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A more geometric and scaling-oriented companion to the discrete ActPC work. It explores how active predictive coding can be accelerated using information geometry, pushing the predictive-coding line toward online neural-symbolic learning that is more plausible as a reusable subsystem inside a larger AGI architecture.
This paper strengthens the predictive-coding side of PRIMUS Full and complements ActPC-Chem by shifting from discrete algorithmic chemistry to scalable geometric learning dynamics.