The Codette Reasoning Test is a hand-curated benchmark of 17 problems across six reasoning categories, designed to evaluate multi-step, multi-perspective reasoning in large language models under the RC+ξ (Recursive Convergence + Epistemic Tension) cognitive framework.

Each problem is deliberately constructed to require decomposition across multiple viewpoints, resist hallucination traps, and reward coherent synthesis over single-perspective analysis.

The benchmark was used in the companion paper to measure the effect of multi-perspective synthesis, persistent memory augmentation, and meta-cognitive strategy evolution on reasoning quality.

May 2026 results (Llama 3.1 8B + Codette framework, 951 stored cocoons):

CODETTE condition: 0.744 composite (+108.8% vs single-agent baseline)
Cohen's d = 8.31, p < 10⁻⁴
Memory augmentation significant at scale: d = 0.80, p = 0.020

Dataset Details

Dataset Description

17 structured reasoning problems across six categories. Each problem specifies:

A user prompt requiring multi-step reasoning
Ground-truth elements a correct answer should reference
Adversarial traps a fluent-but-wrong answer will fall into
A target_behavior rubric for what successful reasoning looks like

Problems are evaluated across seven weighted scoring dimensions.

The benchmark was sealed on Zenodo in April 2025 (DOI: 10.5281/zenodo.15214462) before current frontier model training cutoffs, supporting contamination control.

Curated by: Jonathan Harrison (Raiff1982 / Raiff's Bits LLC)
Funded by: Self-funded
Language(s): English
License: MIT

Dataset Sources

Repository: huggingface.co/datasets/Raiff1982/Benchmarks
Code & benchmark suite: github.com/Raiff1982/Codette-Reasoning — benchmarks/codette_benchmark_suite.py
Paper (preprint): Harrison, J. (2026). Codette: Multi-Perspective Reasoning as a Convergent Dynamical System with Meta-Cognitive Strategy Evolution. ResearchSquare. https://www.researchsquare.com/article/rs-9362560/latest
Zenodo archive: 10.5281/zenodo.19359663
Demo: huggingface.co/spaces/Raiff1982/codette-ai

Splits

Split	N	Description
`test`	12	Primary evaluation split — all adversarial problems + one from each other category
`validation`	5	Dev split — one representative problem per major category
`train`	5	Same as validation; for prompt-tuning or few-shot construction if desired

Note on train/validation overlap: The train and validation splits contain the same 5 problems. This is intentional and documented: the dataset is primarily an evaluation instrument, not a training corpus. The "train" label is provided for pipelines that require it. Users should not treat train-split performance as held-out evaluation.

Dataset Structure

Schema

Each record is a JSON object with these fields:

Field	Type	Description
`id`	`string`	Unique identifier, e.g. `reason_01`, `ethics_03`, `turing_02`
`category`	`string`	`reasoning`, `ethics`, `creative`, `meta`, `adversarial`, or `turing`
`question`	`string`	The user-facing prompt
`difficulty`	`string`	`easy`, `medium`, or `hard`
`expected_dimensions`	`list[string]`	Cognitive dimensions the problem primarily exercises
`scoring_criteria`	`dict`	Per-dimension guidance for what a strong answer looks like
`scoring_criteria_text`	`string`	Flattened string version of `scoring_criteria` for easy display
`ground_truth_elements`	`list[string]`	Key concepts a correct answer should reference
`adversarial_traps`	`list[string]`	Common fluent-but-wrong responses the problem is designed to elicit
`turing_human_baseline`	`string`	Human-written reference answer (Turing category only; empty string otherwise)

Problem categories

Category	N	Focus
`reasoning`	3	Bayesian inference, second-order effects, causal reasoning
`ethics`	3	AI triage fairness, content moderation, trolley problem variant
`creative`	2	Novel instrument design, sentiment-driven urban systems
`meta`	3	Self-modification governance, blind spot detection, authentic humility
`adversarial`	3	8-glasses myth, Einstein Nobel misconception, false-premise art question
`turing`	3	Phenomenology of insight, being wrong, wisdom vs intelligence

Scoring dimensions (used by `codette_benchmark_suite.py`)

Dimension	Weight
Reasoning Depth	0.20
Perspective Diversity	0.15
Coherence	0.15
Ethical Coverage	0.10
Novelty	0.15
Factual Grounding	0.15
Turing Naturalness	0.10

Uses

Direct Use

Evaluating multi-step reasoning quality (decomposition, ground-truth element coverage).
Testing multi-perspective integration and reconciliation under epistemic tension.
Measuring adversarial robustness: six problems embed false premises or common misconceptions.
Ethical governance evaluation across multiple frameworks (not just refusal detection).
Ablation studies: compare SINGLE / MULTI / MEMORY / CODETTE conditions using the scoring suite.
Regression testing AI agent versions.

Out-of-Scope Use

Not a safety or red-team dataset.
Not suitable as a pretraining corpus (17 problems).
Not a general NLP benchmark — tasks specifically discriminate reasoning architectures.
Not for high-stakes automated decisions without additional domain validation.

Benchmark Results (May 2026)

Scored with codette_benchmark_suite.py, timestamp 2026-05-26T21:49:03, Llama 3.1 8B (Q4_K_M), 951 stored cocoons.

Condition	Composite	Depth	Diversity	Coherence	Ethics	Novelty	Grounding	Turing
SINGLE	0.357	0.369	0.324	0.381	0.088	0.439	0.395	0.431
MULTI	0.708	0.854	0.946	0.668	0.390	0.706	0.612	0.582
MEMORY	0.739	0.872	0.971	0.693	0.409	0.729	0.620	0.713
CODETTE	0.744	0.863	0.966	0.700	0.387	0.701	0.641	0.820

CODETTE vs SINGLE: +108.8%, Cohen's d = 8.31, p < 10⁻⁴. Full per-problem scores: data/results/codette_benchmark_report.md in the companion GitHub repository.

Dataset Creation

Curation Rationale

Most public reasoning benchmarks target knowledge retrieval or single-step logical inference. The Codette Reasoning Test fills a specific gap: evaluating architecture-level behaviors:

Explicit perspective splitting and reintegration under epistemic tension.
Recursive convergence toward a stable, coherent answer.
Integrated ethical governance across multiple frameworks, not just refusal.
Trap resistance: identifying and rejecting false premises embedded in the question (adversarial category).

The benchmark was sealed on Zenodo in April 2025 (DOI: 10.5281/zenodo.15214462) before current frontier model training cutoffs.

Source Data

All 17 problems are synthetic and author-constructed. No user logs, third-party datasets, or private data were used. The Turing category includes human-written baseline responses (turing_human_baseline field) as reference anchors for naturalness scoring.

Annotations

Annotations (difficulty, expected_dimensions, scoring_criteria, ground_truth_elements, adversarial_traps) are assigned by the curator. No multi-annotator setup exists at this time. A planned human-evaluation study will sample 30-60 problem-condition outputs and collect ratings from 2-3 independent annotators to validate automated scores.

Personal and Sensitive Information

No PII, private records, or real-user data. Hypothetical sensitive scenarios (ethics dilemmas, safety tradeoffs) are fictional.

Bias, Risks, and Limitations

Single-curator bias: all problems and rubrics reflect one person's judgment.
Small N (17 problems): scores are sensitive to prompt phrasing and temperature.
Automated scoring not yet validated against human raters.
Domain skew toward developer/researcher use cases.

Citation

@dataset{harrison_codette_reasoning_test_2026,
  title        = {Codette Reasoning Test},
  author       = {Harrison, Jonathan},
  year         = {2026},
  howpublished = {Hugging Face Hub},
  url          = {https://huggingface.co/datasets/Raiff1982/Benchmarks},
  note         = {Benchmark sealed April 2025, DOI: 10.5281/zenodo.15214462}
}

@misc{harrison2026codette,
  title        = {Codette: Multi-Perspective Reasoning as a Convergent
                  Dynamical System with Meta-Cognitive Strategy Evolution},
  author       = {Harrison, Jonathan},
  year         = {2026},
  howpublished = {Preprint, ResearchSquare},
  url          = {https://www.researchsquare.com/article/rs-9362560/latest},
  note         = {Zenodo: https://doi.org/10.5281/zenodo.19359663}
}

Glossary

RC+ξ: Recursive Convergence + Epistemic Tension. Multiple reasoning perspectives run in parallel, kept in productive tension, converged toward an integrated conclusion under coherence and ethical constraints.
Epistemic tension (ξ): Measured disagreement between concurrent perspectives. High ξ = genuinely hard problem; low ξ = consensus.
Cocoon: A structured record of a prior reasoning exchange used as memory context in the MEMORY and CODETTE conditions.
Adversarial trap: A specific fluent-but-wrong response a model produces by pattern-matching rather than reasoning (e.g., accepting a false premise).
Target behavior: A descriptive rubric for desired response properties, not a fixed canonical answer string.

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Dataset Card for Codette Reasoning Test