📚 docs: Record field declaration design discussion in papers

## Summary
Documented the "init block vs fields-at-top" design discussion as a valuable example of AI-human collaboration in language design.

## Changes

### Paper G (AI Collaboration)
- Added field-declaration-design.md documenting the entire discussion flow
- Showcased how complex init block proposal evolved to simple "fields at top" rule
- Demonstrates AI's tendency toward complexity vs human intuition for simplicity

### Paper H (AI Practical Patterns)
- Added Pattern #17: "Gradual Refinement Pattern" (段階的洗練型)
- Documents the process: Complex AI proposal → Detailed analysis → Human insight → Convergence
- Field declaration design as a typical example

### Paper K (Explosive Incidents)
- Added Incident #046: "init block vs fields-at-top incident"
- Updated total count to 46 incidents
- Shows how a single human comment redirected entire design approach

## Design Decision
After analysis, decided that BoxIndex should remain a compiler-internal structure, not a core Box:
- Core Boxes: User-instantiable runtime values (String, Integer, Array, Map)
- Compiler internals: BoxIndex for name resolution (compile-time only)
- Clear separation of concerns between language features and compiler tools

## Philosophy
This discussion exemplifies key principles:
- The best design needs no explanation
- Constraints provide clarity, not limitation
- "Everything is Box" doesn't mean "compiler internals are Boxes"
- AI tends toward theoretical completeness; humans toward practical simplicity

🐱 Sometimes the simplest answer is right in front of us\!

docs/private/papers/paper-h-ai-practical-patterns/pattern-categories.md

@@ -270,4 +270,26 @@ AIの診断や実装を鵜呑みにせず、基本に立ち返って検証する
 ### 効果
 - 問題回避: 発生前に防ぐ
 - 拡張性確保: 将来の変更に対応
-- 安心感: 予測可能な成長
+- 安心感: 予測可能な成長
+
+## 17. 段階的洗練型（新規追加）
+
+### 定義
+AIの複雑な提案から始まり、人間の直感的な単純化提案を経て、より洗練された解決策に収束するパターン。
+
+### 典型例
+- **フィールド宣言位置**: initブロック案（複雑）→ 先頭のみルール（単純）
+- **型情報追加**: 300行の型推論（複雑）→ 明示的型フィールド（単純）
+- **PHI生成**: 複数箇所での重複実装（複雑）→ Resolver統一（単純）
+
+### プロセス
+1. **AI初期提案**: 理論的に完全だが複雑
+2. **詳細分析**: メリット・デメリット・他言語比較
+3. **人間の直感**: 「もっと簡単にできないか？」
+4. **AI即座認識**: 単純解の価値を理解
+5. **実装戦略**: 段階的移行計画まで具体化
+
+### 効果
+- 最適解への収束: 複雑→単純の自然な流れ
+- 学習効果: AIも人間も学ぶ
+- 実装容易性: 最終的に簡単な解法に到達