COGITAO: A Visual Reasoning Framework To Study Compositionality & Generalization
#COGITAO #ARC‑AGI #visual domains #compositionality #generalization #data generation framework #benchmark #machine learning #neural compositionality
📌 Key Takeaways
- COGITAO is a flexible framework for generating visual datasets that test compositional reasoning.
- The benchmark draws inspiration from the ARC‑AGI problem‑setting to focus on novel combinations of learned concepts.
- It enables systematic evaluation of how well machine learning models generalize beyond their training distribution.
- The framework is designed to be modular and extensible, facilitating community contributions and future research.
- The paper was last revised to version 2 on arXiv in September 2025.
📖 Full Retelling
The authors of a recent arXiv submission have introduced **COGITAO**, a modular and extensible data‑generation framework and benchmark designed to systematically study compositionality and generalization in visual domains. The work is hosted on arXiv (submission id 2509.05249v2), marking its release in September 2025, with the aim of addressing the persistent gap in state‑of‑the‑art machine learning models’ ability to compose learned concepts and apply them in novel settings.
🏷️ Themes
Artificial Intelligence, Machine Learning, Compositionality, Generalization, Visual Reasoning, Benchmarking, Data Generation
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Original Source
arXiv:2509.05249v2 Announce Type: replace-cross
Abstract: The ability to compose learned concepts and apply them in novel settings is key to human intelligence, but remains a persistent limitation in state-of-the-art machine learning models. To address this issue, we introduce COGITAO, a modular and extensible data generation framework and benchmark designed to systematically study compositionality and generalization in visual domains. Drawing inspiration from ARC-AGI's problem-setting, COGITAO
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