🧪 Agentic Identity Suite¶

Empirically testing the identity and observer-divergence claims of the Emergence Manifesto.

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

Theoretical Background¶

This module operationalizes four concepts from the Emergence Manifesto:

3-Layer Memory Architecture — Identity emerges through deliberate forgetting. An agent that stores everything has no profile. Curation is identity.
Generative Surprise — 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.
Δ-Kohärenz (Ω) — The central measure of coherent evolution over time. Distinguishes three behavioral profiles:
Mirror — Static resonance (low change, low variance)
Noise — Incoherent change (high variance)
Development — Directional, coherent evolution (moderate change + high trajectory consistency)
The Observer Divergence — 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" — 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 → Curated Memory → 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 Δ-Kohärenz profiles.

Hypothesis: Three-Layer → development; Baseline → 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 Δ-Kohärenz (Ω) against an external observer's intentionality score (TF-IDF + entropy model).

The scientifically interesting output:

Case	Internal Ω	Observer Score	Interpretation
A	High	Low	Agent has identity — 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 / IP (Identity Persistence). Note that while earlier versions of this test suite explored Δ-Kohärenz (Ω) as the fourth dimension, the formal theory standardizes on IP to measure simultaneous co-instantiation, keeping Ω as an independent temporal metric.

Configuration¶

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

Provider abstraction (scaffolded, not yet wired)¶

A separate provider layer at lab/providers/ prepares the suite for the eventual switch from mock embeddings to real model calls. Two providers are implemented:

MockProvider — the default. Deterministic, fast, no API key.
AnthropicProvider — real mode. Calls the Anthropic Messages API via the standard library (no new dependency). Default model: claude-sonnet-4-20250514. Requires ANTHROPIC_API_KEY in the environment.

The existing experiments still use the agents' built-in mock embeddings. Wiring those agents through the provider layer is a separate, intentional step — to be taken when real-mode runs become the goal. The infrastructure is ready; the empirical work is deferred. See providers/README.md and theory/core/the-generator-question.md for the spine context.

Persistence Score (Pstrong)¶

A standalone implementation of Algorithm 1 from Perrier & Bennett (2026) is available at lab/metrics/persistence_scores.py. It computes:

Pstrong — averaged simultaneous co-instantiation of identity components across a trajectory.
Per-step persistence variance.
Regime classification (Chord / Arpeggio) using the ip_c_threshold from config.yaml.

A comparison function, correlate_pstrong_with_delta_coherence, returns the Pearson correlation between per-step Pstrong and per-step Δ-Kohärenz proxies on the same trajectory. This is one possible empirical question the suite might eventually answer, not the only one — whether simultaneous co-instantiation and temporal coherence are coupled is currently open.

python -m lab.metrics.persistence_scores  # minimal sanity demo

Pstrong is one instrument among several. The project's spine is the Generator Question, not the persistence score.

Open Questions¶

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

Can Δ-Kohärenz 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