arXiv:2606.01189v1 Announce Type: new 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 be
The accelerating deployment of complex AI models serving billions of users highlights the growing gap between their practical application and our understanding of their inner workings, demanding a more systematic approach.
This marks a foundational call to establish a new scientific discipline for AI model analysis, moving beyond mere benchmarking to address safety, explainability, and future development challenges.
The AI community is explicitly shifting from a singular focus on performance benchmarking to a more holistic science of understanding, verifying, exploring, steering, and refining AI models.
- · AI Safety Researchers
- · Model Explainability Platforms
- · AI Governance Bodies
- · Academic AI Departments
- · Benchmark-only AI Development
- · Black-box AI Deployers
- · AI systems lacking transparency
- · Companies ignoring model analysis
The push for Model Science will standardize methodologies for understanding AI systems beyond performance metrics.
Increased transparency and verifiable models will likely accelerate responsible AI adoption and influence regulatory frameworks.
A deeper scientific understanding of AI models might unlock fundamentally new architectural insights and development paradigms.
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Read at arXiv cs.AI