Paper: "Universally Converging Representations of Matter Across Scientific Foundation Models"
Analysis
This paper investigates the convergence of internal representations in scientific foundation models, a crucial aspect for building reliable and generalizable models. The study analyzes nearly sixty models across various modalities, revealing high alignment in their representations of chemical systems, especially for small molecules. The research highlights two regimes: high-performing models align closely on similar inputs, while weaker models diverge. On vastly different structures, most models collapse to low-information representations, indicating limitations due to training data and inductive bias. The findings suggest that these models are learning a common underlying representation of physical reality, but further advancements are needed to overcome data and bias constraints.
Key Takeaways
- •Scientific foundation models are learning similar internal representations of matter.
- •Model performance correlates with representational convergence, especially for small molecules.
- •Current models are limited by training data and inductive bias, requiring further advancements.
“Models trained on different datasets have highly similar representations of small molecules, and machine learning interatomic potentials converge in representation space as they improve in performance, suggesting that foundation models learn a common underlying representation of physical reality.”