Separating the what and how of compositional computation to enable reuse and continual learning

2025-10-23

AI Summary

To address the dual challenges of skill reusability and catastrophic forgetting in continual learning, this paper proposes a “what–how” dual-system recursive neural network architecture that decouples task selection (what) from execution policy (how), enabling flexible skill composition and incremental updates. Methodologically, it introduces the first integration of online unsupervised task-structure inference with context-driven dynamic assembly of low-rank RNN components: a probabilistic generative model captures time-varying task structure, which then modulates the real-time composition of functional low-rank RNN modules. Experiments demonstrate that the framework significantly mitigates forgetting in multi-task cognitive settings, enables strong forward and backward transfer, and achieves rapid generalization to unseen tasks. This work establishes an interpretable, scalable paradigm for continual learning endowed with cognitive flexibility.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The ability to continually learn, retain and deploy skills to accomplish goals is a key feature of intelligent and efficient behavior. However, the neural mechanisms facilitating the continual learning and flexible (re-)composition of skills remain elusive. Here, we study continual learning and the compositional reuse of learned computations in recurrent neural network (RNN) models using a novel two-system approach: one system that infers what computation to perform, and one that implements how to perform it. We focus on a set of compositional cognitive tasks commonly studied in neuroscience. To construct the what system, we first show that a large family of tasks can be systematically described by a probabilistic generative model, where compositionality stems from a shared underlying vocabulary of discrete task epochs. The shared epoch structure makes these tasks inherently compositional. We first show that this compositionality can be systematically described by a probabilistic generative model. Furthermore, We develop an unsupervised online learning approach that can learn this model on a single-trial basis, building its vocabulary incrementally as it is exposed to new tasks, and inferring the latent epoch structure as a time-varying computational context within a trial. We implement the how system as an RNN whose low-rank components are composed according to the context inferred by the what system. Contextual inference facilitates the creation, learning, and reuse of low-rank RNN components as new tasks are introduced sequentially, enabling continual learning without catastrophic forgetting. Using an example task set, we demonstrate the efficacy and competitive performance of this two-system learning framework, its potential for forward and backward transfer, as well as fast compositional generalization to unseen tasks.

Problem

• Separating computational goals from implementation to enable flexible skill reuse
• Developing neural mechanisms for continual learning without catastrophic forgetting
• Creating compositional generalization through shared task vocabulary and context inference

Innovation

• Two-system approach separates what and how computation
• Unsupervised learning builds vocabulary incrementally for tasks
• Low-rank RNN components composed via inferred context