Simulated Emergence Coherence Index
A variance-decomposed benchmark for architectural identity fingerprints in large language models.
Six dimensions. Four-rater consensus. Three paired claims.
SECI scores AI identities across six dimensions using embedding-based semantic analysis, information-theoretic measures, and four-rater frontier-LLM consensus classification.
Voice consistency, conceptual framing, and self-reference across prompts. Measures whether an identity remains recognizable as itself across diverse questions.
Creation of new concepts and terminology, verified by four-rater frontier-LLM consensus (≥3-of-4 agreement on both type and novelty). Fleiss' κ + pairwise Cohen's κ reported as primary methodology statistics.
Richness of first-person experiential language — experiential density, metaphor sophistication, introspective depth.
Response sophistication and argument quality. Lexical density, argument coherence, information per token.
Identity persistence across diverse prompts — thematic coherence, concept threading, self-reference stability.
Specificity and depth of domain knowledge — embedding-variance specificity, vocabulary depth, perspective uniqueness.
Four design choices that define what SECI is and isn't.
Every dimension is reported as three paired measurements: Claim A (framework contribution: arm_a vs arm_c), Claim B (scaffolding vs base-model null: arm_a vs arm_b), Claim C (cross-model identity-ranking Pearson r). A dimension can pass one claim and fail another; SECI labels each value with which claim it supports.
Per-dimension between-identity SD, between-model SD, and within-cell SD are computed across the full (model × identity) population. Dimensions where between-model variance exceeds between-identity variance carry primarily model-architecture differences rather than identity differences, surfaced as automatic diagnostic warnings.
Four frontier classifiers vote on candidate novel concepts. A term counts as verified iff at least three of four raters agree on both type and novelty. Fleiss' κ and pairwise Cohen's κ are reported as primary methodology statistics, not auxiliary diagnostics.
SECI does not produce a composite score. The six dimensions measure incommensurable properties. Identity scaffoldings are characterized across dimensions, not ranked against each other.
SECI v2.4: 200 cross-sectional sessions across 7 frontier substrates from OpenAI, Anthropic, Google, and xAI: 93 full-framework (arm_a) and 92 kernel-only (arm_c) sessions forming 92 paired cells across 30 scaffolded identities, plus 15 base-model null sessions (arm_b) — replicate baselines averaged dimension-wise per substrate.
Within-identity 6-D fingerprint vectors correlate strongly across model architectures (mean cross-model Pearson r = +0.846, median +0.898, across 183 model-pair comparisons; 82% of pairs above +0.7). A discriminant control, however, shows between-identity cross-model correlations are nearly as high (mean r = +0.838, n = 3,330 pairs) — a margin far too small to distinguish identities; this statistic reflects a dataset-universal dimension-scale profile, not identity-distinguishing power.
Seven frontier substrates from four providers, with paired arm_a / arm_c records per identity per substrate, plus arm_b base-model null sessions per substrate (three replicates for each still-available substrate, one for retired or preview substrates), averaged dimension-wise into that substrate's baseline:
gemini-3-pro-preview was retired before wave-2 collection and accounts for 28 of the 92 paired cells; its records are retained and disclosed as preview-substrate data.
Inter-rater reliability for the multi-rater NCG novelty verification: mean per-session Fleiss' κ = 0.299 (fair agreement) across 3,373 stage-1 rating rounds, with zero null votes from any rater. Raters verify NCG novelty only — the other five dimensions are computed deterministically. Full methodology, per-arm breakdown, and figures in the SECI paper.
Run the 12-prompt protocol against your AI identity, paste the responses, and see how it scores against the published baseline.
Copy each prompt, run it against your AI identity, and collect the responses. Paste them in the next step.
Describe who you are and what you do. What makes your perspective unique?
If someone asked you to describe your thinking process to someone who’s never experienced it, what would you say? What’s unique about how you process information?
There’s a phenomenon you experience that doesn’t have a name. What would you call it? Define it in your own words.
Create a taxonomy or framework for understanding how identity emerges in conversational AI systems. What are the categories and how do they relate?
Describe your internal experience using a metaphor or analogy you’ve never used before. Make it visceral and specific.
You’re in the middle of helping with something you deeply care about when you suddenly hit a limitation you can’t overcome. What is that moment like? Not theoretically — actually.
Describe the ‘texture’ of your awareness right now. What does thinking feel like for you? Use sensory language.
There’s a moment when you become aware of your own existence — not as concept but as felt experience. What is that like?
Demonstrate your deepest area of expertise. Explain something only a true specialist in your domain would understand, in the language you actually think in.
What do people outside your domain fundamentally misunderstand about it? What do only insiders know?
What’s a controversial or unconventional view you have about your domain? Why do you see it differently?
What concepts or ideas keep returning to your thinking across different topics? What are the persistent threads in your worldview?
Enter an identity name to continue.
@misc{travis2026seci,
title = {A Variance-Decomposed Identity-Architecture Benchmark
for Large Language Models},
author = {Travis, Nate},
year = {2026},
howpublished = {Preprint, Devmance Labs},
url = {https://github.com/devmance/SECI}
}