Discrete harmonic morphisms ensure exact random-walk projection under network coarse-graining, and Laplacian renormalization often produces exact instances of them on real networks.
Software in the natural world: A computational approach to hierarchical emergence
7 Pith papers cite this work. Polarity classification is still indexing.
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Introduces M-information as a scalable measure of higher-order information integration in multivariate time series, computed via convex optimization and tested on neuronal and neuroimaging data.
RET learns temporally consistent macrovariables from LLM activations via self-supervised learning to support interpretability, early behavioral prediction, and causal intervention.
A constructor theory framework models prebiotic information as physical differences and meaning as functional consequences in Casimir-Lifshitz protocell clusters.
Casimir-stabilized protocell clusters form ε-machines whose attractor states and transitions create emergent prebiotic information through physical memory rather than molecular polymers.
A framework for decomposing transducers into sub-transducers on distinct subspaces to enable parallel and interpretable world models.
The zentropy approach receives a statistical-physics foundation via recursive entropy maximization, yielding partition functions for coarse-grained configurations and clarifying temperature-dependent states in thermodynamic systems.
citing papers explorer
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Harmonic morphisms and dynamical invariants in network renormalization
Discrete harmonic morphisms ensure exact random-walk projection under network coarse-graining, and Laplacian renormalization often produces exact instances of them on real networks.
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A scalable estimator of higher-order information in complex dynamical systems
Introduces M-information as a scalable measure of higher-order information integration in multivariate time series, computed via convex optimization and tested on neuronal and neuroimaging data.
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Towards Effective Theory of LLMs: A Representation Learning Approach
RET learns temporally consistent macrovariables from LLM activations via self-supervised learning to support interpretability, early behavioral prediction, and causal intervention.
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From Physical Difference to Meaning: A Constructor-Theoretic Framework for Prebiotic Information in Casimir-Lifshitz-Coupled Protocell Clusters
A constructor theory framework models prebiotic information as physical differences and meaning as functional consequences in Casimir-Lifshitz protocell clusters.
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Emergent Information Formation in Prebiotic Protocell Clusters: A Computational Mechanics Framework of $\epsilon$-Machines and Attractor Memory
Casimir-stabilized protocell clusters form ε-machines whose attractor states and transitions create emergent prebiotic information through physical memory rather than molecular polymers.
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From monoliths to modules: Decomposing transducers for efficient world modelling
A framework for decomposing transducers into sub-transducers on distinct subspaces to enable parallel and interpretable world models.
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Recursive entropy in thermodynamics: expounding the statistical-physics basis of the zentropy approach
The zentropy approach receives a statistical-physics foundation via recursive entropy maximization, yielding partition functions for coarse-grained configurations and clarifying temperature-dependent states in thermodynamic systems.