Add 24 headline checkpoints (Qantara + LeWM, 4 envs x 3 seeds)

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - jepa
+  - world-models
+  - model-based-rl
+  - planning
+---
+
+# Qantara — headline checkpoints
+
+Released checkpoints for **Qantara**, a compact (~15M-param) JEPA world model for
+goal-conditioned planning from pixels and actions, and for our **LeWM** reproduction
+baseline. These reproduce the LeWM-suite headline numbers (Table 1) from the ICML 2026
+workshop paper.
+
+- **Code:** https://github.com/corl-team/qantara
+- **Paper:** https://arxiv.org/abs/2607.04978
+
+## Files
+
+24 checkpoints = 2 methods × 4 environments × 3 training seeds.
+
+| Pattern | Method |
+|---|---|
+| `qantara-<env>-s<seed>.ckpt` | Qantara (γ=1, λ_z=3, nulldrop=0) headline |
+| `lewm-<env>-s<seed>.ckpt`    | LeWM reproduction baseline |
+
+`env ∈ {pusht, tworoom, cube, reacher}`, `seed ∈ {11, 22, 33}`.
+
+## Loading
+
+Each file is a full model object (`torch.save` of a `jepa.JEPA`). Clone the code repo,
+then:
+
+```python
+import torch
+model = torch.load("qantara-pusht-s11.ckpt", map_location="cpu", weights_only=False)
+```
+
+The repository's `eval.py` consumes these directly. See the repo README for the full
+train → eval → figure pipeline.
+
+## Citation
+
+```bibtex
+@inproceedings{qantara2026,
+  title     = {Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control},
+  author    = {Rakhimov, Ruslan and Bredis, George and Maksyuta, Yuriy and Gavrilov, Daniil},
+  booktitle = {ICML 2026 Workshop on Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/2607.04978},
+}
+```

lewm-cube-s11.ckpt

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