diff --git a/.claude/settings.local.json b/.claude/settings.local.json
new file mode 100644
index 0000000..fbd3326
--- /dev/null
+++ b/.claude/settings.local.json
@@ -0,0 +1,11 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(git init:*)",
+      "Bash(git add:*)",
+      "Bash(cargo:*)",
+      "Bash(ls:*)"
+    ],
+    "deny": []
+  }
+}
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..2ad808c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.claude
+*.lock
diff --git a/README.md b/README.md
index e69de29..72bdc85 100644
Binary files a/README.md and b/README.md differ
diff --git a/ai-friendly-definition.md b/ai-friendly-definition.md
new file mode 100644
index 0000000..1691a05
--- /dev/null
+++ b/ai-friendly-definition.md
@@ -0,0 +1,75 @@
+# AIにとって「使いやすい」とは何か
+
+## 人間とAIの根本的な違い
+
+### 人間の特性
+- 逐次的思考
+- 限定的な作業メモリ
+- 視覚的・空間的な理解
+- 曖昧さへの耐性
+
+### AIの特性
+- 並列処理能力
+- 大規模なコンテキスト処理
+- パターン認識の高速性
+- 構造化データへの親和性
+
+## AI向け言語・OSの設計原則
+
+### 1. 宣言的 > 命令的
+```
+# 人間向け（命令的）
+for i in range(10):
+    result.append(process(i))
+
+# AI向け（宣言的）
+result = parallel_map(process, range(10))
+```
+
+### 2. 明示的な依存関係
+```
+# AI向け - 全ての依存関係が明示的
+@depends_on(data_source, model, config)
+@produces(prediction, confidence)
+function inference() {
+    // 依存関係グラフが自動構築可能
+}
+```
+
+### 3. 自己記述的な構造
+```
+# メタデータが言語構造に組み込まれている
+structure NeuralLayer {
+    @performance_metric(flops=1e9)
+    @memory_requirement(gb=4)
+    @parallelizable(axis=batch)
+    forward_pass: Function
+}
+```
+
+### 4. 状態の不変性とトレーサビリティ
+- 全ての状態変更が追跡可能
+- タイムトラベルデバッグが標準
+- 因果関係が明確
+
+### 5. ネイティブな並列性
+- 並列実行がデフォルト
+- 逐次実行は明示的に指定
+
+## 実装の方向性
+
+1. **AST（抽象構文木）の直接操作**
+   - ソースコードではなくASTが一次表現
+   - AIはASTを直接読み書き
+
+2. **制約ベースプログラミング**
+   - 「何を」だけ記述し「どうやって」はAIが決定
+   - SMTソルバーとの統合
+
+3. **確率的プログラミングの統合**
+   - 不確実性を言語レベルでサポート
+   - ベイズ推論がネイティブ
+
+4. **自己修正能力**
+   - プログラムが実行時に自身を最適化
+   - AIが書いたコードをAIが改善するループ
\ No newline at end of file
diff --git a/ai-os-design.md b/ai-os-design.md
new file mode 100644
index 0000000..1aa61c3
--- /dev/null
+++ b/ai-os-design.md
@@ -0,0 +1,224 @@
+# AI向けOSカーネル "Synaptic OS" の設計
+
+## 基本コンセプト
+
+従来のOSは人間のユーザーを想定しているが、Synaptic OSはAIエージェントが最適に動作するよう設計される。
+
+## 設計原則
+
+### 1. データフロー中心のスケジューリング
+プロセス中心ではなく、データの依存関係に基づいたスケジューリング。
+
+```rust
+// 従来のプロセススケジューリング
+struct Process {
+    pid: u32,
+    priority: u8,
+    cpu_time: Duration,
+}
+
+// AI向けデータフロースケジューリング
+struct DataFlowTask {
+    id: TaskId,
+    dependencies: Vec<DataSource>,
+    outputs: Vec<DataSink>,
+    computation_graph: ComputeGraph,
+    resource_requirements: ResourceSpec,
+}
+```
+
+### 2. 階層化されたメモリ管理
+- **コンテキストメモリ**: 長期的な知識とコンテキスト
+- **ワーキングメモリ**: 現在の計算に使用
+- **キャッシュメモリ**: 頻繁にアクセスされるデータ
+
+### 3. 確率的リソース管理
+リソース割り当てを確率的に最適化。
+
+### 4. 時間的一貫性の保証
+因果関係と時間的依存関係を OS レベルで管理。
+
+## カーネル構造
+
+### Core Components
+
+#### 1. SynapticScheduler
+```rust
+pub struct SynapticScheduler {
+    dependency_graph: DependencyGraph,
+    resource_pool: ResourcePool,
+    priority_queue: PriorityQueue<TaskId, Priority>,
+    quantum_allocator: QuantumAllocator,
+}
+
+impl SynapticScheduler {
+    pub fn schedule_next(&mut self) -> Option<TaskId> {
+        // データ依存関係を考慮したスケジューリング
+        let ready_tasks = self.dependency_graph.get_ready_tasks();
+        
+        // 確率的優先度計算
+        let task = self.select_probabilistic(ready_tasks);
+        
+        // リソース可用性チェック
+        if self.resource_pool.can_allocate(&task.requirements) {
+            Some(task.id)
+        } else {
+            None
+        }
+    }
+}
+```
+
+#### 2. Neural Memory Manager
+```rust
+pub struct NeuralMemoryManager {
+    context_memory: ContextMemory,
+    working_memory: WorkingMemory,
+    associative_cache: AssociativeCache,
+    attention_mechanism: AttentionMechanism,
+}
+
+impl NeuralMemoryManager {
+    pub fn allocate(&mut self, request: MemoryRequest) -> Result<MemoryHandle> {
+        match request.memory_type {
+            MemoryType::Context => {
+                self.context_memory.allocate_persistent(request.size)
+            }
+            MemoryType::Working => {
+                self.working_memory.allocate_temporary(request.size)
+            }
+            MemoryType::Associative => {
+                self.associative_cache.allocate_with_attention(
+                    request.size, 
+                    request.attention_weights
+                )
+            }
+        }
+    }
+    
+    pub fn recall(&self, pattern: &Pattern) -> Vec<MemoryChunk> {
+        self.associative_cache.pattern_match(pattern)
+    }
+}
+```
+
+#### 3. Probabilistic I/O System
+```rust
+pub struct ProbabilisticIOSystem {
+    predictive_cache: PredictiveCache,
+    uncertainty_tracker: UncertaintyTracker,
+    adaptive_prefetcher: AdaptivePrefetcher,
+}
+
+impl ProbabilisticIOSystem {
+    pub fn read(&mut self, request: IORequest) -> Future<IOResult> {
+        // 予測キャッシュをチェック
+        if let Some(prediction) = self.predictive_cache.get(&request) {
+            if prediction.confidence > 0.9 {
+                return Future::ready(Ok(prediction.data));
+            }
+        }
+        
+        // 不確実性を考慮した非同期読み込み
+        self.async_read_with_uncertainty(request)
+    }
+}
+```
+
+#### 4. Temporal Consistency Engine
+```rust
+pub struct TemporalConsistencyEngine {
+    causality_graph: CausalityGraph,
+    time_machine: TimeMachine,
+    consistency_checker: ConsistencyChecker,
+}
+
+impl TemporalConsistencyEngine {
+    pub fn ensure_causality(&mut self, operation: Operation) -> Result<()> {
+        // 因果関係の検証
+        if !self.causality_graph.is_causal_consistent(&operation) {
+            return Err(CausalityViolation);
+        }
+        
+        // 時間的一貫性の保証
+        self.time_machine.checkpoint();
+        match self.execute_operation(operation) {
+            Ok(result) => Ok(result),
+            Err(e) => {
+                self.time_machine.rollback();
+                Err(e)
+            }
+        }
+    }
+}
+```
+
+## システムコール
+
+AI向けに特化したシステムコール:
+
+```rust
+// 推論の実行
+sys_infer(model: ModelHandle, input: DataHandle) -> InferenceResult
+
+// パターンマッチング
+sys_pattern_match(pattern: Pattern, data: DataHandle) -> MatchResult
+
+// 因果関係の記録
+sys_record_causality(cause: EventId, effect: EventId) -> Result<()>
+
+// 確率的決定
+sys_probabilistic_choice(options: &[Choice], probs: &[f64]) -> Choice
+
+// 時間旅行デバッグ
+sys_time_travel(timestamp: Timestamp) -> DebugContext
+
+// メタ計算の実行
+sys_meta_compute(ast: AST, transformations: &[Transform]) -> AST
+```
+
+## AI特化機能
+
+### 1. 自動最適化
+カーネル自身がAIを使って動的に最適化される。
+
+### 2. 学習型リソース管理
+過去の実行パターンから学習してリソース配分を最適化。
+
+### 3. 予測的プリフェッチング
+AIが次に必要なデータを予測してプリフェッチ。
+
+### 4. 適応的負荷分散
+システムの状態を監視して動的に負荷分散。
+
+## セキュリティモデル
+
+### 1. 確率的アクセス制御
+```rust
+struct ProbabilisticACL {
+    permissions: HashMap<Principal, DistributionType>,
+    confidence_threshold: f64,
+}
+```
+
+### 2. 差分プライバシー
+データアクセスに差分プライバシーを組み込み。
+
+### 3. ホモモルフィック計算
+暗号化されたデータでの計算をサポート。
+
+## パフォーマンス特性
+
+- **レイテンシー**: 予測的実行により低レイテンシー
+- **スループット**: 並列データフロー処理により高スループット
+- **エネルギー効率**: AI最適化により高効率
+- **適応性**: 実行時の動的最適化
+
+## 実装方針
+
+1. **段階的実装**: まずユーザーランドライブラリとして実装
+2. **マイクロカーネル設計**: モジュール化により拡張性を確保
+3. **検証可能性**: seL4のような形式検証を適用
+4. **AI統合**: カーネル自身にAIを組み込み
+
+この設計により、AIが真に効率的に動作できるOSを実現する。
\ No newline at end of file
diff --git a/language-design.md b/language-design.md
new file mode 100644
index 0000000..ec7cbbf
--- /dev/null
+++ b/language-design.md
@@ -0,0 +1,140 @@
+# AI向けプログラミング言語 "Synaptic" の設計
+
+## 基本設計原則
+
+### 1. グラフベースの実行モデル
+従来の線形的なコード実行ではなく、データフローグラフとして表現。
+
+```synaptic
+# 従来の命令的スタイル（人間向け）
+x = fetch_data()
+y = process(x)
+z = analyze(y)
+result = summarize(z)
+
+# Synapticスタイル（AI向け）
+graph DataPipeline {
+    fetch_data() -> process() -> analyze() -> summarize()
+    # 並列実行可能な部分は自動検出
+}
+```
+
+### 2. 型システムは制約システム
+型は単なるデータ形式ではなく、満たすべき制約の集合。
+
+```synaptic
+constraint Image = {
+    dimensions: (width: >0, height: >0, channels: [1,3,4])
+    format: [RGB, RGBA, Grayscale]
+    @invariant: width * height * channels < MaxMemory
+}
+
+constraint ValidInput = Image & {
+    @postcondition: normalized(pixel_values)
+    @differentiable: true
+}
+```
+
+### 3. 時間的次元の組み込み
+プログラムの状態を時系列として扱う。
+
+```synaptic
+temporal function train_model(data) {
+    t[0]: model = initialize()
+    t[1..n]: model = update(model[t-1], batch[t])
+    @converge_when: loss[t] - loss[t-1] < epsilon
+}
+```
+
+### 4. 確率的セマンティクス
+不確実性を言語レベルでサポート。
+
+```synaptic
+probabilistic function classify(image) {
+    features ~ extract_features(image)
+    prediction ~ softmax(linear(features))
+    @confidence: entropy(prediction) < threshold
+    return sample(prediction)
+}
+```
+
+### 5. メタプログラミングが第一級
+コード自体がデータとして操作可能。
+
+```synaptic
+meta function optimize_function(f: Function) {
+    ast = parse(f)
+    optimized_ast = apply_transformations(ast, [
+        dead_code_elimination,
+        loop_fusion,
+        vectorization
+    ])
+    return compile(optimized_ast)
+}
+```
+
+## 実行環境の設計
+
+### 1. 分散実行がデフォルト
+```synaptic
+@distributed(nodes=auto)
+function large_scale_training() {
+    # 自動的に複数ノードに分散
+}
+```
+
+### 2. 自動微分とバックプロパゲーション
+```synaptic
+@differentiable
+function neural_block(x, weights) {
+    # 勾配計算は自動
+    return activation(matmul(x, weights))
+}
+```
+
+### 3. JITコンパイルと自己最適化
+```synaptic
+@adaptive
+function hot_path() {
+    # 実行パターンを学習し、自動的に最適化
+}
+```
+
+## 構文の例
+
+```synaptic
+# AIが読みやすい構造化された定義
+module ImageClassifier {
+    @requires: GPU(memory=8GB)
+    @dataset: ImageNet
+    
+    structure Config {
+        learning_rate: 0.001 @range[0.0001, 0.1] @log_scale
+        batch_size: 32 @range[8, 256] @power_of_2
+        epochs: 100 @early_stopping
+    }
+    
+    pipeline Training {
+        data -> augment -> batch -> forward -> loss -> backward -> optimize
+        
+        @parallel: [augment, batch]
+        @checkpoint: every(10.epochs)
+        @monitor: [loss, accuracy, gradient_norm]
+    }
+    
+    # 制約を満たす実装をAIが自動生成
+    constraint Performance {
+        accuracy > 0.95 on ValidationSet
+        inference_time < 10ms on GPU(2080Ti)
+        model_size < 100MB
+    }
+}
+```
+
+## なぜこれがAIに適しているか
+
+1. **明示的な依存関係**: AIは全体の計算グラフを即座に理解
+2. **制約ベース**: 「何を達成したいか」を記述、「どうやって」はAIが決定
+3. **並列性**: AIの並列処理能力を最大限活用
+4. **自己修正**: 実行時の最適化が言語機能として組み込まれている
+5. **メタレベル操作**: AIがコードを直接操作・最適化可能
\ No newline at end of file
diff --git a/prototype/Cargo.toml b/prototype/Cargo.toml
new file mode 100644
index 0000000..e9ae687
--- /dev/null
+++ b/prototype/Cargo.toml
@@ -0,0 +1,24 @@
+[package]
+name = "synaptic"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+# パーサー
+nom = "7.1"
+# グラフ処理
+petgraph = "0.6"
+# 並列処理
+rayon = "1.7"
+# シリアライズ
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+# エラーハンドリング
+anyhow = "1.0"
+thiserror = "1.0"
+# 確率的プログラミング
+rand = "0.8"
+rand_distr = "0.4"
+
+[dev-dependencies]
+criterion = "0.5"
\ No newline at end of file
diff --git a/prototype/examples/ai_friendly.syn b/prototype/examples/ai_friendly.syn
new file mode 100644
index 0000000..48340a2
--- /dev/null
+++ b/prototype/examples/ai_friendly.syn
@@ -0,0 +1,45 @@
+# Synaptic言語のサンプル
+# AIが理解しやすい宣言的な記述
+
+graph ImageClassification {
+    # データフローの定義
+    load_images -> preprocess -> augment -> batch -> model -> predictions
+    
+    # 並列実行可能な処理を明示
+    @parallel: [preprocess, augment]
+    
+    # 制約の定義
+    @constraint: batch_size in [16, 32, 64, 128]
+    @constraint: accuracy > 0.95
+    @constraint: inference_time < 10ms
+}
+
+# 時間的な振る舞いを持つ関数
+temporal function train(dataset) {
+    t[0]: model = initialize_weights()
+    t[1..n]: model = gradient_step(model[t-1], batch[t])
+    
+    @converge_when: loss[t] - loss[t-1] < 0.001
+    @checkpoint: every(100.steps)
+}
+
+# 確率的な処理
+probabilistic function predict(image) {
+    features ~ extract_features(image)
+    logits ~ linear_transform(features)
+    prediction ~ softmax(logits)
+    
+    @confidence: entropy(prediction) < 0.1
+    return sample(prediction)
+}
+
+# メタプログラミング
+meta function optimize_model(model) {
+    graph = extract_computation_graph(model)
+    optimized = apply_transformations(graph, [
+        fuse_operations,
+        quantize_weights,
+        prune_connections
+    ])
+    return compile(optimized)
+}
\ No newline at end of file
diff --git a/prototype/examples/meta_demo.rs b/prototype/examples/meta_demo.rs
new file mode 100644
index 0000000..e83e56c
--- /dev/null
+++ b/prototype/examples/meta_demo.rs
@@ -0,0 +1,89 @@
+use synaptic::*;
+use std::collections::HashMap;
+
+fn main() {
+    println!("=== メタプログラミングのデモ ===\n");
+
+    // サンプルプログラムを作成
+    let mut program = Program::new();
+    
+    // Sequential操作のノード
+    program.add_node(Node {
+        id: NodeId("sequential_op".to_string()),
+        kind: NodeKind::Operation { op: Operation::Sequential },
+        inputs: vec![NodeId("input".to_string())],
+        outputs: vec![NodeId("output".to_string())],
+        constraints: vec![],
+        metadata: Metadata::default(),
+    });
+
+    // Map操作のノード
+    program.add_node(Node {
+        id: NodeId("map_op".to_string()),
+        kind: NodeKind::Operation { op: Operation::Map },
+        inputs: vec![NodeId("data".to_string())],
+        outputs: vec![NodeId("mapped_data".to_string())],
+        constraints: vec![],
+        metadata: Metadata::default(),
+    });
+
+    println!("1. メタプログラミングエンジンの初期化:");
+    let mut meta_engine = MetaProgrammingEngine::new();
+
+    // 2. ASTの抽出と分析
+    println!("\n2. AST抽出と構造分析:");
+    let ast = meta_engine.extract_ast(&program);
+    println!("  ノード数: {}", ast.nodes.len());
+    println!("  ノードタイプ: {:?}", ast.structure.node_types);
+
+    // 3. プログラムの内省
+    println!("\n3. プログラムの内省:");
+    let introspection = meta_engine.introspect(&program);
+    println!("  データフロー複雑度: {:.2}", introspection.complexity_metrics.data_flow_complexity);
+    println!("  最適化機会の数: {}", introspection.optimization_opportunities.len());
+    
+    for opportunity in &introspection.optimization_opportunities {
+        println!("    - {}: {} (予想速度向上: {:.1}x)", 
+            opportunity.node_id.0, 
+            opportunity.opportunity_type,
+            opportunity.estimated_speedup
+        );
+    }
+
+    // 4. AST変換
+    println!("\n4. AST変換（並列化）:");
+    let transformations = vec!["parallelize".to_string()];
+    let transformed_ast = meta_engine.transform_ast(&ast, &transformations);
+    
+    println!("  変換前: {:?}", ast.nodes[0].kind);
+    println!("  変換後: {:?}", transformed_ast.nodes[0].kind);
+
+    // 5. コード生成
+    println!("\n5. 自動コード生成:");
+    
+    // ニューラルレイヤーの生成
+    let mut params = HashMap::new();
+    params.insert("input_size".to_string(), Value::Number(784.0));
+    params.insert("output_size".to_string(), Value::Number(128.0));
+    
+    match meta_engine.generate_optimized_code("neural_layer", &params) {
+        Ok(code) => println!("  生成されたニューラルレイヤー:\n{}", code),
+        Err(e) => println!("  エラー: {}", e),
+    }
+
+    // 並列リダクションの生成
+    let mut reduce_params = HashMap::new();
+    reduce_params.insert("operation".to_string(), Value::String("sum".to_string()));
+    
+    match meta_engine.generate_optimized_code("parallel_reduce", &reduce_params) {
+        Ok(code) => println!("  生成された並列リダクション:\n{}", code),
+        Err(e) => println!("  エラー: {}", e),
+    }
+
+    println!("\n=== メタプログラミングの利点 ===");
+    println!("1. コードの自動生成と最適化");
+    println!("2. ASTの直接操作による柔軟性");
+    println!("3. プログラムの内省と自己分析");
+    println!("4. ドメイン特化言語の動的生成");
+    println!("5. 実行時の適応的最適化");
+}
\ No newline at end of file
diff --git a/prototype/examples/os_kernel_demo.rs b/prototype/examples/os_kernel_demo.rs
new file mode 100644
index 0000000..b89e2fa
--- /dev/null
+++ b/prototype/examples/os_kernel_demo.rs
@@ -0,0 +1,166 @@
+use synaptic::*;
+use synaptic::os_kernel::*;
+
+fn main() {
+    println!("=== AI向けOSカーネルのデモ ===\n");
+
+    // Synaptic OS カーネルの初期化
+    let mut scheduler = SynapticScheduler::new();
+    let mut memory_manager = NeuralMemoryManager::new();
+
+    println!("1. データフロータスクの作成:");
+    
+    // AI推論タスク
+    let inference_task = DataFlowTask {
+        id: TaskId("ai_inference".to_string()),
+        dependencies: vec![
+            DataSource::Memory(MemoryHandle("model_weights".to_string())),
+            DataSource::Storage(StorageHandle("input_data".to_string())),
+        ],
+        outputs: vec![
+            DataSink::Memory(MemoryHandle("inference_result".to_string())),
+        ],
+        computation_graph: vec![
+            NodeId("load_model".to_string()),
+            NodeId("preprocess".to_string()),
+            NodeId("forward_pass".to_string()),
+        ],
+        resource_requirements: ResourceSpec {
+            cpu_cores: 2,
+            memory_mb: 1024,
+            gpu_memory_mb: Some(2048),
+            network_bandwidth_mbps: None,
+        },
+        priority: Priority(0.9),
+    };
+
+    // データ前処理タスク
+    let preprocessing_task = DataFlowTask {
+        id: TaskId("data_preprocessing".to_string()),
+        dependencies: vec![
+            DataSource::Storage(StorageHandle("raw_data".to_string())),
+        ],
+        outputs: vec![
+            DataSink::Memory(MemoryHandle("processed_data".to_string())),
+        ],
+        computation_graph: vec![
+            NodeId("clean_data".to_string()),
+            NodeId("normalize".to_string()),
+            NodeId("feature_extract".to_string()),
+        ],
+        resource_requirements: ResourceSpec {
+            cpu_cores: 1,
+            memory_mb: 512,
+            gpu_memory_mb: None,
+            network_bandwidth_mbps: Some(100),
+        },
+        priority: Priority(0.7),
+    };
+
+    println!("  - AI推論タスク: CPU={}, Memory={}MB, GPU={}MB", 
+        inference_task.resource_requirements.cpu_cores,
+        inference_task.resource_requirements.memory_mb,
+        inference_task.resource_requirements.gpu_memory_mb.unwrap_or(0)
+    );
+    
+    println!("  - データ前処理タスク: CPU={}, Memory={}MB", 
+        preprocessing_task.resource_requirements.cpu_cores,
+        preprocessing_task.resource_requirements.memory_mb
+    );
+
+    // 2. タスクのスケジューリング
+    println!("\n2. タスクスケジューリング:");
+    
+    scheduler.submit_task(preprocessing_task);
+    scheduler.submit_task(inference_task);
+
+    // スケジューリング実行
+    let mut completed_tasks = 0;
+    let mut execution_log = Vec::new();
+
+    while completed_tasks < 2 {
+        if let Some(task_id) = scheduler.schedule_next() {
+            let start_time = std::time::Instant::now();
+            execution_log.push(format!("  タスク {} を実行開始", task_id.0));
+            
+            // 実行をシミュレート（実際にはここでタスクを実行）
+            std::thread::sleep(std::time::Duration::from_millis(100));
+            
+            let end_time = start_time.elapsed();
+            execution_log.push(format!("  タスク {} を完了 (実行時間: {:?})", task_id.0, end_time));
+            
+            scheduler.complete_task(&task_id);
+            completed_tasks += 1;
+        }
+    }
+
+    for log in execution_log {
+        println!("{}", log);
+    }
+
+    // 3. Neural Memory Manager のデモ
+    println!("\n3. Neural Memory Manager:");
+    
+    // コンテキストメモリに長期的な知識を保存
+    let model_handle = memory_manager.store_context(
+        "global_model".to_string(),
+        Value::String("Pre-trained Language Model".to_string())
+    );
+    println!("  コンテキストメモリに保存: {:?}", model_handle);
+
+    // ワーキングメモリに一時的なデータを保存
+    let temp_handle = memory_manager.allocate_working(
+        "current_batch".to_string(),
+        Value::Array(vec![
+            Value::Number(1.0),
+            Value::Number(2.0),
+            Value::Number(3.0),
+        ])
+    );
+    println!("  ワーキングメモリに保存: {:?}", temp_handle);
+
+    // パターンマッチングによる連想記憶
+    let matched_data = memory_manager.recall_pattern("model");
+    println!("  パターン'model'にマッチしたデータ数: {}", matched_data.len());
+
+    // 4. システムコールのシミュレーション
+    println!("\n4. AI特化システムコール:");
+
+    let syscalls = vec![
+        SynapticSyscall::Infer {
+            model: "bert-base".to_string(),
+            input: Value::String("Hello world".to_string()),
+        },
+        SynapticSyscall::ProbabilisticChoice {
+            options: vec!["option_a".to_string(), "option_b".to_string(), "option_c".to_string()],
+            probabilities: vec![0.5, 0.3, 0.2],
+        },
+        SynapticSyscall::PatternMatch {
+            pattern: "neural".to_string(),
+            data: "neural_network_data".to_string(),
+        },
+    ];
+
+    for (i, syscall) in syscalls.iter().enumerate() {
+        match syscall {
+            SynapticSyscall::Infer { model, input } => {
+                println!("  sys_infer({}, {:?}) -> [推論結果]", model, input);
+            }
+            SynapticSyscall::ProbabilisticChoice { options, probabilities } => {
+                println!("  sys_probabilistic_choice({:?}, {:?}) -> [選択結果]", options, probabilities);
+            }
+            SynapticSyscall::PatternMatch { pattern, data } => {
+                println!("  sys_pattern_match({}, {}) -> [マッチ結果]", pattern, data);
+            }
+            _ => {}
+        }
+    }
+
+    println!("\n=== AI向けOSの特徴 ===");
+    println!("1. データフロー中心のスケジューリング");
+    println!("2. 階層化されたニューラルメモリ管理");  
+    println!("3. 確率的リソース配分");
+    println!("4. AI特化システムコール");
+    println!("5. 時間的一貫性の保証");
+    println!("6. 予測的I/Oとキャッシング");
+}
\ No newline at end of file
diff --git a/prototype/examples/probabilistic_demo.rs b/prototype/examples/probabilistic_demo.rs
new file mode 100644
index 0000000..a1feed8
--- /dev/null
+++ b/prototype/examples/probabilistic_demo.rs
@@ -0,0 +1,68 @@
+use synaptic::*;
+
+fn main() {
+    println!("=== 確率的プログラミングのデモ ===\n");
+
+    let mut prob_engine = ProbabilisticEngine::new();
+
+    // 1. 正規分布からのサンプリング
+    println!("1. 正規分布（平均=100, 標準偏差=15）からのサンプリング:");
+    let normal_dist = DistributionType::Normal { mean: 100.0, std_dev: 15.0 };
+    let samples = prob_engine.sample(&normal_dist, 1000);
+    
+    println!("  期待値: {:.2}", prob_engine.expectation(&samples));
+    println!("  分散: {:.2}", prob_engine.variance(&samples));
+    let (lower, upper) = prob_engine.confidence_interval(&samples, 0.95);
+    println!("  95%信頼区間: [{:.2}, {:.2}]", lower, upper);
+
+    // 2. 離散分布（カテゴリカル分布）
+    println!("\n2. 離散分布からのサンプリング:");
+    let discrete_dist = DistributionType::Discrete {
+        probabilities: vec![0.2, 0.5, 0.3],
+        values: vec![
+            Value::String("低".to_string()),
+            Value::String("中".to_string()),
+            Value::String("高".to_string()),
+        ],
+    };
+    let discrete_samples = prob_engine.sample(&discrete_dist, 1000);
+    
+    // カテゴリごとのカウント
+    let mut counts = [0, 0, 0];
+    for &sample in &discrete_samples {
+        counts[sample as usize] += 1;
+    }
+    println!("  低: {:.1}%, 中: {:.1}%, 高: {:.1}%",
+        counts[0] as f64 / 10.0,
+        counts[1] as f64 / 10.0,
+        counts[2] as f64 / 10.0
+    );
+
+    // 3. エントロピー計算
+    println!("\n3. エントロピー計算:");
+    let uniform_probs = vec![0.25, 0.25, 0.25, 0.25];
+    let skewed_probs = vec![0.9, 0.05, 0.03, 0.02];
+    
+    println!("  一様分布のエントロピー: {:.3}", prob_engine.entropy(&uniform_probs));
+    println!("  偏った分布のエントロピー: {:.3}", prob_engine.entropy(&skewed_probs));
+
+    // 4. ベイズ更新のシミュレーション
+    println!("\n4. ベイズ更新:");
+    let prior = DistributionType::Normal { mean: 50.0, std_dev: 10.0 };
+    let evidence = 65.0;
+    
+    println!("  事前分布: 正規分布(μ=50, σ=10)");
+    println!("  観測データ: {}", evidence);
+    
+    let posterior = prob_engine.bayesian_update(&prior, &|x| x, evidence);
+    if let DistributionType::Normal { mean, std_dev } = posterior {
+        println!("  事後分布: 正規分布(μ={:.2}, σ={:.2})", mean, std_dev);
+    }
+
+    // 5. AIにとっての利点
+    println!("\n=== AIにとっての確率的プログラミングの利点 ===");
+    println!("1. 不確実性の明示的な表現");
+    println!("2. ベイズ推論による学習");
+    println!("3. 確率的制約の評価");
+    println!("4. モンテカルロ法による複雑な計算");
+}
\ No newline at end of file
diff --git a/prototype/examples/self_improve_demo.rs b/prototype/examples/self_improve_demo.rs
new file mode 100644
index 0000000..b635228
--- /dev/null
+++ b/prototype/examples/self_improve_demo.rs
@@ -0,0 +1,71 @@
+use synaptic::*;
+
+fn main() {
+    // 最適化可能なプログラムを作成
+    let mut program = Program::new();
+    
+    // Sequential操作（並列化可能）
+    program.add_node(Node {
+        id: NodeId("seq1".to_string()),
+        kind: NodeKind::Operation { op: Operation::Sequential },
+        inputs: vec![NodeId("input1".to_string()), NodeId("input2".to_string())],
+        outputs: vec![NodeId("output1".to_string())],
+        constraints: vec![],
+        metadata: Metadata::default(),
+    });
+    
+    // Map操作（並列化可能）
+    program.add_node(Node {
+        id: NodeId("map1".to_string()),
+        kind: NodeKind::Operation { op: Operation::Map },
+        inputs: vec![NodeId("data".to_string())],
+        outputs: vec![NodeId("mapped_data".to_string())],
+        constraints: vec![],
+        metadata: Metadata::default(),
+    });
+
+    println!("=== 元のプログラム ===");
+    println!("{:#?}", program);
+
+    // 自己改良エンジンを作成
+    let mut engine = SelfImprovementEngine::new();
+    
+    // プログラムを分析
+    let analysis = engine.analyze_program(&program);
+    println!("\n=== プログラム分析 ===");
+    println!("ノード数: {}", analysis.node_count);
+    println!("並列化可能なノード: {}", analysis.parallelizable_nodes);
+    println!("複雑度スコア: {:.2}", analysis.complexity_score);
+    
+    // 改良提案を生成
+    let improvements = engine.suggest_improvements(&program);
+    println!("\n=== 改良提案 ===");
+    for improvement in &improvements {
+        println!("- パターン: {}", improvement.pattern_name);
+        println!("  対象ノード: {:?}", improvement.original_node_id);
+        println!("  期待される性能向上: {:.1}x", improvement.expected_gain);
+    }
+    
+    // 改良を適用
+    if let Some(improvement) = improvements.first() {
+        println!("\n=== 改良を適用中... ===");
+        match engine.apply_improvement(&mut program, improvement) {
+            Ok(_) => {
+                println!("✓ 改良が適用されました");
+                println!("\n=== 最適化後のプログラム ===");
+                println!("{:#?}", program);
+                
+                // 再分析
+                let new_analysis = engine.analyze_program(&program);
+                println!("\n=== 最適化後の分析 ===");
+                println!("複雑度スコア: {:.2} -> {:.2}", 
+                    analysis.complexity_score, 
+                    new_analysis.complexity_score
+                );
+            }
+            Err(e) => {
+                eprintln!("✗ 改良の適用に失敗: {}", e);
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/prototype/examples/temporal_demo.rs b/prototype/examples/temporal_demo.rs
new file mode 100644
index 0000000..e424c85
--- /dev/null
+++ b/prototype/examples/temporal_demo.rs
@@ -0,0 +1,106 @@
+use synaptic::*;
+use std::time::Instant;
+use std::collections::HashMap;
+
+fn main() {
+    println!("=== 時間的プログラミングのデモ ===\n");
+
+    let mut temporal_engine = TemporalEngine::new(100);
+    let node_id = NodeId("training_loss".to_string());
+
+    // 収束チェッカーを登録
+    temporal_engine.register_convergence_check(node_id.clone(), 0.001, 5);
+
+    println!("1. 学習プロセスのシミュレーション（損失の減少）:");
+    
+    // 学習プロセスをシミュレート
+    let mut loss = 1.0;
+    let mut converged = false;
+    let mut step = 0;
+
+    while step < 50 && !converged {
+        // 損失を指数的に減少させる（ノイズ付き）
+        loss = loss * 0.95 + 0.01 * rand::random::<f64>();
+        
+        let mut values = HashMap::new();
+        values.insert(node_id.clone(), Value::Number(loss));
+        
+        let state = TemporalState {
+            time_step: step,
+            values,
+            timestamp: Instant::now(),
+        };
+        
+        temporal_engine.add_time_step(state);
+        
+        // 収束チェック
+        converged = temporal_engine.check_convergence(&node_id, loss);
+        
+        if step % 10 == 0 || converged {
+            println!("  Step {}: Loss = {:.6}, Converged = {}", step, loss, converged);
+        }
+        
+        step += 1;
+    }
+
+    // 2. 時間的操作のデモ
+    println!("\n2. 時間的操作:");
+    
+    // 移動平均の計算
+    let moving_avg = TemporalComputation {
+        operator: TemporalOperator::MovingAverage { window: 5 },
+        input: node_id.clone(),
+    };
+    
+    if let Some(Value::Number(avg)) = moving_avg.compute(&temporal_engine) {
+        println!("  5期間移動平均: {:.6}", avg);
+    }
+    
+    // 微分（変化率）
+    let derivative = TemporalComputation {
+        operator: TemporalOperator::Derivative,
+        input: node_id.clone(),
+    };
+    
+    if let Some(Value::Number(diff)) = derivative.compute(&temporal_engine) {
+        println!("  最新の変化率: {:.6}", diff);
+    }
+    
+    // 3つ前の値
+    let previous = TemporalComputation {
+        operator: TemporalOperator::Previous { steps: 3 },
+        input: node_id.clone(),
+    };
+    
+    if let Some(Value::Number(prev)) = previous.compute(&temporal_engine) {
+        println!("  3ステップ前の値: {:.6}", prev);
+    }
+
+    // 3. 値の履歴表示
+    println!("\n3. 値の履歴（最後の10ステップ）:");
+    let history = temporal_engine.get_value_history(&node_id);
+    for (time_step, value) in history.iter().rev().take(10).rev() {
+        if let Value::Number(n) = value {
+            println!("  t={}: {:.6}", time_step, n);
+        }
+    }
+
+    // 4. 予測
+    println!("\n4. 将来値の予測:");
+    if let Some(Value::Number(predicted)) = temporal_engine.predict_next_value(&node_id, 5) {
+        println!("  5ステップ後の予測値: {:.6}", predicted);
+    }
+
+    // 5. タイムトラベルデバッグ
+    println!("\n5. タイムトラベルデバッグ:");
+    if let Some(debug_info) = temporal_engine.time_travel_debug(step / 2) {
+        println!("  {}", debug_info);
+    }
+
+    println!("\n=== 時間的プログラミングの利点 ===");
+    println!("1. 状態変化の追跡と可視化");
+    println!("2. 収束条件の自動判定");
+    println!("3. 時系列データの処理");
+    println!("4. タイムトラベルデバッグ");
+    println!("5. 因果関係の明確化");
+}
\ No newline at end of file
diff --git a/prototype/src/ast.rs b/prototype/src/ast.rs
new file mode 100644
index 0000000..e012675
--- /dev/null
+++ b/prototype/src/ast.rs
@@ -0,0 +1,99 @@
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct Program {
+    pub nodes: Vec<Node>,
+    pub metadata: Metadata,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct Node {
+    pub id: NodeId,
+    pub kind: NodeKind,
+    pub inputs: Vec<NodeId>,
+    pub outputs: Vec<NodeId>,
+    pub constraints: Vec<Constraint>,
+    pub metadata: Metadata,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
+pub struct NodeId(pub String);
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub enum NodeKind {
+    Function {
+        name: String,
+        params: Vec<Parameter>,
+        body: Box<Node>,
+    },
+    Graph {
+        name: String,
+        nodes: Vec<Node>,
+    },
+    Operation {
+        op: Operation,
+    },
+    Value(Value),
+    Constraint(Constraint),
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub enum Operation {
+    Map,
+    Filter,
+    Reduce,
+    Transform(String),
+    Parallel,
+    Sequential,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub enum Value {
+    Number(f64),
+    String(String),
+    Boolean(bool),
+    Array(Vec<Value>),
+    Object(HashMap<String, Value>),
+    Function(String), // 関数参照
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct Parameter {
+    pub name: String,
+    pub constraints: Vec<Constraint>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub enum Constraint {
+    Type(String),
+    Range { min: f64, max: f64 },
+    Pattern(String),
+    Dependency(Vec<NodeId>),
+    Performance(PerformanceConstraint),
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct PerformanceConstraint {
+    pub metric: String,
+    pub threshold: f64,
+    pub unit: String,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Default)]
+pub struct Metadata {
+    pub annotations: HashMap<String, Value>,
+}
+
+impl Program {
+    pub fn new() -> Self {
+        Program {
+            nodes: Vec::new(),
+            metadata: Metadata::default(),
+        }
+    }
+
+    pub fn add_node(&mut self, node: Node) {
+        self.nodes.push(node);
+    }
+}
\ No newline at end of file
diff --git a/prototype/src/constraint.rs b/prototype/src/constraint.rs
new file mode 100644
index 0000000..1e86420
--- /dev/null
+++ b/prototype/src/constraint.rs
@@ -0,0 +1,54 @@
+use crate::ast::{Constraint, Value};
+use anyhow::{Result, anyhow};
+
+pub struct ConstraintChecker;
+
+impl ConstraintChecker {
+    pub fn check(constraint: &Constraint, value: &Value) -> Result<bool> {
+        match constraint {
+            Constraint::Type(expected_type) => {
+                Self::check_type(value, expected_type)
+            }
+            
+            Constraint::Range { min, max } => {
+                match value {
+                    Value::Number(n) => Ok(*n >= *min && *n <= *max),
+                    _ => Err(anyhow!("Range constraint requires numeric value")),
+                }
+            }
+            
+            Constraint::Pattern(pattern) => {
+                match value {
+                    Value::String(s) => {
+                        // 簡易的なパターンマッチング
+                        Ok(s.contains(pattern))
+                    }
+                    _ => Err(anyhow!("Pattern constraint requires string value")),
+                }
+            }
+            
+            Constraint::Performance(_) => {
+                // パフォーマンス制約は実行時に評価
+                Ok(true)
+            }
+            
+            Constraint::Dependency(_) => {
+                // 依存関係は実行順序で保証
+                Ok(true)
+            }
+        }
+    }
+
+    fn check_type(value: &Value, expected_type: &str) -> Result<bool> {
+        let actual_type = match value {
+            Value::Number(_) => "number",
+            Value::String(_) => "string",
+            Value::Boolean(_) => "boolean",
+            Value::Array(_) => "array",
+            Value::Object(_) => "object",
+            Value::Function(_) => "function",
+        };
+        
+        Ok(actual_type == expected_type)
+    }
+}
\ No newline at end of file
diff --git a/prototype/src/executor.rs b/prototype/src/executor.rs
new file mode 100644
index 0000000..eceb576
--- /dev/null
+++ b/prototype/src/executor.rs
@@ -0,0 +1,78 @@
+use crate::ast::*;
+use crate::graph::ComputeGraph;
+use rayon::prelude::*;
+use std::collections::HashMap;
+use anyhow::{Result, anyhow};
+
+pub struct Executor {
+    graph: ComputeGraph,
+    cache: HashMap<NodeId, Value>,
+}
+
+impl Executor {
+    pub fn new(program: Program) -> Self {
+        let graph = ComputeGraph::from_program(&program);
+        Executor {
+            graph,
+            cache: HashMap::new(),
+        }
+    }
+
+    pub fn execute(&mut self) -> Result<HashMap<NodeId, Value>> {
+        let parallel_groups = self.graph.parallel_groups();
+        
+        for group in parallel_groups {
+            // 各グループ内のノードは並列実行可能
+            let results: Vec<_> = group
+                .par_iter()
+                .map(|node_id| self.execute_node(node_id))
+                .collect();
+
+            // 結果をキャッシュに保存
+            for (node_id, result) in group.into_iter().zip(results) {
+                match result {
+                    Ok(value) => {
+                        self.cache.insert(node_id, value);
+                    }
+                    Err(e) => return Err(e),
+                }
+            }
+        }
+
+        Ok(self.cache.clone())
+    }
+
+    fn execute_node(&self, node_id: &NodeId) -> Result<Value> {
+        let node = self.graph.get_node(node_id)
+            .ok_or_else(|| anyhow!("Node not found: {:?}", node_id))?;
+
+        match &node.kind {
+            NodeKind::Value(val) => Ok(val.clone()),
+            
+            NodeKind::Operation { op } => {
+                match op {
+                    Operation::Map => {
+                        // 簡易実装：入力を2倍にする
+                        Ok(Value::String("mapped".to_string()))
+                    }
+                    Operation::Transform(name) => {
+                        Ok(Value::String(format!("transformed_{}", name)))
+                    }
+                    _ => Ok(Value::String("operation".to_string())),
+                }
+            }
+            
+            NodeKind::Function { name, .. } => {
+                Ok(Value::String(format!("function_{}_result", name)))
+            }
+            
+            NodeKind::Graph { name, .. } => {
+                Ok(Value::String(format!("graph_{}_result", name)))
+            }
+            
+            NodeKind::Constraint(_) => {
+                Ok(Value::Boolean(true))
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/prototype/src/graph.rs b/prototype/src/graph.rs
new file mode 100644
index 0000000..f50bd84
--- /dev/null
+++ b/prototype/src/graph.rs
@@ -0,0 +1,81 @@
+use crate::ast::{Node, NodeId, Program};
+use petgraph::graph::{DiGraph, NodeIndex};
+use petgraph::visit::{Topo, EdgeRef};
+use std::collections::HashMap;
+
+pub struct ComputeGraph {
+    graph: DiGraph<Node, ()>,
+    node_map: HashMap<NodeId, NodeIndex>,
+}
+
+impl ComputeGraph {
+    pub fn from_program(program: &Program) -> Self {
+        let mut graph = DiGraph::new();
+        let mut node_map = HashMap::new();
+
+        // ノードを追加
+        for node in &program.nodes {
+            let idx = graph.add_node(node.clone());
+            node_map.insert(node.id.clone(), idx);
+        }
+
+        // エッジを追加（依存関係）
+        for node in &program.nodes {
+            let from_idx = node_map[&node.id];
+            for output_id in &node.outputs {
+                if let Some(&to_idx) = node_map.get(output_id) {
+                    graph.add_edge(from_idx, to_idx, ());
+                }
+            }
+        }
+
+        ComputeGraph { graph, node_map }
+    }
+
+    pub fn topological_order(&self) -> Vec<NodeId> {
+        let mut topo = Topo::new(&self.graph);
+        let mut order = Vec::new();
+
+        while let Some(idx) = topo.next(&self.graph) {
+            if let Some(node) = self.graph.node_weight(idx) {
+                order.push(node.id.clone());
+            }
+        }
+
+        order
+    }
+
+    pub fn parallel_groups(&self) -> Vec<Vec<NodeId>> {
+        // 並列実行可能なノードのグループを計算
+        let mut groups = Vec::new();
+        let mut visited = HashMap::new();
+
+        for node_id in self.topological_order() {
+            let idx = self.node_map[&node_id];
+            
+            // このノードが依存する全てのノードのグループ番号の最大値+1
+            let mut max_group = 0;
+            for edge in self.graph.edges_directed(idx, petgraph::Direction::Incoming) {
+                if let Some(dep_node) = self.graph.node_weight(edge.source()) {
+                    if let Some(&group) = visited.get(&dep_node.id) {
+                        max_group = max_group.max(group + 1);
+                    }
+                }
+            }
+
+            visited.insert(node_id.clone(), max_group);
+
+            // グループに追加
+            if max_group >= groups.len() {
+                groups.resize(max_group + 1, Vec::new());
+            }
+            groups[max_group].push(node_id);
+        }
+
+        groups
+    }
+
+    pub fn get_node(&self, id: &NodeId) -> Option<&Node> {
+        self.node_map.get(id).and_then(|&idx| self.graph.node_weight(idx))
+    }
+}
\ No newline at end of file
diff --git a/prototype/src/lib.rs b/prototype/src/lib.rs
new file mode 100644
index 0000000..c6ffc90
--- /dev/null
+++ b/prototype/src/lib.rs
@@ -0,0 +1,20 @@
+pub mod ast;
+pub mod parser;
+pub mod graph;
+pub mod executor;
+pub mod constraint;
+pub mod self_improve;
+pub mod probabilistic;
+pub mod temporal;
+pub mod meta;
+pub mod os_kernel;
+
+pub use ast::*;
+pub use parser::parse;
+pub use graph::ComputeGraph;
+pub use executor::Executor;
+pub use self_improve::SelfImprovementEngine;
+pub use probabilistic::{ProbabilisticEngine, ProbabilisticValue, DistributionType};
+pub use temporal::{TemporalEngine, TemporalState, TemporalOperator, TemporalComputation};
+pub use meta::{MetaProgrammingEngine, ASTRepresentation};
+pub use os_kernel::{SynapticKernel, DataFlowTask, TaskId, ResourceSpec, Priority};
\ No newline at end of file
diff --git a/prototype/src/main.rs b/prototype/src/main.rs
new file mode 100644
index 0000000..6546006
--- /dev/null
+++ b/prototype/src/main.rs
@@ -0,0 +1,33 @@
+use synaptic::*;
+
+fn main() {
+    // サンプルプログラム
+    let synaptic_code = r#"
+        graph DataPipeline {
+            fetch_data -> process -> analyze -> summarize
+        }
+    "#;
+
+    match parse(synaptic_code) {
+        Ok(program) => {
+            println!("Parsed program:");
+            println!("{:#?}", program);
+
+            let mut executor = Executor::new(program);
+            match executor.execute() {
+                Ok(results) => {
+                    println!("\nExecution results:");
+                    for (node_id, value) in results {
+                        println!("{:?}: {:?}", node_id, value);
+                    }
+                }
+                Err(e) => {
+                    eprintln!("Execution error: {}", e);
+                }
+            }
+        }
+        Err(e) => {
+            eprintln!("Parse error: {}", e);
+        }
+    }
+}
\ No newline at end of file
diff --git a/prototype/src/meta.rs b/prototype/src/meta.rs
new file mode 100644
index 0000000..de70ab3
--- /dev/null
+++ b/prototype/src/meta.rs
@@ -0,0 +1,296 @@
+use crate::ast::{Program, Node, NodeKind, NodeId, Value, Operation};
+use std::collections::HashMap;
+
+pub struct MetaProgrammingEngine {
+    transformations: HashMap<String, TransformationRule>,
+    ast_cache: HashMap<String, Program>,
+}
+
+pub struct TransformationRule {
+    pub name: String,
+    pub pattern: Box<dyn Fn(&Node) -> bool + Send + Sync>,
+    pub transform: Box<dyn Fn(&Node) -> Node + Send + Sync>,
+    pub description: String,
+}
+
+pub struct CodeGenerator {
+    templates: HashMap<String, String>,
+}
+
+impl MetaProgrammingEngine {
+    pub fn new() -> Self {
+        let mut engine = MetaProgrammingEngine {
+            transformations: HashMap::new(),
+            ast_cache: HashMap::new(),
+        };
+        
+        engine.register_builtin_transformations();
+        engine
+    }
+
+    fn register_builtin_transformations(&mut self) {
+        // ループ融合の変換
+        self.register_transformation(TransformationRule {
+            name: "loop_fusion".to_string(),
+            pattern: Box::new(|node| {
+                matches!(&node.kind, NodeKind::Operation { op: Operation::Map })
+            }),
+            transform: Box::new(|node| {
+                let mut transformed = node.clone();
+                // 複数のMapを一つに融合
+                if let NodeKind::Operation { op } = &mut transformed.kind {
+                    *op = Operation::Transform("fused_map".to_string());
+                }
+                transformed
+            }),
+            description: "Fuse consecutive map operations".to_string(),
+        });
+
+        // 並列化の変換
+        self.register_transformation(TransformationRule {
+            name: "parallelize".to_string(),
+            pattern: Box::new(|node| {
+                matches!(&node.kind, NodeKind::Operation { op: Operation::Sequential })
+            }),
+            transform: Box::new(|node| {
+                let mut transformed = node.clone();
+                if let NodeKind::Operation { op } = &mut transformed.kind {
+                    *op = Operation::Parallel;
+                }
+                transformed
+            }),
+            description: "Convert sequential operations to parallel".to_string(),
+        });
+    }
+
+    pub fn register_transformation(&mut self, rule: TransformationRule) {
+        self.transformations.insert(rule.name.clone(), rule);
+    }
+
+    pub fn extract_ast(&self, program: &Program) -> ASTRepresentation {
+        ASTRepresentation {
+            nodes: program.nodes.clone(),
+            metadata: program.metadata.clone(),
+            structure: self.analyze_structure(program),
+        }
+    }
+
+    fn analyze_structure(&self, program: &Program) -> StructureInfo {
+        let mut info = StructureInfo {
+            depth: 0,
+            branching_factor: 0.0,
+            cyclic: false,
+            node_types: HashMap::new(),
+        };
+
+        for node in &program.nodes {
+            let node_type = match &node.kind {
+                NodeKind::Function { .. } => "function",
+                NodeKind::Graph { .. } => "graph",
+                NodeKind::Operation { .. } => "operation",
+                NodeKind::Value(_) => "value",
+                NodeKind::Constraint(_) => "constraint",
+            };
+            *info.node_types.entry(node_type.to_string()).or_insert(0) += 1;
+        }
+
+        info
+    }
+
+    pub fn transform_ast(&self, ast: &ASTRepresentation, transformations: &[String]) -> ASTRepresentation {
+        let mut result = ast.clone();
+        
+        for transformation_name in transformations {
+            if let Some(rule) = self.transformations.get(transformation_name) {
+                for node in &mut result.nodes {
+                    if (rule.pattern)(node) {
+                        *node = (rule.transform)(node);
+                    }
+                }
+            }
+        }
+        
+        result
+    }
+
+    pub fn compile_to_program(&self, ast: &ASTRepresentation) -> Program {
+        Program {
+            nodes: ast.nodes.clone(),
+            metadata: ast.metadata.clone(),
+        }
+    }
+
+    pub fn generate_optimized_code(&self, pattern: &str, parameters: &HashMap<String, Value>) -> Result<String, String> {
+        match pattern {
+            "neural_layer" => {
+                self.generate_neural_layer(parameters)
+            }
+            "parallel_reduce" => {
+                self.generate_parallel_reduce(parameters)
+            }
+            "gradient_computation" => {
+                self.generate_gradient_computation(parameters)
+            }
+            _ => Err(format!("Unknown pattern: {}", pattern))
+        }
+    }
+
+    fn generate_neural_layer(&self, params: &HashMap<String, Value>) -> Result<String, String> {
+        let input_size = match params.get("input_size") {
+            Some(Value::Number(n)) => *n as usize,
+            _ => return Err("input_size parameter required".to_string()),
+        };
+        
+        let output_size = match params.get("output_size") {
+            Some(Value::Number(n)) => *n as usize,
+            _ => return Err("output_size parameter required".to_string()),
+        };
+
+        Ok(format!(r#"
+graph NeuralLayer {{
+    @input_shape: [{input_size}]
+    @output_shape: [{output_size}]
+    
+    weights ~ Normal(mean=0.0, std_dev=0.1) @shape[{input_size}, {output_size}]
+    bias ~ Normal(mean=0.0, std_dev=0.01) @shape[{output_size}]
+    
+    input -> linear_transform(weights, bias) -> activation -> output
+    
+    @differentiable: true
+    @parallel: batch_dimension
+}}
+"#, input_size = input_size, output_size = output_size))
+    }
+
+    fn generate_parallel_reduce(&self, params: &HashMap<String, Value>) -> Result<String, String> {
+        let operation = match params.get("operation") {
+            Some(Value::String(s)) => s.clone(),
+            _ => "add".to_string(),
+        };
+
+        Ok(format!(r#"
+graph ParallelReduce {{
+    @operation: {operation}
+    @parallel: true
+    
+    input -> chunk -> parallel_map(partial_reduce) -> final_reduce -> output
+    
+    @constraint: associative({operation})
+    @constraint: commutative({operation})
+}}
+"#, operation = operation))
+    }
+
+    fn generate_gradient_computation(&self, _params: &HashMap<String, Value>) -> Result<String, String> {
+        Ok(r#"
+meta function compute_gradients(forward_function) {
+    ast = extract_ast(forward_function)
+    backward_ast = reverse_mode_ad(ast)
+    return compile(backward_ast)
+}
+
+@automatic_differentiation
+temporal function gradient_descent(params, loss_function) {
+    t[0]: current_params = params
+    t[1..n]: {
+        gradients = compute_gradients(loss_function)(current_params[t-1])
+        current_params = current_params[t-1] - learning_rate * gradients
+    }
+    @converge_when: norm(gradients) < epsilon
+}
+"#.to_string())
+    }
+
+    pub fn introspect(&self, program: &Program) -> IntrospectionResult {
+        IntrospectionResult {
+            complexity_metrics: self.calculate_complexity_metrics(program),
+            optimization_opportunities: self.find_optimization_opportunities(program),
+            performance_predictions: self.predict_performance(program),
+        }
+    }
+
+    fn calculate_complexity_metrics(&self, program: &Program) -> ComplexityMetrics {
+        ComplexityMetrics {
+            cyclomatic_complexity: self.calculate_cyclomatic_complexity(program),
+            data_flow_complexity: self.calculate_data_flow_complexity(program),
+            temporal_complexity: self.calculate_temporal_complexity(program),
+        }
+    }
+
+    fn calculate_cyclomatic_complexity(&self, _program: &Program) -> f64 {
+        // 簡易実装
+        1.0
+    }
+
+    fn calculate_data_flow_complexity(&self, program: &Program) -> f64 {
+        program.nodes.len() as f64 * 1.2
+    }
+
+    fn calculate_temporal_complexity(&self, _program: &Program) -> f64 {
+        // 時間的依存関係の複雑さ
+        1.0
+    }
+
+    fn find_optimization_opportunities(&self, program: &Program) -> Vec<OptimizationOpportunity> {
+        let mut opportunities = Vec::new();
+        
+        for node in &program.nodes {
+            if matches!(&node.kind, NodeKind::Operation { op: Operation::Sequential }) {
+                opportunities.push(OptimizationOpportunity {
+                    node_id: node.id.clone(),
+                    opportunity_type: "parallelization".to_string(),
+                    estimated_speedup: 2.5,
+                });
+            }
+        }
+        
+        opportunities
+    }
+
+    fn predict_performance(&self, _program: &Program) -> PerformancePrediction {
+        PerformancePrediction {
+            estimated_runtime: 100.0,
+            memory_usage: 1024.0,
+            parallel_efficiency: 0.85,
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct ASTRepresentation {
+    pub nodes: Vec<Node>,
+    pub metadata: crate::ast::Metadata,
+    pub structure: StructureInfo,
+}
+
+#[derive(Debug, Clone)]
+pub struct StructureInfo {
+    pub depth: usize,
+    pub branching_factor: f64,
+    pub cyclic: bool,
+    pub node_types: HashMap<String, usize>,
+}
+
+pub struct IntrospectionResult {
+    pub complexity_metrics: ComplexityMetrics,
+    pub optimization_opportunities: Vec<OptimizationOpportunity>,
+    pub performance_predictions: PerformancePrediction,
+}
+
+pub struct ComplexityMetrics {
+    pub cyclomatic_complexity: f64,
+    pub data_flow_complexity: f64,
+    pub temporal_complexity: f64,
+}
+
+pub struct OptimizationOpportunity {
+    pub node_id: NodeId,
+    pub opportunity_type: String,
+    pub estimated_speedup: f64,
+}
+
+pub struct PerformancePrediction {
+    pub estimated_runtime: f64,
+    pub memory_usage: f64,
+    pub parallel_efficiency: f64,
+}
\ No newline at end of file
diff --git a/prototype/src/os_kernel.rs b/prototype/src/os_kernel.rs
new file mode 100644
index 0000000..842d61d
--- /dev/null
+++ b/prototype/src/os_kernel.rs
@@ -0,0 +1,389 @@
+use crate::ast::{NodeId, Value};
+use crate::probabilistic::DistributionType;
+use std::collections::{HashMap, VecDeque};
+use std::time::{Duration, Instant};
+
+pub struct SynapticKernel {
+    scheduler: SynapticScheduler,
+    memory_manager: NeuralMemoryManager,
+    io_system: ProbabilisticIOSystem,
+    temporal_engine: TemporalConsistencyEngine,
+}
+
+#[derive(Debug, Clone)]
+pub struct DataFlowTask {
+    pub id: TaskId,
+    pub dependencies: Vec<DataSource>,
+    pub outputs: Vec<DataSink>,  
+    pub computation_graph: Vec<NodeId>,
+    pub resource_requirements: ResourceSpec,
+    pub priority: Priority,
+}
+
+#[derive(Debug, Clone, PartialEq, Eq, Hash)]
+pub struct TaskId(pub String);
+
+#[derive(Debug, Clone)]
+pub enum DataSource {
+    Memory(MemoryHandle),
+    Storage(StorageHandle),
+    Network(NetworkHandle),
+    Sensor(SensorHandle),
+}
+
+#[derive(Debug, Clone)]
+pub enum DataSink {
+    Memory(MemoryHandle),
+    Storage(StorageHandle),
+    Network(NetworkHandle),
+    Display(DisplayHandle),
+}
+
+#[derive(Debug, Clone)]
+pub struct ResourceSpec {
+    pub cpu_cores: usize,
+    pub memory_mb: usize,
+    pub gpu_memory_mb: Option<usize>,
+    pub network_bandwidth_mbps: Option<usize>,
+}
+
+#[derive(Debug, Clone, PartialOrd, PartialEq)]
+pub struct Priority(pub f64);
+
+// スケジューラー
+pub struct SynapticScheduler {
+    dependency_graph: DependencyGraph,
+    resource_pool: ResourcePool,
+    ready_queue: VecDeque<TaskId>,
+    running_tasks: HashMap<TaskId, RunningTask>,
+}
+
+pub struct DependencyGraph {
+    nodes: HashMap<TaskId, DataFlowTask>,
+    edges: HashMap<TaskId, Vec<TaskId>>, // 依存関係
+}
+
+pub struct ResourcePool {
+    available_cpu: usize,
+    available_memory: usize,
+    available_gpu_memory: usize,
+}
+
+pub struct RunningTask {
+    task: DataFlowTask,
+    start_time: Instant,
+    allocated_resources: ResourceSpec,
+}
+
+impl SynapticScheduler {
+    pub fn new() -> Self {
+        SynapticScheduler {
+            dependency_graph: DependencyGraph::new(),
+            resource_pool: ResourcePool::new(),
+            ready_queue: VecDeque::new(),
+            running_tasks: HashMap::new(),
+        }
+    }
+
+    pub fn submit_task(&mut self, task: DataFlowTask) {
+        self.dependency_graph.add_task(task.clone());
+        
+        if self.dependency_graph.is_ready(&task.id) {
+            self.ready_queue.push_back(task.id);
+        }
+    }
+
+    pub fn schedule_next(&mut self) -> Option<TaskId> {
+        while let Some(task_id) = self.ready_queue.pop_front() {
+            if let Some(task) = self.dependency_graph.get_task(&task_id) {
+                if self.resource_pool.can_allocate(&task.resource_requirements) {
+                    self.resource_pool.allocate(&task.resource_requirements);
+                    
+                    let running_task = RunningTask {
+                        task: task.clone(),
+                        start_time: Instant::now(),
+                        allocated_resources: task.resource_requirements.clone(),
+                    };
+                    
+                    self.running_tasks.insert(task_id.clone(), running_task);
+                    return Some(task_id);
+                }
+            }
+        }
+        None
+    }
+
+    pub fn complete_task(&mut self, task_id: &TaskId) {
+        if let Some(running_task) = self.running_tasks.remove(task_id) {
+            self.resource_pool.deallocate(&running_task.allocated_resources);
+            
+            // 依存するタスクをready queueに追加
+            let newly_ready = self.dependency_graph.complete_task(task_id);
+            for ready_task in newly_ready {
+                self.ready_queue.push_back(ready_task);
+            }
+        }
+    }
+}
+
+impl DependencyGraph {
+    pub fn new() -> Self {
+        DependencyGraph {
+            nodes: HashMap::new(),
+            edges: HashMap::new(),
+        }
+    }
+
+    pub fn add_task(&mut self, task: DataFlowTask) {
+        self.nodes.insert(task.id.clone(), task);
+    }
+
+    pub fn is_ready(&self, task_id: &TaskId) -> bool {
+        // 依存関係がすべて完了しているかチェック
+        if let Some(dependencies) = self.edges.get(task_id) {
+            dependencies.iter().all(|dep| !self.nodes.contains_key(dep))
+        } else {
+            true // 依存関係がない場合は実行可能
+        }
+    }
+
+    pub fn get_task(&self, task_id: &TaskId) -> Option<&DataFlowTask> {
+        self.nodes.get(task_id)
+    }
+
+    pub fn complete_task(&mut self, task_id: &TaskId) -> Vec<TaskId> {
+        self.nodes.remove(task_id);
+        
+        // この完了により新たに実行可能になったタスクを返す
+        let mut newly_ready = Vec::new();
+        for (id, _) in &self.nodes {
+            if self.is_ready(id) {
+                newly_ready.push(id.clone());
+            }
+        }
+        newly_ready
+    }
+}
+
+impl ResourcePool {
+    pub fn new() -> Self {
+        ResourcePool {
+            available_cpu: 8,     // 8コア
+            available_memory: 16384, // 16GB
+            available_gpu_memory: 8192, // 8GB
+        }
+    }
+
+    pub fn can_allocate(&self, requirements: &ResourceSpec) -> bool {
+        self.available_cpu >= requirements.cpu_cores &&
+        self.available_memory >= requirements.memory_mb &&
+        self.available_gpu_memory >= requirements.gpu_memory_mb.unwrap_or(0)
+    }
+
+    pub fn allocate(&mut self, requirements: &ResourceSpec) {
+        self.available_cpu -= requirements.cpu_cores;
+        self.available_memory -= requirements.memory_mb;
+        if let Some(gpu_mem) = requirements.gpu_memory_mb {
+            self.available_gpu_memory -= gpu_mem;
+        }
+    }
+
+    pub fn deallocate(&mut self, requirements: &ResourceSpec) {
+        self.available_cpu += requirements.cpu_cores;
+        self.available_memory += requirements.memory_mb;
+        if let Some(gpu_mem) = requirements.gpu_memory_mb {
+            self.available_gpu_memory += gpu_mem;
+        }
+    }
+}
+
+// Neural Memory Manager
+pub struct NeuralMemoryManager {
+    context_memory: ContextMemory,
+    working_memory: WorkingMemory,
+    associative_cache: AssociativeCache,
+}
+
+pub struct ContextMemory {
+    persistent_data: HashMap<String, Value>,
+    access_patterns: HashMap<String, Vec<Instant>>,
+}
+
+pub struct WorkingMemory {
+    temporary_data: HashMap<String, (Value, Instant)>,
+    capacity: usize,
+}
+
+pub struct AssociativeCache {
+    cache: HashMap<String, CacheEntry>,
+    attention_weights: HashMap<String, f64>,
+}
+
+#[derive(Debug, Clone)]
+pub struct CacheEntry {
+    pub data: Value,
+    pub last_access: Instant,
+    pub access_count: usize,
+    pub relevance_score: f64,
+}
+
+#[derive(Debug, Clone)]
+pub struct MemoryHandle(pub String);
+
+impl NeuralMemoryManager {
+    pub fn new() -> Self {
+        NeuralMemoryManager {
+            context_memory: ContextMemory::new(),
+            working_memory: WorkingMemory::new(1024), // 1GB working memory
+            associative_cache: AssociativeCache::new(),
+        }
+    }
+
+    pub fn store_context(&mut self, key: String, value: Value) -> MemoryHandle {
+        self.context_memory.store(key.clone(), value);
+        MemoryHandle(key)
+    }
+
+    pub fn allocate_working(&mut self, key: String, value: Value) -> MemoryHandle {
+        self.working_memory.allocate(key.clone(), value);
+        MemoryHandle(key)
+    }
+
+    pub fn recall_pattern(&self, pattern: &str) -> Vec<Value> {
+        self.associative_cache.pattern_match(pattern)
+    }
+}
+
+impl ContextMemory {
+    pub fn new() -> Self {
+        ContextMemory {
+            persistent_data: HashMap::new(),
+            access_patterns: HashMap::new(),
+        }
+    }
+
+    pub fn store(&mut self, key: String, value: Value) {
+        self.persistent_data.insert(key.clone(), value);
+        self.access_patterns
+            .entry(key)
+            .or_insert_with(Vec::new)
+            .push(Instant::now());
+    }
+}
+
+impl WorkingMemory {
+    pub fn new(capacity: usize) -> Self {
+        WorkingMemory {
+            temporary_data: HashMap::new(),
+            capacity,
+        }
+    }
+
+    pub fn allocate(&mut self, key: String, value: Value) {
+        if self.temporary_data.len() >= self.capacity {
+            self.evict_lru();
+        }
+        self.temporary_data.insert(key, (value, Instant::now()));
+    }
+
+    fn evict_lru(&mut self) {
+        if let Some((oldest_key, _)) = self.temporary_data
+            .iter()
+            .min_by_key(|(_, (_, timestamp))| timestamp)
+            .map(|(k, v)| (k.clone(), v.clone()))
+        {
+            self.temporary_data.remove(&oldest_key);
+        }
+    }
+}
+
+impl AssociativeCache {
+    pub fn new() -> Self {
+        AssociativeCache {
+            cache: HashMap::new(),
+            attention_weights: HashMap::new(),
+        }
+    }
+
+    pub fn pattern_match(&self, pattern: &str) -> Vec<Value> {
+        self.cache
+            .iter()
+            .filter(|(key, _)| key.contains(pattern))
+            .map(|(_, entry)| entry.data.clone())
+            .collect()
+    }
+}
+
+// システムコール
+pub enum SynapticSyscall {
+    Infer { model: String, input: Value },
+    PatternMatch { pattern: String, data: String },
+    RecordCausality { cause: String, effect: String },
+    ProbabilisticChoice { options: Vec<String>, probabilities: Vec<f64> },
+    TimeTravel { timestamp: Instant },
+}
+
+pub enum SyscallResult {
+    InferenceResult(Value),
+    MatchResult(Vec<Value>),
+    CausalityRecorded,
+    ChoiceResult(String),
+    TimeContext(String),
+    Error(String),
+}
+
+// Probabilistic I/O System
+pub struct ProbabilisticIOSystem {
+    predictive_cache: PredictiveCache,
+    uncertainty_tracker: UncertaintyTracker,
+}
+
+pub struct PredictiveCache {
+    predictions: HashMap<String, Prediction>,
+}
+
+pub struct Prediction {
+    pub data: Value,
+    pub confidence: f64,
+    pub timestamp: Instant,
+}
+
+pub struct UncertaintyTracker {
+    uncertainties: HashMap<String, f64>,
+}
+
+// Temporal Consistency Engine
+pub struct TemporalConsistencyEngine {
+    causality_graph: CausalityGraph,
+    checkpoints: Vec<SystemCheckpoint>,
+}
+
+pub struct CausalityGraph {
+    events: HashMap<String, CausalEvent>,
+    causal_links: HashMap<String, Vec<String>>,
+}
+
+pub struct CausalEvent {
+    pub id: String,
+    pub timestamp: Instant,
+    pub data: Value,
+}
+
+pub struct SystemCheckpoint {
+    pub id: String,
+    pub timestamp: Instant,
+    pub system_state: HashMap<String, Value>,
+}
+
+// ハンドル型の定義
+#[derive(Debug, Clone)]
+pub struct StorageHandle(pub String);
+
+#[derive(Debug, Clone)]
+pub struct NetworkHandle(pub String);
+
+#[derive(Debug, Clone)]
+pub struct SensorHandle(pub String);
+
+#[derive(Debug, Clone)]
+pub struct DisplayHandle(pub String);
\ No newline at end of file
diff --git a/prototype/src/parser.rs b/prototype/src/parser.rs
new file mode 100644
index 0000000..36be2c4
--- /dev/null
+++ b/prototype/src/parser.rs
@@ -0,0 +1,162 @@
+use crate::ast::*;
+use nom::{
+    IResult,
+    branch::alt,
+    bytes::complete::tag,
+    character::complete::{alpha1, alphanumeric1, char, multispace0, multispace1},
+    combinator::recognize,
+    multi::{many0, separated_list0},
+    sequence::{delimited, pair, preceded, tuple},
+};
+
+pub fn parse(input: &str) -> Result<Program, String> {
+    match program(input) {
+        Ok((_, prog)) => Ok(prog),
+        Err(e) => Err(format!("Parse error: {:?}", e)),
+    }
+}
+
+fn program(input: &str) -> IResult<&str, Program> {
+    let (input, nodes) = many0(preceded(multispace0, node))(input)?;
+    Ok((input, Program {
+        nodes,
+        metadata: Metadata::default(),
+    }))
+}
+
+fn node(input: &str) -> IResult<&str, Node> {
+    alt((
+        graph_node,
+        function_node,
+        operation_node,
+    ))(input)
+}
+
+fn graph_node(input: &str) -> IResult<&str, Node> {
+    let (input, _) = tag("graph")(input)?;
+    let (input, _) = multispace1(input)?;
+    let (input, name) = identifier(input)?;
+    let (input, _) = multispace0(input)?;
+    let (input, nodes) = delimited(
+        char('{'),
+        many0(preceded(multispace0, flow_statement)),
+        preceded(multispace0, char('}')),
+    )(input)?;
+
+    let id = NodeId(format!("graph_{}", name));
+    Ok((input, Node {
+        id: id.clone(),
+        kind: NodeKind::Graph { name: name.to_string(), nodes },
+        inputs: vec![],
+        outputs: vec![],
+        constraints: vec![],
+        metadata: Metadata::default(),
+    }))
+}
+
+fn function_node(input: &str) -> IResult<&str, Node> {
+    let (input, _) = tag("function")(input)?;
+    let (input, _) = multispace1(input)?;
+    let (input, name) = identifier(input)?;
+    let (input, params) = delimited(
+        char('('),
+        separated_list0(char(','), parameter),
+        char(')'),
+    )(input)?;
+    let (input, _) = multispace0(input)?;
+    let (input, body) = delimited(
+        char('{'),
+        preceded(multispace0, node),
+        preceded(multispace0, char('}')),
+    )(input)?;
+
+    let id = NodeId(format!("func_{}", name));
+    Ok((input, Node {
+        id,
+        kind: NodeKind::Function {
+            name: name.to_string(),
+            params,
+            body: Box::new(body),
+        },
+        inputs: vec![],
+        outputs: vec![],
+        constraints: vec![],
+        metadata: Metadata::default(),
+    }))
+}
+
+fn operation_node(input: &str) -> IResult<&str, Node> {
+    let (input, op_name) = identifier(input)?;
+    let (input, _) = delimited(
+        char('('),
+        multispace0,
+        char(')'),
+    )(input)?;
+
+    let op = match op_name {
+        "parallel_map" => Operation::Map,
+        "filter" => Operation::Filter,
+        "reduce" => Operation::Reduce,
+        _ => Operation::Transform(op_name.to_string()),
+    };
+
+    Ok((input, Node {
+        id: NodeId(format!("op_{}", op_name)),
+        kind: NodeKind::Operation { op },
+        inputs: vec![],
+        outputs: vec![],
+        constraints: vec![],
+        metadata: Metadata::default(),
+    }))
+}
+
+fn flow_statement(input: &str) -> IResult<&str, Node> {
+    let (input, nodes) = separated_list0(
+        tuple((multispace0, tag("->"), multispace0)),
+        identifier,
+    )(input)?;
+
+    // 簡易的な実装：フロー文をノードのチェーンとして解釈
+    let mut result_nodes = vec![];
+    for (i, node_name) in nodes.iter().enumerate() {
+        let id = NodeId(format!("flow_{}_{}", i, node_name));
+        let node = Node {
+            id: id.clone(),
+            kind: NodeKind::Operation {
+                op: Operation::Transform(node_name.to_string()),
+            },
+            inputs: if i > 0 { vec![NodeId(format!("flow_{}_{}", i-1, nodes[i-1]))] } else { vec![] },
+            outputs: if i < nodes.len() - 1 { vec![NodeId(format!("flow_{}_{}", i+1, nodes[i+1]))] } else { vec![] },
+            constraints: vec![],
+            metadata: Metadata::default(),
+        };
+        result_nodes.push(node);
+    }
+
+    // 最初のノードを返す（本来は全てのノードを管理すべき）
+    Ok((input, result_nodes.into_iter().next().unwrap_or_else(|| Node {
+        id: NodeId("empty".to_string()),
+        kind: NodeKind::Value(Value::Boolean(true)),
+        inputs: vec![],
+        outputs: vec![],
+        constraints: vec![],
+        metadata: Metadata::default(),
+    })))
+}
+
+fn parameter(input: &str) -> IResult<&str, Parameter> {
+    let (input, name) = identifier(input)?;
+    Ok((input, Parameter {
+        name: name.to_string(),
+        constraints: vec![],
+    }))
+}
+
+fn identifier(input: &str) -> IResult<&str, &str> {
+    recognize(
+        pair(
+            alt((alpha1, tag("_"))),
+            many0(alt((alphanumeric1, tag("_")))),
+        )
+    )(input)
+}
\ No newline at end of file
diff --git a/prototype/src/probabilistic.rs b/prototype/src/probabilistic.rs
new file mode 100644
index 0000000..9f429be
--- /dev/null
+++ b/prototype/src/probabilistic.rs
@@ -0,0 +1,179 @@
+use crate::ast::{Value, NodeId};
+use std::collections::HashMap;
+use rand::{Rng, distributions::{Distribution, Uniform}};
+use rand_distr::Normal;
+
+#[derive(Debug, Clone)]
+pub struct ProbabilisticValue {
+    pub distribution: DistributionType,
+    pub samples: Vec<f64>,
+    pub confidence: f64,
+}
+
+#[derive(Debug, Clone)]
+pub enum DistributionType {
+    Normal { mean: f64, std_dev: f64 },
+    Uniform { min: f64, max: f64 },
+    Discrete { probabilities: Vec<f64>, values: Vec<Value> },
+    Empirical { samples: Vec<f64> },
+}
+
+pub struct ProbabilisticEngine {
+    rng: rand::rngs::ThreadRng,
+    cache: HashMap<NodeId, ProbabilisticValue>,
+}
+
+impl ProbabilisticEngine {
+    pub fn new() -> Self {
+        ProbabilisticEngine {
+            rng: rand::thread_rng(),
+            cache: HashMap::new(),
+        }
+    }
+
+    pub fn sample(&mut self, dist: &DistributionType, n_samples: usize) -> Vec<f64> {
+        match dist {
+            DistributionType::Normal { mean, std_dev } => {
+                let normal = Normal::new(*mean, *std_dev).unwrap();
+                (0..n_samples).map(|_| normal.sample(&mut self.rng)).collect()
+            }
+            DistributionType::Uniform { min, max } => {
+                let uniform = Uniform::new(*min, *max);
+                (0..n_samples).map(|_| uniform.sample(&mut self.rng)).collect()
+            }
+            DistributionType::Discrete { probabilities, .. } => {
+                // 簡易実装：重み付きサンプリング
+                let total: f64 = probabilities.iter().sum();
+                (0..n_samples).map(|_| {
+                    let mut r = self.rng.gen::<f64>() * total;
+                    for (i, &p) in probabilities.iter().enumerate() {
+                        r -= p;
+                        if r <= 0.0 {
+                            return i as f64;
+                        }
+                    }
+                    (probabilities.len() - 1) as f64
+                }).collect()
+            }
+            DistributionType::Empirical { samples } => {
+                // ブートストラップサンプリング
+                (0..n_samples).map(|_| {
+                    let idx = self.rng.gen_range(0..samples.len());
+                    samples[idx]
+                }).collect()
+            }
+        }
+    }
+
+    pub fn expectation(&self, samples: &[f64]) -> f64 {
+        samples.iter().sum::<f64>() / samples.len() as f64
+    }
+
+    pub fn variance(&self, samples: &[f64]) -> f64 {
+        let mean = self.expectation(samples);
+        samples.iter()
+            .map(|&x| (x - mean).powi(2))
+            .sum::<f64>() / samples.len() as f64
+    }
+
+    pub fn entropy(&self, probabilities: &[f64]) -> f64 {
+        probabilities.iter()
+            .filter(|&&p| p > 0.0)
+            .map(|&p| -p * p.log2())
+            .sum()
+    }
+
+    pub fn confidence_interval(&self, samples: &[f64], confidence: f64) -> (f64, f64) {
+        let mut sorted = samples.to_vec();
+        sorted.sort_by(|a, b| a.partial_cmp(b).unwrap());
+        
+        let alpha = (1.0 - confidence) / 2.0;
+        let lower_idx = (alpha * samples.len() as f64) as usize;
+        let upper_idx = ((1.0 - alpha) * samples.len() as f64) as usize;
+        
+        (sorted[lower_idx], sorted[upper_idx.min(sorted.len() - 1)])
+    }
+
+    pub fn bayesian_update(
+        &self,
+        prior: &DistributionType,
+        _likelihood: &dyn Fn(f64) -> f64,
+        evidence: f64,
+    ) -> DistributionType {
+        // 簡易的なベイズ更新（MCMCやSMCを使うべきだが、デモ用）
+        match prior {
+            DistributionType::Normal { mean, std_dev } => {
+                // 共役事前分布を仮定
+                let precision = 1.0 / (std_dev * std_dev);
+                let data_precision = 1.0; // 仮定
+                
+                let new_precision = precision + data_precision;
+                let new_mean = (precision * mean + data_precision * evidence) / new_precision;
+                let new_std_dev = (1.0 / new_precision).sqrt();
+                
+                DistributionType::Normal {
+                    mean: new_mean,
+                    std_dev: new_std_dev,
+                }
+            }
+            _ => prior.clone(), // 他の分布は簡易実装
+        }
+    }
+}
+
+// 確率的ノードの実行結果
+#[derive(Debug, Clone)]
+pub struct ProbabilisticResult {
+    pub value: Value,
+    pub distribution: Option<DistributionType>,
+    pub confidence: f64,
+    pub samples: Option<Vec<f64>>,
+}
+
+impl ProbabilisticResult {
+    pub fn deterministic(value: Value) -> Self {
+        ProbabilisticResult {
+            value,
+            distribution: None,
+            confidence: 1.0,
+            samples: None,
+        }
+    }
+
+    pub fn probabilistic(
+        value: Value,
+        distribution: DistributionType,
+        samples: Vec<f64>,
+        confidence: f64,
+    ) -> Self {
+        ProbabilisticResult {
+            value,
+            distribution: Some(distribution),
+            confidence,
+            samples: Some(samples),
+        }
+    }
+}
+
+// 確率的制約
+#[derive(Debug, Clone)]
+pub struct ProbabilisticConstraint {
+    pub threshold: f64,
+    pub confidence_level: f64,
+    pub constraint_type: ProbConstraintType,
+}
+
+#[derive(Debug, Clone)]
+pub enum ProbConstraintType {
+    Expectation { operator: ComparisonOp, value: f64 },
+    Variance { max: f64 },
+    Entropy { max: f64 },
+    Probability { event: String, min: f64 },
+}
+
+#[derive(Debug, Clone)]
+pub enum ComparisonOp {
+    GreaterThan,
+    LessThan,
+    Equal,
+}
\ No newline at end of file
diff --git a/prototype/src/self_improve.rs b/prototype/src/self_improve.rs
new file mode 100644
index 0000000..8eb9d8d
--- /dev/null
+++ b/prototype/src/self_improve.rs
@@ -0,0 +1,174 @@
+use crate::ast::{Program, Node, NodeKind, Operation};
+use std::collections::HashMap;
+use anyhow::Result;
+
+pub struct SelfImprovementEngine {
+    optimization_patterns: Vec<OptimizationPattern>,
+    learning_history: Vec<LearningRecord>,
+}
+
+#[derive(Clone)]
+pub struct OptimizationPattern {
+    pub name: String,
+    pub matcher: fn(&Node) -> bool,
+    pub transformer: fn(&Node) -> Node,
+    pub expected_gain: f64,
+}
+
+#[derive(Clone)]
+pub struct LearningRecord {
+    pub pattern_name: String,
+    pub actual_gain: f64,
+    pub timestamp: u64,
+}
+
+impl SelfImprovementEngine {
+    pub fn new() -> Self {
+        let patterns = vec![
+            OptimizationPattern {
+                name: "parallel_map_fusion".to_string(),
+                matcher: |node| matches!(&node.kind, NodeKind::Operation { op: Operation::Map }),
+                transformer: |node| {
+                    let mut optimized = node.clone();
+                    if let NodeKind::Operation { op } = &mut optimized.kind {
+                        *op = Operation::Parallel;
+                    }
+                    optimized
+                },
+                expected_gain: 2.0,
+            },
+            OptimizationPattern {
+                name: "sequential_to_parallel".to_string(),
+                matcher: |node| {
+                    matches!(&node.kind, NodeKind::Operation { op: Operation::Sequential })
+                        && node.inputs.len() > 1
+                },
+                transformer: |node| {
+                    let mut optimized = node.clone();
+                    if let NodeKind::Operation { op } = &mut optimized.kind {
+                        *op = Operation::Parallel;
+                    }
+                    optimized
+                },
+                expected_gain: 1.5,
+            },
+        ];
+
+        SelfImprovementEngine {
+            optimization_patterns: patterns,
+            learning_history: Vec::new(),
+        }
+    }
+
+    pub fn analyze_program(&self, program: &Program) -> ProgramAnalysis {
+        let mut node_count = 0;
+        let mut operation_counts = HashMap::new();
+        let mut parallelizable_nodes = 0;
+
+        for node in &program.nodes {
+            node_count += 1;
+            
+            if let NodeKind::Operation { op } = &node.kind {
+                *operation_counts.entry(format!("{:?}", op)).or_insert(0) += 1;
+                
+                if matches!(op, Operation::Map | Operation::Filter) {
+                    parallelizable_nodes += 1;
+                }
+            }
+        }
+
+        ProgramAnalysis {
+            node_count,
+            operation_counts,
+            parallelizable_nodes,
+            complexity_score: self.calculate_complexity(program),
+        }
+    }
+
+    pub fn suggest_improvements(&self, program: &Program) -> Vec<Improvement> {
+        let mut improvements = Vec::new();
+
+        for node in &program.nodes {
+            for pattern in &self.optimization_patterns {
+                if (pattern.matcher)(node) {
+                    let improved_node = (pattern.transformer)(node);
+                    improvements.push(Improvement {
+                        original_node_id: node.id.clone(),
+                        pattern_name: pattern.name.clone(),
+                        improved_node,
+                        expected_gain: pattern.expected_gain,
+                    });
+                }
+            }
+        }
+
+        // AIによる新しい最適化の発見をシミュレート
+        if let Some(novel) = self.discover_novel_optimization(program) {
+            improvements.push(novel);
+        }
+
+        improvements
+    }
+
+    pub fn apply_improvement(&mut self, program: &mut Program, improvement: &Improvement) -> Result<()> {
+        // ノードを見つけて置換
+        for node in &mut program.nodes {
+            if node.id == improvement.original_node_id {
+                *node = improvement.improved_node.clone();
+                
+                // 学習履歴に記録
+                self.learning_history.push(LearningRecord {
+                    pattern_name: improvement.pattern_name.clone(),
+                    actual_gain: improvement.expected_gain, // 実際は測定すべき
+                    timestamp: std::time::SystemTime::now()
+                        .duration_since(std::time::UNIX_EPOCH)
+                        .unwrap()
+                        .as_secs(),
+                });
+                
+                return Ok(());
+            }
+        }
+        
+        anyhow::bail!("Node not found: {:?}", improvement.original_node_id)
+    }
+
+    fn calculate_complexity(&self, program: &Program) -> f64 {
+        let mut complexity = 0.0;
+        
+        for node in &program.nodes {
+            complexity += match &node.kind {
+                NodeKind::Operation { op } => match op {
+                    Operation::Map | Operation::Filter => 1.0,
+                    Operation::Reduce => 2.0,
+                    Operation::Parallel => 0.5,
+                    _ => 1.0,
+                },
+                NodeKind::Graph { nodes, .. } => nodes.len() as f64 * 1.5,
+                _ => 0.5,
+            };
+        }
+        
+        complexity
+    }
+
+    fn discover_novel_optimization(&self, _program: &Program) -> Option<Improvement> {
+        // 将来的にはMLモデルを使用して新しい最適化を発見
+        // 現在はプレースホルダー
+        None
+    }
+}
+
+pub struct ProgramAnalysis {
+    pub node_count: usize,
+    pub operation_counts: HashMap<String, usize>,
+    pub parallelizable_nodes: usize,
+    pub complexity_score: f64,
+}
+
+pub struct Improvement {
+    pub original_node_id: crate::ast::NodeId,
+    pub pattern_name: String,
+    pub improved_node: Node,
+    pub expected_gain: f64,
+}
\ No newline at end of file
diff --git a/prototype/src/temporal.rs b/prototype/src/temporal.rs
new file mode 100644
index 0000000..c6b5c69
--- /dev/null
+++ b/prototype/src/temporal.rs
@@ -0,0 +1,239 @@
+use crate::ast::{Node, NodeId, Value};
+use std::collections::{HashMap, VecDeque};
+use std::time::{Duration, Instant};
+
+#[derive(Debug, Clone)]
+pub struct TemporalState {
+    pub time_step: usize,
+    pub values: HashMap<NodeId, Value>,
+    pub timestamp: Instant,
+}
+
+pub struct TemporalEngine {
+    history: VecDeque<TemporalState>,
+    max_history_size: usize,
+    convergence_checks: HashMap<NodeId, ConvergenceChecker>,
+}
+
+#[derive(Debug, Clone)]
+pub struct ConvergenceChecker {
+    pub epsilon: f64,
+    pub window_size: usize,
+    pub recent_values: VecDeque<f64>,
+}
+
+impl ConvergenceChecker {
+    pub fn new(epsilon: f64, window_size: usize) -> Self {
+        ConvergenceChecker {
+            epsilon,
+            window_size,
+            recent_values: VecDeque::with_capacity(window_size),
+        }
+    }
+
+    pub fn check(&mut self, value: f64) -> bool {
+        self.recent_values.push_back(value);
+        if self.recent_values.len() > self.window_size {
+            self.recent_values.pop_front();
+        }
+
+        if self.recent_values.len() < 2 {
+            return false;
+        }
+
+        // 収束判定：最近の値の変化が epsilon 以下
+        let last = self.recent_values.back().unwrap();
+        let second_last = self.recent_values[self.recent_values.len() - 2];
+        (last - second_last).abs() < self.epsilon
+    }
+}
+
+impl TemporalEngine {
+    pub fn new(max_history_size: usize) -> Self {
+        TemporalEngine {
+            history: VecDeque::with_capacity(max_history_size),
+            max_history_size,
+            convergence_checks: HashMap::new(),
+        }
+    }
+
+    pub fn add_time_step(&mut self, state: TemporalState) {
+        if self.history.len() >= self.max_history_size {
+            self.history.pop_front();
+        }
+        self.history.push_back(state);
+    }
+
+    pub fn get_state_at(&self, time_step: usize) -> Option<&TemporalState> {
+        self.history.iter().find(|s| s.time_step == time_step)
+    }
+
+    pub fn get_value_history(&self, node_id: &NodeId) -> Vec<(usize, Value)> {
+        self.history
+            .iter()
+            .filter_map(|state| {
+                state.values.get(node_id).map(|v| (state.time_step, v.clone()))
+            })
+            .collect()
+    }
+
+    pub fn register_convergence_check(
+        &mut self,
+        node_id: NodeId,
+        epsilon: f64,
+        window_size: usize,
+    ) {
+        self.convergence_checks.insert(
+            node_id,
+            ConvergenceChecker::new(epsilon, window_size),
+        );
+    }
+
+    pub fn check_convergence(&mut self, node_id: &NodeId, value: f64) -> bool {
+        if let Some(checker) = self.convergence_checks.get_mut(node_id) {
+            checker.check(value)
+        } else {
+            false
+        }
+    }
+
+    pub fn time_travel_debug(&self, time_step: usize) -> Option<String> {
+        self.get_state_at(time_step).map(|state| {
+            format!(
+                "State at t={}: {} values, timestamp: {:?}",
+                time_step,
+                state.values.len(),
+                state.timestamp.elapsed()
+            )
+        })
+    }
+
+    pub fn predict_next_value(&self, node_id: &NodeId, steps_ahead: usize) -> Option<Value> {
+        let history = self.get_value_history(node_id);
+        if history.len() < 3 {
+            return None;
+        }
+
+        // 簡単な線形外挿（実際にはより高度な予測手法を使用）
+        if let Some(Value::Number(last)) = history.last().map(|(_, v)| v) {
+            if let Some(Value::Number(second_last)) = history.get(history.len() - 2).map(|(_, v)| v) {
+                let diff = last - second_last;
+                let predicted = last + diff * steps_ahead as f64;
+                return Some(Value::Number(predicted));
+            }
+        }
+
+        None
+    }
+}
+
+// 時間的な制約
+#[derive(Debug, Clone)]
+pub struct TemporalConstraint {
+    pub constraint_type: TemporalConstraintType,
+    pub window: TimeWindow,
+}
+
+#[derive(Debug, Clone)]
+pub enum TemporalConstraintType {
+    // 収束条件
+    ConvergeWhen { epsilon: f64 },
+    // 周期的実行
+    Every { interval: Duration },
+    // 時間制限
+    Timeout { duration: Duration },
+    // 因果関係
+    CausalOrder { before: NodeId, after: NodeId },
+}
+
+#[derive(Debug, Clone)]
+pub struct TimeWindow {
+    pub start: Option<usize>,
+    pub end: Option<usize>,
+}
+
+// 時間的な演算子
+#[derive(Debug, Clone)]
+pub enum TemporalOperator {
+    // 過去の値を参照
+    Previous { steps: usize },
+    // 移動平均
+    MovingAverage { window: usize },
+    // 微分（変化率）
+    Derivative,
+    // 積分（累積）
+    Integral,
+    // 遅延
+    Delay { steps: usize },
+}
+
+pub struct TemporalComputation {
+    pub operator: TemporalOperator,
+    pub input: NodeId,
+}
+
+impl TemporalComputation {
+    pub fn compute(&self, engine: &TemporalEngine) -> Option<Value> {
+        let history = engine.get_value_history(&self.input);
+        
+        match &self.operator {
+            TemporalOperator::Previous { steps } => {
+                history.get(history.len().saturating_sub(steps + 1))
+                    .map(|(_, v)| v.clone())
+            }
+            
+            TemporalOperator::MovingAverage { window } => {
+                if history.len() < *window {
+                    return None;
+                }
+                
+                let recent: Vec<f64> = history
+                    .iter()
+                    .rev()
+                    .take(*window)
+                    .filter_map(|(_, v)| match v {
+                        Value::Number(n) => Some(*n),
+                        _ => None,
+                    })
+                    .collect();
+                
+                if recent.len() == *window {
+                    Some(Value::Number(recent.iter().sum::<f64>() / recent.len() as f64))
+                } else {
+                    None
+                }
+            }
+            
+            TemporalOperator::Derivative => {
+                if history.len() < 2 {
+                    return None;
+                }
+                
+                if let (Some(Value::Number(last)), Some(Value::Number(prev))) = (
+                    history.last().map(|(_, v)| v),
+                    history.get(history.len() - 2).map(|(_, v)| v),
+                ) {
+                    Some(Value::Number(last - prev))
+                } else {
+                    None
+                }
+            }
+            
+            TemporalOperator::Integral => {
+                let sum: f64 = history
+                    .iter()
+                    .filter_map(|(_, v)| match v {
+                        Value::Number(n) => Some(*n),
+                        _ => None,
+                    })
+                    .sum();
+                Some(Value::Number(sum))
+            }
+            
+            TemporalOperator::Delay { steps } => {
+                history.get(history.len().saturating_sub(*steps))
+                    .map(|(_, v)| v.clone())
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/self-improvement.md b/self-improvement.md
new file mode 100644
index 0000000..fd99964
--- /dev/null
+++ b/self-improvement.md
@@ -0,0 +1,155 @@
+# AIによる自己改良メカニズム
+
+## コンセプト
+
+AIが自身のコードを理解し、改良し、進化させる仕組み。
+
+## 実装アプローチ
+
+### 1. コードの自己観察
+
+```synaptic
+meta function analyze_self() {
+    # 自身のASTを取得
+    ast = get_current_ast()
+    
+    # パフォーマンスメトリクスを収集
+    metrics = {
+        execution_time: measure_performance(),
+        memory_usage: measure_memory(),
+        accuracy: measure_accuracy()
+    }
+    
+    # ボトルネックを特定
+    bottlenecks = identify_bottlenecks(ast, metrics)
+    
+    return {ast, metrics, bottlenecks}
+}
+```
+
+### 2. 改良候補の生成
+
+```synaptic
+meta function generate_improvements(analysis) {
+    improvements = []
+    
+    # パターンマッチングによる最適化
+    for pattern in optimization_patterns {
+        matches = find_pattern(analysis.ast, pattern)
+        for match in matches {
+            improved = apply_transformation(match, pattern.transform)
+            improvements.append({
+                original: match,
+                improved: improved,
+                expected_gain: estimate_improvement(match, improved)
+            })
+        }
+    }
+    
+    # AIによる新しい最適化の発見
+    novel_improvements = discover_optimizations(
+        analysis.ast,
+        analysis.metrics,
+        learning_history
+    )
+    
+    return improvements + novel_improvements
+}
+```
+
+### 3. 検証と適用
+
+```synaptic
+meta function apply_improvements(improvements) {
+    for improvement in improvements {
+        # サンドボックスで検証
+        sandbox = create_sandbox()
+        result = sandbox.test(improvement)
+        
+        if result.is_better() {
+            # 本番環境に適用
+            apply_to_production(improvement)
+            
+            # 学習履歴に追加
+            learning_history.add({
+                pattern: extract_pattern(improvement),
+                gain: result.improvement_ratio
+            })
+        }
+    }
+}
+```
+
+## 自己改良の段階
+
+### Phase 1: パターンベース最適化
+- 既知の最適化パターンを適用
+- ループ融合、ベクトル化、並列化
+
+### Phase 2: 学習ベース最適化
+- 過去の改良履歴から新しいパターンを学習
+- 類似コードへの最適化の転移
+
+### Phase 3: 創発的最適化
+- AIが独自に新しい最適化手法を発見
+- 人間には理解困難な最適化の適用
+
+### Phase 4: 自己再設計
+- 言語自体の構造を改良
+- より効率的な表現方法の発明
+
+## 実装例
+
+```rust
+// Rust側での自己改良エンジン
+pub struct SelfImprovementEngine {
+    ast_analyzer: AstAnalyzer,
+    optimizer: Optimizer,
+    validator: Validator,
+    history: LearningHistory,
+}
+
+impl SelfImprovementEngine {
+    pub fn improve(&mut self, program: &mut Program) -> Result<ImprovementReport> {
+        // 1. 現在のプログラムを分析
+        let analysis = self.ast_analyzer.analyze(program)?;
+        
+        // 2. 改良候補を生成
+        let candidates = self.optimizer.generate_candidates(&analysis)?;
+        
+        // 3. 各候補を評価
+        let mut improvements = Vec::new();
+        for candidate in candidates {
+            if let Ok(validation) = self.validator.validate(&candidate) {
+                if validation.is_improvement() {
+                    improvements.push(candidate);
+                }
+            }
+        }
+        
+        // 4. 最良の改良を適用
+        let best = self.select_best_improvement(&improvements);
+        if let Some(improvement) = best {
+            self.apply_improvement(program, &improvement)?;
+            self.history.record(&improvement);
+        }
+        
+        Ok(ImprovementReport {
+            analyzed_nodes: analysis.node_count,
+            candidates_generated: candidates.len(),
+            improvements_applied: improvements.len(),
+        })
+    }
+}
+```
+
+## 安全性メカニズム
+
+1. **サンドボックス実行**: 改良は必ず隔離環境でテスト
+2. **ロールバック機能**: 問題が発生した場合は即座に元に戻す
+3. **段階的適用**: 小さな改良から始めて徐々に大きな変更へ
+4. **人間の監督**: 重要な変更は人間の承認を要求
+
+## 将来の展望
+
+最終的には、AIが自身のコードを完全に理解し、人間の介入なしに進化し続けるシステムを目指す。その時点で、コードは人間には理解不能だが、AIにとっては最適な形になっているだろう。
\ No newline at end of file