refactor: 将子模块转为普通目录，移除外部 git 依赖

- 移除 JEPA/lejepa-identifiability 子模块 gitlink
- 移除 research/multiply/MultiPLY 子模块 gitlink
- 删除 .gitmodules（不再有外部 URL 依赖）
- 两个目录内容作为普通文件纳入主仓库追踪
- 删除各自内部 .git 目录，消除嵌套 git 仓库

JEPA/lejepa-identifiability/experiments/lejepa_id/models.py

@@ -0,0 +1,55 @@
+"""Encoder architectures."""
+
+import torch
+import torch.nn as nn
+import numpy as np
+
+
+def make_mlp_encoder(N, hidden=256, n_layers=4, device="cuda"):
+    """MLP encoder."""
+    layers = [nn.Linear(N, hidden), nn.GELU()]
+    for _ in range(n_layers - 1):
+        layers += [nn.Linear(hidden, hidden), nn.GELU()]
+    layers.append(nn.Linear(hidden, N))
+    return nn.Sequential(*layers).to(device)
+
+
+class MatchedEncoder(nn.Module):
+    """Inverse coupling-layer encoder matched to NVP mixing architecture."""
+
+    def __init__(self, N, n_layers=4, device="cuda"):
+        super().__init__()
+        half = N // 2
+        self.half = half
+        self.n_layers = n_layers
+        self.Ws = nn.ParameterList([
+            nn.Parameter(torch.randn(half, half, device=device) / np.sqrt(half))
+            for _ in range(n_layers)
+        ])
+
+    def forward(self, x):
+        for i, W in reversed(list(enumerate(self.Ws))):
+            z1, z2 = x[..., :self.half], x[..., self.half:]
+            if i % 2 == 0:
+                z2 = z2 - torch.tanh(z1 @ W)
+            else:
+                z1 = z1 - torch.tanh(z2 @ W)
+            x = torch.cat([z1, z2], dim=-1)
+        return x
+
+
+def make_matched_encoder(N, n_layers=4, seed=42, device="cuda"):
+    torch.manual_seed(seed)
+    return MatchedEncoder(N, n_layers=n_layers, device=device).to(device)
+
+
+def make_cnn_encoder(d_latent=2, device="cuda"):
+    return nn.Sequential(
+        nn.Conv2d(3, 32, 4, 2, 1), nn.BatchNorm2d(32), nn.GELU(),
+        nn.Conv2d(32, 64, 4, 2, 1), nn.BatchNorm2d(64), nn.GELU(),
+        nn.Conv2d(64, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.GELU(),
+        nn.Conv2d(128, 256, 4, 2, 1), nn.BatchNorm2d(256), nn.GELU(),
+        torch.nn.AvgPool2d(4), nn.Flatten(),
+        nn.Linear(256, 256), nn.BatchNorm1d(256), nn.GELU(),
+        nn.Linear(256, d_latent),
+    ).to(device)