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saju_inference.py

@@ -98,12 +98,23 @@ class SajuAttentionModel(nn.Module):
         self.attention = nn.MultiheadAttention(embed_dim=embedding_dim, num_heads=num_heads, batch_first=True, dropout=0.3)
         self.alpha = nn.Parameter(torch.tensor(0.5))
         
+        # Red Neuronal Profunda de Proyección (Non-linear mapping)
+        self.projection = nn.Sequential(
+            nn.Linear(embedding_dim, embedding_dim * 2),
+            nn.GELU(),
+            nn.Dropout(0.2),
+            nn.Linear(embedding_dim * 2, embedding_dim)
+        )
+        
     def forward(self, year_emb, month_emb, day_emb, time_emb):
         pillars_seq = torch.stack([year_emb, month_emb, day_emb, time_emb], dim=1)
         attn_out, attn_weights = self.attention(pillars_seq, pillars_seq, pillars_seq)
         saju_context = attn_out.mean(dim=1)
         base_saju = pillars_seq.mean(dim=1)
-        return base_saju + (self.alpha * saju_context), attn_weights
+        
+        # Conexión residual (Deltas perfectos)
+        combined = base_saju + (self.alpha * saju_context)
+        return combined + self.projection(combined), attn_weights
 
 # -------------------------------------------------------------------
 # 3. Genuine Universal Engine (Hugging Face Ready)