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New paper details Renormalising Generative Models for Active Inference

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A new paper published on arXiv details Renormalising Generative Models (RGMs) for Active Inference, aiming to overcome challenges in scaling these models to complex domains. The research provides a clear, derivation-oriented explanation of RGMs, clarifying how hierarchies are constructed and how information flows between levels. The authors also offer an open, verified implementation to improve transparency, auditability, and reproducibility of RGMs, thereby lowering barriers for future research and quantitative evaluation. AI

IMPACT Provides a foundation for advancing active inference models in complex spatial and temporal domains.

RANK_REASON The cluster contains an academic paper detailing a novel approach to generative models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New paper details Renormalising Generative Models for Active Inference

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The cluster contains an academic paper detailing a novel approach to generative models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Karim Zaghw, Andrew Pashea, Marc Pritsch, Wouter Nuijten, Karl Friston, Lancelot Da Costa ·

    Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification

    arXiv:2608.09512v1 Announce Type: new Abstract: Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficult. Renormalising generative models (RGMs) address th…

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