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ActPC-Geom: Towards Scalable Online Neural-Symbolic Learning via Accelerating Active Predictive Coding with Information Geometry

Author: Ben Goertzel
Year: 2025
Venue: arXiv:2501.04832
Links: arXiv abstract

Summary

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.

Relevance to Hyperon

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.

Key References

• RawData source
• ActPC-Chem
• PRIMUS Full
• +Papers