A Neuroscience-Inspired Dual-Process Model of Compositional Generalization

• URL: http://arxiv.org/abs/2507.18868v3
• Date: Tue, 28 Oct 2025 02:48:15 GMT
• Title: A Neuroscience-Inspired Dual-Process Model of Compositional Generalization
• Authors: Alex Noviello, Claas Beger, Jacob Groner, Kevin Ellis, Weinan Sun,
• Abstract summary: We propose textscMirage, a neuro-inspired dual-process model.<n>It combines a fast, intuitive System1'' (a meta-trained Transformer) with a deliberate, rule-based System2'' (a Engine)<n>Mirage achieves $>$99% accuracy on all splits of the SCAN benchmark in a task-agnostic setting.
• Score: 12.494200165412186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models struggle with systematic compositional generalization, a hallmark of human cognition. We propose \textsc{Mirage}, a neuro-inspired dual-process model that offers a processing account for this ability. It combines a fast, intuitive ``System~1'' (a meta-trained Transformer) with a deliberate, rule-based ``System~2'' (a Schema Engine), mirroring the brain's neocortical and hippocampal--prefrontal circuits. Trained to perform general, single-step decomposition on a stream of random grammars, Mirage achieves $>$99\% accuracy on all splits of the SCAN benchmark in a task-agnostic setting. Ablations confirm that the model's systematic behavior emerges from the architectural interplay of its two systems, particularly its use of explicit, prioritized schemas and iterative refinement. In line with recent progress on recursive/recurrent Transformer approaches, Mirage preserves an iterative neural update while externalizing declarative control into an interpretable schema module. Our work provides a concrete computational model for interpreting how compositional reasoning can arise from a modular cognitive architecture.

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