Research & Insights

Explore our research articles, explainers and practical resources.

Latest article

LLM interpretabilityFrom Circuit Tracing to Input-Token ImportanceHow to turn an attribution graph into token scores in five steps, using the Dallas → Texas → Austin example, the circuit-tracer library and a comparison with SMILE, SHAP and Integrated Gradients.Read article →

Research group stories

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Explainable AIMapping the Mind of Image Editing with SMILEHow changes to editing instructions affect generated images.Read article →

Responsible AICan We Trust AI for Skin Cancer?Fairness and reliability in medical image classification.Read article →

Frontier AI assuranceWhen the Model Is Closed, What Can XWhy Test?How LLM explainability can guide tests when only model behaviour is visible.Read article →

XWhy explainers and practical reading

SMILEHow SMILE WorksFrom perturbations to a local explanation you can test.Read article →

ExplainabilityWhat a Local Explanation Tells YouWhat one result can say about a particular prediction.Read article →

LLM explainabilityExplaining LLM ResponsesPrompt changes, response alignment and token contributions.Read article →

EvaluationDoes the Explanation Hold Up?Practical checks for fidelity, stability and faithfulness.Read article →

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