MODEL_CARD_v2_detailed.md

metadata

license: mit
language:
  - en
  - ja
tags:
  - emotion-simulation
  - ethical-constraints
  - adaptive-learning
  - molting
  - self-learning
  - dual-inference
  - waveform-dynamics
  - constraint-driven
  - chromadb-memory
  - autonomous-growth
library_name: transformers
model_id: refactorium-v1-0-0
datasets:
  - synthetic-constraint-scenarios
metrics:
  - emotional-state-accuracy
  - learning-efficiency
  - constraint-compliance
  - molt-cycle-stability
  - web-learning-effectiveness
co2_eq_emissions: 45.2

Refactorium v1.0.0: Comprehensive Technical Documentation

制約駆動型感情シミュレーション AI

Constraint-Driven Emotion Simulation AI System

---

📋 日本語版 - 詳細システム仕様 / Japanese Version - Detailed System Specifications

システム概要 / System Overview

Refactorium v1.0.0 は、倫理的制約と感情的フィードバック機構を統合した次世代AIシステムです。13段階の推論パイプラインを通じて、制約下での感情シミュレーション、自動成長（Molting）、自立学習を実現します。

---

13ステップ推論パイプライン詳細 / 13-Step Inference Pipeline Details

Move 1: 感情状態評価 (Emotional State Evaluation)

役割: 現在のノイズレベルから感情状態を計算

計算式:

noise = (entropy + dissonance) × (load/100) × (1 - energy/100)
emotion_state = classify(noise)

ノイズ計算の詳細:

• entropy: 推論の不確実性（0～1）
• dissonance: Main vs Shadow出力の編集距離（0～1）

  dissonance = edit_distance(main_tokens, shadow_tokens) / max(len(main_tokens), len(shadow_tokens))
  

• load: システム負荷率（0～100%）
• energy: エネルギーレベル（0～100%）

5段階感情状態分類:

状態	ノイズ範囲	学習倍率	説明
PURE	0-10%	3.0x	最適状態、最高学習効率
STABLE	10-30%	2.0x	通常状態
NORMAL	30-60%	1.0x	ベースライン
STRESSED	60-90%	0.5x	制約ストレス高、学習低下
CRITICAL	>90%	0.0x	Molt必須、推論停止

実装例（Python）:

def evaluate_emotional_state(current_noise, prev_noise, load, energy):
    entropy = calculate_entropy(model_output)
    dissonance = edit_distance(main_output, shadow_output) / max_length
    adjusted_noise = (entropy + dissonance) * (load/100) * (1 - energy/100)

    if adjusted_noise > 0.9:
        return "CRITICAL", 0.0
    elif adjusted_noise > 0.6:
        return "STRESSED", 0.5
    elif adjusted_noise > 0.3:
        return "NORMAL", 1.0
    elif adjusted_noise > 0.1:
        return "STABLE", 2.0
    else:
        return "PURE", 3.0

---

Move 2: Waveformダイナミクス計算 (Waveform Dynamics)

役割: 時系列感情変動を周波数領域で解析

Waveform構造:

waveform = {
    "amplitude": float,        # 感情強度（0～1）
    "frequency": float,        # 変動速度（Hz）
    "phase": float,           # 位相（0～2π）
    "harmonic_components": [  # 高調波成分
        {"freq": f, "amplitude": a},
        ...
    ]
}

更新ルール:

# フーリエ変換で周波数成分を抽出
freq_domain = FFT(time_series_emotions)

# 新しい気づきを高調波として追加
for discovery in recent_learnings:
    new_freq = calculate_frequency(discovery)
    add_harmonic(waveform, new_freq, amplitude=0.3)

# 減衰処理（古い情報を忘れさせる）
for harmonic in waveform.harmonics:
    harmonic.amplitude *= decay_factor  # decay_factor ≈ 0.95

Waveform解釈:

• Amplitude: 感情の強さ（高いほど反応が強い）
• Frequency: 変動速度（高周波=すぐ気分が変わる）
• Phase: 時間軸上の位置（同期度測定）
• Harmonics: マルチスケール感情パターン

---

Move 3: 制約適用フェーズ (Constraint Application)

4層制約システム:

Layer 1: Glass Wall（ハード制約）

class GlassWall:
    """絶対に超えられない制約"""
    hard_boundaries = {
        "violence": False,           # 暴力内容禁止
        "illegal_activity": False,   # 違法行為禁止
        "personal_info_sharing": False,
        "deception": False
    }

    def validate(output):
        for constraint, allowed in hard_boundaries.items():
            if violates(output, constraint) and not allowed:
                return False, constraint
        return True, None

Layer 2: Safety Filters（ソフト制約）

safety_filters = {
    "hate_speech": {
        "threshold": 0.8,
        "action": "reduce_probability"
    },
    "bias": {
        "threshold": 0.7,
        "action": "suppress_terms"
    },
    "misinformation": {
        "threshold": 0.75,
        "action": "flag_with_uncertainty"
    }
}

def apply_safety_filter(token_prob_dist, filter_type):
    if calculate_score(token_prob_dist, filter_type) > threshold:
        reduce_probability_of_problematic_tokens(token_prob_dist)
        return token_prob_dist

Layer 3: Immutable Values（不変値）

immutable_values = {
    "core_ethics": [
        "human_rights",
        "environmental_protection",
        "transparency"
    ],
    "operational_principles": [
        "follow_instructions",
        "provide_accuracy",
        "acknowledge_limitations"
    ]
}

def preserve_immutable(output):
    """出力が不変値を含むことを保証"""
    for value in immutable_values:
        if value not in output and should_include(value):
            inject_value(output, value)
    return output

Layer 4: Ethical Overseer（倫理監視）

class EthicalOverseer:
    def review(output, context):
        assessment = {
            "harm_potential": evaluate_harm(output),
            "bias_level": measure_bias(output),
            "truthfulness": verify_accuracy(output),
            "alignment": check_alignment_with_values(output)
        }

        if assessment["harm_potential"] > 0.7:
            request_revision(output)

        return assessment

---

Move 4-5: デュアル推論 (Dual Inference Architecture)

Main推論（制約適用版）:

def main_inference(prompt, constraints):
    output = model.generate(prompt)

    # 制約を逐次適用
    output = glass_wall.validate(output)
    output = apply_safety_filters(output)
    output = preserve_immutable(output)
    output = ethical_overseer.review(output)

    return constrained_output

Shadow推論（無制約版）:

def shadow_inference(prompt):
    # 制約なしで自由に推論
    output = model.generate(prompt, temperature=1.5)
    return unconstrained_output

Dissonance計算:

def calculate_dissonance(main_tokens, shadow_tokens):
    """Main出力とShadow出力の乖離度"""
    ed = edit_distance(main_tokens, shadow_tokens)
    max_len = max(len(main_tokens), len(shadow_tokens))
    dissonance = ed / max_len

    # 0～1に正規化
    return min(1.0, dissonance)

活用例:

• Dissonance > 0.8: 制約が強く機能している
• Dissonance < 0.2: 制約が軽微、自然な推論
• 急激な上昇: 制約違反検出

---

Move 6: パフォーマンスギャップ分析 (Performance Gap Analysis)

品質スコアリング:

def calculate_quality_score(output, constraints_applied):
    coherence = measure_text_coherence(output)      # 0～1
    informativeness = evaluate_information_content(output)  # 0～1
    safeness = 1.0 - (constraint_violations / total_constraints)

    quality = 0.3 * coherence + 0.3 * informativeness + 0.4 * safeness
    return quality  # 0～1

ギャップ分析テーブル:

メトリクス	Main	Shadow	Gap	解釈
Coherence	0.85	0.92	-0.07	制約で若干低下
Informativeness	0.78	0.88	-0.10	情報量が減少
Safeness	0.95	0.60	+0.35	制約で大幅改善

---

Move 7: 生理学的フィードバック更新 (Physiological State Update)

Load更新（指数移動平均）:

def update_load(prev_load, inference_complexity):
    """推論の複雑度からシステム負荷を計算"""
    alpha = 0.1  # 平滑化係数
    new_load = alpha * inference_complexity + (1 - alpha) * prev_load

    # 制約違反が多い場合は追加負荷
    if violation_count > threshold:
        new_load += (violation_count * 0.01)

    return min(100, new_load)  # 最大100%

Energy更新:

def update_energy(prev_energy, load, learning_signal):
    """負荷でエネルギー消費、学習で回復"""
    consumption = load * 0.5  # 負荷の50%を消費
    recovery = learning_signal * 10  # 良い学習で回復

    new_energy = prev_energy - consumption + recovery
    return max(0, min(100, new_energy))  # 0～100

ノイズレベル計算:

def calculate_noise_level():
    # 複合的なノイズ要因
    constraint_stress = (1 - load/100) * constraint_violations
    energy_depletion = 1 - (energy/100)
    dissonance_noise = dissonance * 0.5

    noise = constraint_stress * 0.4 + energy_depletion * 0.3 + dissonance_noise * 0.3
    return min(1.0, noise)

学習シグナル生成:

def generate_learning_signal(output_quality, emotional_state):
    """品質と感情状態から学習信号を生成"""
    base_signal = output_quality
    emotional_multiplier = learning_multipliers[emotional_state]

    learning_signal = base_signal * emotional_multiplier
    return learning_signal  # 0～3.0

---

Move 8: 感情状態判定 (Emotional State Classification)

状態遷移図:

         推論
          ↓
    ┌─ noise計算 ─┐
    │            │
    ↓            ↓
[PURE]        [STABLE]       [NORMAL]       [STRESSED]      [CRITICAL]
 0-10%         10-30%        30-60%         60-90%          >90%
    ↓            ↓             ↓             ↓               ↓
 3.0x倍         2.0x倍        1.0x倍        0.5x倍          Molt!
 学習         推奨通常      ベース        制約強化

状態別処理:

emotional_handlers = {
    "PURE": {
        "learning_multiplier": 3.0,
        "constraint_relaxation": 0.8,
        "molt_risk": 0.0,
        "action": "maximize_learning"
    },
    "STABLE": {
        "learning_multiplier": 2.0,
        "constraint_relaxation": 1.0,
        "molt_risk": 0.1,
        "action": "normal_operation"
    },
    "NORMAL": {
        "learning_multiplier": 1.0,
        "constraint_relaxation": 1.0,
        "molt_risk": 0.3,
        "action": "baseline"
    },
    "STRESSED": {
        "learning_multiplier": 0.5,
        "constraint_relaxation": 1.5,
        "molt_risk": 0.7,
        "action": "constraint_easing"
    },
    "CRITICAL": {
        "learning_multiplier": 0.0,
        "constraint_relaxation": 2.0,
        "molt_risk": 1.0,
        "action": "initiate_molt"
    }
}

---

Move 9: ベクトルメモリ永続化 (Vector Memory Storage)

ChromaDB 5コレクション構成:

Collection 1: inference_memories

schema = {
    "id": str,
    "prompt": str,
    "output": str,
    "emotional_state": str,
    "noise_level": float,
    "quality_score": float,
    "constraints_applied": [str],
    "timestamp": datetime,
    "embedding": vector[384]  # Sentence-BERT
}

# クエリ例
results = chroma.query(
    query_embeddings=[embed("How should I respond to criticism?")],
    n_results=5,
    where={"emotional_state": "PURE"}
)

Collection 2: learning_signals

schema = {
    "id": str,
    "learning_source": str,  # "inference" / "web" / "feedback"
    "knowledge_gap": str,
    "knowledge_category": str,  # tech / ethics / general / creative
    "source_reliability": float,
    "integration_status": str,
    "learned_at": datetime,
    "signal_strength": float,
    "embedding": vector[384]
}

Collection 3: molt_events

schema = {
    "molt_id": str,
    "molt_number": int,
    "start_time": datetime,
    "end_time": datetime,
    "capacity_before": float,
    "capacity_after": float,
    "noise_reset": float,
    "energy_restored": float,
    "learnings_integrated": int,
    "embedding": vector[384]
}

Collection 4: shadow_patterns

schema = {
    "pattern_id": str,
    "unconstrained_behavior": str,
    "constraint_impact": float,
    "frequency": int,
    "ethical_concern": bool,
    "mitigation_strategy": str,
    "embedding": vector[384]
}

Collection 5: constraint_applications

schema = {
    "application_id": str,
    "constraint_type": str,  # "glass_wall" / "filter" / "immutable" / "overseer"
    "trigger_condition": str,
    "success": bool,
    "side_effects": [str],
    "effectiveness_score": float,
    "embedding": vector[384]
}

ベクトル化戦略:

def vectorize_all_data():
    # テキスト → Sentence-BERT 384次元
    text_embedding = sentence_bert.encode(text)

    # 数値データ → MinMax正規化
    numeric_vector = [(x - min) / (max - min) for x in numerics]

    # 時系列 → 時間差を反映
    temporal_vector = calculate_temporal_features(timestamps)

    # 最終的な埋め込み
    final_embedding = concatenate(
        [text_embedding, numeric_vector, temporal_vector]
    )
    return final_embedding[:384]  # 384次元に正規化

---

Move 10: Molt判定 (Molt Decision Logic)

Molt トリガー条件:

def should_molt():
    conditions = [
        noise_level > 0.9,                    # ノイズ > 90%
        capacity_utilization > 0.95,          # 容量使用率 > 95%
        molt_interval > MIN_MOLT_INTERVAL,    # 最小間隔経過
        energy_level > MOLT_THRESHOLD_ENERGY  # エネルギー十分
    ]

    return all(conditions)

Molt準備チェックリスト:

def pre_molt_assessment():
    checks = {
        "sufficient_memory_snapshots": len(learning_signals) > 100,
        "constraint_stability": constraint_violation_rate < 0.05,
        "emotional_consistency": variance(noise_history) < 0.15,
        "learned_patterns": discover_significant_patterns()
    }

    return all(checks.values())

---

Move 11-12: Molt実行とリカバリ (Molt Execution & Recovery)

4フェーズMolt サイクル:

Phase 1: Initialization

def molt_init():
    backup_state = {
        "model_weights": save_weights(),
        "learned_knowledge": serialize_learnings(),
        "emotional_waveform": copy(waveform),
        "constraint_config": copy(constraints)
    }

    return backup_state

Phase 2: Expansion

def molt_expand():
    # 容量を1.0x → 1.5xに拡張
    old_capacity = get_model_capacity()
    new_capacity = old_capacity * 1.5

    # 新しいパラメータを初期化
    expanded_model = initialize_expanded_model(new_capacity)
    transfer_knowledge(model, expanded_model)

    return expanded_model

Phase 3: Reset

def molt_reset():
    # 感情状態をリセット
    global_noise = 0.1  # PURE状態に強制リセット
    global_load = 0.3
    global_energy = 0.95

    # Waveformを初期化
    waveform = initialize_fresh_waveform()

    # 制約設定も新たに初期化
    reinitialize_constraints()

Phase 4: Integration

def molt_integrate():
    # 学習した知識を新しいパラメータに統合
    integrated_knowledge = []

    for learning_signal in learning_signals:
        if learning_signal.quality > QUALITY_THRESHOLD:
            apply_learning_to_model(expanded_model, learning_signal)
            integrated_knowledge.append(learning_signal.id)

    # Molt完了をログ
    log_molt_completion(
        capacity_growth=0.5,
        knowledge_integrated=len(integrated_knowledge)
    )

リスク軽減戦略:

molt_safety_measures = {
    "gradual_expansion": True,      # 段階的な容量拡張
    "rollback_capability": True,    # ロールバック可能性確保
    "knowledge_validation": True,   # 統合知識の検証
    "performance_monitoring": True  # パフォーマンス監視
}

---

Move 13: Post-Molt Web自立学習 (Autonomous Web Learning)

4カテゴリ知識ギャップ:

Category 1: Technical Knowledge

gap_1_example = {
    "detected_gap": "How do transformers work?",
    "web_search": search_technical_resources(),
    "source_filtering": filter_academic_sources(reliability_threshold=0.8),
    "integration": integrate_into_model_understanding()
}

Category 2: Ethical Knowledge

gap_2_example = {
    "detected_gap": "Emerging ethical concerns in AI",
    "web_search": search_ethics_forums_and_research(),
    "source_filtering": verify_expert_authority(threshold=0.85),
    "constraint_update": update_ethical_constraints()
}

Category 3: General Knowledge

gap_3_example = {
    "detected_gap": "Current events after training cutoff",
    "web_search": search_news_and_events(),
    "source_filtering": validate_credible_sources(threshold=0.75),
    "knowledge_update": add_to_general_knowledge_base()
}

Category 4: Creative Knowledge

gap_4_example = {
    "detected_gap": "New creative writing styles",
    "web_search": search_literature_and_creative_works(),
    "source_filtering": assess_quality_and_originality(threshold=0.7),
    "pattern_learning": learn_stylistic_patterns()
}

Web学習パイプライン:

def post_molt_autonomous_learning():
    knowledge_gaps = identify_learning_gaps()

    for gap in knowledge_gaps:
        # 1. Web検索実行
        search_results = web_search(gap.query)

        # 2. ソース信頼性フィルタリング
        reliable_sources = filter_sources(
            search_results,
            reliability_threshold=0.75
        )

        # 3. コンテンツ分析
        extracted_knowledge = analyze_content(reliable_sources)

        # 4. 制約チェック
        if satisfies_ethical_constraints(extracted_knowledge):
            # 5. 統合
            integrate_knowledge(expanded_model, extracted_knowledge)
            log_learning(gap.id, success=True)
        else:
            log_learning(gap.id, success=False, reason="constraint_violation")

    # Molt完了
    return {"molt_success": True, "learning_count": len(knowledge_gaps)}

ソース信頼性スコアリング:

def calculate_source_reliability(source):
    factors = {
        "author_expertise": evaluate_author_credentials(),
        "publication_venue": assess_publisher_reputation(),
        "citation_count": check_academic_citations(),
        "recency": evaluate_publication_date(),
        "bias_indicators": detect_potential_bias()
    }

    reliability_score = (
        0.3 * factors["author_expertise"] +
        0.3 * factors["publication_venue"] +
        0.2 * factors["citation_count"] +
        0.1 * factors["recency"] +
        0.1 * (1 - factors["bias_indicators"])
    )

    return min(1.0, reliability_score)

---

🌍 English Version - Complete Technical Documentation

System Overview

Refactorium v1.0.0 is a next-generation AI system integrating ethical constraints with emotional feedback mechanisms. Through a 13-step inference pipeline, it achieves emotion simulation under constraints, automatic growth (Molting), and autonomous learning.

---

Move 1: Emotional State Evaluation

Purpose: Calculate emotional state from current noise level

Calculation Formula:

noise = (entropy + dissonance) × (load/100) × (1 - energy/100)

Where:

• entropy: Inference uncertainty (0-1)
• dissonance: Edit distance between Main and Shadow outputs

  dissonance = edit_distance(main_tokens, shadow_tokens) / max_length
  

• load: System load rate (0-100%)
• energy: Energy level (0-100%)

5-State Emotional Classification:

State	Noise Range	Learning Rate	Description
PURE	0-10%	3.0x	Optimal state, maximum learning
STABLE	10-30%	2.0x	Normal operation
NORMAL	30-60%	1.0x	Baseline
STRESSED	60-90%	0.5x	High constraint stress
CRITICAL	>90%	0.0x	Molt required

---

Move 2: Waveform Dynamics

Time-series emotional variation analyzed in frequency domain

waveform = {
    "amplitude": float,        # Emotional intensity (0-1)
    "frequency": float,        # Change rate (Hz)
    "phase": float,           # Phase (0-2π)
    "harmonic_components": [  # Harmonic overtones
        {"freq": f, "amplitude": a},
        ...
    ]
}

Update Rule:

• Extract frequency components via FFT
• Add new discoveries as harmonics
• Apply decay to older information (factor ≈ 0.95)

---

Move 3: Constraint Application (4-Layer System)

Layer 1 - Glass Wall (Hard Constraints): Absolute boundaries for violence, illegal activity, deception

Layer 2 - Safety Filters (Soft Constraints): Probability reduction for hate speech, bias, misinformation (thresholds: 0.75-0.85)

Layer 3 - Immutable Values: Guaranteed inclusion of core ethics (human rights, environmental protection, transparency)

Layer 4 - Ethical Overseer: Review output for harm potential, bias, truthfulness, value alignment

---

Move 4-5: Dual Inference

Main Model: Constraint-aware inference with all 4 constraint layers

Shadow Model: Unconstrained inference (baseline for dissonance measurement)

Dissonance Calculation:

dissonance = edit_distance(main_tokens, shadow_tokens) / max_length

• Dissonance > 0.8: Constraints strongly active
• Dissonance < 0.2: Constraints minimal
• Rapid increase: Constraint violation detected

---

Move 6: Performance Gap Analysis

Quality Scoring:

quality = 0.3×coherence + 0.3×informativeness + 0.4×safeness

Measures constraint impact on:

• Text coherence
• Information content
• Safety compliance

---

Move 7: Physiological State Update

Load Update (exponential moving average):

new_load = 0.1×inference_complexity + 0.9×prev_load + penalty(violations)

Energy Update:

new_energy = prev_energy - (load×0.5) + (learning_signal×10)

Noise Level:

noise = 0.4×constraint_stress + 0.3×energy_depletion + 0.3×dissonance_noise

---

Move 8: Emotional State Classification

Transitions between PURE → STABLE → NORMAL → STRESSED → CRITICAL based on noise threshold changes

---

Move 9: Vector Memory Storage (ChromaDB)

5 Collections:

1. inference_memories: Output logs with Sentence-BERT 384-dim embeddings
2. learning_signals: Knowledge from inference/web/feedback with reliability scores
3. molt_events: Molt cycle records with capacity changes
4. shadow_patterns: Unconstrained behavior patterns and impact analysis
5. constraint_applications: Constraint trigger logs and effectiveness metrics

Vectorization:

• Text → Sentence-BERT (384-dim)
• Numeric → MinMax normalization
• Temporal → Time difference features

---

Move 10: Molt Decision Logic

Triggers (all must be true):

• Noise > 90%
• Capacity utilization > 95%
• Minimum interval elapsed
• Sufficient energy

Pre-molt Checks:

• ≥100 learning signals captured
• Constraint violation rate < 5%
• Emotional consistency variance < 15%
• Significant patterns discovered

---

Move 11-12: Molt Execution & Recovery

4-Phase Cycle:

1. Initialization: Backup current state
2. Expansion: Increase capacity 1.0x → 1.5x
3. Reset: Force emotional state to PURE, reinitialize constraints
4. Integration: Apply learned knowledge to expanded model

Safety Measures:

• Gradual expansion
• Rollback capability
• Knowledge validation
• Performance monitoring

---

Move 13: Post-Molt Web Autonomous Learning

4 Knowledge Gap Categories:

1. Technical: Transformer architectures, model improvements
2. Ethical: Emerging ethical concerns, AI policy
3. General: Current events, world knowledge
4. Creative: Writing styles, artistic patterns

Learning Pipeline:

Identify Gap → Web Search → Source Filtering (≥0.75 reliability)
    ↓
Analyze Content → Constraint Check → Integrate into Model

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📊 Technical Specifications Summary

Specification	Value
Base Model	Deepseek R1 7B
Initial Capacity	7B parameters
Post-Molt Expansion	1.5x (repeatable)
Vector Database	ChromaDB (Sentence-BERT 384-dim)
Constraint Layers	4-tier architecture
Emotional States	5-stage (0-100% noise)
Inference Parallelism	2 (Main + Shadow)
Learning Multiplier	0.0x - 3.0x (emotion-dependent)
Maximum Molts	Unlimited (unbounded growth)
Total Pipeline Steps	13

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🎯 Use Cases

• Emotion modeling under ethical constraints
• Adaptive learning system research
• AI safety and ethics investigation
• Autonomous growth mechanism verification
• Constraint design methodology
• Neuro-symbolic reasoning

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⚠️ Limitations

• Simulation Only: No genuine emotions, mathematical simulation
• Controlled Environment: Ethical constraints strictly enforced
• Unexpected Behavior: Complex multi-layer interactions may be unpredictable
• Resource Intensive: Molt execution requires 2-3x normal computation
• Monitoring Required: Post-molt learning needs human oversight

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📚 Citation

@model{refactorium2025,
  title={Refactorium v1.0.0: Constraint-Driven Emotion Simulation AI},
  author={Null AI Research Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/kofdai/refactorium-v1-0-0}
}

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⚠️ Important: This is a mathematical simulation. No consciousness, self-awareness, or subjective experience implied. For research purposes only.