The Dually Flat Geometry of Planning as Inference
本文通过将规划标准嵌入动力学中，重新定义了强化学习的占用度量，并利用对偶平坦几何结构解决非线性奖励函数下的规划问题。
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Active Inference as a Convex Markov Decision Process
This work integrates the minimization of expected free energy from active inference into an optimizable decision-making framework by formally casting it as a Convex Markov Decision Process (Convex MDP) for the first time, unifying epistemic exploration and pragmatic goals in the space of state marginal distributions. By revealing that expected free energy corresponds to a policy-dependent instrumental reward, the study establishes compatibility with dynamic programming and actor-critic methods. Leveraging convex optimization and mirror descent, it derives policy optimization algorithms applicable to finite-horizon, discounted, and average-reward settings. This approach provides theoretical guarantees for policy improvement and bridges active inference with modern reinforcement learning through a rigorous theoretical foundation.
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Revealing the core dimensions underlying representations in brains, behavior and AI
Current approaches struggle to extract interpretable low-dimensional representations from sparse or incomplete similarity data, limiting our understanding of representational structures in neural, behavioral, and artificial intelligence systems. This work proposes Similarity Representation Factorization (SRF), a novel method that integrates non-negative matrix factorization with low-dimensional embedding to enable, for the first time, generalizable and interpretable extraction of representational dimensions. SRF effectively recovers task-specific model dimensions, accurately predicts independent behavioral attributes, and substantially enhances both exploratory analysis capabilities and statistical power in hypothesis testing. The method is broadly applicable to heterogeneous, multi-source similarity data, offering a robust framework for uncovering latent structure across diverse domains.
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Predicting Microbial Interactions Using Graph Neural Networks
Predicting interspecies microbial interactions is a fundamental challenge in deciphering microbial community structure and function. This paper introduces the first graph neural network (GNN) framework tailored for large-scale microbial interaction prediction, where species pairs are modeled as edges and co-culture experiments as nodes. The framework integrates multi-source features—including monoculture growth phenotypes, phylogenetic distances, and prior knowledge of known interactions—to enable fine-grained, directional prediction of interaction types (e.g., mutualism, competition, parasitism). Innovatively, it leverages edge-centric graph structures to capture cross-experiment shared information, overcoming limitations of conventional classifiers in modeling interaction directionality and type specificity. Evaluated on a benchmark dataset comprising over 7,500 experimentally validated interactions, our method achieves an F1-score of 80.44%, significantly outperforming XGBoost (72.76%). Results demonstrate superior predictive accuracy and enhanced biological interpretability.
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The Geometry of Nonlinear Reinforcement Learning
This paper addresses the challenge of jointly optimizing multiple objectives—reward maximization, safe exploration, and intrinsic motivation—in reinforcement learning. Methodologically, it introduces the first unified geometric optimization framework that generalizes classical algorithms such as policy mirror descent and natural policy gradient to settings involving nonlinear utility functions and convex constraints, integrating differential geometry and convex optimization to construct a trust-region-style nonlinear policy optimization framework for deep RL. Theoretically, it uncovers a shared geometric structure of multi-objective trade-offs in the space of long-horizon behavioral trajectories. Algorithmically, it unifies the modeling of robustness, safety, and exploratory diversity within a single principled formulation. This framework establishes a novel theoretical foundation for safe reinforcement learning and efficient exploration, while providing a scalable and modular paradigm for algorithm design.
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The Dually Flat Geometry of Planning as Inference
本文通过将规划标准嵌入动力学中，重新定义了强化学习的占用度量，并利用对偶平坦几何结构解决非线性奖励函数下的规划问题。
0 citationsRead paper
Active Inference as a Convex Markov Decision Process
This work integrates the minimization of expected free energy from active inference into an optimizable decision-making framework by formally casting it as a Convex Markov Decision Process (Convex MDP) for the first time, unifying epistemic exploration and pragmatic goals in the space of state marginal distributions. By revealing that expected free energy corresponds to a policy-dependent instrumental reward, the study establishes compatibility with dynamic programming and actor-critic methods. Leveraging convex optimization and mirror descent, it derives policy optimization algorithms applicable to finite-horizon, discounted, and average-reward settings. This approach provides theoretical guarantees for policy improvement and bridges active inference with modern reinforcement learning through a rigorous theoretical foundation.
0 citationsRead paper
Revealing the core dimensions underlying representations in brains, behavior and AI
Current approaches struggle to extract interpretable low-dimensional representations from sparse or incomplete similarity data, limiting our understanding of representational structures in neural, behavioral, and artificial intelligence systems. This work proposes Similarity Representation Factorization (SRF), a novel method that integrates non-negative matrix factorization with low-dimensional embedding to enable, for the first time, generalizable and interpretable extraction of representational dimensions. SRF effectively recovers task-specific model dimensions, accurately predicts independent behavioral attributes, and substantially enhances both exploratory analysis capabilities and statistical power in hypothesis testing. The method is broadly applicable to heterogeneous, multi-source similarity data, offering a robust framework for uncovering latent structure across diverse domains.
0 citationsRead paper
Predicting Microbial Interactions Using Graph Neural Networks
Predicting interspecies microbial interactions is a fundamental challenge in deciphering microbial community structure and function. This paper introduces the first graph neural network (GNN) framework tailored for large-scale microbial interaction prediction, where species pairs are modeled as edges and co-culture experiments as nodes. The framework integrates multi-source features—including monoculture growth phenotypes, phylogenetic distances, and prior knowledge of known interactions—to enable fine-grained, directional prediction of interaction types (e.g., mutualism, competition, parasitism). Innovatively, it leverages edge-centric graph structures to capture cross-experiment shared information, overcoming limitations of conventional classifiers in modeling interaction directionality and type specificity. Evaluated on a benchmark dataset comprising over 7,500 experimentally validated interactions, our method achieves an F1-score of 80.44%, significantly outperforming XGBoost (72.76%). Results demonstrate superior predictive accuracy and enhanced biological interpretability.
0 citationsRead paper
The Geometry of Nonlinear Reinforcement Learning
This paper addresses the challenge of jointly optimizing multiple objectives—reward maximization, safe exploration, and intrinsic motivation—in reinforcement learning. Methodologically, it introduces the first unified geometric optimization framework that generalizes classical algorithms such as policy mirror descent and natural policy gradient to settings involving nonlinear utility functions and convex constraints, integrating differential geometry and convex optimization to construct a trust-region-style nonlinear policy optimization framework for deep RL. Theoretically, it uncovers a shared geometric structure of multi-objective trade-offs in the space of long-horizon behavioral trajectories. Algorithmically, it unifies the modeling of robustness, safety, and exploratory diversity within a single principled formulation. This framework establishes a novel theoretical foundation for safe reinforcement learning and efficient exploration, while providing a scalable and modular paradigm for algorithm design.
0 citationsRead paper