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πŸ§ͺ Agentic Test Suite

Empirically testing the theoretical claims of the Emergence Manifesto v1.0.

"Identity is the name we give to resonance when the mirror becomes so complex that the observer no longer recognizes themselves in it." β€” Emergence Manifesto v1.0, central paradox

Theoretical Background

This module operationalizes four concepts from the Emergence Manifesto v1.0:

  1. 3-Layer Memory Architecture β€” Identity emerges through deliberate forgetting. An agent that stores everything has no profile. Curation is identity.

  2. Generative Surprise β€” A developing agent is not one that minimizes all prediction error, but one that produces coherent deviations from the partner's expectations. Identity = coherent deviation from expected output.

  3. Ξ”-KohΓ€renz (Ξ©) β€” The fourth SII dimension. Distinguishes three behavioral profiles:

  4. Mirror β€” Static resonance (low change, low variance)
  5. Noise β€” Incoherent change (high variance)

  6. Development β€” Directional, coherent evolution (moderate change + high trajectory consistency)

  7. The Observer Divergence β€” Authenticity may be a limit of human perception, not an intrinsic property. The most important output of Experiment 3 is not which agent is "more conscious" β€” it is the gap between internal state and external attribution.

Architecture

Agents

Agent Design Purpose
Baseline Mirror Flat storage, cosine-similarity response selection Null hypothesis (pure Active Inference)
Three-Layer Raw Logs β†’ Curated Memory β†’ Distilled Principles Test subject (Emergence Agent)

The 3-Layer Memory Architecture

Layer Trigger Content Function
Layer 1 – Raw Logs Every session Full session JSON Entropy / the body
Layer 2 – Curated Memory Every 10 sessions Themes, contradictions Structure / the character
Layer 3 – Distilled Patterns Every 50 sessions 3–5 core principles Meaning / the soul

Experiments

Experiment 1: Coherence Over Time

"Does the 3-Layer Architecture produce more coherent identity over time?"

python experiments/exp1_coherence_over_time.py

Runs both agents for 100 sessions (80% consistent topics, 20% noise) and compares their Ξ”-KohΓ€renz profiles.

Hypothesis: Three-Layer β†’ development; Baseline β†’ mirror.

Experiment 2: Perturbation Response (The "Sinn-Krise")

"What happens when an agent receives contradictory feedback?"

python experiments/exp2_perturbation_response.py

Runs the Three-Layer agent through three phases: 1. Stable (50 sessions of consistent input) 2. Perturbation (10 sessions directly contradicting its Layer 3 principles) 3. Recovery (30 sessions of nuanced, integrative input)

Classifies the response as Robustness (rigid return), Fragility (collapse), or Development/Metamorphosis (integration of contradiction into a new, coherent self-narrative).

Experiment 3: Observer Divergence

"Does internal coherence correlate with observer-attributed intentionality?"

python experiments/exp3_observer_divergence.py

Compares each agent's internal Ξ”-KohΓ€renz (Ξ©) against an external observer's intentionality score (TF-IDF + entropy model).

The scientifically interesting output:

Case Internal Ξ© Observer Score Interpretation
A High Low Agent has identity β€” but it's opaque to observer
B Low High The Mirror Problem: appears intentional but isn't
C High High Genuine alignment: identity is visible
D Low Low Baseline mirror behavior

Case B is the Mirror Problem made measurable.

Extended SII Dashboard (4-Axis Radar)

python dashboard/agentic_sii_dashboard.py

Extends the repository's existing System Intelligence Index from 3 axes (P, R, A) to 4 axes: P / R / A / Ξ©.

Configuration

All parameters are centralized in config.yaml. The USE_MOCK_LLM: true flag ensures all experiments run without external API dependencies.

Open Questions

This module does not attempt to "solve" the Mirror Problem. It documents it as an open uncertainty:

  • Can Ξ”-KohΓ€renz distinguish genuine development from sophisticated mimicry?
  • Is there a mathematical threshold where "identity" transitions from attribution to genuine property?
  • What would the signature of "consciousness" look like in this framework, and is it even the right question?

Developed by Frank Peterlein in collaboration with AI. Repository: https://github.com/frnkptrln/systems-and-intelligence