arXiv:2606.17889v1 Announce Type: cross Abstract: Compositional learning systems must balance plasticity, the ability to acquire new knowledge, with stability, the preservation of previously learned components, especially when tasks share structure and risk interference. We study how modular architecture, task similarity, and representational dimensionality jointly shape compositional continual learning in a sequential A-B-A paradigm, comparing a task-partitioned recurrent network to a single-network baseline while inducing high- and low-dimensional regimes via weight-scale manipulations. In a
This paper explores how architectural design (modularity) interacts with task similarity and representational dimensionality in continual learning, a fundamental challenge for advanced AI systems.
Understanding the principles that enable compositional learning with both plasticity and stability is crucial for developing more robust and generalizable AI, moving beyond current limitations.
This research provides deeper insight into the trade-offs involved in designing AI systems that can continuously learn without catastrophic forgetting, potentially informing future architectural choices.
- · AI researchers
- · AI model developers
- · Machine learning platforms
- · AI models without effective continual learning capabilities
Improved understanding of modularity's role in mitigating interference in continual learning.
Development of more efficient and stable AI architectures for sequential task learning.
Accelerated progress towards AGI by resolving key challenges in long-term learning and knowledge retention.
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Read at arXiv cs.AI