Add ChronoLoop model card

README.md

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+---
+license: apache-2.0
+tags: [robotics, world-action-model, libero, loopwam, chronoloop]
+---
+# ChronoLoop checkpoints
+
+ChronoLoop adds a small recurrent memory to LoopWAM v0: 16 memory tokens ride through the
+pre-blocks and the shared core loops with the observation, are read out after the last core
+loop, squashed and mixed into a carried state, and are fed back at the next replanning query.
+Non-memory tokens read the memory through a per-head gate `tanh(alpha)` that starts at 0, so
+every run starts exactly at its parent.
+
+Every run fine-tunes the same parent, `anhdao69/LoopWAM_NT/libero-all-suites/v0-video4-action4`
+(LoopWAM v0, 4 video / 4 action core loops, 388/400 on the four LIBERO suites), on all four
+LIBERO suites (1,712 demonstrations, 277,713 windows) for 10 epochs with global batch 128
+(32 streams x 4 consecutive windows, truncated BPTT S = 4). The schedule, noise and data order
+are shared by all runs, so they differ only in the memory flags.
+
+| Folder | Run | Definition |
+|---|---|---|
+| `CL-0_no-memory_a4` | CL-0 | No memory (control), K_a = 4 |
+| `CL-A_mem16-learned-loopwrite_a4` | CL-A | Memory written by the shared core loops (default ChronoLoop) |
+| `CL-REG_mem16-reset-registers_a4` | CL-REG | Same 16 tokens reset to s = 0 every query |
+| `CL-W2_mem16-learned-external-updater_a4` | CL-W2 | Read-only memory in the backbone; separate 1-block updater |
+| `CL-0-at1_no-memory_a1` | CL-0@1 | CL-0 with 1 action core loop |
+| `CL-A-at1_mem16-learned-loopwrite_a1` | CL-A@1 | CL-A with 1 action core loop |
+| `CL-FRAME_history-frame-k3_a4` | CL-FRAME | No memory; observation from query k-3 as extra gated clean frame |
+
+Each folder has `epoch_08/`, `epoch_09/` and `epoch_10/` with `policy.pt` (weights only, format
+`chronoloop-v1`) and `config.json`, plus the run's `manifest.json`, `metrics.jsonl` and `timing.json`.
+A folder appears only after its training has written those epochs, and every upload is checked against
+the local SHA-256.
+
+Load with the `chrono` branch of LoopWAM_NT:
+
+```python
+from fastwam.models.wan22.chronoloop import ChronoConfig, create_chronoloop
+import torch
+payload = torch.load('policy.pt', map_location='cpu', weights_only=False, mmap=True)
+model, _ = create_chronoloop(ChronoConfig(**payload['chrono']), 'Wan2.1_VAE.pth', checkpoint_path='policy.pt', device='cuda')
+# per replanning query: out = model.chrono_infer_action(image, proprio, context, mask, state=state); state = out['state']
+```
+
+Rollouts: `scripts/evaluate_chronoloop_libero.py` (700 policy steps, 30 settling steps, replan every 10 actions,
+memory reset at episode start, fixed or fresh noise).