5 papers
Modelpedia: A Catalog of Model Findings for the Meta-Science of AI
Franciszek Bernat, Dawid Płudowski, Michał Jan Włodarczyk +6
Scientific knowledge about AI models is produced faster than the community can organize it. Every few months a new foundation model reshapes the field and hundreds of papers, blogs…
The Case for Model Science: Verify, Explore, Steer, Refine
Przemyslaw Biecek, Luca Longo, Jianlong Zhou +3
We argue that the AI community is now ready to move beyond benchmarking and consolidate scattered efforts in model analysis into a systematic discipline, a direction we term Model…
-TCAV: A Unified Framework for Testing with Concept Activation Vectors
Ekkehard Schnoor, Jawher Said, Malik Tiomoko +2
Concept Activation Vectors (CAVs) are a fundamental tool for concept-based explainability in deep learning, yet their practical utility is limited by statistical instability. We an…
Judge Circuits Explain Format-Induced Inconsistency in LLM-as-a-Judge
Nils Feldhus, Tanja Baeumel, Elena Golimblevskaia +10
LLM-as-a-judge has become the dominant paradigm for grading model outputs at scale, yet the same model assigns systematically different scores when its output format changes (e.g.,…
Model Guidance via Explanations Turns Image Classifiers into Segmentation Models
Xiaoyan Yu, Jannik Franzen, Wojciech Samek +2
Heatmaps generated on inputs of image classification networks via explainable AI methods like Grad-CAM and LRP have been observed to resemble segmentations of input images in many…