feat(cnn_v3/training): load prev.png when available; document web tool prev gapHEADmain

- assemble_features() accepts optional prev ndarray (None → zeros)
- _load_sample() loads prev.png if present, else None
- __getitem__ slices/resizes prev alongside other channels
- TODO.md: note that cnn_v3/tools/shaders.js hardcodes prev=0 in both
  pack shaders while C++ gbuf_pack.wgsl reads a real prev_cnn texture

handoff(Gemini): prev.png now used in training when present; web tool gap documented in TODO.md

2 files changed, 18 insertions, 6 deletions

diff --git a/TODO.md b/TODO.md
index 0184f0a..c5b71e9 100644
--- a/TODO.md
+++ b/TODO.md
@@ -76,6 +76,10 @@ Ongoing shader code hygiene for granular, reusable snippets.
 **Pending (lower priority):**
 - [ ] GBufferEffect: Pass 3 transparency (transp=0 placeholder)
 - [ ] GBufferEffect: `resize()` support
+- [ ] Web tool (`cnn_v3/tools/shaders.js`): `prev_cnn` always zero in both pack shaders
+  (`FULL_PACK_SHADER` line ~313 and simple pack line ~39 hardcode `prev=0`).
+  C++ `gbuf_pack.wgsl` reads a real `prev_cnn` texture (binding 6).
+  Fix: add a `prev` texture binding to both JS pack shaders and wire it up in `tester.js`.
 
 ## Future: CNN v3 "2D Mode" (G-buffer-free)
 
diff --git a/cnn_v3/training/cnn_v3_utils.py b/cnn_v3/training/cnn_v3_utils.py
index 50707a2..68c0798 100644
--- a/cnn_v3/training/cnn_v3_utils.py
+++ b/cnn_v3/training/cnn_v3_utils.py
@@ -128,10 +128,11 @@ def _upsample_nearest(a: np.ndarray, h: int, w: int) -> np.ndarray:
 
 def assemble_features(albedo: np.ndarray, normal: np.ndarray,
                       depth: np.ndarray, matid: np.ndarray,
-                      shadow: np.ndarray, transp: np.ndarray) -> np.ndarray:
+                      shadow: np.ndarray, transp: np.ndarray,
+                      prev: np.ndarray | None = None) -> np.ndarray:
     """Build (H,W,20) f32 feature tensor.
 
-    prev set to zero (no temporal history during training).
+    prev: (H,W,3) f32 [0,1] previous frame RGB, or None → zeros.
     mip1/mip2 computed from albedo.  depth_grad computed via finite diff.
     dif (ch18) = max(0, dot(oct_decode(normal), KEY_LIGHT)) * shadow.
     """
@@ -140,7 +141,8 @@ def assemble_features(albedo: np.ndarray, normal: np.ndarray,
     mip1    = _upsample_nearest(pyrdown(albedo), h, w)
     mip2    = _upsample_nearest(pyrdown(pyrdown(albedo)), h, w)
     dgrad   = depth_gradient(depth)
-    prev    = np.zeros((h, w, 3), dtype=np.float32)
+    if prev is None:
+        prev = np.zeros((h, w, 3), dtype=np.float32)
     nor3    = oct_decode(normal)
     diffuse = np.maximum(0.0, (nor3 * _KEY_LIGHT).sum(-1))
     dif     = diffuse * shadow
@@ -348,11 +350,13 @@ class CNNv3Dataset(Dataset):
         shadow = load_gray(sd / 'shadow.png')
         transp = load_gray(sd / 'transp.png')
         h, w   = albedo.shape[:2]
+        prev_path = sd / 'prev.png'
+        prev = load_rgb(prev_path) if prev_path.exists() else None
         target_img = Image.open(sd / 'target.png').convert('RGBA')
         if target_img.size != (w, h):
             target_img = target_img.resize((w, h), Image.LANCZOS)
         target = np.asarray(target_img, dtype=np.float32) / 255.0
-        return albedo, normal, depth, matid, shadow, transp, target
+        return albedo, normal, depth, matid, shadow, transp, prev, target
 
     def __getitem__(self, idx):
         if self.full_image:
@@ -360,7 +364,7 @@ class CNNv3Dataset(Dataset):
         else:
             sample_idx = idx // self.patches_per_image
 
-        albedo, normal, depth, matid, shadow, transp, target = self._cache[sample_idx]
+        albedo, normal, depth, matid, shadow, transp, prev, target = self._cache[sample_idx]
         h, w = albedo.shape[:2]
 
         if self.full_image:
@@ -382,6 +386,8 @@ class CNNv3Dataset(Dataset):
             matid  = _resize_gray(matid)
             shadow = _resize_gray(shadow)
             transp = _resize_gray(transp)
+            if prev is not None:
+                prev = _resize_img(prev)
             target = _resize_img(target)
         else:
             ps        = self.patch_size
@@ -398,6 +404,8 @@ class CNNv3Dataset(Dataset):
             matid  = matid[sl]
             shadow = shadow[sl]
             transp = transp[sl]
+            if prev is not None:
+                prev = prev[sl]
 
             # Apply cached target offset (if search was enabled at init).
             if self._target_offsets:
@@ -408,7 +416,7 @@ class CNNv3Dataset(Dataset):
             else:
                 target = target[sl]
 
-        feat = assemble_features(albedo, normal, depth, matid, shadow, transp)
+        feat = assemble_features(albedo, normal, depth, matid, shadow, transp, prev)
 
         if self.augment:
             feat = apply_channel_dropout(feat,