Programs: Bridging the Gap Between Performance and Interpretability in Transformers

Quoc-Vinh Lai-Dang, Taemin Kang, Seungah Son

ICLR 2025

ICLR 2025arXiv:2402.02933

#20

Intrinsic User-Centric Interpretability through Global Mixture of Experts

Vinitra Swamy, Syrielle Montariol, Julian Blackwell et al.

NeurIPS 2025arXiv:2511.20273

#21

Beyond Components: Singular Vector-Based Interpretability of Transformer Circuits

Areeb Ahmad, Abhinav Joshi, Ashutosh Modi

transformer interpretabilitymechanistic interpretabilitysingular vector analysiscircuit analysis+4

mechanistic interpretabilityvision language modelsactivation patchingsparse autoencoders+3

#22

Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?

Maxime Méloux, Silviu Maniu, François Portet et al.

Mechanistic Interpretability Meets Vision Language Models: Insights and Limitations

Yiming Liu, Yuhui Zhang, Serena Yeung

ICLR 2025

mechanistic interpretabilityneural network interpretabilitylow-dimensional representationsnuclear physics concepts+2

#24

From Neurons to Neutrons: A Case Study in Interpretability

Ouail Kitouni, Niklas Nolte, Víctor Samuel Pérez-Díaz et al.

ICML 2024

reinforcement learningprobabilistic inferencepolicy optimizationinterpretable ai+4

#25

Probabilistic Constrained Reinforcement Learning with Formal Interpretability

YANRAN WANG, QIUCHEN QIAN, David Boyle

ICML 2024

ICML 2024arXiv:2406.02981

#26

Local vs. Global Interpretability: A Computational Complexity Perspective

Shahaf Bassan, Guy Amir, Guy Katz

interpretability of ml modelscomputational complexity theorylocal explanationsglobal explanations+4

#27

Learning Robust Rationales for Model Explainability: A Guidance-Based Approach

Integrated Decision Gradients: Compute Your Attributions Where the Model Makes Its Decision

Weibo Gao, Qi Liu, Hao Wang et al.

AAAI 2024