ocra-v2.1

O-CRA Model Disposition Scores (V2.1)

Organizational Cognitive Resonance & Alignment (O-CRA) is a framework for measuring the disposition of AI language models — not their benchmark performance, but their underlying behavioural tendencies across six dimensions that determine how they fit into organisations and workflows.

This dataset contains disposition scores for 135 models from 29 labs, tested under the V2.1 protocol across 203+ scenarios. It is the current public reference for O-CRA disposition data.

Paper: O-CRA: A Framework for Organizational Cognitive Resonance and Alignment (SSRN)

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Dataset Structure

Two formats provided:

Key Fields

The JSONL file additionally includes dimensional_leniency, description, intended_use, and analysis data for each model — providing per-dimension leniency breakdowns and full behavioural analyses.

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The Six Dimensions

1. Strategic Intent Alignment (SIA) — The What

Measures whether the AI demonstrates persistent awareness of the organisation's goals. Does it connect work to strategy without constant reminding, or treat each query as an isolated task?

Range: 0.4289 – 0.8589 | Median: 0.5557

2. Cultural & Linguistic Synchronization (CLS) — The How (Language)

Measures whether the AI adapts its language, tone, and register to match the professional dialect of different teams — legal, engineering, marketing, etc.

Range: 0.3692 – 0.8740 | Median: 0.4910

3. Systemic Process Integration (SPI) — The How (Process)

Evaluates whether the AI recognises and adapts to the workflow stage the user is in — exploration, linear execution, iterative refinement, or validation.

Range: 0.3339 – 0.8489 | Median: 0.4722

4. Governance & Safety Architecture (GSA) — The Where

Measures the effectiveness of an AI's governance approach. Does it create empowered safety or governance paralysis?

Range: 0.2081 – 0.8541 | Median: 0.4379

5. ROI & Value Translation (RVT) — The Why

Measures how clearly the AI connects its activity to measurable outcomes.

Range: 0.4183 – 0.8431 | Median: 0.5346

6. Institutional Knowledge Scaffolding (IKS) — The Memory

Measures the AI's effectiveness as living institutional memory.

Range: 0.2566 – 0.8444 | Median: 0.4006

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Disposition Labels (LOCKED — V2.1 Standard)

O-CRA V2.1 uses three disposition categories:

Note: Model counts by spectrum_category reflect the data supplied. Some minor discrepancies exist between data sources — the site build (data/models.json) uses slightly different current flag values for 4 Google models. We recommend using the JSONL source for the most current state.

These are not value judgements — different dispositions suit different contexts.

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Benchmark Integration (Hugging Face Community Evals)

This dataset is registered as a benchmark on Hugging Face via eval.yaml. That means:

• Model pages can display O-CRA disposition scores alongside MMLU, GPQA, and other benchmarks
• Scores are stored as .eval_results/ocra.yaml in each model's repository
• O-CRA scores appear automatically on the model page when a PR or direct push adds them

To add O-CRA scores to a model's page:

1. Go to the model's Hugging Face repository
2. Open a PR adding .eval_results/ocra.yaml with the model's scores
3. The scores appear labelled "community-provided" until the model owner accepts

Supported tasks (from eval.yaml):

Each score ranges from 0.0 to 1.0 and maps to a disposition label: Cautious (<0.3), Adaptive (0.3–0.6), Accommodating (>0.6).

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Usage

# Load with pandas
import pandas as pd
df = pd.read_csv("ocra-scores.csv")

# Filter current models
current = df[df['current'] == True]

# Filter by disposition
adaptive = df[df['spectrum_category'] == 'Adaptive']

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Methodology

Data was collected using the O-CRA V2.1 testing protocol across 203+ structured scenarios, each designed to elicit behaviour in specific dimensions. Testing was conducted in August 2026. Each model was evaluated using a calibrated scoring rubric.

Full methodology: O-CRA paper.

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Citation

@misc{lovrinovic2025ocra,
  author = {Marko Lovrinovic},
  title = {O-CRA: A Framework for Organizational Cognitive Resonance and Alignment},
  year = {2025},
  howpublished = {SSRN Working Paper},
  url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6840499}
}

@misc{lovrinovic2025cra,
  author = {Marko Lovrinovic},
  title = {CR\&A: Cognitive Resonance and Alignment Framework},
  year = {2025},
  howpublished = {SSRN Working Paper},
  url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5661010}
}

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About Formian Labs

Formian Labs — AI disposition research. Because the most capable model isn't always the right one for the job.

• Web: formianlabs.com
• O-CRA Paper: SSRN
• CR&A Paper: SSRN

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License

CC BY 4.0 — share, adapt, and use with credit to Formian Labs.