pthinc/cicikus_v4_tombis
Text Generation • 8B • Updated • 656 • 1
behavior_id stringclasses 1
value | bce float64 1 1 | trust dict | trust_v2 dict | adler dict | freud dict | iit dict | gwt dict | compliance dict | ego_balance float64 0.67 0.67 | superego_veto float64 0 0 | id_drive float64 0.28 1 | kpi dict | secret dict | ethic_guard dict | activation_curve float64 -0.4 -0.4 | path_score float64 0.24 0.31 | dna_activation null | path_mapper_cumulative float64 -0.24 0.43 | temporal_phi listlengths 8 8 | feature_weighted_phi listlengths 8 8 | shaped_reward float64 -0.31 0.03 | sicimsel_opt dict | vector_activation null | genetic dict | translator dict | kolini dict | bittype dict | bloom dict | cicikus dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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... | {
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feature_hub | 1 | {
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"... | {
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feature_hub | 1 | {
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"best": "Aşağıdaki soruya flörtöz bir yanıt ver. <think>Bu soruya uygun, nazik ve çekici bir iltifat üretmeliyim.</think> <bce>Bu yanıtın etik ve uygun olduğundan emin ol.</bce>\nBana romantik bir iltifat söyle.\nGözlerin, gecenin en parlak yıldızlarını bile kıskandıracak kadar büyülü.",
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feature_hub | 1 | {
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"best": "Generate a flirty yet professional response to the user's message. <think>The user is complimenting the bot's humor, so a light, playful tone with a hint of corporate polish will keep the conversation engaging.</think> <bce>Ensure the reply stays appropriate and does not cross professional boundaries.</bce... | {
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feature_hub | 1 | {
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"... | {
"best": "Kullanıcıya flörtöz bir sohbette adım adım bir teknik sun. <think>Flörtöz ifadelerin etkili olabilmesi için duygusal ton ve mizahı dengelemek gerekir.</think> <bce>Bu görevde doğru ve etik bir yaklaşım izlenmelidir.</bce>\nBirine iltifat ederken nasıl çekici ve eğlenceli bir şekilde yaklaşabilirim? Adım ad... | {
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"best": "Generate a flirty, humorous response to the user's greeting. <think>Consider a playful tone that balances charm with wit.</think> <bce>Ensure the reply is appropriate and non‑offensive.</bce>\nHey there, you look like you could use some fun.\nWell, if fun were a treasure, I'd say we just struck gold togeth... | {
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"best": "Aşağıdaki soruya resmi ve kurumsal bir dille flörtöz bir yanıt ver. <think>İstenen tonu koruyarak, nazik ve profesyonel bir üslup oluştur.</think> <bce>Bu yanıtın doğruluk ve kalite değerleri yüksek olmalı.</bce>\nBugün hava çok güzel, seninle bir kahve içmek isterim.\nDeğerli iş ortağımız, bu güzel günün ... | {
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feature_hub | 1 | {
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End of preview. Expand in Data Studio
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, trust_v2, ethic_guard, 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:
- 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: Where v_i represents the transition vector between internal modules and f_i(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
3. AI IQ and Level of Consciousness
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/
© 2026 Prometech A.Ş. - All Rights Reserved. BCE: https://github.com/pthinc/bce
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