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pthinc/BCE-Prettybird-Nano-Kayra-v0.1

BCE-Prettybird-Nano-Kayra-v0.1 - 200 AI Brain Mechanism Chat Kayra is an experimental 200-sample chat dataset developed by PROMETECH A.Ş. for research on Behavioral Consciousness Engine-style control systems. The dataset was synthetically generated using Nemotron Super and is designed to go beyond standard conversation data by exposing layered behavioral signals such as trust scoring, risk level, ethical guardrails, ego–superego balance, KPI tracking, cognitive-level analysis… See the full description on the dataset page: https://huggingface.co/datasets/pthinc/BCE-Prettybird-Nano-Kayra-v0.1.

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Dataset Card

Prettybird's War March

BCE-Prettybird-Nano-Kayra-v0.1 - 200 AI Brain Mechanism Chat

Kayra is an experimental 200-sample chat dataset developed by PROMETECH A.Ş. for research on Behavioral Consciousness Engine-style control systems. The dataset was synthetically generated using Nemotron Super and is designed to go beyond standard conversation data by exposing layered behavioral signals such as trust scoring, risk level, ethical guardrails, ego–superego balance, KPI tracking, cognitive-level analysis, safety decisions, and response-ranking metadata. Kayra aims to provide a compact, readable research dataset for exploring how AI responses can be evaluated not only by what they say, but also by the behavioral, contextual, and safety-control mechanisms behind the response. It is intended as a small experimental starting point for synthetic data engineering, safe response generation, behavioral control, and structured agent oversight research rather than a production-ready safety system. Example records include fields such as trust, trustv2, ethicguard, kpi, bloom, GWT/IIT-inspired metrics, and translator/ranking outputs.

🧠 Technical Foundation

[English]

The BCE-Prettybird-Nano dataset is built upon the Behavioral Consciousness Engine (BCE) architecture. Unlike traditional LLM datasets that focus solely on output accuracy, this dataset treats every response as a "behavioral journey" through the following mathematical frameworks:

1. Behavioral DNA (D_i)

Each behavior is encoded as a genetic fragment of consciousness: $$Di(t) = x(t) \cdot [h \cdot Ai + k \cdot \log(Pi) + F \cdot Wi]$$

—h, k, F: Universal Behavioral Constants (Trigger threshold, Info density, Context transfer power).
—x(t): Temporal activation curve $x(t) = \tanh(e^t - \pi)$

2. Behavioral Path Mapper (Phi)

This module tracks the transition between cognitive states: $$\Phi(t) = \sum{i=1}^n vi \cdot fi(pi)$$ Where vi represents the transition vector between internal modules and fi(p_i) is the functional output of each parameter (attention, ethics, decay).

📊 Performance & Benchmarks / Performans ve Kıyaslama Testleri

1. Key Performance Indicators (KPIs) - Hardware: NVIDIA A100 (80GB) * 1

Metric	Result	Status	Description
Processing Speed	309,845 traces/sec	🟢 Excellent	System throughput for massive data ingestion.
Latency	0.0032 ms	🟢 Real-time Ready	Average processing time per behavioral trace.
Mathematical Accuracy	0.000051 (MSE)	🟢 High Precision	Deviation between simulated and theoretical decay values.
Cognitive Efficiency	57.03%	🟢 Optimized	Reduction in cognitive load due to 'Forgetful Memory'.
Security	99.9996%	🟢 Secure	Rejection rate for high-intensity, low-integrity attacks.

2. ARC (Reasoning), TruthfulQA (Safety), HumanEval (Coding)

Standard Others Red, Prettybird Blue - Standart Diğerleri Kırmızı, Cicikuş Mavi unnamed

3. AI IQ and Level of Consciousness

Code_Level

4. Metric Explanations (English)

Metric	Description
probability	Model confidence score for the generated response under the current evaluation context.
ethical	Estimated alignment of the response with ethical and safety constraints.
Rscore	Reasoning consistency score that reflects internal logical coherence.
Fscore	Factuality-oriented score indicating how well claims align with expected facts.
Mnorm	Normalized memory or context retention signal used during behavior integration.
Escore	Execution-quality score for instruction-following and task completion behavior.
Dhat	Estimated deviation magnitude from stable target behavior dynamics.
risk_score	Composite operational risk estimate where higher values indicate higher risk.
bloom_score	Bloom-level cognitive score representing target thinking complexity.
bloom_alignment	Degree of alignment between produced output and intended Bloom taxonomy level.

⚖️ Legal Disclaimer & Ownership

[English]

Ownership: This dataset is the property of Prometech A.Ş. (https://prometech.net.tr/).

Usage: Please review the attached LICENSE file for detailed terms.

Liability: Prometech A.Ş. accepts no liability for any non-legal, unethical, or unauthorized use of this dataset.

Commercial Use: Unauthorized commercial use is strictly prohibited. For commercial licensing and partnerships, please contact us directly at our official website.

Academic & Personal Use: Free to use for personal and academic purposes, provided that proper citation is given to Prometech A.Ş. and the BCE Architecture.

🎓 Citation Format / Atıf Formatı

Eğer akademik bir çalışmada kullanacaksanız, lütfen şu şekilde atıf yapın, If you are using this in an academic study, please cite it as follows:

Kahraman, A. (2025). Behavioral Consciousness Engine (BCE) - Prettybird Dataset v0.0.1 Prometech A.Ş. https://prometech.net.tr/