# Scalable Evaluation and Neural Models for Compositional Generalization

arXiv: https://arxiv.org/abs/2511.02667 · HTML: https://mlsanity.com/paper/2511.02667/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [Global Geometry Is Not Enough for Vision Representations](https://mlsanity.com/paper/2602.03282/) (2026)
- [Ternary Gamma Semirings: From Neural Implementation to Categorical Foundations](https://mlsanity.com/paper/2603.19317/) (2026)

## Abstract (arXiv)

Compositional generalization-a key open challenge in modern machine learning-requires models to predict unknown combinations of known concepts. However, assessing compositional generalization remains a fundamental challenge due to the lack of standardized evaluation protocols and the limitations of current benchmarks, which often favor efficiency over rigor. At the same time, general-purpose vision architectures lack the necessary inductive biases, and existing approaches to endow them compromise scalability. As a remedy, this paper introduces: 1) a rigorous evaluation framework that unifies and extends previous approaches while reducing computational requirements from combinatorial to constant; 2) an extensive and modern evaluation on the status of compositional generalization in supervised vision backbones, training more than 5000 models; 3) Attribute Invariant Networks, a class of models establishing a new Pareto frontier in compositional generalization, achieving a 23.43% accuracy improvement over baselines while reducing parameter overhead from 600% to 16% compared to fully disentangled counterparts. Our code is available at https://github.com/IBM/scalable-compositional-generalization.