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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 wikithe public source linkscontext 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 {{Hyperon AI Algorithms+MetaMo (Motivational Framework)+MetaMo Full|view:link;title:MetaMo Full}}{{Hyperon AI Algorithms+MetaMo (Motivational Framework)+MetaMo Deep Dive|view:link;title:MetaMo Full}} and one of the key theory papers for the motivation layer in the current Hyperon stack. It is also directly relevant to {{Ecosystem+Magi+Magi Full|view:link;title:Magi Full}}{{Ecosystem+Magi+Magi Deep Dive|view:link;title:Magi Full}}, because MAGUS and related systems are framed as concrete motivational realizations rather than unrelated products.
Key References

{{RawData+Publications+AGI 25 METAMO One|view:link;title:RawData source}}Lian, R.; Goertzel, B. (2025). MetaMo: A Robust Motivational Framework for Open-Ended AGI. AGI 2025.
{{Hyperon AI Algorithms+MetaMo (Motivational Framework)+MetaMo Full|view:link;title:MetaMo Full}}{{Hyperon AI Algorithms+MetaMo (Motivational Framework)+MetaMo Deep Dive|view:link;title:MetaMo Deep Dive}}
{{Ecosystem+Magi+Magi Full|view:link;title:Magi Full}}{{Ecosystem+Magi+Magi Deep Dive|view:link;title:Magi Full}}
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