Neural networks for abstraction and reasoning: Towards broad generalization in machines (2402.03507v1)

Abstract: For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find this easy. While specific neural networks are able to solve an impressive range of problems, broad generalisation to situations outside their training data has proved elusive.In this work, we look at several novel approaches for solving the Abstraction & Reasoning Corpus (ARC), a dataset of abstract visual reasoning tasks introduced to test algorithms on broad generalization. Despite three international competitions with $100,000 in prizes, the best algorithms still fail to solve a majority of ARC tasks and rely on complex hand-crafted rules, without using machine learning at all. We revisit whether recent advances in neural networks allow progress on this task. First, we adapt the DreamCoder neurosymbolic reasoning solver to ARC. DreamCoder automatically writes programs in a bespoke domain-specific language to perform reasoning, using a neural network to mimic human intuition. We present the Perceptual Abstraction and Reasoning Language (PeARL) language, which allows DreamCoder to solve ARC tasks, and propose a new recognition model that allows us to significantly improve on the previous best implementation.We also propose a new encoding and augmentation scheme that allows LLMs to solve ARC tasks, and find that the largest models can solve some ARC tasks. LLMs are able to solve a different group of problems to state-of-the-art solvers, and provide an interesting way to complement other approaches. We perform an ensemble analysis, combining models to achieve better results than any system alone. Finally, we publish the arckit Python library to make future research on ARC easier.

• The paper demonstrates that integrating DreamCoder with the new PeARL language yields improved performance on ARC tasks, achieving 16.5% on ARC-Easy and 4.5% on ARC-Hard challenges.
• The paper shows that large language models, when applied to ARC tasks, reveal complementary strengths compared to traditional solvers, offering a novel pathway for abstract reasoning.
• The paper introduces an ensemble approach combined with the arckit library, establishing a robust framework for future research in broad machine generalization and abstract visual reasoning.

Neural Networks for Abstraction and Reasoning: Towards Broad Generalization in Machines

Introduction to ARC Challenges

The field of AI has long pursued the goal of fostering broad generalization in machines—enabling them to learn new concepts from a minimal set of examples in ways akin to human cognition. The Abstraction and Reasoning Corpus (ARC), designed to measure such capabilities, presents abstract visual reasoning tasks requiring broad generalization beyond the training data. Despite significant interest and efforts, including international competitions, solving ARC tasks remains a formidable challenge, with state-of-the-art algorithms either failing to tackle the majority of ARC tasks or resorting to complex hand-crafted rules without leveraging ML.

Novel Approaches Examined

This paper investigates two novel approaches to tackling ARC tasks through recent advancements in neural networks. Firstly, the adaptation of the DreamCoder algorithm demonstrates significant strides, with the introduction of the Perceptual Abstraction & Reasoning Language (PeARL) enhancing DreamCoder's capabilities. Secondly, a novel methodology enabling LLMs to address ARC tasks reveals that the most advanced models can solve certain problems, indicating a complementary pathway to traditional solvers.

Ensemble Analysis and arckit Library

Beyond individual model performance, this paper proposes an ensemble approach, combining various models to surpass the capabilities of any single system. This methodology not only highlights the heterogeneity of the ARC challenges but also underpins the creation of the arckit Python library, designed to facilitate future research in this vein.

Reflections on Performance

Performance analysis sheds light on the significant challenge ARC poses, with even the most advanced ML models solving only a fraction of the tasks. Despite this, the paper's findings underscore the effectiveness of DreamCoder and its enhancements, achieving a 16.5% accuracy on ARC-Easy and 4.5% on ARC-Hard tasks, marking a notable improvement over previous implementations. The investigation into LLMs further contributes to this discourse, showcasing their potential despite the inherent limitations of current models.

Findings on Model Complementarity

A particularly compelling aspect of this research is the discovery of the complementary nature of different models in tackling ARC tasks. The low overlap in solved tasks between models like DreamCoder and GPT-4 suggests that an ensemble approach, leveraging the diverse strengths of various algorithms, could dramatically improve overall performance.

Future Directions

The ongoing challenge ARC presents invites further exploration, with potential avenues including the extension of DreamCoder to incorporate more complex DSL features, the application of LLMs through advanced prompting techniques, and the exploration of Large Vision Models for visual reasoning. Additionally, the impending release of ARC2 promises to heighten the benchmark for broad generalization in AI, encouraging continued innovation in this field.

Conclusion

In conclusion, this paper not only propels our understanding of ML models' capabilities and limitations in abstract reasoning and generalization but also provides a robust framework for future research. By embracing the complexity of ARC tasks and exploring novel methodologies and ensemble approaches, this research moves us closer to achieving the goal of broad generalization in machines—a cornerstone in the journey toward advanced artificial intelligence.