Modelpedia: A Catalog of Model Findings for the Meta-Science of AI

arXiv:2609.01090 · cs.LG · Submitted 2026-09-01 · Read on arXiv

cs.LG

Submitted: 2026-09-01

Updated: 2026-09-03

Code: https://github.com/CredibleAI/modelpedia

Project page: https://credibleai.github.io/modelpedia

Terminology

• Benchmark — 6× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Accountability — 2× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Activation Patching — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hallucination — 1× in this paper · explained in 4 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• The Case for Model Science: Verify, Explore, Steer, Refine
• Datasheets for Datasets
• Gemma 3 Technical Report
• Model Cards for Model Reporting
• DINOv2: Learning Robust Visual Features without Supervision
• Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions
• Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

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