MetaMo Robust Motivational Framework

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MetaMo: A Robust Motivational Framework for Open-Ended AGI

Authors: Ben Goertzel, Ruiting Lian
Year: 2025
Venue: AGI 2025
Links: no public URL yet; see wiki source links below

Summary

The abstract theory paper for MetaMo. It presents a unified motivational framework combining category theory, functional analysis, and topology to describe open-ended agents that can self-modify without collapsing motivational coherence. The paper centers on a composite appraisal-then-decision operator with comonadic and monadic structure, plus contractive updates and tubular topology guarantees that keep motivational change inside a feasible region.

Relevance to Hyperon

This is the formal foundation behind MetaMo Full and one of the key theory papers for the motivation layer in the current Hyperon stack. It is also directly relevant to Magi Full, because MAGUS and related systems are framed as concrete motivational realizations rather than unrelated products.

Key References



Discussion