Hyperon AI Algorithms+MetaMo (Motivational Framework)+MetaMo Full

Motivational State

MetaMo represents motivation as a structured interaction between goal intensities and modulatory variables. Formally, the motivational state is an object \(X = G \times M\) in a category \(\mathcal{C}\), where \(G = (g_1, \ldots, g_P)\) is a vector of goal intensities and \(M = (\mu_1, \ldots, \mu_K)\) is a vector of modulator parameter values. The state includes:

• Goal vectors: 8-dimensional representations of active goal intensities (e.g., competence, affiliation, energy)
• Modulator vectors: 6-dimensional representations of modulatory variables (valence, arousal, risk sensitivity, ethical vigilance, social drive, energy level)
• Dual overgoals: Two meta-level drives — Individuation (maintaining coherent identity) and Transcendence (evolving beyond current limits)

Appraisal and Decision

MetaMo explicitly couples two processes:

• Appraisal updates modulators in response to contextual novelty and task relevance — assessing "what's happening and how does it matter?"
• Decision scores candidate actions relative to active goals and current modulators — determining "what should I do about it?"

The critical insight is that these processes interact asymmetrically: "feeling then choosing" differs slightly from "choosing then feeling."

Pseudo-Bimonad Structure

MetaMo models cognitive motivation through a pseudo-bimonad coupling two components:

• Appraisal comonad (\(\Psi\)): Equipped with counit \(\varepsilon\) (extract current assessment) and comultiplication \(\delta\) (extend assessments to broader contexts). Maps \(\Psi(X) \to X\).
• Decision monad (\(D\)): Equipped with unit \(\eta\) (embed a state into a decision context) and multiplication \(\mu\) (flatten nested decisions). Maps \(X \to D(X)\).

These are linked by a lax distributive law \(\lambda_X : \Psi(D(X)) \Rightarrow D(\Psi(X))\). The composite \(F = D \circ \Psi\) inherits both monad and comonad structure. A motivational coalgebra \(\alpha : X \to F(X)\) governs one full cycle of appraisal plus decision.

Stability Guarantees

• Contractive update law: Small disturbances fade via damping, with tighter control near boundary conditions. Prevents runaway dynamics without external safety constraints.
• Tubular topology: Any achievable target motivational state lies on a "thick, feasible path" of incremental steps — gradual convergence guaranteed, no discontinuous jumps.

Five Design Principles

1. Modular Appraisal-Decision Interface: Separate mood updates from goal selection with controlled feedback
2. Homeostatic Drive Stability: Apply damping so small disturbances fade
3. Reciprocal State Simulation: Share state-translation maps for multi-agent empathy and hand-off
4. Parallel Motivational Compositionality: Run multiple motivational subsystems in parallel, merge with small coherence corrections
5. Incremental Objective Embodiment: Blend partway toward preferred states each cycle, guaranteeing gradual convergence

Implementation Backing (cluster-pilot extraction, Source 2 close 2026-05-06)

The Non-clustered Hyperon AI Algorithms cluster pilot Source 2 (Goertzel & Lian 2025 MetaMo papers + reference implementations) extracted a [PAPER-LEANING-HYBRID] / [IMPLEMENTATION-BACKED-CORE] / [REFERENCE-IMPLEMENTATION-NOT-PRODUCTION] verdict against iCog-Labs-Dev/MetaMo-Pythonat3ec409b HEAD . The formalism above has direct reference-code anchors at the skeleton level, with one important caveat:

[IMPLEMENTATION-BACKED-CORE] — the core state/operator skeleton is implemented:

• \(X = G \times M\) — core/state.py:8 (class MotivationalState with NUM_GOALS=8, NUM_MODULATORS=6 at core/config.py:1-7)
• Appraisal comonad \(\Psi\) — category/functors.py:7 (class AppraisalComonad); concrete OpenPsiAppraisal(AppraisalComonad) at openpsi/appraisal.py:17
• Decision monad \(D\) — category/functors.py:32 (class DecisionMonad); concrete MagusDecision(DecisionMonad) at magus/decision.py
• Composite \(F = D \circ \Psi\) — category/bimonad.py:12 (class MetaMoPseudoBimonad)
• Lax distributive law \(\lambda_X\) check — category/bimonad.py:42 (check_lax_distributive_law; bounds numerical distortion ≤ LAX_DISTRIBUTIVE_DELTA, NOT a categorical proof)
• Parallel-merge / lax-monoidal structure — category/bimonad.py:62 (parallel_merge)
• Safe-region + boundary-band — dynamics/stability.py:18 + :43
• Contractive update law — dynamics/stability.py:54 (check_contractive_update_law)
• Homeostatic damping (Principle 4) — dynamics/stability.py:84 (apply_homeostatic_damping)
• Blend factor + incremental embodiment (Principle 5) — dynamics/coherence.py:10 + :24
• Translation functor (Principle 2) — category/functors.py:57 (class TranslationFunctor); applied at applications/research_assistant.py:88 via simulate_peer (the demo uses an identity translation matrix, so this counts as [SKELETON-IMPLEMENTATION] for Principle 2 — the class runs but the demo is degenerate)

[FORMAL-LAWS-PAPER-ONLY] — the categorical laws (monad/comonad coherence, lax distributive law, pseudo-bimonad equations) are described in the paper and named in the code, but the code checks numeric distortion under sample inputs, not categorical coherence. There is no formal proof apparatus and no checked-in test suite (rg --files for tests in MetaMo-Python returns nothing; AGENTS.md:23 confirms no formal test suite).

This split also explains why the overall verdict is [REFERENCE-IMPLEMENTATION-NOT-PRODUCTION]: MetaMo-Python is small enough to map paper concepts to executable references, but it does not bind to MeTTa/AtomSpace, has no Hyperon runtime integration, and treats the principles as demo logic rather than validated system behavior. Production-grade MetaMo over Hyperon remains a roadmap item (see Status and Resources).

Cluster-pilot extraction archive: scripts/archive/non_clustered_haa_pilot/source2_metamo_paper/ (findings_codex.txt + findings_gemini.txt + findings_reconciled_crossmodel.txt).