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Active perception and disentangled representations allow continual, episodic zero and few-shot learning

Feb 22, 2026

This work addresses the challenge in conventional continual and few-shot learning methods, where entangled representations are prone to catastrophic interference, hindering the simultaneous achievement of rapid adaptation and strong generalization. To overcome this, the authors propose a Complementary Learning System (CLS) that disentangles representations by implementing a fast learner as a context-driven episodic memory module—used not merely for replay but to generate contextual biases. These biases guide a slower statistical learner to encode novel stimuli in a structured manner. Integrated with an active perception mechanism, the architecture enables continual learning in both zero-shot and few-shot settings. Experiments demonstrate that the approach effectively mitigates representational interference and exhibits robust rapid learning and generalization capabilities under observational variability and uncertainty.

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HRM-Agent: Training a recurrent reasoning model in dynamic environments using reinforcement learning

Oct 26, 2025

Existing Hierarchical Reasoning Models (HRMs) are confined to static, fully observable supervised learning tasks and cannot handle dynamic, uncertain, or partially observable environments; moreover, they lack mechanisms for reusing historical computational states. Method: We propose HRM-Agent, the first framework integrating a lightweight hierarchical reasoning model (HRM) into a reinforcement learning architecture, leveraging a recurrent neural network (RNN)-based cyclic reasoning mechanism to enable cross-temporal computation reuse and continual inference. This design supports autonomous decision-making and goal-directed navigation even under action-label scarcity. Contribution/Results: Evaluated in complex maze environments, HRM-Agent successfully accomplishes long-horizon navigation tasks while significantly improving inference efficiency and environmental adaptability. Our results empirically validate the feasibility of jointly modeling hierarchical reasoning and sequential decision-making, establishing a foundation for scalable, adaptive agents in partially observable, non-stationary settings.

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Latest Papers

Active perception and disentangled representations allow continual, episodic zero and few-shot learning

Feb 22, 2026

This work addresses the challenge in conventional continual and few-shot learning methods, where entangled representations are prone to catastrophic interference, hindering the simultaneous achievement of rapid adaptation and strong generalization. To overcome this, the authors propose a Complementary Learning System (CLS) that disentangles representations by implementing a fast learner as a context-driven episodic memory module—used not merely for replay but to generate contextual biases. These biases guide a slower statistical learner to encode novel stimuli in a structured manner. Integrated with an active perception mechanism, the architecture enables continual learning in both zero-shot and few-shot settings. Experiments demonstrate that the approach effectively mitigates representational interference and exhibits robust rapid learning and generalization capabilities under observational variability and uncertainty.

0 citationsRead paper

HRM-Agent: Training a recurrent reasoning model in dynamic environments using reinforcement learning

Oct 26, 2025

Existing Hierarchical Reasoning Models (HRMs) are confined to static, fully observable supervised learning tasks and cannot handle dynamic, uncertain, or partially observable environments; moreover, they lack mechanisms for reusing historical computational states. Method: We propose HRM-Agent, the first framework integrating a lightweight hierarchical reasoning model (HRM) into a reinforcement learning architecture, leveraging a recurrent neural network (RNN)-based cyclic reasoning mechanism to enable cross-temporal computation reuse and continual inference. This design supports autonomous decision-making and goal-directed navigation even under action-label scarcity. Contribution/Results: Evaluated in complex maze environments, HRM-Agent successfully accomplishes long-horizon navigation tasks while significantly improving inference efficiency and environmental adaptability. Our results empirically validate the feasibility of jointly modeling hierarchical reasoning and sequential decision-making, establishing a foundation for scalable, adaptive agents in partially observable, non-stationary settings.

0 citationsRead paper