Interpretable machine learning through teaching

Challenges in Evaluating Explanation Methods for Static and Evolving Data

arXiv:2608. 06351v1 Announce Type: new Abstract: This paper addresses the limitations of Explainable Artificial Intelligence (XAI) with respect to insufficient evaluation.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information

arXiv:2608. 10766v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) seeks to explain how an Artificial Intelligence (AI) system arrived at a particular decision.

Teaching AI to see the world more like we do

Our new paper analyzes the important ways AI systems organize the visual world differently from humans.

Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers

arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.

Interpretable and pedagogical examples

A Definition of Good Explanations and the Challenges Explaining LLM Outputs

arXiv:2606. 14838v1 Announce Type: new Abstract: How to define a good explanation is a long-standing philosophical debate which has found recent renewed interest in the context of AI outputs.

Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers

arXiv:2606. 06664v1 Announce Type: cross Abstract: Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before safe deployment.

Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics

arXiv:2606. 26228v1 Announce Type: cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics.

Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction

arXiv:2607. 08233v1 Announce Type: new Abstract: A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form hypotheses about hidden patterns, and design informative experiments to test them.

Position: Reasoning is a Learnable Rule-Based Process

arXiv:2608. 12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today.

Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation

arXiv:2604. 10098v2 Announce Type: replace Abstract: As the foundational architecture of modern machine learning, Transformers have driven remarkable progress across diverse AI domains.

DigitalCoach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching

arXiv:2606. 31980v1 Announce Type: cross Abstract: Agents are increasingly capable of automating software tasks, but can they teach humans how to use software themselves?