Selected work

Representative Papers

Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings

Oct 02, 2026

This study addresses the challenge of learning control policies in the absence of action records by proposing a hierarchical model-based reinforcement learning framework. Rather than relying on reconstruction dynamics errors, the method extracts low-dimensional dynamics embeddings via piecewise linear recurrent neural networks and explicit intervention models, which are subsequently reused to parameterize a shared policy network. Experimental evaluations demonstrate that this framework significantly improves transfer performance and cumulative rewards on benchmark systems such as Lorenz attractors, achieving cross-system generalization and interpretable intervention effects. Furthermore, the proposed approach is successfully applied to the suppression of neurally predicted movements, highlighting its practical utility in real-world control scenarios.

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Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating

Sep 29, 2026

This study addresses the challenges of catastrophic forgetting and capacity exhaustion encountered by recurrent neural networks during the continual learning of dynamical systems. To this end, we propose CRUG, a novel approach that introduces a recyclable unit gating mechanism. By integrating L0 regularization, nearly linear RNNs, and directed connectivity techniques, CRUG enables the recycling and isolation of network units through differentiable gating, thereby achieving compact allocation and forward transfer within a fixed-capacity architecture. Experimental results demonstrate that CRUG attains zero forgetting across heterogeneous nonlinear chaotic systems and sequential cognitive tasks. Furthermore, it achieves an optimal reconstruction–capacity trade-off, effectively overcoming the capacity bottlenecks inherent in conventional continual learning paradigms.

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A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems

Jul 16, 2026

This work addresses the lack of interpretability in existing foundation models for zero-shot dynamical system reconstruction, which often obscure their prediction mechanisms. The authors propose DynaBase, a minimal interpretable architecture comprising only two parameters, that predicts future states via a linear combination of the current latent state and the nearest neighbors—along with their successors—in a contextual memory bank. By integrating model parsimony, nearest-neighbor retrieval, and analytical optimization, DynaBase achieves high-performance zero-shot reconstruction for the first time and yields a one-parameter family of maps that unifies chaotic and periodic dynamics, reconciling conflicting views in the literature. Evaluated across diverse systems, DynaBase outperforms existing models while using orders of magnitude fewer parameters and admits a closed-form MSE solution, enabling direct optimization toward reconstruction metrics.

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Position: Why a Dynamical Systems Perspective is Needed to Advance Time Series Modeling

Feb 18, 2026

This work addresses a fundamental limitation in current time series modeling approaches: their general lack of grounding in the underlying dynamical systems, which impedes long-term statistical forecasting, generalization to unseen regimes (e.g., critical transitions), and sample-efficient learning. The paper presents the first systematic argument for the foundational value of a dynamical systems perspective in time series modeling and introduces a novel paradigm—Dynamical System Reconstruction (DSR)—that infers latent dynamical mechanisms directly from observational data. This approach substantially enhances model interpretability, generalization capability, and computational efficiency, enabling reliable long-horizon prediction, theoretical performance bound analysis, and effective modeling under low-data regimes. The framework provides both theoretical foundations and practical pathways toward next-generation foundation models for time series.

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Recent publications

Latest Papers

Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings

Oct 02, 2026

This study addresses the challenge of learning control policies in the absence of action records by proposing a hierarchical model-based reinforcement learning framework. Rather than relying on reconstruction dynamics errors, the method extracts low-dimensional dynamics embeddings via piecewise linear recurrent neural networks and explicit intervention models, which are subsequently reused to parameterize a shared policy network. Experimental evaluations demonstrate that this framework significantly improves transfer performance and cumulative rewards on benchmark systems such as Lorenz attractors, achieving cross-system generalization and interpretable intervention effects. Furthermore, the proposed approach is successfully applied to the suppression of neurally predicted movements, highlighting its practical utility in real-world control scenarios.

0 citationsRead paper

Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating

Sep 29, 2026

This study addresses the challenges of catastrophic forgetting and capacity exhaustion encountered by recurrent neural networks during the continual learning of dynamical systems. To this end, we propose CRUG, a novel approach that introduces a recyclable unit gating mechanism. By integrating L0 regularization, nearly linear RNNs, and directed connectivity techniques, CRUG enables the recycling and isolation of network units through differentiable gating, thereby achieving compact allocation and forward transfer within a fixed-capacity architecture. Experimental results demonstrate that CRUG attains zero forgetting across heterogeneous nonlinear chaotic systems and sequential cognitive tasks. Furthermore, it achieves an optimal reconstruction–capacity trade-off, effectively overcoming the capacity bottlenecks inherent in conventional continual learning paradigms.

0 citationsRead paper

A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems

Jul 16, 2026

This work addresses the lack of interpretability in existing foundation models for zero-shot dynamical system reconstruction, which often obscure their prediction mechanisms. The authors propose DynaBase, a minimal interpretable architecture comprising only two parameters, that predicts future states via a linear combination of the current latent state and the nearest neighbors—along with their successors—in a contextual memory bank. By integrating model parsimony, nearest-neighbor retrieval, and analytical optimization, DynaBase achieves high-performance zero-shot reconstruction for the first time and yields a one-parameter family of maps that unifies chaotic and periodic dynamics, reconciling conflicting views in the literature. Evaluated across diverse systems, DynaBase outperforms existing models while using orders of magnitude fewer parameters and admits a closed-form MSE solution, enabling direct optimization toward reconstruction metrics.

0 citationsRead paper

Position: Why a Dynamical Systems Perspective is Needed to Advance Time Series Modeling

Feb 18, 2026

This work addresses a fundamental limitation in current time series modeling approaches: their general lack of grounding in the underlying dynamical systems, which impedes long-term statistical forecasting, generalization to unseen regimes (e.g., critical transitions), and sample-efficient learning. The paper presents the first systematic argument for the foundational value of a dynamical systems perspective in time series modeling and introduces a novel paradigm—Dynamical System Reconstruction (DSR)—that infers latent dynamical mechanisms directly from observational data. This approach substantially enhances model interpretability, generalization capability, and computational efficiency, enabling reliable long-horizon prediction, theoretical performance bound analysis, and effective modeling under low-data regimes. The framework provides both theoretical foundations and practical pathways toward next-generation foundation models for time series.

0 citationsRead paper