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README.md

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+---
+license: apache-2.0
+language:
+- en
+- ja
+tags:
+- machine-learning
+- deep-learning
+- transformer
+- architecture-design
+- adaptive-algorithms
+- resonant-contraction
+- resonant-projection-field
+---
+# D-RNA：Dual‑Helix Resonance Neural Architecture (DRNA)  
+
+D-RNA is a new neural architecture centered on a dual helix structure and a rotation field produced by RoPE.  
+
+In this architecture, Attention and MLP are synchronized into a dual helix, and information is holographically compressed through Resonant Contraction.  
+This method rearranges sparse representations into dense ones to achieve high expressiveness using the depth‑direction structure alone, without increasing the number of dimensions.  
+A key feature of this approach is its ability to preserve the full connectivity of the Transformer architecture while suppressing catastrophic forgetting and retaining subtle fluctuations and phase information.  
+
+---
+
+### Features  
+High structural compatibility: It has the exact same input–output shape as a standard Transformer Block, allowing it to be smoothly substituted as the core of an architecture.  
+Resonant Contraction: By synchronizing Attention and the MLP in a double‑helix pattern and converging information into a phase field, it dramatically increases representational density.  
+Depth as an alternative to dimensionality: The spiral rotation (depth‑wise operations) compensates for limited dimensionality and enables holographic information retention without increasing parameter count.  
+Excellent learning efficiency: The spiral‑based information attraction (synchronization) achieves astonishing early convergence with far fewer steps than a Transformer.  
+Fine‑grained phase preservation: The rotational field powered by RoPE preserves subtle fluctuations and relative contextual relationships that are often lost in conventional architectures.  
+Re‑synchronization of knowledge: Existing weights can be transplanted as initialization and gently adapted to the spiral phase with a low learning rate, allowing existing intelligence to be evolved or overwritten into the D-RNA structure.
+
+
+### Notes  
+Optimization of learning rate (LR):  
+Because D-RNA synchronizes information extremely quickly through Resonant Contraction, it converges sufficiently — and rapidly — even with a lower learning rate compared to a standard Transformer.  
+If the LR is set too high, the resonance may be excessively amplified and cause oscillation, so starting with a modest LR is recommended.  
+Synergistic gradient effects:  
+Since Attention (recall) and the MLP (memory) are synchronized in a double‑helix sequence, the “settling” of weights from a single update is very strong.  
+This is an advantage for fast convergence, but it also means that careful updates are key to stability.  
+Parameter commonality:  
+Hyperparameters such as weight initialization seeds and batch size can be inherited directly from standard Transformer settings.  
+
+---
+
+### Conceptual Diagram
+
+```
+	Synchronizing “searching” (Attention) and “knowing” (MLP) in the phase of a spiral.
+
+	RoPE Rotation Field (Phase-Preserving)  
+	Holographic Compression: Turning Sparse into Dense  
+
+		A     M  
+		 \   /  
+		  \ /    ← This is Resonance  
+		  / \      Synchronization occurs naturally through the seed  
+		 /   \     Naturally, meaning emerges through a chain of synchronicities  
+		A     M  
+
+	Repeats in the depth direction to form a dual helix  
+	(acts as a substitute for increasing dimensionality)  
+```
+---
+
+### Minimal Block  
+
+```python
+class ResonantBlock(nn.Module):
+    def __init__(self, dim, n_heads):
+        super().__init__()
+        self.qkv = nn.Linear(dim, dim * 3)
+        self.out = nn.Linear(dim, dim)
+        self.mlp = MLP(dim)
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.n_heads = n_heads
+        self.d_head = dim // n_heads
+
+    def forward(self, x, cos, sin):
+        # --- Attention ---
+        q, k, v = project_qkv(x, self.qkv, self.n_heads, self.d_head)
+        q, k = apply_rope(q, k, cos, sin)
+        attn_out = attention(q, k, v)
+        x = self.norm1(x + self.out(attn_out))
+
+        # --- MLP ---
+        x = self.norm2(x + self.mlp(x))
+        return x
+```
+
+---
+
+### Example: Replacing a Transformer block with a D-RNA block  
+
+```python
+class DRNA_ResonantBlock(nn.Module):
+    """
+    Replace the existing TransformerBlock with this ResonantBlock.
+    I/O: [Batch, Seq, Dim] -> [Batch, Seq, Dim] (Fully compatible)
+    """
+    def __init__(self, dim, n_heads, mlp_dim_forward=None):
+        super().__init__()
+        self.n_heads = n_heads
+        self.d_head = dim // n_heads
+        
+        # 1. Spiral Projection Layer (A)
+        self.qkv = nn.Linear(dim, dim * 3)
+        self.out = nn.Linear(dim, dim)
+        
+        # 2. Spiral Memory Layer (B)
+        mlp_dim = mlp_dim_forward if mlp_dim_forward else dim * 4
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, mlp_dim),
+            nn.GELU(),
+            nn.Linear(mlp_dim, dim)
+        )
+        
+        # 3. Normalization layer for compression
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+    def forward(self, x, cos, sin):
+        """
+        Phase information for RoPE as an argument (cos, sin)
+        """
+        # Attention：Spiral Projection Layer (A)
+        # QKV -> RoPE -> Norm
+        q, k, v = project_qkv(x, self.qkv, self.n_heads, self.d_head)
+        q, k = apply_rope(q, k, cos, sin)
+        
+        attn_out = attention(q, k, v)
+        x = self.norm1(x + self.out(attn_out)) # Synchronization with context
+
+        # MLP：Spiral Memory Layer (B)
+        # MLP -> Norm
+        x = self.norm2(x + self.mlp(x)) # Determined by memory
+        
+        return x
+```
+
+### Replacement and Utilization of D-RNA  
+A direct drop‑in replacement is not possible, but it can be utilized through “redefinition and re‑synchronization.”  
+Why it cannot be used as‑is:  
+While a standard Transformer stores information using an “absolute address” (absolute position), D-RNA processes information using the “phase of a spiral” (relative position), meaning the coordinate systems are fundamentally different.  
+Even if the weights are copied directly, the phases do not align and no resonance occurs.  
+How to replace it (implementation):  
+The network’s input–output shapes are fully compatible.  
+By rewriting the existing layers as ResonantBlock and migrating positional information into RoPE’s rotational field, the core upgrade is complete.  
+How to utilize and adapt it (training):  
+After transferring the existing model’s weights as initialization, continue training with a low learning rate.  
+The previously static knowledge (existing weights) begins to synchronize with the spiral rotation, gradually blending into D-RNA’s “Resonant Contraction” process and evolving beyond the original performance.  
+
+---
+
+BPC Comparison Chart  
+
+<img width="800" alt="bpc_only" src="bpc_only.png" />  
+
+---
+
+License：  
+This project is licensed under the Apache License 2.0. (See the LICENSE for details).  
+
+#### Acknowledgments：  
+This work builds upon the foundation established by the Transformer architecture.  
+I would like to express my gratitude to the researchers and open-source communities
+whose contributions to attention mechanisms, positional encoding, and large-scale
+model design made this work possible.  

README_JA.md

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+---
+license: apache-2.0
+language:
+- en
+- ja
+tags:
+- machine-learning
+- deep-learning
+- transformer
+- architecture-design
+- adaptive-algorithms
+- resonant-contraction
+- resonant-projection-field
+---
+# D‑RNA：Dual‑Helix Resonance Neural Architecture (DRNA)  
+
+D-RNA は、二重らせん(Dual‑Helix)構造と RoPE による回転場を中核に据えた新しいニューラルアーキテクチャです  
+
+本アーキテクチャでは、Attention と MLP を二重らせんとして同期させ、共鳴収縮(Resonant Contraction)により情報をホログラフィックに圧縮します  
+これは 疎な表現を密に再配置 し、次元を増やすことなく、深さ方向の構造だけで高い表現力を獲得します  
+Transformer の全接続性を維持しつつ、破壊的忘却を抑制し、微細なゆらぎや位相情報を保持できる点が特徴です  
+
+---
+
+### D-RNA の主な特徴  
+
+高い構造的互換性：標準的な Transformer Block と入出力形状が同一であり、アーキテクチャの心臓部としてスムーズな置き換えが可能です。  
+共鳴収縮 (Resonant Contraction)：Attention と MLP を二重らせん状に同期させ、情報を位相場に収束させることで、表現密度を劇的に向上させます。  
+次元の代替としての深さ：螺旋の回転(深さ方向の演算)が次元の不足を補い、パラメータ数を増やさずホログラフィックな情報保持を実現します。  
+優れた学習効率：螺旋構造による情報の引き寄せ(同期)により、Transformer よりも極めて少ないステップ数で驚異的な早期収束を達成します。  
+微細な位相保持：RoPE を活用した回転場により、従来の構造では失われがちな微細なゆらぎや文脈の相対関係を精度高く保持します。  
+知識の再同調が可能：既存の重みを初期値として移植し、低学習率で螺旋の位相に馴染ませることで、既存知能を D-RNA 構造へ進化･上書きできます。 
+
+### ご注意ください  
+
+学習率(LR)の最適化：D-RNA は共鳴収縮により情報の同期が極めて速いため、標準的な Transformer よりも低い学習率で十分に、かつ高速に収束します。 設定が高すぎると、共鳴が過剰に増幅され振動を招く可能性があるため、控えめな LR からの開始を推奨します。  
+勾配の相乗効果：Attention(回想)と MLP(記憶)が二重らせん状に直列同期しているため、一回の更新による｢重みのなじみ｣が非常に強く働きます。 これは高速収束の利点であると同時に、慎重な更新が安定化の鍵であることを意味します。  
+パラメータの共通性：重みの初期化シードやバッチサイズなどのハイパーパラメータは、通常の Transformer 設定をそのまま継承できます。  
+
+---
+
+### 概念図(Conceptual Diagram) 
+```
+	｢探す｣(Attention)、｢知っている｣(MLP)この２つを螺旋の位相で同期させる
+
+	RoPE の回転場 (位相保持)  
+	疎を密にするホログラフィック圧縮  
+
+		A     M  
+		 \   /  
+		  \ /    ← ここが共鳴(Resonance)  
+		  / \      seed により自然に同期が生まれる  
+		 /   \     同期の連鎖で意味などを自然に引き寄せる  
+		A     M  
+
+	深さ方向へ繰り返すことで二重らせんを形成  
+	(次元の代替として機能)  
+```
+
+---
+
+### 最小コード(Minimal Block)  
+
+```python
+class ResonantBlock(nn.Module):
+    def __init__(self, dim, n_heads):
+        super().__init__()
+        self.qkv = nn.Linear(dim, dim * 3)
+        self.out = nn.Linear(dim, dim)
+        self.mlp = MLP(dim)
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.n_heads = n_heads
+        self.d_head = dim // n_heads
+
+    def forward(self, x, cos, sin):
+        # --- Attention ---
+        q, k, v = project_qkv(x, self.qkv, self.n_heads, self.d_head)
+        q, k = apply_rope(q, k, cos, sin)
+        attn_out = attention(q, k, v)
+        x = self.norm1(x + self.out(attn_out))
+
+        # --- MLP ---
+        x = self.norm2(x + self.mlp(x))
+        return x
+```
+
+---
+
+### 例：Transformer のブロックを DRNA ブロックに置き換える  
+
+```python
+class DRNA_ResonantBlock(nn.Module):
+    """
+    既存の TransformerBlock をこの ResonantBlock に置き換える
+    I/O: [Batch, Seq, Dim] -> [Batch, Seq, Dim] (完全互換)
+    """
+    def __init__(self, dim, n_heads, mlp_dim_forward=None):
+        super().__init__()
+        self.n_heads = n_heads
+        self.d_head = dim // n_heads
+        
+        # 1. 螺旋の射影層 (らせんA)
+        self.qkv = nn.Linear(dim, dim * 3)
+        self.out = nn.Linear(dim, dim)
+        
+        # 2. 記���の層 (らせんB)
+        mlp_dim = mlp_dim_forward if mlp_dim_forward else dim * 4
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, mlp_dim),
+            nn.GELU(),
+            nn.Linear(mlp_dim, dim)
+        )
+        
+        # 3. 収縮のための正規化層
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+    def forward(self, x, cos, sin):
+        """
+        引数に RoPE 用の位相情報 (cos, sin) を追加するのが唯一の違い
+        """
+        # --- らせんA: 文脈の共鳴 (Attention) ---
+        # QKV 抽出 -> RoPE 回転 -> 収縮(Norm)
+        q, k, v = project_qkv(x, self.qkv, self.n_heads, self.d_head)
+        q, k = apply_rope(q, k, cos, sin)
+        
+        attn_out = attention(q, k, v)
+        x = self.norm1(x + self.out(attn_out)) # 文脈との同期
+
+        # --- らせんB: 記憶の共鳴 (MLP) ---
+        # 知識の照会 -> 収縮(Norm)
+        x = self.norm2(x + self.mlp(x)) # 記憶による確定
+        
+        return x
+```
+
+### D-RNAへの置き換えと活用について  
+そのまま置換(Drop-in replacement)はできませんが｢再定義と再同調｣による活用は可能です  
+なぜそのままではダメなのか･･･  
+標準的なTransformerが｢絶対的な住所｣(絶対位置)で記憶しているのに対し、D-RNAは｢螺旋の位相｣(相対位置)で情報を処理するため、座標系が根本から異なります。 重みをそのままコピーしても、位相が合わず共鳴が起きません。  
+どう置き換えるのか(実装)  
+ネットワークの入出力形状は完全互換です。 既存の層を ResonantBlock に書き換え、位置情報を RoPE の回転場へ移行するだけで、心臓部のアップグレードが完了します。  
+どう活用し、なじませるのか(学習)  
+既存モデルの重みを初期値として転写(Transfer)した後、低学習率で継続学習を行います。 止まっていた知識(既存の重み)が螺旋の回転に同期し始め、次第にD-RNAの｢共鳴収縮｣のプロセスへ溶け込み、元の性能を超えて進化します。  
+
+---
+
+BPC 比較図  
+
+<img width="800" alt="bpc_only" src="bpc_only.png" />  
+
+---
+
+ライセンス：  
+本プロジェクトは Apache License 2.0 の下で公開されています(詳細は LICENSE をご覧ください)  
+
+### 謝辞：  
+本研究は Transformer アーキテクチャによって築かれた基盤の上に成り立っています  
+Attention 機構、位置エンコーディング、大規模モデル設計に関する研究とオープンソースコミュニティの貢献に深く感謝いたします  

drna.py

@@ -0,0 +1,108 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+'''
+D‑RNA: Dual‑Helix Resonance Neural Architecture (DRNA) 260420
+Transformerの全接続性を継承しつつ、二重らせん(Dual-Helix)構造による
+｢共鳴収縮｣(Resonant Contraction)を物理的に再現したニューラルアーキテクチャです
+螺旋の同期: Attention(文脈の回想)とMLP(知識の定着)を直列に配置し、情報を一段ずつ絞り込む
+位相の保持: RoPE(Rotary Positional Embedding)を回転場として利用し、相対位置を保つ
+高密度圧縮: Post-Norm構造により、各らせんの出力直後に情報を収縮させ、意味を確定させる
+'''
+
+class DRNA_RoPE(nn.Module):
+    """二重らせんの位相を決定する回転場"""
+    def __init__(self, d_model, base=10000):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, d_model, 2).float() / d_model))
+        self.register_buffer("inv_freq", inv_freq)
+
+    def forward(self, x, seq_len):
+        t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return emb.cos()[None, :, :], emb.sin()[None, :, :]
+
+def apply_drna_rope(q, k, cos, sin):
+    """位相回転の適用"""
+    def rotate_half(x):
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    cos, sin = cos[:, None, :, :], sin[:, None, :, :]
+    return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
+
+class DRNA_Block(nn.Module):
+    """DRNA共鳴ブロック：元設計に忠実な直列共鳴構造"""
+    def __init__(self, d_model, n_heads, d_ff=None, dropout=0.1):
+        super().__init__()
+        self.n_heads = n_heads
+        self.d_head = d_model // n_heads
+        
+        # らせんA: 回想系 (Attention)
+        self.qkv = nn.Linear(d_model, d_model * 3)
+        self.out_proj = nn.Linear(d_model, d_model)
+        self.norm1 = nn.LayerNorm(d_model)
+        
+        # らせんB: 記憶系 (MLP)
+        d_ff = d_ff or d_model * 4
+        self.mlp = nn.Sequential(
+            nn.Linear(d_model, d_ff),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(d_ff, d_model)
+        )
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, cos, sin):
+        b, s, d = x.shape
+        
+        # --- らせんA (Attention Resonance) ---
+        qkv = self.qkv(x).reshape(b, s, 3, self.n_heads, self.d_head).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+        
+        q, k = apply_drna_rope(q, k, cos, sin)
+        
+        # Scaled Dot-Product Attention
+        attn = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(self.d_head))
+        attn = F.softmax(attn, dim=-1)
+        
+        a_out = (attn @ v).transpose(1, 2).reshape(b, s, d)
+        
+        # 収縮統合1
+        x = self.norm1(x + self.dropout(self.out_proj(a_out)))
+        
+        # --- らせんB (MLP Resonance) ---
+        # 収縮統合2
+        x = self.norm2(x + self.dropout(self.mlp(x)))
+        
+        return x
+
+class DRNA_Model(nn.Module):
+    """汎用 DRNA モデルコンテナ"""
+    def __init__(self, vocab_size, d_model=256, n_layers=16, n_heads=8, d_ff=1024):
+        super().__init__()
+        self.embed = nn.Embedding(vocab_size, d_model)
+        self.rope = DRNA_RoPE(d_model // n_heads)
+        
+        self.layers = nn.ModuleList([
+            DRNA_Block(d_model, n_heads, d_ff) for _ in range(n_layers)
+        ])
+        
+        self.output_head = nn.Linear(d_model, vocab_size)
+
+    def forward(self, x):
+        cos, sin = self.rope(x, x.size(1))
+        x = self.embed(x)
+        
+        for layer in self.layers:
+            x = layer(x, cos, sin)
+            
+        return self.output_head(x)
+
+'''
+汎用型 D-RNA コード License: Apache License 2.0
+https://github.com/muooon/DRNA
+'''