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MetaMo: A Robust Motivational Framework for Open-Ended AGI
Authors: {{+author|content;item:link}}
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 {{Hyperon AI Algorithms+MetaMo+MetaMo Full|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 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}}
{{Hyperon AI Algorithms+MetaMo+MetaMo Full|view:link;title:MetaMo Full}}
{{Ecosystem+Magi+Magi Deep Dive|view:link;title:Magi Full}}
{{Publications+Papers|view:link;title:Publications+Papers}}
{{+tags|titled;title:Tags}}
{{+discussion|titled;title:Discussion}}
Implementation & component card: a Python reference implementation exists at iCog-Labs-Dev/MetaMo-Python (verified by the Cross-Org sweeps, HAA S2); the predecessor OpenPsi substrate is iCog-Labs-Dev/hyperon-openpsi. See the {{Hyperon AI Algorithms+MetaMo (Motivational Framework)+MetaMo Deep Dive|view:link;title:MetaMo Deep Dive}}.
MetaMo: A Robust Motivational Framework for Open-Ended AGI
Authors: {{+author|content;item:link}}
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 {{Hyperon AI Algorithms+MetaMo+MetaMo Full|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 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}}
{{Hyperon AI Algorithms+MetaMo+MetaMo Full|view:link;title:MetaMo Full}}
{{Ecosystem+Magi+Magi Deep Dive|view:link;title:Magi Full}}
{{Publications+Papers|view:link;title:Publications+Papers}}
{{+tags|titled;title:Tags}}
{{+discussion|titled;title:Discussion}}
Implementation & component card: a Python reference implementation exists at iCog-Labs-Dev/MetaMo-Python (verified by the Cross-Org sweeps, HAA S2); the predecessor OpenPsi substrate is iCog-Labs-Dev/hyperon-openpsi. See the {{Hyperon AI Algorithms+MetaMo (Motivational Framework)+MetaMo Deep Dive|view:link;title:MetaMo Deep Dive}}.