diff --git a/README.md b/README.md
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index 0000000000000000000000000000000000000000..ab270dab0fcdccb5b19030e185eb6612aa1738c3
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+---
+license: mit
+tags:
+  - operations-research
+  - vehicle-routing
+  - reinforcement-learning
+  - combinatorial-optimization
+---
+
+# TTP-D solver weights
+
+Trained checkpoints for the Travelling Thief Problem with Drone (TTP-D) study.
+Benchmark instances and result tables live in the companion dataset repo
+[`Murjani/ttpd-benchmarks`](https://huggingface.co/datasets/Murjani/ttpd-benchmarks); the code is at
+https://github.com/Kcbir/ttpd.
+
+## Layout
+
+```
+<variant>/<model>/<family>/n<N>/best.pt
+```
+
+- **variant** — `a280` (fixed drone endurance) or `ttd300` (endurance-conditioned)
+- **model** — `gat` (attention encoder), `mlp` (the encoder ablation), `lisa`
+  (behaviour cloning)
+- **family** — what the policy was trained on:
+
+| family | trained on | use it for |
+|---|---|---|
+| `benchmark-tuned/` | the five benchmark instances of that size | the headline results |
+| `sampled/` | randomly sampled a280 subsets | generalisation; warm start for the above |
+| `specialists/` | early small-n runs | the n<=20 rows using a dedicated small model |
+
+LISA weights live at `<variant>/lisa/bc/n<N>.pt`, with
+`a280/lisa/bc-initial/` and `a280/lisa/gat-n50/` kept for provenance.
+
+`best.pt` is the deliverable (best held-out beam evaluation); `last.pt` is a
+resume artifact; `best_milp.pt` is selected by MILP gap instead of eval return.
+
+## Loading
+
+Checkpoints are `torch.save` payloads with a `"policy"` state-dict key:
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download("Murjani/ttpd-weights", "a280/gat/bench-tuned/n30/best.pt")
+payload = torch.load(path, map_location="cpu", weights_only=False)
+policy.load_state_dict(payload["policy"])
+```
+
+The repo code resolves these automatically -- see `ttpd_hub.ensure_local`.
+
+`manifest.json` records, for every file, the path it was published from and its
+sha256.
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