The Case for Model Science: Verify, Explore, Steer, Refine

2026-05-31

AI Summary

This work addresses the critical gap between the widespread deployment of AI models and the limited understanding of their internal mechanisms, as conventional benchmarks often fail to uncover root causes of failures such as hallucination and shortcut learning. It proposes the first systematic “model science” framework, integrating paradigms from cognitive science, neuroscience, and related disciplines to enable in-depth analysis of individual model instances through four complementary lenses: Verify, Explore, Steer, and Refine. By establishing a shared knowledge repository and collaborative research infrastructure, the framework transcends the limitations of population-level performance evaluation, offering both theoretical foundations and practical pathways to enhance AI interpretability, reliability, and continuous improvement. This paradigm shift moves AI research beyond performance-centric metrics toward a deeper, understanding-driven approach.

Abstract

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 Science. Complex AI models now serve billions of users, yet our understanding of how they work lags far behind our ability to deploy them. Decades of benchmark-driven research have delivered remarkable progress: extensive leaderboards, a wide range of performance metrics, tracking capability gains across diverse tasks; yet this success has also revealed the limits of benchmarks as they tell us whether models perform but not why they succeed or fail, they miss critical failure modes, such as hallucinations or shortcuts. Precedents from established sciences point the way forward: cognitive science shows that understanding complex systems requires complementary levels of analysis; neuroscience demonstrates that deep study of single cases reveals what population studies miss; medicine teaches that specialised training must develop alongside research practice; and agriculture models how shared infrastructure and principles enable cumulative progress. These lessons inform three foundations for Model Science. First, we propose to consolidate research around four functional perspectives: Verify, Explore, Steer, and Refine that address complementary questions about model behaviour. Second, we discuss the required infrastructure for cumulative knowledge: catalogues of datasets, models and findings. Third, we highlight the need for deep analysis of individual model instances, not just model families, because single cases can reveal what population studies miss.

Problem

• Model Science
• benchmarking limitations
• model understanding
• failure modes
• AI model analysis

Innovation

• Model Science
• Verify
• Explore
• Steer
• Refine