MetaMo (Motivational Framework)

MetaMo is a framework for modeling motivation in open-ended intelligent agents, concerned with how goals, priorities, and evaluative signals can be updated over time while preserving coherence, stability, and interpretability. Rather than relying on scalar reward functions or manually engineered drive hierarchies, MetaMo treats motivation itself as a dynamical system, explicitly coupling appraisal processes (which evaluate situations in terms of salience, risk, and opportunity) with decision processes (which select actions and allocate computational and behavioral resources). The objective is not affective realism, but the construction of a principled motivational substrate capable of supporting adaptive behavior, safe self-modification, and long-horizon deliberation.

MetaMo represents motivational state as a structured interaction between goal intensities and modulatory variables. Appraisal updates modulators such as valence, arousal, and risk sensitivity in response to contextual novelty and task relevance, while decision mechanisms score candidate actions relative to active goals under the current modulatory configuration. These processes are designed to commute up to bounded error, ensuring consistency between “appraise-then-decide” and “decide-then-appraise” cycles. System stability is enforced via contractive update dynamics that draw motivational state away from pathological extremes, while goal evolution proceeds incrementally to maintain continuity of self-model during learning and self-modification.

Within the Hyperon ecosystem, MetaMo serves as the motivational backbone linking inference, learning, and attention allocation. It shapes control dynamics in Probabilistic Logic Networks by biasing search and inference toward contextually appropriate goals, regulates exploration–exploitation tradeoffs, and embeds safety and ethical constraints directly within motivational dynamics rather than as externally imposed rules. In practice, this enables agents such as adaptive research assistants or autonomous scientific systems that must balance curiosity, caution, and long-term coherence. MetaMo does not encode specific values; instead, it provides the structural machinery through which an AGI system can form, maintain, and revise its priorities in a controlled and intelligible manner.