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

Researchers propose 'Model Science,' a systematic discipline for understanding AI models beyond traditional benchmarking. The framework consolidates analysis around four functional perspectives—Verify, Explore, Steer, and Refine—and emphasizes deep study of individual models rather than population-level comparisons, drawing lessons from established sciences like neuroscience and medicine.

The AI research community faces a fundamental gap between deployment scale and scientific understanding. While benchmarking has driven impressive performance gains, this approach reveals only whether models work, not why they fail or succeed. Critical failure modes like hallucinations and reasoning shortcuts remain invisible to traditional metrics, leaving practitioners unable to diagnose problems or build reliable systems. This paper advocates shifting from quantity-focused benchmarking to quality-focused model science.

The proposal draws legitimacy from established scientific disciplines. Cognitive science demonstrates that understanding complex systems requires multiple analytical levels; neuroscience shows that detailed case studies reveal patterns population studies miss; medicine illustrates how specialized expertise evolves alongside research; and agriculture demonstrates how shared infrastructure enables cumulative progress. These precedents suggest AI research needs structural changes, not merely incremental refinement.

Model Science rests on three pillars: organizing research around four complementary functional perspectives addressing different behavioral questions; building shared infrastructure including datasets, models, and findings catalogues; and prioritizing deep analysis of individual model instances over family-level comparisons. This shift has significant implications for AI safety, interpretability, and reliability. Organizations developing large-scale AI systems face mounting pressure to understand failure modes and demonstrate trustworthiness to regulators and users.

The framework suggests a maturation of AI research toward engineering discipline standards. Success requires coordinated infrastructure investment, standardized analysis methodologies, and incentive structures rewarding thorough mechanistic understanding over benchmark score improvements. This transition could reshape how companies and researchers allocate resources, potentially slowing headline capability advances while accelerating robustness improvements.

• →Model Science proposes consolidating AI analysis around four functional perspectives—Verify, Explore, Steer, Refine—rather than relying solely on benchmark leaderboards.
• →The framework emphasizes deep study of individual model instances over population-level comparisons, borrowing methodologies from neuroscience and cognitive science.
• →Shared infrastructure for datasets, models, and findings is essential for cumulative progress in understanding AI behavior beyond performance metrics.
• →Traditional benchmarking reveals whether models work but cannot explain failure modes like hallucinations or identify reasoning shortcuts.
• →This systematic approach addresses growing demands for AI interpretability, safety, and reliability in systems serving billions of users.