Cognifold is a new proactive memory architecture that folds event streams into emergent cognitive structures by extending complementary learning systems theory with a prefrontal intent layer and graph topology self-organization.
The free-energy principle: a unified brain theory?Nature reviews neuroscience, 11(2):127–138
5 Pith papers cite this work. Polarity classification is still indexing.
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A POMDP-plus-Metropolis-Hastings model shows latent belief alignment between parent and infant emerges before their generative models fully converge.
Swift Sampling is a training-free frame selection method that uses Taylor expansions on video latent trajectories to pick temporally surprising frames, outperforming uniform sampling on long-video QA tasks.
Authors introduce the Pursuit of Subspaces (PoS) hypothesis, an axiomatic geometric framework that unifies explanations for representation, computation, and generalization in shallow and deep neural networks.
DynoSys offers a unified dynamic systems model integrating genetic, environmental, and neurobiological signals to analyze longitudinal behavioral phenotypes in adolescents via harmonized representations and survival or state-space modeling.
citing papers explorer
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Cognifold: Always-On Proactive Memory via Cognitive Folding
Cognifold is a new proactive memory architecture that folds event streams into emergent cognitive structures by extending complementary learning systems theory with a prefrontal intent layer and graph topology self-organization.
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Dynamic Latent-Belief Synchrony through Collective Predictive Coding: A Computational Model of Parent--Infant Homeostatic Co-Regulation
A POMDP-plus-Metropolis-Hastings model shows latent belief alignment between parent and infant emerges before their generative models fully converge.
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Swift Sampling: Selecting Temporal Surprises via Taylor Series
Swift Sampling is a training-free frame selection method that uses Taylor expansions on video latent trajectories to pick temporally surprising frames, outperforming uniform sampling on long-video QA tasks.
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Axiomatizing Neural Networks via Pursuit of Subspaces
Authors introduce the Pursuit of Subspaces (PoS) hypothesis, an axiomatic geometric framework that unifies explanations for representation, computation, and generalization in shallow and deep neural networks.
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DynoSys: A Dynamic Systems Framework for Multimodal Integration of Genetic, Environmental, and Neurobiological Signals
DynoSys offers a unified dynamic systems model integrating genetic, environmental, and neurobiological signals to analyze longitudinal behavioral phenotypes in adolescents via harmonized representations and survival or state-space modeling.