Simplify model: shared DINOv3 WEB + MONA in last 12/24 blocks

Three related architecture changes, driven by a cost/simplicity trade-off:

1. **Shared encoder**: one DINOv3 LVD-1689M (WEB) processes both drone
   and satellite images. Previously asymmetric — separate WEB (drone) and
   SAT-493M (satellite) encoders. Saves ~303M frozen params and halves
   VRAM for the image tower. Expected to lose some satellite-domain
   inductive bias; MONA adapters pick up the slack.

2. **MONA in last 12/24 blocks**: adapters injected only in the top half
   of the ViT. The lowest 12 blocks keep their pretrained features
   untouched. Trainable MONA count drops from 14.0M (48 adapters × 2
   encoders) to 3.5M (24 adapters × 1 encoder).

3. **No DINO_SAT**: `nn_models/DINO_SAT` is no longer loaded by the
   default config. It stays on disk and the path param is kept for
   backward compat with asymmetric checkpoints.

Parameter counts (with text fusion + LoRA + gates):
  Before: 17.6M trainable / 733M total (2.35%)
  After:   7.06M trainable / 434M total (1.63%)

Also fixes a pre-existing resume bug: checkpoints now record
`shared_encoder`, `baseline_mode`, `mona_bottleneck`, `mona_last_n_blocks`
so `AsymmetricEncoder.load_checkpoint` can rebuild the right architecture.
Old checkpoints still load (missing keys fall back to asymmetric defaults
via `ckpt.get(..., <default>)`).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

conf/gtauav_balanced.gin

@@ -22,7 +22,9 @@ TrainConfigGTAUAV.device = "cuda"
 # ---- Model ----
 TrainConfigGTAUAV.init_gate = 0.7
 TrainConfigGTAUAV.baseline_mode = False
-TrainConfigGTAUAV.shared_encoder = False
+TrainConfigGTAUAV.shared_encoder = True  # single DINOv3 WEB for both branches
+TrainConfigGTAUAV.mona_bottleneck = 64
+TrainConfigGTAUAV.mona_last_n_blocks = 12  # inject MONA only in last 12/24 ViT blocks
 TrainConfigGTAUAV.gradient_checkpointing = True
 
 # ---- Loss ----