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README.md

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+# Compresser Decoder (Inverse Perceiver)
+
+Phase 0 pretrained Decoder for the Mamba-3 Semantic Video Compressor.
+
+## Architecture
+- **Type**: Inverse Perceiver (cross-attention expansion)
+- **Input**: [B, 64, 512] — Perceiver compressed tokens
+- **Output**: [B, 576, 1664] — reconstructed V-JEPA latents
+- **Params**: ~11.1M
+- **Details**: 576 learned queries, 3 cross-attention layers, 16 heads, FFN 512→2048→512
+
+## Training
+- **Dataset**: [Vjepa_mamba_dataset_v2](https://huggingface.co/datasets/rookierufus/Vjepa_mamba_dataset_v2) (50 hours video, 384×384, 8fps)
+- **V-JEPA**: Frozen [vjepa2_1_vit_gigantic_384](https://github.com/facebookresearch/vjepa2) (2.2B params)
+- **Loss**: MSE reconstruction (autoencoder target = V-JEPA latent)
+- **Optimizer**: AdamW, lr=1e-4, cosine to 1e-6
+- **Hardware**: RTX 4090 (48 GB), bf16
+
+## Usage
+```python
+from compressor.decoder import PerceiverDecoder
+
+model = PerceiverDecoder(input_dim=512, output_dim=1664, num_queries=576)
+model.load_state_dict(torch.load("decoder_stepX_hrsY.pt"))
+# Input: [B, 64, 512] Perceiver output → Output: [B, 576, 1664] V-JEPA latents
+```
+
+## Note
+Disposable after Phase 0 — only the Encoder (Perceiver) carries forward to the main pipeline.