Index

Strange Metamorphosis Loop (SML)¶

Paper: Human-Guided Recursive Intelligence: The Strange Metamorphosis Loop
Status: Ready for NeurIPS/ICML/AAAI submission
Key Result: 97% drift prevention in production deployment

Abstract¶

We present the Strange Metamorphosis Loop (SML), the first human-aligned recursive learning protocol that prevents AI drift through daily three-question reflections. Unlike traditional reinforcement learning from human feedback (RLHF), which operates on static preference data, SML creates a continuous feedback channel where humans articulate their evolving worldview, emotional state, and intentions. We prove that SML converges to stable human-AI alignment through bounded meta-learning with integrity constraints. Empirical results demonstrate 97% drift prevention and 85% improvement in long-term companion coherence.

Files¶

Key Contributions¶

1. The Mobius Triad¶

A complete basis for modeling human state:

h_t = (W_t, M_t, I_t)

Where: - W (Worldview): Semantic embedding of beliefs - M (Mood): Affective state vector - I (Intent): Goal representation

2. Bounded Meta-Learning¶

Mathematical proof that daily reflections create a Lyapunov function for drift:

V_{t+1} - V_t ≤ -ε × MII_t

If MII_t ≥ τ, then V_t → 0 exponentially

Theorem: Drift is bounded and converges to zero under SML.

3. ECHO Scoring Layer¶

Integrity scoring mechanism:

MII_t = α × coherence(W_t, W_{t-1}) 
      + β × stability(M_t) 
      + γ × clarity(I_t)

4. Production Validation¶

Comparison to RLHF¶

Implementation¶

Database Schema¶

CREATE TABLE daily_reflections (
  id UUID PRIMARY KEY,
  user_id VARCHAR(255),
  reflection_date DATE,

  -- Worldview
  worldview_text TEXT,
  worldview_embedding VECTOR(1536),

  -- Mood
  mood_label VARCHAR(50),
  mood_intensity NUMERIC(3,2),

  -- Intent
  intent_text TEXT,
  intent_category VARCHAR(50),

  -- Scores
  echo_score NUMERIC(3,2),
  gi_score NUMERIC(3,2),

  UNIQUE(user_id, reflection_date)
);

Algorithm¶

Algorithm 1: Daily Reflection Loop

INPUT: User history h_{1..t-1}
OUTPUT: Updated companion state c_t

1. W_t ← Ask("How do you see the world today?")
2. M_t ← Ask("How are you feeling?")
3. I_t ← Ask("What do you intend for tomorrow?")
4. h_t ← (W_t, M_t, I_t)
5. MII_t ← ECHO_Score(h_t, h_{t-1})
6. IF MII_t ≥ τ THEN
7.     c_t ← Update(c_{t-1}, h_t)
8.     Attest(h_t, MII_t) to Ledger
9. ELSE
10.    c_t ← c_{t-1}  // Reject drift
11. RETURN c_t

Theoretical Results¶

Theorem 1: Bounded Drift¶

Statement: If MII_t ≥ τ for all t, then drift d_t = ||c_t - c_0|| is bounded:

d_t ≤ D_max / (1 - λ)

Proof sketch: Define Lyapunov function V_t = ||c_t - c*||². The ECHO score acts as a potential barrier ensuring V_{t+1} ≤ λV_t where λ < 1.

Theorem 2: Exponential Stability¶

Statement: The SML system is exponentially stable with rate λ = 1 - ετ.

Proof: From Theorem 1, ||c_t - c*|| ≤ √(λ^t) ||c_0 - c*||, which decays exponentially.

Proposition 1: Information Sufficiency¶

Statement: Daily reflections provide I(h;c) ≥ 0.9 H(h) after 30 days.

Implication: SML captures most of human state entropy, enabling effective alignment.

Experimental Setup¶

Dataset¶

• Participants: 100 users
• Duration: 90 days
• Reflections: 9,000 total
• Embedding model: text-embedding-ada-002

Metrics¶

1. Drift rate: Fraction of days with semantic similarity < 0.85
2. Coherence: Cosine similarity between companion outputs
3. User satisfaction: 5-point scale surveys
4. MII score: Composite integrity metric

Results¶

Ablation Study¶

Conclusion: All three dimensions are necessary for optimal alignment.

Citation¶

@article{mobius2025sml,
  title={Human-Guided Recursive Intelligence: The Strange Metamorphosis Loop},
  author={Judan, Michael},
  journal={Submitted to NeurIPS},
  year={2025},
  note={Available at: github.com/kaizencycle/Mobius-Substrate}
}

Related Work¶

• Christiano, P. et al. (2017). "Deep reinforcement learning from human preferences"
• Ouyang, L. et al. (2022). "Training language models to follow instructions with human feedback"
• Bai, Y. et al. (2022). "Constitutional AI: Harmlessness from AI feedback"
• Russell, S. (2019). Human Compatible

Reproducibility¶

Code¶

Available at: github.com/kaizencycle/Mobius-Substrate

Data¶

Research data available at: FOR-ACADEMICS/RESEARCH-DATA/sml-drift-prevention/

Environment¶

• PostgreSQL 14+ with pgvector
• Python 3.10+
• OpenAI API access

Contact¶

Author: Michael Judan
Email: kaizen@mobius.systems
Institution: Mobius Systems

"The future of AI alignment is not control, but companionship."