Archive paper Figures 2–4 and S3–S6 for private review

README.md

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+---
+language:
+- en
+tags:
+- reinforcement-learning
+- neuroscience
+- drosophila
+- simulation
+datasets:
+- UncleStrawberryBear/FlyControl-data
+---
+# FlyControl — archived paper policies
+
+This is a private, author-review snapshot for **A Drosophila connectome prior enables adaptable embodied control**. It covers Figure 2, Figure 3, Figure 4, and Supplementary Figures S3–S6 from the final manuscript supplied on 2026-11-02. Figure 5 and Figure 6 are outside this snapshot.
+
+The manuscript identity is SHA-256 `cb7dac9a2c20305f6228c98766d7d0f361ce59c058e1e13b88bee59d38152a30`. The manuscript itself is not distributed here. The corresponding code is [FlyControl](https://github.com/THU-NeuroML/FlyControl/tree/1a49c33bf1e6eab17cb2953123e02ecc2549a5f4), revision `1a49c33bf1e6eab17cb2953123e02ecc2549a5f4`.
+
+The repository contains **226 original PyTorch checkpoint files** (190 final policies and 36 initial states), their effective configuration files, `model_index.json`, and a small loading helper. Files retain their original bytes, graph buffers, and saved normalization/training state. The total checkpoint size is approximately 11.01 GiB.
+
+| Paper item | Evidence / task | Checkpoint files |
+|---|---|---:|
+| Figure 2 | Terrain; Adult, MLP and ER, 20 seeds each | 60 |
+| Figure 3 | Turning; Adult/MLP, 24 seeds each, plus 24 Adult initial states | 72 |
+| Figure 4 | Speed; Adult/MLP, 20 seeds each | 40 |
+| S3 | Developmental direction; Adult/Larva, 6 seeds each | 12 |
+| S4 | Developmental speed; Adult/Larva, 6 seeds each | 12 |
+| S5 | BANC descending/motor readouts, 6 seeds each, initial and final | 24 |
+| S6 | BANC regional recovery, 3 seeds per readout | 6 |
+
+These are task-specific simulator policies. Main-figure checkpoints and supplemental checkpoints use different numerical engines. In particular, S5 speed and S6 recovery policies are distinct despite overlapping seed labels. The index records original paths and SHA-256 identities to disambiguate historical batches. For S4, Adult seed 3 uses the recorded `seed3_attempt1` policy. The S3 larval checkpoints use a dense recurrent graph; S4 larval checkpoints use sparse edges.
+
+## Download and load
+
+Use an authenticated Hugging Face account with access. Install the dependencies specified by the pinned FlyControl code, then download the model snapshot and the matching data archive:
+
+```python
+from huggingface_hub import snapshot_download
+models = snapshot_download('UncleStrawberryBear/FlyControl-models')
+```
+
+For a single cohort, use `allow_patterns` to select its checkpoints/configs together with `model_index.json` and `load_policy.py`. Main-figure policies require the matching graph in the extracted [data repository](https://huggingface.co/datasets/UncleStrawberryBear/FlyControl-data). Extract that figure archive into an empty `paper-data` directory.
+
+```bash
+git clone https://github.com/THU-NeuroML/FlyControl.git
+git -C FlyControl checkout 1a49c33bf1e6eab17cb2953123e02ecc2549a5f4
+python /path/to/model-snapshot/load_policy.py   --model-id figure2/connectome/seed_2011/final   --code-dir ./FlyControl --data-dir ./paper-data
+```
+
+This performs a strict state load and a numerical forward pass on zero input; it does not start a simulator. Supplemental policies reconstruct their graph from the saved checkpoint and do not need `--data-dir` for this numerical check. Run the helper in a fresh Python process so that another checkout's `fly_control` or `larva` modules cannot shadow the pinned code.
+
+The helper returns the policy and saved observation normalization state. Inputs must follow the task's original observation construction and normalization. Figure 2 uses 688 inputs (four 172-channel frames) and 6 actions; Figure 3 uses 64 inputs and 2 actions; Figure 4 and S3–S6 use 51 inputs and 2 actions. Main-figure Gaussian coordinates and supplemental action semantics are interpreted by their respective simulator engines; a raw network output is not a physical motor command.
+
+Legacy initial checkpoints contain Python training state, so the helper uses `torch.load(..., weights_only=False)` on these known, checksum-verified research files. Original configurations and checkpoints retain historical filesystem paths as provenance; they are not portable filesystem instructions. Map identities through `model_index.json` and use a new run directory with the code's task interfaces.
+
+## Validation and boundaries
+
+All 226 files were copied without transformation and verified against source SHA-256 values (and historical recorded hashes where available). Every policy tensor was checked for finite values. Eighteen representatives covering every figure/method/stage combination passed strict state loading, exact state equality, and three finite forward steps with the pinned code. Main-figure checkpoint budgets were checked: 32,768 / 8,192 / 15,872 steps for Figures 2 / 3 / 4. Details are in `validation.json`.
+
+This upload did not retrain policies or rerun full simulator evaluations. Historical source data are provided separately; the numerical loading checks do not certify a fresh end-to-end reproduction of all manuscript results. See `RIGHTS.md` for the current private-review licensing status.

RIGHTS.md

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+This snapshot is private and is prepared for author review. A public redistribution license for the authors' research data and weights has not yet been selected; the code repository's Apache-2.0 license does not automatically apply to these files.
+
+Derived connectome material retains the upstream FlyWire FAFB v783, larval Drosophila, and BANC v626 attribution and terms. Exact upstream input manifests are included under `external_inputs/` in the data archives. FlyGym/NeuroMechFly assets and numerical engines retain their upstream attribution; see the pinned code repository's THIRD_PARTY_NOTICES.md. This snapshot does not relicense upstream material.

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