标题： 一种受神经科学启发的组合泛化双过程模型 显示英文标题

标题： A Neuroscience-Inspired Dual-Process Model of Compositional Generalization

摘要： 系统性组合泛化——构建和理解已知构建块的新组合——仍然是人工智能系统的核心挑战。 人类认知通过海马体（HPC）和前额叶皮层（PFC）的相互作用实现这种灵活性：海马体快速编码事件，而前额叶皮层将它们巩固为可用于推理的可重用模式。 基于这些见解，我们提出了MIRAGE（基于通用经验的规则和抽象的元推理），这是一个在组合任务上实现系统泛化的框架。 MIRAGE有两个相互作用的模块，模仿大脑的深思熟虑的HPC-PFC循环和直觉性的新皮层模式识别。 （1）元训练的Transformer神经分解器，类比新皮层“系统1”计算，在任务无关的随机采样的组合语法流上进行训练，每次遍历应用一次分解步骤，后续遍历迭代地优化序列表示。 （2）模式引擎，类似于HPC-PFC“系统2”循环，动态提取、排序并应用可重用模式，将变量绑定存储在情景记忆中，并在需要时扩展它们。 通过显式地为MIRAGE的Transformer组件配备主动管理的模式结构，我们的模型通过显式的模式应用和转换进行系统性组合操作，在解决完全新颖任务时仅依赖冻结的权重。 这种方法在SCAN基准测试中展示了系统性组合泛化，仅使用变压器模块中的1.19M参数，在所有任务分割上达到了> 99%的准确率。 消融研究证实，MIRAGE的系统性关键取决于提取模式的质量和模型的迭代优化过程。 显示英文摘要

摘要： Systematic compositional generalization - constructing and understanding novel combinations of known building blocks - remains a core challenge for AI systems. Human cognition achieves this flexibility via the interplay of the hippocampus (HPC) and prefrontal cortex (PFC): the hippocampus rapidly encodes episodes, and the prefrontal cortex consolidates them into reusable schemas for reasoning. Drawing on these insights, we present MIRAGE (Meta-Inference with Rules and Abstractions from Generalized Experience), a framework that achieves systematic generalization on compositional tasks. MIRAGE has two interacting modules mirroring the brain's deliberative HPC-PFC loop and intuitive neocortical pattern recognition. (1) The meta-trained Transformer Neural Decomposer, paralleling neocortical "System 1" computation, is trained on a task-agnostic stream of randomly sampled compositional grammars and applies one decomposition step per pass, with successive passes iteratively refining the sequence representation. (2) The Schema Engine, analogous to the HPC-PFC "System 2" loop, dynamically extracts, ranks, and applies reusable schemas, storing variable bindings in episodic memory and expanding them when needed. By explicitly equipping the Transformer component of MIRAGE with actively managed schematic structures, our model performs systematic compositional operations through explicit schema application and transformation, relying solely on frozen weights when solving entirely novel tasks. This approach demonstrates systematic compositional generalization on the SCAN benchmark, achieving > 99% accuracy on all task splits with only 1.19M parameters in the transformer module. Ablation studies confirm that MIRAGE's systematicity critically depends on the quality of extracted schemas and the model's iterative refinement process.