Prepare AdaPrune checkpoint 1000 public inference release

PUBLICATION_SCOPE.json

@@ -1,5 +0,0 @@
-{
-  "scope": "model weights, masks, architecture, normalization and provenance only",
-  "optimizer_resume_states": "deferred by explicit user instruction",
-  "original_files_modified": false
-}

README.md

@@ -3,59 +3,63 @@ language: en
 tags:
 - robotics
 - vpp2
-- model-compression
 - structured-pruning
 - robodojo
 ---
 
 # AdaPrune — Bagel-Robotics-1 checkpoint 1000
 
-Complete: listed model artifacts verified against local receipts and remote checksums.
+Public inference release of the **exact checkpoint 1000** used in the Bagel-Robotics-1 evaluation campaign. Downloads require no account, token or access request. This is distinct from Bagel-Robotics-1-Preview.
 
-This is the **physically pruned policy checkpoint selected for the full-suite evaluation campaign**. The uploaded inference files match the SHA-256 fingerprints in all five suite protocols (Precision, Memory, Generalization, Long Horizon and Open). This establishes artifact identity, not completion of every evaluation episode.
+The video expert has **12,468,409,664 parameters**, 23.95% fewer than its recorded parent. Learned masks were converted into smaller FFN and cross-attention layers. Self-attention heads and the action expert are retained. No teacher or learned-mask module is required for inference. Parameter reduction is not a measured inference-speedup claim.
 
-Keep these four files together: `video.pt`, `action.pt`, `compact_architecture.json`, `dataset_stats.json`. No learned-mask module or full teacher is needed for deployed inference.
+## Fixed checkpoint identity
 
-### Evaluation settings
+- Original checkpoint revision: `2651631c5d08819088475e8778676f5cf6d9afcf`.
+- Identity: `790bffc9464fa7a026aa43b217aec2033d186895cad643166daa0bcc01914d5c`.
+- [artifact-manifest.json](artifact-manifest.json) pins all six bundle files and the separate public encoder/tokenizer assets.
+- [SHA256SUMS](SHA256SUMS) records the unchanged SHA-256 hashes of all six bundle files.
 
-- Action horizon: 32; execute 16 actions per query.
-- Action sampling: 10 Euler steps; shift 1; policy seed 1.
-- Temporal ensembling: 90% newly predicted actions, 10% previous overlapping predictions, using the recorded quaternion handling in the evaluation code.
+The release adds documentation and inference packaging without changing any checkpoint file. The original revision is preserved, so historical provenance remains accessible in Git history. Current release files focus on inference. `COMPLETE.json` is the original export receipt, including export-time paths and `simulation_completed: false`; it is retained unchanged for integrity, not used as a current evaluation-status claim. Those historical paths are not required for deployment.
 
-The full-suite campaign comprises 400 Precision, 300 Memory, 600 Generalization, 400 Long Horizon and 400 Open episodes. Frozen protocol snapshots are included under `evaluation/`. They record intended evaluation coverage and checkpoint hashes; this repository does not claim a completed full-suite score.
+## Download the exact checkpoint
 
-### Training and validation context
-
-Recovery after checkpoint 55 used ground-truth action supervision (1.0) and reliability-weighted teacher-video supervision (0.05), with K/V and teacher-action imitation losses both zero. Video weights trained; Action2B remained frozen. Curated 30,000-window subset, global batch 256, training seed 100008. The complete source config is preserved.
-
-At update 1,000, the fixed 140-window offline validation panel reported task-macro normalized native-action MSE of **0.0005726480308242441** against ground truth. The offline action metric scores up to the first 24 valid actions; it differs from the simulation execution interval of 16. `validation.json` contains the original metrics. Lower offline error alone does not establish improved robot success.
+```bash
+hf download bageldotcom/adaprune \
+  --revision 2651631c5d08819088475e8778676f5cf6d9afcf \
+  --include video.pt action.pt compact_architecture.json dataset_stats.json COMPLETE.json validation.json \
+  --local-dir /absolute/path/to/checkpoints/cp1000
+```
 
-The original `COMPLETE.json` has `simulation_completed: false`, reflecting its export-time status. It is preserved unedited; subsequent evaluation provenance is separate.
+There are approximately 29.0 GB of checkpoint tensors. Keep all six files together. The inference downloader checks their sizes and SHA-256 hashes and downloads approximately 16.7 GB of separately pinned VAE/conditioning/tokenizer assets. Allow extra disk space for caches and installation.
 
-## Download
+## Inference adapter and reevaluation
 
-Authenticate with an account that has access to this private repository, then run:
+The standalone XPolicyLab adapter is provided under [`inference/`](inference/), at release tag `adaprune-cp1000-inference-v1`. No private research repository is required.
 
 ```bash
 hf download bageldotcom/adaprune \
-  --revision 2651631c5d08819088475e8778676f5cf6d9afcf \
-  --local-dir /absolute/path/to/checkpoints/cp1000
-```
+  --revision adaprune-cp1000-inference-v1 \
+  --include 'inference/**' \
+  --local-dir /absolute/path/to/adaprune-release
 
-Use the code and setup link below to load these files. Keep the integrity receipts with the downloaded weights.
+git clone https://github.com/XPolicyLab/XPolicyLab.git /absolute/path/to/workspace/XPolicyLab
+git -C /absolute/path/to/workspace/XPolicyLab checkout 6baccd57ff958570d0db1588370289ee36c8bb70
+python /absolute/path/to/adaprune-release/inference/install_adapter.py /absolute/path/to/workspace/XPolicyLab
+```
 
-For the prepared XPolicyLab submission, the policy directory is `policy/bagel_robotics_1` and the checkpoint name is `cp1000`. Its pinned downloader fetches the six required bundle files and the separate VAE/conditioning assets, verifies their hashes, and supplies the standalone inference runtime. The adapter PR is being prepared; no public XPolicyLab PR link is available yet. The technical report and training/pruning/export code are planned for November 20, 2026. Evaluator access must be arranged while this repository remains private.
+Follow the included [policy README](inference/policy/bagel_robotics_1/README.md) to install the Python 3.10 environment, download/verify assets and run both debug checks. Follow [REEVALUATION.md](inference/policy/bagel_robotics_1/REEVALUATION.md) for task, suite and three-seed commands. Install RoboDojo and simulator assets separately using its [official guide](https://robodojo-benchmark.com/doc/usage/install-and-download/).
 
-## Lineage and code
+The adapter uses `policy_name=bagel_robotics_1`, `ckpt_name=cp1000`, ARX X5 end-effector actions, and one environment per policy process. Fixed inference settings: BF16; predict 32 actions and execute 16; ten Euler steps; shift 1; 90% new / 10% previous temporal blending. Qualification used a 96 GB RTX PRO 6000 Blackwell, Python 3.10.21 and PyTorch 2.11.0+cu130; a lower minimum GPU capacity has not been qualified.
 
-Mask learning update 1,500 selected the channels. The physically pruned video expert has **12,468,409,664 parameters**, compared with 16,394,878,784 before pruning (23.95% fewer video parameters). The frozen Action2B expert is separate from this count. Recovery checkpoint 55 already had 55 recovery updates with K/V supervision; the later no-K/V branch continued from it to update 1,000.
+These are compact-aware VPP2 artifacts, not Transformers `AutoModel` checkpoints. The XPolicyLab PR has not been created; publishing this inference release does not imply committee acceptance.
 
-[Historical research code (access may be restricted)](https://github.com/bageldotcom/bagel-robotics/tree/2fc74c246136e543d72ff9af1732936814d3baf8/workflows/distillation) · [Historical research integration PR](https://github.com/bageldotcom/bagel-robotics/pull/169)
+## Validation and release scope
 
-These artifacts require a compact-aware VPP2 loader; they are not Transformers `AutoModel` checkpoints. The prepared XPolicyLab adapter includes that inference runtime and does not require access to the historical research repository. Until its PR is available, reviewers need the standalone adapter package from Bagel Labs. External VAE/conditioning encoders and RoboDojo data remain separate dependencies. Original upstream model/data terms continue to apply; this backup does not assert a new redistribution license.
+The adapter's existing qualification includes 15 CPU tests, both real-checkpoint debug transports, four valid native simulator episodes, and eight exact action/history/reset/backend checks on the tested recordings. Full multi-seed official leaderboard validation remains separate. Historical research scores and scene exceptions are documented in the adapter's EVALUATION_RESULTS.md; they are not official leaderboard results.
 
-## Integrity and training continuation
+The technical report and training/pruning/export code are planned for **November 20, 2026**. This release contains inference code and weights; optimizer, scheduler, distributed RNG/sampler state, training datasets and simulator assets are not included. It does not support exact training continuation from weights alone.
 
-`UPLOAD_INVENTORY.json` records every uploaded artifact's original path, size, and SHA-256. Upload verifies the source checksum against its recorded receipt and the remote file checksum. Original files are never modified or deleted.
+## Attribution and terms
 
-**Optimizer, scheduler, distributed RNG/sampler state and ZeRO shards are not included in this model-weight upload.** Model weights alone cannot exactly resume training. `RESUME_STATE_LOCAL.json` identifies the preserved local full checkpoint and its checksums; it does not mean those shards are on Hugging Face.
+Derived from [VPP2](https://github.com/roboterax/video-prediction-policy-2) and Wan2.1 components. See [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md) and the adapter's retained MIT/Apache-2.0 notices. Model and auxiliary-asset upstream terms continue to apply; an inference-code license is not a new blanket license for all weights or datasets.

RELEASE.json

@@ -0,0 +1,36 @@
+{
+  "model_name": "Bagel-Robotics-1",
+  "release_name": "AdaPrune checkpoint 1000",
+  "repository": "bageldotcom/adaprune",
+  "checkpoint_revision": "2651631c5d08819088475e8778676f5cf6d9afcf",
+  "checkpoint_identity": "790bffc9464fa7a026aa43b217aec2033d186895cad643166daa0bcc01914d5c",
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+    }
+  },
+  "scope": "Public inference checkpoint and supporting adapter; training/pruning/export source planned November 20, 2026",
+  "historical_provenance": "Original checkpoint revision and checksums preserved. Older commits retain original provenance and export records; the current release view contains inference materials.",
+  "official_leaderboard_result": false
+}

RESUME_STATE_LOCAL.json

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SHA256SUMS

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THIRD_PARTY_NOTICES.md

@@ -0,0 +1,13 @@
+# Source and artifact notices
+
+- The retained VPP2/FastWAM-derived runtime carries the original MIT notice in
+  `LICENSE`, copyright 2026 The FastWAM Authors. Bagel-specific packaging does not
+  remove that attribution. See `source-provenance.json` for frozen-source hashes.
+- Wan2.1/DiffSynth-derived model components retain Apache-2.0 terms; see
+  `licenses/Apache-2.0.txt` and upstream Wan2.1/DiffSynth source notices.
+- The XPolicyLab interface follows its upstream license, included in
+  `licenses/XPolicyLab.txt`. RoboDojo and simulator assets are not bundled.
+- Encoder weights, tokenizer files and model weights have their own upstream
+  terms. The code license does not assign a new license to those artifacts.
+- The existing preview PR by Soroush Mehraban was consulted as a reference; its
+  alternate checkpoint, code and release promises are not this submission.

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UPLOAD_VERIFICATION.json

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-{
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-  "verified_at": "2026-11-04T20:00:51.244557+00:00"
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artifact-manifest.json

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+{
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evaluation/full-suite-manifest.json

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-{
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evaluation/generalization-protocol.json

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-{
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evaluation/memory-protocol.json

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evaluation/open-protocol.json

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evaluation/precision-completion.json

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evaluation/precision-protocol.json

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-}

inference/README.md

@@ -0,0 +1,13 @@
+# Bagel-Robotics-1 public inference adapter
+
+This folder is the standalone XPolicyLab adapter for AdaPrune checkpoint 1000. It contains inference code and evaluation instructions, with no training repository dependency. Model tensors download separately from `bageldotcom/adaprune`, pinned to `2651631c5d08819088475e8778676f5cf6d9afcf` and verified against `policy/bagel_robotics_1/artifact-manifest.json`.
+
+Install this adapter into an existing XPolicyLab checkout (the installer refuses to overwrite an existing policy):
+
+```bash
+python install_adapter.py /absolute/path/to/XPolicyLab
+```
+
+Then follow [Installation](policy/bagel_robotics_1/README.md#installation) for the Python 3.10 environment, exact checkpoint and encoder download, and debug checks. [REEVALUATION.md](policy/bagel_robotics_1/REEVALUATION.md) gives native simulator task/suite/three-seed commands. RoboDojo and its assets are installed separately.
+
+The adapter is prepared against XPolicyLab `6baccd57ff958570d0db1588370289ee36c8bb70`; source and dependency pins are preserved in the policy folder. This public release is not an XPolicyLab PR or an official leaderboard result. Training/pruning/export source and the technical report are planned for November 20, 2026.

inference/SHA256SUMS

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inference/install_adapter.py

@@ -0,0 +1,40 @@
+"""Install this standalone policy into an existing XPolicyLab checkout.
+
+Refuses to overwrite any existing adapter. Does not publish, submit, or load GPUs.
+"""
+import argparse
+import json
+from pathlib import Path
+import shutil
+import subprocess
+
+ROOT = Path(__file__).resolve().parent
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("xpolicylab", type=Path)
+    args = parser.parse_args()
+    xpl = args.xpolicylab.expanduser().resolve()
+    for rel in ["model_template.py", "setup_policy_server.py", "utils/checkpoint_resolver.py"]:
+        if not (xpl / rel).is_file():
+            parser.error(f"Not a compatible XPolicyLab checkout: missing {rel}")
+    source = ROOT / "policy/bagel_robotics_1"
+    destination = xpl / "policy/bagel_robotics_1"
+    if destination.exists():
+        parser.error(f"Refusing to overwrite {destination}; choose a clean checkout")
+    shutil.copytree(source, destination,
+                    ignore=shutil.ignore_patterns("__pycache__", "*.pyc", "*.egg-info",
+                                                  "checkpoints", ".venv", "build", "dist", ".pytest_cache", "validation-output"))
+    expected = json.loads((source / "source-provenance.json").read_text())["xpolicylab_base"]
+    version = subprocess.run(["git", "-C", str(xpl), "rev-parse", "HEAD"],
+                             capture_output=True, text=True)
+    print(f"Installed: {destination}")
+    print(f"Prepared against XPolicyLab commit: {expected}")
+    if version.returncode == 0 and version.stdout.strip() != expected:
+        print("This checkout differs from the pinned reference; rerun adapter checks.")
+    print(f"Next: bash {destination}/install.sh /absolute/path/to/new-policy-venv")
+
+
+if __name__ == "__main__":
+    main()

inference/policy/bagel_robotics_1/.gitignore

@@ -0,0 +1,9 @@
+checkpoints/
+.venv/
+__pycache__/
+*.pyc
+*.egg-info/
+.pytest_cache/
+build/
+dist/
+validation-output/

inference/policy/bagel_robotics_1/EVALUATION_RESULTS.md

@@ -0,0 +1,103 @@
+# Checkpoint-1000 research evaluation results
+
+Bagel-Robotics-1 / AdaPrune Final, checkpoint 1000. Success means full task completion; partial scores average credited progress over all reported outcomes, including failures. Partial values here are out of 100. Generalization combines standard and randomized episodes; other suites use their recorded standard scene selections.
+
+| Suite | Successful / reported outcomes | Success rate | Mean partial /100 |
+|---|---:|---:|---:|
+| Precision | 126/400 | 31.50% | 40.43 |
+| Memory† | 173/300 | 57.67% | 58.00 |
+| Long Horizon‡ | 137/400 | 34.25% | 53.00 |
+| Generalization | 183/600 | 30.50% | 36.82 |
+| Open§ | 16/400 | 4.00% | 4.48 |
+| **Equal-weight five-suite average** | **635/2,100 total; not the averaging formula** | **31.58%** | **38.55** |
+
+## Original scenes, repairs and replacements
+
+† Memory has 299 valid original results: 173 successes, **57.86% success and 58.19/100 partial**. Original sorting-sequence episode 47 remains unscored. Its separately repaired variant is a valid failure with score zero; the headline includes that variant, giving 300 reported outcomes. The repaired variant is not an unchanged-original result.
+
+‡ Long Horizon includes 398 original results plus egg-holder replacements 50 and 51 for invalid original layouts 15 and 20. Both replacements failed the task, with partial scores 0 and 0.25. Original-only totals: 137/398 successes, 53.2035/100 partial.
+
+§ Open includes 399 original results plus pickup replacement 50 for invalid original layout 5. That replacement failed with partial zero. Original-only totals: 16/399 successes, 4.4862/100 partial.
+
+Generalization combines standard and randomized episodes over all 12 tasks (600 results). The nine user-supplied completed tasks contribute 131/450 successes; the three node-2 tasks contribute 52/150. The original stack-block layout 6 and laptop/headphones layout 22 succeeded on retry; these are original scenes, not replacements. Cancellation of duplicate queue entries does not discard the separately supplied completed results.
+
+These totals contain **2,096 original results, three replacements and one repaired variant**. The overall score gives each suite equal weight, not each episode. Organizer acceptance of local substitutions or repairs is not assumed.
+
+## Sources and scope
+
+Precision, Open and Long Horizon retain the original receipt hashes and replacement evidence in `evidence/local-results.json`. The completed five-suite calculation is in `evidence/five-suite-results.json`.
+
+Memory and nine Generalization tasks come from the user's supplied completed task summaries. Their raw episode logs were not independently re-audited during this packaging update. Stack blocks, store laptop/headphones and sort nesting dolls use node-2 receipts verified in the preceding results review, including successful original-layout retries. No new multi-seed estimate is implied.
+
+These results belong to the frozen research runner. Packaged-adapter debug, smoke and matched-action validation are reported separately in `VALIDATION.md`; research scores do not substitute for those tests. This is not an official leaderboard entry.
+
+## Task-level breakdown
+
+### Precision
+
+| Task | Successful / reported outcomes | Success rate | Mean partial /100 |
+|---|---:|---:|---:|
+| Build tower | 32/50 | 64.00% | 73.60 |
+| Deposit coin | 11/50 | 22.00% | 26.80 |
+| Fasten screws | 18/50 | 36.00% | 66.80 |
+| Insert key | 0/50 | 0.00% | 15.00 |
+| Insert tubes | 32/50 | 64.00% | 75.20 |
+| Play xylophone | 0/50 | 0.00% | 0.00 |
+| Plug in charger | 2/50 | 4.00% | 4.00 |
+| Pour balls into vase | 31/50 | 62.00% | 62.00 |
+
+### Memory
+
+| Task | Successful / reported outcomes | Success rate | Mean partial /100 |
+|---|---:|---:|---:|
+| Cover blocks | 50/50 | 100.00% | 100.00 |
+| Imitate sorting sequence | 0/50 | 0.00% | 2.00 |
+| Match and pick from conveyor | 46/50 | 92.00% | 92.00 |
+| Press by number | 50/50 | 100.00% | 100.00 |
+| Swap T | 11/50 | 22.00% | 22.00 |
+| Swap blocks | 16/50 | 32.00% | 32.00 |
+
+### Long Horizon
+
+| Task | Successful / reported outcomes | Success rate | Mean partial /100 |
+|---|---:|---:|---:|
+| Classify objects | 16/50 | 32.00% | 44.50 |
+| Fill egg holder | 2/50 | 4.00% | 13.80 |
+| Fill pen holder | 11/50 | 22.00% | 43.90 |
+| Make kong | 43/50 | 86.00% | 86.00 |
+| Organize table | 9/50 | 18.00% | 64.50 |
+| Play stacking toy | 6/50 | 12.00% | 32.00 |
+| Play tic tac toe | 1/50 | 2.00% | 40.50 |
+| Put bottles into dustbin | 49/50 | 98.00% | 98.80 |
+
+### Generalization
+
+| Task | Successful / reported outcomes | Success rate | Mean partial /100 |
+|---|---:|---:|---:|
+| Arrange largest number | 2/50 | 4.00% | 11.00 |
+| Fold clothes | 36/50 | 72.00% | 74.40 |
+| Hang mugs | 14/50 | 28.00% | 38.50 |
+| Make toast | 22/50 | 44.00% | 52.50 |
+| Pack objects into box | 6/50 | 12.00% | 35.40 |
+| Pour liquid into cup | 22/50 | 44.00% | 44.00 |
+| Push T | 1/50 | 2.00% | 2.00 |
+| Sort nesting dolls by size | 10/50 | 20.00% | 20.00 |
+| Stack blocks | 22/50 | 44.00% | 50.00 |
+| Stack bowls | 26/50 | 52.00% | 58.90 |
+| Store laptop and headphones | 20/50 | 40.00% | 51.20 |
+| Sweep blocks | 2/50 | 4.00% | 4.00 |
+
+### Open
+
+| Task | Successful / reported outcomes | Success rate | Mean partial /100 |
+|---|---:|---:|---:|
+| Align blocks | 0/50 | 0.00% | 0.00 |
+| General pickup | 12/50 | 24.00% | 24.00 |
+| Stack blocks by language | 4/50 | 8.00% | 9.20 |
+| Solve equation | 0/50 | 0.00% | 0.00 |
+| Classify objects by language | 0/50 | 0.00% | 2.60 |
+| Pick from conveyor by image | 0/50 | 0.00% | 0.00 |
+| Store tools in toolbox | 0/50 | 0.00% | 0.00 |
+| Pour by language | 0/50 | 0.00% | 0.00 |
+
+Checkpoint identity: `790bffc9464fa7a026aa43b217aec2033d186895cad643166daa0bcc01914d5c`.

inference/policy/bagel_robotics_1/LICENSE

@@ -0,0 +1,24 @@
+MIT License
+
+Copyright (c) 2026 The FastWAM Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+Except for third-party code and files that retain their original license
+headers, this repository is released under the MIT License.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

inference/policy/bagel_robotics_1/README.md

@@ -0,0 +1,219 @@
+# Bagel-Robotics-1
+
+**Contributor:** Bagel Labs | **Paper:** Technical report in preparation | **arXiv:** Not yet available | **Original code:** [VPP2](https://github.com/roboterax/video-prediction-policy-2)
+
+Evaluation-only adapter for the physically compact VPP2 recovery checkpoint 1000.
+The video expert has 12,468,409,664 parameters, 23.95% fewer than the recorded
+16,394,878,784-parameter parent video expert. Action2B remains unchanged. The
+`runtime/` directory vendors a minimal isolated inference runtime and compact
+loader; this is a different artifact from Bagel-Robotics-1-Preview.
+
+Shared conventions — argument meanings, checkpoint naming, split-machine deployment, `EVAL_ENV_TYPE` — are documented in the [XPolicyLab README](https://github.com/XPolicyLab/XPolicyLab/blob/main/README.md). Official results: [RoboDojo LeaderBoard](https://robodojo-benchmark.com/LeaderBoard).
+
+## Installation
+
+Install this directory under `XPolicyLab/policy/bagel_robotics_1` in a RoboDojo
+workspace. `env_cfg/` belongs next to XPolicyLab, not inside the policy. Install
+RoboDojo and its simulator/assets separately using the
+[official installation guide](https://robodojo-benchmark.com/doc/usage/install-and-download/).
+
+**1. Set paths and install the policy environment.** Replace the example checkout
+path below. Run the following sections in the same shell so these variables remain
+available. Use **Python 3.10**, the version used for the GPU qualification; set
+`PYTHON_BIN` to its executable if it is not named `python3.10`.
+
+```bash
+BAGEL_XPL_ROOT=/absolute/path/to/XPolicyLab
+BAGEL_POLICY_DIR="$BAGEL_XPL_ROOT/policy/bagel_robotics_1"
+BAGEL_POLICY_ENV="$BAGEL_POLICY_DIR/.venv"
+cd "$BAGEL_POLICY_DIR"
+PYTHON_BIN=python3.10 bash install.sh "$BAGEL_POLICY_ENV"
+```
+
+The installer creates a venv, installs PyTorch 2.11 / torchvision 0.26 CUDA 13.0
+wheels and installs the isolated runtime plus XPolicyLab. `TORCH_INDEX_URL` changes
+the PyTorch wheel index. GPU qualification used Python 3.10.21 and PyTorch
+2.11.0+cu130 on an RTX PRO 6000 Blackwell; installation on another stack does not
+establish numerical equivalence. The recorded GPU has 96 GB VRAM; a lower
+minimum has not been qualified. The isolated validation jobs reserved 128 GiB
+host RAM and eight CPUs; these are tested allocations, not measured minima.
+
+**2. Download and verify before evaluation.** The checkpoint is public at
+[bageldotcom/adaprune](https://huggingface.co/bageldotcom/adaprune). No login, token
+or access request is required.
+
+```bash
+PYTHON_BIN="$BAGEL_POLICY_ENV/bin/python" bash download_checkpoint.sh
+```
+
+The download is approximately **45.7 GB** before cache/installation overhead.
+To check pinned metadata before downloading tensors, run
+`PYTHON_BIN="$BAGEL_POLICY_ENV/bin/python" bash download_checkpoint.sh --metadata-only`
+first. This checks four small JSON files only; run the full downloader above
+before evaluation. The downloader verifies every checkpoint and auxiliary-asset hash. For existing
+files, follow [Model Assets](#model-assets) instead. Continue to
+[Evaluation](#evaluation) only after the download or verification succeeds.
+
+## Data Processing
+
+No conversion is needed for inference. The research recovery run used a curated
+30,000-window RoboDojo EE16 export; training reproduction is outside this initial
+adapter. `process_data.sh` is intentionally omitted under the proposed eval-only
+exception. Inputs are decoded RGB arrays from XPolicyLab's shared server.
+
+## Training
+
+`train.sh` is intentionally omitted. Training, mask learning and export are not included in this adapter; the
+standalone review package contains a method note. The technical report and training,
+pruning, and export code are planned for release on **November 20, 2026**.
+The proposed eval-only integration and release schedule remain subject to maintainer
+coordination; this preparation does not claim that the exception is approved.
+
+## Evaluation
+
+Supported: `bench_name=RoboDojo`, `env_cfg_type=arx_x5`, `action_type=ee`.
+Use the same checkpoint and settings for every task. Complete Installation first.
+
+**3. Check both debug transports.** These load the real model but use synthetic
+observations. Run from the policy directory with the environment defined above.
+On a dedicated workstation, choose a GPU with sufficient free memory:
+
+```bash
+cd "$BAGEL_POLICY_DIR"
+BAGEL_GPU_ID=0
+```
+
+On a scheduled cluster, run inside an allocation and set `BAGEL_GPU_ID` to its
+assigned single GPU instead; preserve the scheduler's `CUDA_VISIBLE_DEVICES`.
+For a one-GPU allocation that exports this variable:
+
+```bash
+BAGEL_GPU_ID="${CUDA_VISIBLE_DEVICES:?Run inside a one-GPU allocation}"
+```
+
+Run both commands sequentially:
+
+```bash
+EVAL_ENV_TYPE=debug DEBUG_OBS_ENCODED=0 DEBUG_EVAL_EPISODES=1 bash eval.sh \
+  RoboDojo stack_blocks cp1000 arx_x5 ee 1 \
+  "$BAGEL_GPU_ID" "$BAGEL_GPU_ID" "$BAGEL_POLICY_ENV" "$BAGEL_POLICY_ENV"
+
+EVAL_ENV_TYPE=debug DEBUG_OBS_ENCODED=1 DEBUG_EVAL_EPISODES=1 bash eval.sh \
+  RoboDojo stack_blocks cp1000 arx_x5 ee 1 \
+  "$BAGEL_GPU_ID" "$BAGEL_GPU_ID" "$BAGEL_POLICY_ENV" "$BAGEL_POLICY_ENV"
+```
+
+Each must finish with `[TestEnv] Episode finished` and `[MAIN] eval finished`,
+exit zero, and contain no traceback. Debug completion is an interface check,
+not a task success result. A venv path in argument 10 is supported for debug
+only; simulation uses the official conda client launcher.
+
+**4. Run a small native simulator check.** Set the installed RoboDojo path and
+simulator conda environment name. The following command matches the tested
+`insert_tubes` smoke: seed 1, two episodes, original layouts 0 and 1. It uses
+the same GPU selected above for the policy and simulator.
+
+```bash
+BAGEL_SIM_ROOT=/absolute/path/to/RoboDojo
+BAGEL_SIM_ENV=your_simulator_conda_env
+cd "$BAGEL_SIM_ROOT"
+EVAL_ENV_TYPE=sim bash scripts/robodojo.sh eval \
+  --policy-dir "$BAGEL_POLICY_DIR" \
+  --task insert_tubes --ckpt cp1000 --env-cfg arx_x5 --action-type ee \
+  --seed 1 --eval-num 2 --policy-env "$BAGEL_POLICY_ENV" \
+  --eval-env "$BAGEL_SIM_ENV" \
+  --policy-gpu "$BAGEL_GPU_ID" --env-gpu "$BAGEL_GPU_ID"
+```
+
+For the second tested task, repeat with `--task store_laptop_and_headphones`.
+Preserve native `_result.json` files under RoboDojo's `eval_result/` and record
+source revisions and any startup errors. See [VALIDATION.md](VALIDATION.md) for
+measured outcomes and the shared-installation startup workaround used in our
+qualification. Keep these two-episode checks separate from full benchmark runs.
+
+For a full task, suite, or three-seed run, follow [Reevaluation](REEVALUATION.md).
+It uses the official runner, native episode counts and original layouts.
+
+The direct launcher retains XPolicyLab's ten-argument contract (run from the
+policy directory):
+
+```text
+bash eval.sh <bench_name> <task_name> <ckpt_name> <env_cfg_type> <action_type> <seed> \
+  <policy_gpu_id> <env_gpu_id> <policy_env_or_venv_path> <simulator_conda_env>
+```
+
+The custom `deploy.py` records every executed observation, calls reset at episode
+boundaries and stops sending actions when the environment terminates. This is
+necessary for the checkpoint's history conditioning. Batched execution is disabled;
+the batch methods only support a single environment and reject larger batches.
+
+## Model Assets
+
+The Installation quickstart authenticates and downloads before evaluation.
+The script downloads the six exact bundle files into `checkpoints/cp1000` and
+the pinned public VAE/CLIP/UMT5/tokenizer assets into
+`checkpoints/Wan2.1-I2V-14B-480P`. Bundle repository:
+[bageldotcom/adaprune](https://huggingface.co/bageldotcom/adaprune),
+revision `2651631c5d08819088475e8778676f5cf6d9afcf` (**public; no login required**).
+Access must be arranged for evaluators. No token is included.
+
+For existing artifacts, set `BAGEL_CP1000_BUNDLE` and `BAGEL_CP1000_WAN_ROOT`.
+`bundle_path` and `wan_model_dir` in `deploy.yml` are alternatives. The environment
+variables take precedence; otherwise the shared XPolicyLab checkpoint resolver
+uses `ckpt_name`. `artifact-manifest.json` pins every file and encoder revision.
+
+```bash
+export BAGEL_CP1000_BUNDLE=/absolute/path/to/update_001000
+export BAGEL_CP1000_WAN_ROOT=/absolute/path/to/Wan2.1-I2V-14B-480P
+"$BAGEL_POLICY_ENV/bin/python" "$BAGEL_POLICY_DIR/download_checkpoint.py" \
+  --verify-only --bundle "$BAGEL_CP1000_BUNDLE" --wan-root "$BAGEL_CP1000_WAN_ROOT"
+```
+
+Full file hashes are checked before loading. `--metadata-only` downloads only
+small checkpoint metadata for CPU architecture checks and does not qualify weights.
+
+## Configuration
+
+BF16, action horizon 32, execute/replan 16, ten Euler steps, sigma shift 1,
+90/10 temporal blending, and `eval_batch: false` are fixed and validated.
+`seed` is supplied by the evaluator; task names never select alternate models.
+The server launcher also preserves the recorded deterministic backend controls
+(`PYTHONHASHSEED=1`, `CUBLAS_WORKSPACE_CONFIG=:4096:8`, deterministic algorithms,
+and the recorded TF32/matmul settings). Direct API users must set those startup
+environment variables before launching Python. The checkpoint loader accepts only the compact channel architecture and loads
+weights strictly. There is no teacher, mask-training module or dense fallback.
+
+Optional validation: setting `BAGEL_VALIDATION_TRACE` to a new absolute output
+directory enables lossless capture around the official policy server. It records
+the first 209 observations per episode and fingerprints preprocessing, history
+and actions. It changes no predictions or scoring and is disabled by default.
+See [VALIDATION.md](VALIDATION.md) for capture/replay scope.
+
+## Notes
+
+Mask-based structured pruning and recovery produce physically smaller,
+nonuniform FFN and cross-attention layers. The method is intended for compatible
+WAM backbones; the evidence here is limited to VPP2. The technical report will
+provide the detailed algorithm and recovery recipe.
+
+
+The recorded research results cover all five suites:
+
+| Suite | Successful / reported outcomes | Success rate | Mean partial /100 |
+|---|---:|---:|---:|
+| Precision | 126/400 | 31.50% | 40.43 |
+| Memory† | 173/300 | 57.67% | 58.00 |
+| Long Horizon‡ | 137/400 | 34.25% | 53.00 |
+| Generalization | 183/600 | 30.50% | 36.82 |
+| Open§ | 16/400 | 4.00% | 4.48 |
+| **Equal-weight five-suite average** | **635/2,100 total; not the averaging formula** | **31.58%** | **38.55** |
+
+Memory includes one repaired variant, Long Horizon two labeled replacements, and Open one labeled replacement. Generalization combines standard and randomized episodes. Memory and nine Generalization tasks use user-supplied summaries; see [EVALUATION_RESULTS.md](EVALUATION_RESULTS.md) for complete counts, task results, source limitations and original-only figures. These are research-runner results, not official leaderboard scores or proof of packaged-adapter equivalence.
+
+The learned masks have been exported into nonuniform per-layer FFN/cross-attention
+reductions. Action2B and all video self-attention heads are retained. Eligibility
+as a new model and the evaluation-only exception remain subject to committee review.
+
+Package validation so far: 15 CPU tests, real compact-video strict tensor loading, all checkpoint/encoder hashes, static checks, isolated wheel processor loading, and clean-checkout installation. Both real-checkpoint GPU debug transports passed in job 53609. Four native simulator episodes completed with valid results, and all eight action/history/reset/backend comparison checks passed with zero recorded differences. See [VALIDATION.md](VALIDATION.md) for the measured task scores and test scope.
+Reviewer access remains pending. The technical report and training/pruning/export code are planned for **November 20, 2026**. Full-suite
+coverage is a separate leaderboard-verification milestone. See [validation evidence](VALIDATION.md) and [historical task results](EVALUATION_RESULTS.md). The standalone review package contains the fuller internal method note.

inference/policy/bagel_robotics_1/REEVALUATION.md

@@ -0,0 +1,131 @@
+# Reevaluate Bagel-Robotics-1 checkpoint 1000
+
+Use the [README quickstart](README.md#installation) to install Python 3.10 policy
+dependencies, download and verify the pinned files, and pass both
+debug checks. The standalone inference runtime is included; the private research
+training repository is not needed. Install RoboDojo and its assets separately.
+
+The only policy checkpoint is `cp1000`, from
+[bageldotcom/adaprune](https://huggingface.co/bageldotcom/adaprune),
+revision `2651631c5d08819088475e8778676f5cf6d9afcf`. The downloader and loader reject
+files that do not match `artifact-manifest.json`. The VAE, image/text encoders and
+tokenizer are downloaded automatically at their separately pinned public revision.
+The checkpoint and all required public encoder assets download without authentication.
+
+## Set the local paths
+
+Replace the example paths and simulator conda environment. Reuse the policy
+venv created in the README. GPU commands on a cluster belong inside an allocation;
+set `BAGEL_GPU_ID` to its assigned single GPU. On a dedicated workstation, the
+example selects GPU 0. Do not overwrite a scheduler's GPU visibility assignment.
+
+```bash
+BAGEL_XPL_ROOT=/absolute/path/to/XPolicyLab
+BAGEL_POLICY_DIR="$BAGEL_XPL_ROOT/policy/bagel_robotics_1"
+BAGEL_POLICY_ENV="$BAGEL_POLICY_DIR/.venv"
+BAGEL_SIM_ROOT=/absolute/path/to/RoboDojo
+BAGEL_SIM_ENV=your_simulator_conda_env
+BAGEL_GPU_ID=0
+cd "$BAGEL_SIM_ROOT"
+```
+
+Qualification used XPolicyLab `6baccd57ff958570d0db1588370289ee36c8bb70` and
+RoboDojo `f57e1633ffed59761bc8e568cfe7530c4b33381c`. Record the revisions used for
+reevaluation. A different simulator or numerical backend requires fresh checking.
+The [official installation guide](https://robodojo-benchmark.com/doc/usage/install-and-download/)
+provides the simulator workspace layout, assets and conda setup.
+
+## Inspect the command, then check two episodes
+
+The dry run does not load the model or simulator:
+
+```bash
+EVAL_ENV_TYPE=sim bash scripts/robodojo.sh eval \
+  --policy-dir "$BAGEL_POLICY_DIR" --task insert_tubes --ckpt cp1000 \
+  --env-cfg arx_x5 --action-type ee --seed 1 --eval-num 2 \
+  --policy-env "$BAGEL_POLICY_ENV" --eval-env "$BAGEL_SIM_ENV" \
+  --policy-gpu "$BAGEL_GPU_ID" --env-gpu "$BAGEL_GPU_ID" --dry-run
+```
+
+Remove `--dry-run` to execute the check. The recorded qualification used original
+layouts 0/1 and produced 2/2 successes for `insert_tubes`. Repeating with
+`--task store_laptop_and_headphones` produced 1/2 successes and 60/100 mean partial
+score. These are four integration episodes, not full-suite performance guarantees.
+[Validation evidence](VALIDATION.md) records their scope and backend.
+
+## Evaluate complete tasks or suites
+
+Use the official `benchmark` entry point with `--eval-num native`: the benchmark
+then selects its 25/50 episode counts. The following runs the entire Memory suite
+sequentially with seed 1. Append `--dry-run` first to inspect all resolved commands.
+
+```bash
+EVAL_ENV_TYPE=sim bash scripts/robodojo.sh benchmark \
+  --policy-dir "$BAGEL_POLICY_DIR" --ckpt cp1000 \
+  --env-cfg arx_x5 --action-type ee --seed 1 --eval-num native \
+  --policy-env "$BAGEL_POLICY_ENV" --eval-env "$BAGEL_SIM_ENV" \
+  --policy-gpu "$BAGEL_GPU_ID" --env-gpu "$BAGEL_GPU_ID" \
+  --dimension memory
+```
+
+Use `--dimension generalization`, `precision`, `long-horizon`, `open`, or `all`.
+For one complete task, use `--only insert_tubes` with `--dimension precision`.
+Generalization includes each standard task and its `_random` sibling; each has
+25 episodes per seed. The five suites contain 54 runnable configurations, merged
+into 42 task rows. Preserve canonical names such as `play_Xylophone`, `swap_T`,
+`push_T` and `push_T_random`.
+
+## Run the three-seed verification scope
+
+Use a dedicated result workspace for a complete run, separate from the smoke
+checks. This command is a full benchmark, not a quick installation test. Run it
+only with the intended evaluation allocation and disk space. It runs tasks and
+seeds sequentially; it never changes the checkpoint or inference settings.
+
+```bash
+set -euo pipefail
+for BAGEL_EVAL_SEED in 0 1 2; do
+  EVAL_ENV_TYPE=sim bash scripts/robodojo.sh benchmark \
+    --policy-dir "$BAGEL_POLICY_DIR" --ckpt cp1000 \
+    --env-cfg arx_x5 --action-type ee --seed "$BAGEL_EVAL_SEED" \
+    --eval-num native --dimension all \
+    --policy-env "$BAGEL_POLICY_ENV" --eval-env "$BAGEL_SIM_ENV" \
+    --policy-gpu "$BAGEL_GPU_ID" --env-gpu "$BAGEL_GPU_ID"
+done
+bash scripts/robodojo.sh summarize
+```
+
+The official [Quick Evaluation guide](https://robodojo-benchmark.com/doc/usage/quick-evaluation/)
+also documents multi-GPU task sharding. Use one unbatched policy process per
+worker; `eval_batch: false` is required. Shared state across independent episodes
+or changing inference settings is not part of this submission.
+
+## Preserve and report the results
+
+Keep each run's native `_result.json`, logs, source revisions and videos. Run
+`bash scripts/robodojo.sh summarize` from the matching RoboDojo checkout and retain
+`eval_result/RoboDojo/_summary.md`. The native summary distinguishes incomplete
+coverage; its across-seed statistics must not be replaced with statistics over
+episodes or combined with the two-episode smoke results.
+
+The default reevaluation uses original supplied layouts. If a scene fails before
+any robot action, preserve its startup error and retry history; do not silently
+replace the scene or score an infrastructure failure as a policy failure. Seek
+the committee's handling decision. The historical research run used three
+labeled replacements and one repaired Memory variant, described in
+[EVALUATION_RESULTS.md](EVALUATION_RESULTS.md); those exceptions are not activated
+by these commands. Official verification and acceptance remain with RoboDojo.
+
+## Common setup problems
+
+| Symptom | Check |
+|---|---|
+| HTTP 401/403 or repository not found | Use the canonical public repository and pinned revision; retry without a stale cached credential (`HF_HUB_DISABLE_IMPLICIT_TOKEN=1`). |
+| Hash/size mismatch | Check the pinned revision and local file completeness; do not disable verification. |
+| Missing encoder or tokenizer | Run the full downloader; `--metadata-only` does not fetch tensors or encoders. |
+| Missing `env_cfg` or simulator | Complete the official workspace setup; `env_cfg/` belongs beside XPolicyLab. |
+| Shared Isaac/Kit cache permissions | Use a writable installation/cache; the recorded local workaround is documented in VALIDATION.md. No task or scoring patches are supplied. |
+| Insufficient GPU memory | Qualification used a 96 GB RTX PRO 6000 Blackwell; a smaller minimum has not been qualified. |
+
+The policy logs the checkpoint identity after file verification. Keep that log
+with the run so the evaluated checkpoint is unambiguous.

inference/policy/bagel_robotics_1/THIRD_PARTY_NOTICES.md

@@ -0,0 +1,13 @@
+# Source and artifact notices
+
+- The retained VPP2/FastWAM-derived runtime carries the original MIT notice in
+  `LICENSE`, copyright 2026 The FastWAM Authors. Bagel-specific packaging does not
+  remove that attribution. See `source-provenance.json` for frozen-source hashes.
+- Wan2.1/DiffSynth-derived model components retain Apache-2.0 terms; see
+  `licenses/Apache-2.0.txt` and upstream Wan2.1/DiffSynth source notices.
+- The XPolicyLab interface follows its upstream license, included in
+  `licenses/XPolicyLab.txt`. RoboDojo and simulator assets are not bundled.
+- Encoder weights, tokenizer files and model weights have their own upstream
+  terms. The code license does not assign a new license to those artifacts.
+- The existing preview PR by Soroush Mehraban was consulted as a reference; its
+  alternate checkpoint, code and release promises are not this submission.

inference/policy/bagel_robotics_1/VALIDATION.md

@@ -0,0 +1,45 @@
+# Adapter validation
+
+Status: **Technical qualification passed: 15 CPU tests, both real-checkpoint debug transports, four valid native simulator episodes, and all eight exact replay/backend checks. The technical report and training/pruning/export code are planned for November 20, 2026; evaluator access and user review remain before submission.**
+
+| Check | Evidence and scope |
+|---|---|
+| Static/template audit | 50 Python files compiled, five shell scripts checked, clean diff, exact PR-template headings |
+| CPU regression | 15 tests passed: compact architecture metadata, corrupt artifacts, blending, observation timing, reset, backend settings, lossless array integrity and comparison failures |
+| Real video tensors | Strict CPU loading passed; 12,468,409,664 compact video parameters |
+| Artifact integrity | All six bundle files and nine auxiliary assets matched pinned sizes/hashes |
+| Fresh policy installation | Python 3.10.21, PyTorch 2.11.0+cu130; dependency consistency check passed |
+| Source preservation | 30 numerical modules and 16 policy functions match the frozen reference after namespace normalization; factory and prompt match |
+| Simulator preflight | Official doctor: 12 passes, zero failures; Isaac and conda checks intentionally skipped at this non-GPU stage |
+| Plain and encoded debug | Passed with real checkpoint: job 53609, exit 0, both evaluation-finished markers |
+| Simulator smoke | 4/4 valid, original layouts 0/1, seed 1: Insert tubes 2/2 successes, 100/100 partial; Store laptop/headphones 1/2 successes, 60/100 partial |
+| Numerical action comparison | Passed: all eight checks, zero recorded differences; job 53612 completed with exit 0 |
+
+XPolicyLab base: `6baccd57ff958570d0db1588370289ee36c8bb70`.
+Simulator validation checkout: `f57e1633ffed59761bc8e568cfe7530c4b33381c`.
+
+Two real recordings of 209 observations / 14 action chunks each were replayed in A/B/A order, giving 42 chunks per implementation. Reference-versus-package, original-capture-versus-replay, reset and backend checks all passed with zero recorded differences. Evidence: `evidence/gpu-validation.json` and `evidence/action-comparison.json`. Equality is established for these fixtures on PyTorch 2.11.0+cu130 and NVIDIA RTX PRO 6000 Blackwell, not every input or backend.
+
+The original Store laptop/headphones layout 1 failed with 0.2/1 partial and is retained. Debug completion is an interface check; four simulator smoke episodes do not establish full-suite performance. Historical research scores are separately documented in EVALUATION_RESULTS.md.
+
+## Reproduce the transport checks
+
+Use the policy README's debug command with the actual checkpoint. Repeat it with `DEBUG_OBS_ENCODED=1`. Both runs must finish normally and print the evaluation-finished marker.
+
+## Optional lossless capture
+
+`BAGEL_VALIDATION_TRACE=/absolute/path/to/new-capture-directory` enables `validation_server.py` through the normal server launcher. The wrapper uses the official server and records already-decoded observations, inference inputs, history fingerprints, raw predictions, blending and commands. It does not modify actions, seeds, task termination or scoring.
+
+Capture is limited to the first 209 observations per episode. The default server path does not import or enable the recorder. Array blobs are local test artifacts and are excluded from the PR.
+
+`validation_replay.py` replays two fixtures in A/B/A order. `validation_compare.py` checks exact equality of reference versus packaged outputs, capture versus replay, history and resets, and verifies matched GPU/backend settings. The original frozen research implementation is needed for the reference comparison and is not redistributed in this inference package.
+
+All planned qualification stages completed. Failed attempts remain preserved in the outer verification packet; full camera fixtures and videos remain on the validation machine and are excluded from the PR.
+
+## Packaging repair
+
+The first debug attempt (53541) failed before opening the server because serialized processor targets still referred to the original `vpp2` namespace and the processor modules were missing. Its dependent simulator job 53542 was cancelled before execution. The repair vendors the evaluated processor/transform modules unchanged except import namespace, and recursively maps Hydra targets into `bagel_cp1000`. Four new CPU cases test the real processor metadata and legacy target formats while rejecting research-package imports. Failed evidence is preserved in the outer review packet.
+
+## Shared simulator installation
+
+The local qualification environment reuses an existing shared Isaac Sim installation. Attempt 53610 stopped at its noninteractive startup prompt; attempt 53611 then stopped on read-only shared cache permissions. Neither reached a scored episode. Attempt 53612 restores the same noninteractive flag and user-owned portable Kit directory used by the frozen evaluation runner. A recorded one-line change to the validation checkout's launch script permits the portable-root argument; task, physics, layout and scoring code remain unchanged. This launch-only patch is in the outer verification packet and is not part of the policy PR. See [Kit portable configuration](https://docs.omniverse.nvidia.com/kit/docs/kit-manual/latest/guide/configuring.html).

inference/policy/bagel_robotics_1/artifact-manifest.json

@@ -0,0 +1,112 @@
+{
+  "schema": "bagel-cp1000-submission-v1",
+  "display_name": "Bagel-Robotics-1",
+  "checkpoint_identity": "790bffc9464fa7a026aa43b217aec2033d186895cad643166daa0bcc01914d5c",
+  "update": 1000,
+  "video_parameters": 12468409664,
+  "repo_id": "bageldotcom/adaprune",
+  "revision": "2651631c5d08819088475e8778676f5cf6d9afcf",
+  "private": false,
+  "files": {
+    "COMPLETE.json": {
+      "sha256": "eee062fbcb8c92a881117aa7f06b5cd039dfb9912d85571b59a20aa30837b0e1",
+      "size": 2491
+    },
+    "action.pt": {
+      "sha256": "9b04836abe5a30972e69ee84e5f5e39c09bf53dd45865551e1d04b42ead90ec3",
+      "size": 4057916577
+    },
+    "compact_architecture.json": {
+      "sha256": "a7f6696ab9a9beca785531c2402b81ccd1550745cb6e9ccaad67f658630f6b32",
+      "size": 6563270
+    },
+    "dataset_stats.json": {
+      "sha256": "2649d4e3ff3d6d400c9953b7c20c79541474f9c38a8a9916b9caacecd8578f16",
+      "size": 100256
+    },
+    "validation.json": {
+      "sha256": "9806ad2b0b58f879351da58eede46507a79433ec9e4eb8f37f415cf58f2ef70e",
+      "size": 88781
+    },
+    "video.pt": {
+      "sha256": "fb62db0b3a74a53aeab60f4b1acc8a5be47274f5c9db166684043521fe2a40bb",
+      "size": 24937214042
+    }
+  },
+  "wan": {
+    "repo": "Haodong082399/VPP2",
+    "revision": "04824ddb3aae11f13db50984ed7d540c3b77dd9f",
+    "files": [
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/LICENSE.txt",
+        "blobId": "261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64",
+        "size": 11357
+      },
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/Wan2.1_VAE.pth",
+        "blobId": "5897fba405232a6b07a947d6188d19a8e050ccfb",
+        "size": 507609880,
+        "lfs": {
+          "sha256": "38071ab59bd94681c686fa51d75a1968f64e470262043be31f7a094e442fd981",
+          "size": 507609880,
+          "pointerSize": 134
+        }
+      },
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/config.json",
+        "blobId": "f5a12b285ec9c2497cafc4a4443fc6ba61b1a30b",
+        "size": 250
+      },
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/google/umt5-xxl/special_tokens_map.json",
+        "blobId": "14855e7052ffbb595057dfd791d293c1c940db2c",
+        "size": 6623
+      },
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/google/umt5-xxl/spiece.model",
+        "blobId": "2fe5f347e9f9367585589ae89e997dfbd5cf802c",
+        "size": 4548313,
+        "lfs": {
+          "sha256": "e3909a67b780650b35cf529ac782ad2b6b26e6d1f849d3fbb6a872905f452458",
+          "size": 4548313,
+          "pointerSize": 132
+        }
+      },
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/google/umt5-xxl/tokenizer.json",
+        "blobId": "7bd4ee3cf5633e49c0e06a3aac2a621559e6222a",
+        "size": 16837417,
+        "lfs": {
+          "sha256": "6e197b4d3dbd71da14b4eb255f4fa91c9c1f2068b20a2de2472967ca3d22602b",
+          "size": 16837417,
+          "pointerSize": 133
+        }
+      },
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/google/umt5-xxl/tokenizer_config.json",
+        "blobId": "4e1cc1cd85599ce0b47fd0a746af188fe4043ff2",
+        "size": 61728
+      },
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth",
+        "blobId": "cbdd1a5bd944b2b4c47e21435106316971ac76d8",
+        "size": 4772359047,
+        "lfs": {
+          "sha256": "628c9998b613391f193eb67ff68da9667d75f492911e4eb3decf23460a158c38",
+          "size": 4772359047,
+          "pointerSize": 135
+        }
+      },
+      {
+        "rfilename": "checkpoints/Wan2.1-I2V-14B-480P/models_t5_umt5-xxl-enc-bf16.pth",
+        "blobId": "d5dad910304ab4b909a2c8a225a71840606e6de4",
+        "size": 11361920418,
+        "lfs": {
+          "sha256": "7cace0da2b446bbbbc57d031ab6cf163a3d59b366da94e5afe36745b746fd81d",
+          "size": 11361920418,
+          "pointerSize": 136
+        }
+      }
+    ]
+  }
+}

inference/policy/bagel_robotics_1/assets.py

@@ -0,0 +1,104 @@
+"""Pinned CP1000 artifact and inference contract; safe to import without torch."""
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from pathlib import Path
+
+POLICY_DIR = Path(__file__).resolve().parent
+MANIFEST = json.loads((POLICY_DIR / "artifact-manifest.json").read_text())
+FIXED_SETTINGS = {
+    "bench_name": "RoboDojo", "env_cfg_type": "arx_x5", "action_type": "ee",
+    "protocol": "ws", "eval_batch": False, "mixed_precision": "bf16",
+    "action_horizon": 32, "replan_steps": 16, "num_inference_steps": 10,
+    "sigma_shift": 1.0, "deployment_adapter": "robodojo_ee16",
+    "temporal_ensemble": {"enabled": True, "new_weight": 0.9, "old_weight": 0.1},
+}
+
+
+def file_digest(path, algorithm="sha256", git_blob=False):
+    digest = hashlib.new(algorithm)
+    if git_blob:
+        digest.update(f"blob {path.stat().st_size}\0".encode())
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def verify_bundle(bundle, *, full_hash=True):
+    bundle = Path(bundle).expanduser().resolve()
+    for name, expected in MANIFEST["files"].items():
+        path = bundle / name
+        if not path.is_file():
+            raise FileNotFoundError(f"Missing checkpoint artifact: {path}")
+        if path.stat().st_size != expected["size"]:
+            raise ValueError(f"Checkpoint size mismatch: {name}")
+        if full_hash or name not in {"action.pt", "video.pt"}:
+            if file_digest(path) != expected["sha256"]:
+                raise ValueError(f"Checkpoint SHA-256 mismatch: {name}")
+    receipt = json.loads((bundle / "COMPLETE.json").read_text())
+    if receipt.get("identity") != MANIFEST["checkpoint_identity"] or receipt.get("update") != 1000:
+        raise ValueError("Not the approved recovery checkpoint 1000")
+    architecture = json.loads((bundle / "compact_architecture.json").read_text())
+    if architecture.get("schema") != "vpp2-compact-channels-v1" or architecture.get("parameters") != MANIFEST["video_parameters"]:
+        raise ValueError("Unexpected compact architecture")
+    return {"checkpoint_identity": receipt["identity"], "update": 1000,
+            "files": len(MANIFEST["files"]), "full_hash": full_hash}
+
+
+def verify_wan(wan_root, *, full_hash=True):
+    wan_root = Path(wan_root).expanduser().resolve()
+    prefix = "checkpoints/Wan2.1-I2V-14B-480P/"
+    for entry in MANIFEST["wan"]["files"]:
+        path = wan_root / entry["rfilename"].removeprefix(prefix)
+        if not path.is_file():
+            raise FileNotFoundError(f"Missing encoder asset: {path}")
+        if path.stat().st_size != entry["size"]:
+            raise ValueError(f"Encoder asset size mismatch: {path.name}")
+        if full_hash:
+            lfs = entry.get("lfs")
+            actual = file_digest(path, "sha256" if lfs else "sha1", git_blob=not bool(lfs))
+            expected = lfs["sha256"] if lfs else entry["blobId"]
+            if actual != expected:
+                raise ValueError(f"Encoder asset checksum mismatch: {path.name}")
+    return {"repo": MANIFEST["wan"]["repo"], "revision": MANIFEST["wan"]["revision"],
+            "files": len(MANIFEST["wan"]["files"]), "full_hash": full_hash}
+
+
+def validate_settings(model_cfg):
+    cfg = dict(model_cfg)
+    for key, expected in FIXED_SETTINGS.items():
+        actual = cfg.get(key, expected)
+        if actual != expected:
+            raise ValueError(f"CP1000 requires {key}={expected!r}; got {actual!r}")
+        cfg[key] = expected
+    seed = cfg.get("seed", 1)
+    if isinstance(seed, bool) or not isinstance(seed, int) or seed < 0:
+        raise ValueError("seed must be a nonnegative integer supplied by the evaluator")
+    cfg["seed"] = seed
+    return cfg
+
+
+def prepare_model_config(model_cfg, policy_dir=POLICY_DIR):
+    from XPolicyLab.utils.checkpoint_resolver import resolve_checkpoint_root
+
+    cfg = validate_settings(model_cfg)
+    explicit = os.environ.get("BAGEL_CP1000_BUNDLE") or cfg.get("bundle_path")
+    lookup = dict(cfg)
+    if explicit:
+        lookup["model_path"] = explicit
+    bundle = resolve_checkpoint_root(lookup, Path(policy_dir) / "checkpoints",
+                                     policy_dir=policy_dir)
+    wan_root = os.environ.get("BAGEL_CP1000_WAN_ROOT") or cfg.get("wan_model_dir")
+    if not wan_root:
+        wan_root = Path(policy_dir) / "checkpoints/Wan2.1-I2V-14B-480P"
+    # Verify before loading any tensors. No silently accepted alternate weights.
+    verification = {"checkpoint": verify_bundle(bundle), "encoders": verify_wan(wan_root)}
+    print("[Bagel-Robotics-1] " + json.dumps(verification), flush=True)
+    cfg.update(checkpoint_path=str(bundle / "action.pt"),
+               video_checkpoint_path=str(bundle / "video.pt"),
+               dataset_stats_path=str(bundle / "dataset_stats.json"),
+               wan_model_dir=str(Path(wan_root).expanduser().resolve()))
+    return cfg

inference/policy/bagel_robotics_1/backend.py

@@ -0,0 +1,26 @@
+"""Numerical controls recorded by the checkpoint-1000 node-2 evaluation."""
+import os
+import random
+
+
+def configure_backend(seed):
+    if os.environ.get("PYTHONHASHSEED") != "1":
+        raise RuntimeError("Launch Python with PYTHONHASHSEED=1 (see setup_eval_policy_server.sh)")
+    if os.environ.get("CUBLAS_WORKSPACE_CONFIG") != ":4096:8":
+        raise RuntimeError("Launch Python with CUBLAS_WORKSPACE_CONFIG=:4096:8")
+    import numpy as np
+    import torch
+    random.seed(seed)
+    np.random.seed(seed % (2 ** 32))
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.set_float32_matmul_precision("highest")
+    torch.use_deterministic_algorithms(True, warn_only=False)
+    return {"torch": torch.__version__, "cuda": torch.version.cuda,
+            "deterministic_algorithms": torch.are_deterministic_algorithms_enabled(),
+            "float32_matmul_precision": torch.get_float32_matmul_precision()}

inference/policy/bagel_robotics_1/deploy.py

@@ -0,0 +1,36 @@
+import os
+import socket
+import uuid
+
+# VPP2 keeps per-episode observation history on the policy server.  Every
+# executed action records exactly one observation through update_obs, and each
+# call carries this process's client id so one server can serve several
+# simulator clients without mixing their histories.  A single-client official
+# evaluation behaves exactly as without the id.
+_CLIENT_ID_FIELD = "_vpp2_client_id"
+_CLIENT_ID = f"{socket.gethostname()}:{os.getpid()}:{uuid.uuid4().hex[:8]}"
+
+
+def _with_client_id(obs):
+    tagged = dict(obs)
+    tagged[_CLIENT_ID_FIELD] = _CLIENT_ID
+    return tagged
+
+
+def eval_one_episode(TASK_ENV, model_client):
+    model_client.call(func_name="reset")
+    model_client.call(func_name="reset_client", obs={_CLIENT_ID_FIELD: _CLIENT_ID})
+
+    while not TASK_ENV.is_episode_end():  # Check whether the episode ends
+        obs = TASK_ENV.get_obs()  # Get Observation
+        model_client.call(func_name="update_obs", obs=_with_client_id(obs))
+
+        actions = model_client.call(func_name="get_action", obs={_CLIENT_ID_FIELD: _CLIENT_ID})
+        for action_idx, action in enumerate(actions):
+            if TASK_ENV.is_episode_end():
+                break
+            TASK_ENV.take_action(action)
+
+            if action_idx != len(actions) - 1 and not TASK_ENV.is_episode_end():
+                obs = TASK_ENV.get_obs()  # Get Observation
+                model_client.call(func_name="update_obs", obs=_with_client_id(obs))

inference/policy/bagel_robotics_1/deploy.yml

@@ -0,0 +1,26 @@
+policy_name: bagel_robotics_1
+protocol: ws
+host: localhost
+port: null
+bench_name: RoboDojo
+task_name: null
+ckpt_name: cp1000
+env_cfg_type: arx_x5
+action_type: ee
+seed: 1
+gpu_id: null
+eval_batch: false
+bundle_path: null
+wan_model_dir: null
+deployment_adapter: robodojo_ee16
+device: cuda
+mixed_precision: bf16
+action_horizon: 32
+replan_steps: 16
+num_inference_steps: 10
+sigma_shift: 1.0
+default_instruction: follow the instruction
+temporal_ensemble:
+  enabled: true
+  new_weight: 0.9
+  old_weight: 0.1

inference/policy/bagel_robotics_1/download_checkpoint.py

@@ -0,0 +1,48 @@
+"""Download the pinned existing private CP1000 artifact and public encoder assets."""
+from pathlib import Path
+import argparse
+import json
+from assets import MANIFEST, POLICY_DIR, verify_bundle, verify_wan
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--bundle", type=Path, default=POLICY_DIR / "checkpoints/cp1000")
+    parser.add_argument("--wan-root", type=Path, default=POLICY_DIR / "checkpoints/Wan2.1-I2V-14B-480P")
+    parser.add_argument("--verify-only", action="store_true")
+    parser.add_argument("--metadata-only", action="store_true",
+                        help="Download/check small checkpoint metadata; does not qualify tensor weights")
+    args = parser.parse_args()
+    if args.metadata_only and args.verify_only:
+        parser.error("Choose metadata-only or verify-only")
+    if not args.verify_only:
+        from huggingface_hub import hf_hub_download
+        for filename in MANIFEST["files"]:
+            if args.metadata_only and filename in {"action.pt", "video.pt"}:
+                continue
+            hf_hub_download(MANIFEST["repo_id"], filename, revision=MANIFEST["revision"],
+                            local_dir=args.bundle)
+        if args.metadata_only:
+            from assets import file_digest
+            for name, expected in MANIFEST["files"].items():
+                if name not in {"action.pt", "video.pt"}:
+                    if file_digest(args.bundle / name) != expected["sha256"]:
+                        raise ValueError(f"Metadata mismatch: {name}")
+            print("Pinned metadata verified. Tensor weights and encoders NOT checked.")
+            return
+        prefix = "checkpoints/Wan2.1-I2V-14B-480P/"
+        # Download just the encoders/tokenizer, never the original dense video model.
+        for entry in MANIFEST["wan"]["files"]:
+            source = hf_hub_download(MANIFEST["wan"]["repo"], entry["rfilename"],
+                                     revision=MANIFEST["wan"]["revision"])
+            import shutil
+            destination = args.wan_root / entry["rfilename"].removeprefix(prefix)
+            destination.parent.mkdir(parents=True, exist_ok=True)
+            if not destination.exists():
+                shutil.copyfile(source, destination)
+    print(json.dumps({"checkpoint": verify_bundle(args.bundle),
+                      "encoders": verify_wan(args.wan_root)}, indent=2))
+
+
+if __name__ == "__main__":
+    main()

inference/policy/bagel_robotics_1/download_checkpoint.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+set -euo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+exec "${PYTHON_BIN:-python}" "${SCRIPT_DIR}/download_checkpoint.py" "$@"

inference/policy/bagel_robotics_1/eval.sh

@@ -0,0 +1,68 @@
+#!/bin/bash
+set -euo pipefail
+
+bench_name=${1}
+task_name=${2}
+ckpt_name=${3}
+env_cfg_type=${4}
+action_type=${5}
+seed=${6}
+policy_gpu_id=${7}
+env_gpu_id=${8}
+policy_conda_env=${9}
+eval_env_conda_env=${10}
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+XPL_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
+BENCH_ROOT="$(cd "${XPL_ROOT}/.." && pwd)"
+UTILS_DIR="${XPL_ROOT}/utils"
+
+SERVER_SCRIPT="${SCRIPT_DIR}/setup_eval_policy_server.sh"
+CLIENT_SCRIPT="${SCRIPT_DIR}/setup_eval_env_client.sh"
+
+policy_server_port=$(bash "${UTILS_DIR}/get_free_port.sh")
+policy_server_ip="localhost"
+
+additional_info="ckpt_name=${ckpt_name},action_type=${action_type}"
+
+cleanup() {
+    if [[ -n "${SERVER_PID:-}" ]]; then
+        echo "[MAIN] kill server ${SERVER_PID}"
+        kill "${SERVER_PID}" 2>/dev/null || true
+    fi
+}
+trap cleanup EXIT
+
+echo "[MAIN] start server, policy_server_port=${policy_server_port}"
+
+bash "${SERVER_SCRIPT}" \
+    "${bench_name}" \
+    "${task_name}" \
+    "${ckpt_name}" \
+    "${env_cfg_type}" \
+    "${action_type}" \
+    "${seed}" \
+    "${policy_gpu_id}" \
+    "${policy_conda_env}" \
+    "${policy_server_port}" &
+
+SERVER_PID=$!
+
+bash "${UTILS_DIR}/wait_for_policy_server.sh" "${policy_server_ip}" "${policy_server_port}" "${SERVER_PID}" "Policy server" 1200
+
+echo "[MAIN] start client, server=${policy_server_ip}:${policy_server_port}"
+
+bash "${CLIENT_SCRIPT}" \
+    "${bench_name}" \
+    "${task_name}" \
+    "${ckpt_name}" \
+    "${env_cfg_type}" \
+    "${action_type}" \
+    "${seed}" \
+    "${env_gpu_id}" \
+    "${eval_env_conda_env}" \
+    "${additional_info}" \
+    "${policy_server_port}" \
+    "${policy_server_ip}"
+
+echo "[MAIN] eval finished"

inference/policy/bagel_robotics_1/evidence/action-comparison.json

@@ -0,0 +1,105 @@
+{
+  "passed": true,
+  "checks": {
+    "reference_vs_packaged_0": {
+      "passed": true,
+      "events": [
+        280,
+        280
+      ],
+      "max_abs_stored_array_difference": 0.0,
+      "difference_count": 0,
+      "differences": []
+    },
+    "reference_vs_packaged_1": {
+      "passed": true,
+      "events": [
+        280,
+        280
+      ],
+      "max_abs_stored_array_difference": 0.0,
+      "difference_count": 0,
+      "differences": []
+    },
+    "reference_vs_packaged_2": {
+      "passed": true,
+      "events": [
+        280,
+        280
+      ],
+      "max_abs_stored_array_difference": 0.0,
+      "difference_count": 0,
+      "differences": []
+    },
+    "captured_vs_packaged_0": {
+      "passed": true,
+      "events": [
+        280,
+        280
+      ],
+      "max_abs_stored_array_difference": 0.0,
+      "difference_count": 0,
+      "differences": []
+    },
+    "captured_vs_packaged_1": {
+      "passed": true,
+      "events": [
+        280,
+        280
+      ],
+      "max_abs_stored_array_difference": 0.0,
+      "difference_count": 0,
+      "differences": []
+    },
+    "reference_reset_A_to_B_to_A": {
+      "passed": true,
+      "events": [
+        280,
+        280
+      ],
+      "max_abs_stored_array_difference": 0.0,
+      "difference_count": 0,
+      "differences": []
+    },
+    "packaged_reset_A_to_B_to_A": {
+      "passed": true,
+      "events": [
+        280,
+        280
+      ],
+      "max_abs_stored_array_difference": 0.0,
+      "difference_count": 0,
+      "differences": []
+    },
+    "matching_backend": {
+      "passed": true,
+      "reference": {
+        "torch": "2.11.0+cu130",
+        "cuda": "13.0",
+        "cudnn": 91900,
+        "gpu": "NVIDIA RTX PRO 6000 Blackwell Server Edition",
+        "deterministic": true,
+        "warn_only": false,
+        "cudnn_benchmark": false,
+        "cudnn_deterministic": true,
+        "cudnn_tf32": true,
+        "matmul_tf32": false,
+        "matmul_precision": "highest"
+      },
+      "packaged": {
+        "torch": "2.11.0+cu130",
+        "cuda": "13.0",
+        "cudnn": 91900,
+        "gpu": "NVIDIA RTX PRO 6000 Blackwell Server Edition",
+        "deterministic": true,
+        "warn_only": false,
+        "cudnn_benchmark": false,
+        "cudnn_deterministic": true,
+        "cudnn_tf32": true,
+        "matmul_tf32": false,
+        "matmul_precision": "highest"
+      }
+    }
+  },
+  "scope": "Same observations, seed 1, hardware and backend; not a benchmark score."
+}

inference/policy/bagel_robotics_1/evidence/five-suite-results.json

@@ -0,0 +1,448 @@
+{
+  "schema": "bagel-cp1000-five-suite-report-v1",
+  "checkpoint": 1000,
+  "display_name": "Bagel-Robotics-1",
+  "official_leaderboard_result": false,
+  "scope": "Research-runner reported results; not packaged-adapter validation",
+  "aggregation": "Equal-weight mean of five suites; Generalization combines standard and randomized episodes",
+  "suites": {
+    "precision": {
+      "scored": 400,
+      "successes": 126,
+      "success_rate": 0.315,
+      "mean_partial_score": 0.40425,
+      "tasks": {
+        "build_tower": {
+          "name": "Build tower",
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+          "name": "Push T",
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+          "mean_partial_score": 0.02,
+          "source": "user-supplied completed task summary"
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+          "source": "user-supplied completed task summary"
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+        "store_laptop_and_headphones": {
+          "name": "Store laptop and headphones",
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+          "mean_partial_score": 0.512,
+          "source": "node-2 receipts verified in prior results review"
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+          "source": "saved checkpoint-1000 research evidence with receipt hashes"
+        },
+        "store_tools_in_toolbox": {
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+}

inference/policy/bagel_robotics_1/evidence/gpu-validation.json

@@ -0,0 +1,168 @@
+{
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+  "date_utc": "2026-11-06",
+  "public_model_name": "Bagel-Robotics-1",
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+    {
+      "JobID": "53542",
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+      "ExitCode": "0:0",
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+  "simulator_launch_adjustments": [
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+    "Read-path bridge to native eval_result; no result contents changed"
+  ],
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+    "slurm-final.json": "766906b88c3153fb1abb641579302b9639234ccac995015bcb58ead33622344d"
+  }
+}

inference/policy/bagel_robotics_1/evidence/local-results.json

@@ -0,0 +1,569 @@
+{
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+            "native_complete": true,
+            "reporting_complete": true,
+            "scored": 50,
+            "success_rate": 0.08,
+            "successes": 4,
+            "unscored": 0,
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+          },
+          "store_tools_in_toolbox": {
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+            "expected": 50,
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+            "native_complete": true,
+            "reporting_complete": true,
+            "scored": 50,
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+            "unscored": 0,
+            "valid_failures": 50
+          }
+        },
+        "unscored": 0,
+        "update": 1000,
+        "valid_failures": 383,
+        "variants": {
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+            "accounted": 399,
+            "expected": 400,
+            "mean_partial_score": 0.04486215538847117,
+            "native_complete": false,
+            "reporting_complete": false,
+            "scored": 399,
+            "success_rate": 0.040100250626566414,
+            "successes": 16,
+            "unscored": 0,
+            "valid_failures": 383
+          }
+        }
+      },
+      "reported_with_labeled_replacements": {
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+        "success_rate": 0.04,
+        "mean_partial_score": 0.04475,
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+          },
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+            "success_rate": 0.24,
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+          },
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+            "success_rate": 0.0,
+            "successes": 0
+          },
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+            "scored": 50,
+            "success_rate": 0.0,
+            "successes": 0
+          },
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+            "success_rate": 0.0,
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+          },
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+            "scored": 50,
+            "success_rate": 0.08,
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+            "success_rate": 0.0,
+            "successes": 0
+          }
+        }
+      }
+    },
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+          },
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+            "expected": 50,
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+            "native_complete": true,
+            "reporting_complete": true,
+            "scored": 50,
+            "success_rate": 0.86,
+            "successes": 43,
+            "unscored": 0,
+            "valid_failures": 7
+          },
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+            "native_complete": true,
+            "reporting_complete": true,
+            "scored": 50,
+            "success_rate": 0.18,
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+            "unscored": 0,
+            "valid_failures": 41
+          },
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+            "expected": 50,
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+            "native_complete": true,
+            "reporting_complete": true,
+            "scored": 50,
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+            "successes": 6,
+            "unscored": 0,
+            "valid_failures": 44
+          },
+          "play_tic_tac_toe": {
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+            "expected": 50,
+            "mean_partial_score": 0.405,
+            "native_complete": true,
+            "reporting_complete": true,
+            "scored": 50,
+            "success_rate": 0.02,
+            "successes": 1,
+            "unscored": 0,
+            "valid_failures": 49
+          },
+          "put_bottles_into_dustbin": {
+            "accounted": 50,
+            "expected": 50,
+            "mean_partial_score": 0.988,
+            "native_complete": true,
+            "reporting_complete": true,
+            "scored": 50,
+            "success_rate": 0.98,
+            "successes": 49,
+            "unscored": 0,
+            "valid_failures": 1
+          }
+        },
+        "unscored": 0,
+        "update": 1000,
+        "valid_failures": 261,
+        "variants": {
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+            "expected": 400,
+            "mean_partial_score": 0.532035175879397,
+            "native_complete": false,
+            "reporting_complete": false,
+            "scored": 398,
+            "success_rate": 0.3442211055276382,
+            "successes": 137,
+            "unscored": 0,
+            "valid_failures": 261
+          }
+        }
+      },
+      "reported_with_labeled_replacements": {
+        "scored": 400,
+        "successes": 137,
+        "success_rate": 0.3425,
+        "mean_partial_score": 0.53,
+        "replacement_count": 2,
+        "tasks": {
+          "classify_objects": {
+            "mean_partial_score": 0.445,
+            "scored": 50,
+            "success_rate": 0.32,
+            "successes": 16
+          },
+          "fill_egg_holder": {
+            "mean_partial_score": 0.138,
+            "scored": 50,
+            "success_rate": 0.04,
+            "successes": 2
+          },
+          "fill_pen_holder": {
+            "mean_partial_score": 0.439,
+            "scored": 50,
+            "success_rate": 0.22,
+            "successes": 11
+          },
+          "make_kong": {
+            "mean_partial_score": 0.86,
+            "scored": 50,
+            "success_rate": 0.86,
+            "successes": 43
+          },
+          "organize_table": {
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+            "scored": 50,
+            "success_rate": 0.18,
+            "successes": 9
+          },
+          "play_stacking_toy": {
+            "mean_partial_score": 0.32,
+            "scored": 50,
+            "success_rate": 0.12,
+            "successes": 6
+          },
+          "play_tic_tac_toe": {
+            "mean_partial_score": 0.405,
+            "scored": 50,
+            "success_rate": 0.02,
+            "successes": 1
+          },
+          "put_bottles_into_dustbin": {
+            "mean_partial_score": 0.988,
+            "scored": 50,
+            "success_rate": 0.98,
+            "successes": 49
+          }
+        }
+      }
+    }
+  },
+  "replacements": [
+    {
+      "suite": "open",
+      "episode": "general_pickup--0050",
+      "outcome": {
+        "executed_steps": 200,
+        "layout_id": 50,
+        "score": 0.0,
+        "step_budget": 200,
+        "success": false,
+        "termination_reason": "step_budget"
+      },
+      "receipt_sha256": "4f2a6cebf43dc12ea30ee63981e720e7afcec93b03ad3f6a4ba460c59f2d9cf4",
+      "protocol": "e32aa0e95d1bdfe638c65520e8512c1e0da1cab16744ac560ba2c8ad59dcbc5d",
+      "source_sha256": "a9c67e0796355fb49b13de78212bb5ef0983d3e2d18bb0480d0c38f2bcbe1eae",
+      "source_file": "replacement-result.json"
+    },
+    {
+      "suite": "long_horizon",
+      "episode": "fill_egg_holder--0050",
+      "outcome": {
+        "executed_steps": 700,
+        "layout_id": 50,
+        "score": 0.0,
+        "step_budget": 700,
+        "success": false,
+        "termination_reason": "step_budget"
+      },
+      "receipt_sha256": "e997fd981a5855be673538943400865788b20f4850d0da684d9752ff4778bafa",
+      "protocol": "a876503909848ac16a62806bed0177445f8ee0681970f82f1d49150a1ab5b5b6",
+      "source_sha256": "af24963ead6a0db6175e26b51df2a551e3a3b7c4854659c8b7995ff24a4fb927",
+      "source_file": "fill_egg_holder--0050-result.json"
+    },
+    {
+      "suite": "long_horizon",
+      "episode": "fill_egg_holder--0051",
+      "outcome": {
+        "executed_steps": 700,
+        "layout_id": 51,
+        "score": 0.25,
+        "step_budget": 700,
+        "success": false,
+        "termination_reason": "step_budget"
+      },
+      "receipt_sha256": "8a9be90e270a00edd75615061418570fe83af25e28eb742ce2548d10b792713f",
+      "protocol": "a876503909848ac16a62806bed0177445f8ee0681970f82f1d49150a1ab5b5b6",
+      "source_sha256": "48566afa4d9dd6f1c53c601974cc583fc4c519a57c96c94515e361ba1daf03b0",
+      "source_file": "fill_egg_holder--0051-result.json"
+    }
+  ],
+  "note": "Local standard-layout results. Open/Long Horizon include labeled startup-invalid replacements. No five-suite benchmark aggregate or packaged-adapter rollout claim."
+}

inference/policy/bagel_robotics_1/evidence/pr-audit.json

@@ -0,0 +1,20 @@
+{
+  "passed": true,
+  "errors": [],
+  "python_files": 50,
+  "shell_scripts": 5,
+  "cpu_tests": {
+    "tests": 15,
+    "failures": 0,
+    "errors": 0,
+    "skipped": 0
+  },
+  "pr_template_headings_exact": true,
+  "gpu_qualification": "passed",
+  "ready_for_pr": false,
+  "remaining_gates": [
+    "dedicated private reviewer access",
+    "maintainer agreement on proposed eval-only integration and release timeline",
+    "user review before PR creation"
+  ]
+}

inference/policy/bagel_robotics_1/install.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+set -euo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+XPL_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
+ENV_DIR="${1:-${SCRIPT_DIR}/.venv}"
+"${PYTHON_BIN:-python3}" -m venv "${ENV_DIR}"
+"${ENV_DIR}/bin/python" -m pip install --upgrade pip
+"${ENV_DIR}/bin/python" -m pip install torch==2.11.0 torchvision==0.26.0 \
+  --index-url "${TORCH_INDEX_URL:-https://download.pytorch.org/whl/cu130}"
+"${ENV_DIR}/bin/python" -m pip install -c "${SCRIPT_DIR}/requirements-transport.txt" -e "${SCRIPT_DIR}/runtime" -e "${XPL_ROOT}"
+echo "Policy environment: ${ENV_DIR}"

inference/policy/bagel_robotics_1/licenses/Apache-2.0.txt

@@ -0,0 +1,201 @@
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+
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+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
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+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
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+      same "printed page" as the copyright notice for easier
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+   Copyright [yyyy] [name of copyright owner]
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+   Licensed under the Apache License, Version 2.0 (the "License");
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inference/policy/bagel_robotics_1/licenses/RoboDojo-MIT.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Yue Chen <yuechen020614@gmail.com>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

inference/policy/bagel_robotics_1/licenses/XPolicyLab.txt

@@ -0,0 +1,201 @@
+                                 Apache License
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+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+
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+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
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+   See the License for the specific language governing permissions and
+   limitations under the License.

inference/policy/bagel_robotics_1/model.py

@@ -0,0 +1,361 @@
+import os
+from pathlib import Path
+from typing import Any
+import cv2
+import numpy as np
+from .temporal_ensemble import TemporalEnsemble
+from XPolicyLab.model_template import ModelTemplate
+from XPolicyLab.utils.process_data import get_robot_action_dim_info
+from .assets import prepare_model_config
+from .backend import configure_backend
+
+POLICY_DIR = Path(__file__).resolve().parent
+
+
+def _is_none_like(value: Any) -> bool:
+    if value is None:
+        return True
+    return isinstance(value, str) and value.strip().lower() in {"", "none", "null"}
+
+
+def _standardize_rgb(image: np.ndarray, *, resize: bool = True) -> np.ndarray:
+    image = np.asarray(image)
+    if image.ndim != 3 or image.shape[-1] != 3:
+        raise ValueError(f"Expected HWC image with 3 channels, got {image.shape}")
+    if image.dtype != np.uint8:
+        image = np.clip(image, 0, 255).astype(np.uint8)
+    if resize and image.shape[:2] != (240, 320):
+        image = cv2.resize(image, (320, 240), interpolation=cv2.INTER_AREA)
+    if resize and image.shape != (240, 320, 3):
+        raise ValueError(f"Expected standardized RGB shape (240, 320, 3), got {image.shape}")
+    return image
+
+
+def _get_instruction(obs: dict, fallback: str) -> str:
+    value = obs.get("task_instruction")
+    if value is None:
+        value = obs.get("instruction", obs.get("instructions"))
+    if isinstance(value, (list, tuple)):
+        return str(value[0]) if value else fallback
+    if value is None:
+        return fallback
+    if hasattr(value, "item"):
+        value = value.item()
+    text = str(value).strip()
+    return text if text else fallback
+
+
+class Model(ModelTemplate):
+    def __init__(self, model_cfg):
+        self.model_cfg = prepare_model_config(model_cfg, POLICY_DIR)
+        self.backend_settings = configure_backend(self.model_cfg["seed"])
+        info = get_robot_action_dim_info(self.model_cfg["env_cfg_type"])
+        if len(info["arm_dim"]) != 2 or list(info["ee_dim"]) != [1, 1]:
+            raise ValueError("The checkpoint requires two arms and one gripper value per arm")
+        self.robot_action_dim_info = info
+        self.action_dim = sum(7 + int(d) for d in info["ee_dim"])
+        if self.action_dim != 16:
+            raise ValueError("Checkpoint action width differs from the registered robot")
+        self.action_type = self.model_cfg["action_type"]
+        if (
+            self.action_type != "ee"
+            or self.model_cfg.get("deployment_adapter", "robodojo_ee16") != "robodojo_ee16"
+        ):
+            raise ValueError("This adapter only supports RoboDojo EE16")
+        self.env_cfg_type = self.model_cfg["env_cfg_type"]
+        self.action_horizon = 1
+        self.replan_steps = int(self.model_cfg.get("replan_steps") or 16)
+        self.default_instruction = str(
+            self.model_cfg.get("default_instruction")
+            or self.model_cfg.get("prompt")
+            or "follow the instruction"
+        )
+        self.deployment_adapter = (
+            str(self.model_cfg.get("deployment_adapter") or "robodojo_ee16").strip().lower()
+        )
+        self.last_obs = None
+        self.last_instruction = self.default_instruction
+        self.model = None
+        self._request_context = {}
+        self._virtual_env_ids = {}
+        self._next_virtual_env_id = 0
+        self._client_last_use = {}
+        self._client_use_counter = 0
+        self._max_retained_clients = int(self.model_cfg.get("max_retained_clients") or 64)
+        self._last_infer_step = {}
+        self.history_guard_duplicates = 0
+        self.history_guard_mismatches = 0
+        self._active_client = None
+        self._seen_client_ids = False
+        self._last_obs_by_client = {}
+        self._last_instruction_by_client = {}
+        self._last_virtual_by_client = {}
+        self._infer_count_by_virtual = {}
+        self._ensemble_by_virtual = {}
+        self._last_ensemble = None
+        checkpoint_path = self.model_cfg.get("checkpoint_path") or self.model_cfg.get(
+            "ckpt_setting"
+        )
+        dataset_stats_path = self.model_cfg.get("dataset_stats_path")
+        if _is_none_like(checkpoint_path):
+            raise FileNotFoundError(
+                "VPP2 requires checkpoint_path/ckpt_setting for real deployment."
+            )
+        if _is_none_like(dataset_stats_path):
+            raise FileNotFoundError("VPP2 requires dataset_stats_path for real deployment.")
+        from bagel_cp1000.policy import get_model
+
+        upstream_cfg = dict(self.model_cfg)
+        upstream_cfg["ckpt_setting"] = str(Path(checkpoint_path).expanduser().resolve())
+        upstream_cfg["dataset_stats_path"] = str(Path(dataset_stats_path).expanduser().resolve())
+        self.model = get_model(upstream_cfg)
+        video = self.model.model.video_expert
+        if sum(p.numel() for p in video.parameters()) != 12468409664:
+            raise ValueError("Loaded video expert is not the pinned compact CP1000 model")
+        if any("mask_logits" in name for name, _ in video.named_parameters()):
+            raise ValueError("Training mask parameters must not be present at inference")
+        self.action_horizon = int(self.model.action_horizon)
+        self.replan_steps = int(self.model.replan_steps)
+        if self.model_cfg.get("temporal_ensemble") != {"enabled": True, "new_weight": 0.9, "old_weight": 0.1}:
+            raise ValueError("Expected the authorized 90/10 temporal ensemble")
+        if (self.action_horizon, self.replan_steps) != (32, 16):
+            raise ValueError("Expected horizon 32, execute 16")
+
+    @staticmethod
+    def _pack_robodojo_ee16_state(obs: dict) -> np.ndarray:
+        state = obs["state"]
+        parts = []
+        for side in ("left", "right"):
+            pose = np.asarray(state[f"{side}_ee_pose"], dtype=np.float32).reshape(7)
+            gripper = np.asarray(state[f"{side}_ee_joint_state"], dtype=np.float32).reshape(1)
+            parts.extend((pose, gripper))
+        packed = np.concatenate(parts, axis=0)
+        if packed.shape != (16,):
+            raise ValueError(f"Expected RoboDojo EE state [16], got {packed.shape}")
+        return packed
+
+    @staticmethod
+    def _unpack_robodojo_ee16_actions(actions: np.ndarray):
+        packed = np.asarray(actions, dtype=np.float32)
+        if packed.ndim == 1:
+            packed = packed[None, :]
+        if packed.ndim != 2 or packed.shape[1] != 16:
+            raise ValueError(f"Expected RoboDojo EE actions [T,16], got {packed.shape}")
+        result = []
+        for action in packed:
+            result.append(
+                {
+                    "left_ee_pose": action[0:7],
+                    "left_ee_joint_state": action[7:8],
+                    "right_ee_pose": action[8:15],
+                    "right_ee_joint_state": action[15:16],
+                }
+            )
+        return result
+
+    def _encode_observation(self, obs: dict) -> dict:
+        vision = obs["vision"]
+        adapted = {
+            "env_idx": self._virtual_env_idx(int(obs.get("env_idx", 0))),
+            "observation": {
+                "head_camera": {
+                    "rgb": _standardize_rgb(vision["cam_head"]["color"], resize=False)
+                },
+                "left_camera": {
+                    "rgb": _standardize_rgb(vision["cam_left_wrist"]["color"], resize=False)
+                },
+                "right_camera": {
+                    "rgb": _standardize_rgb(vision["cam_right_wrist"]["color"], resize=False)
+                },
+            },
+            "joint_action": {"vector": self._pack_robodojo_ee16_state(obs)},
+        }
+        return adapted
+
+    CLIENT_ID_FIELD = "_vpp2_client_id"
+
+    def _bind_client(self, obs) -> None:
+        client_id = obs.get(self.CLIENT_ID_FIELD) if isinstance(obs, dict) else None
+        if client_id is None:
+            self._active_client = None
+            return
+        self._seen_client_ids = True
+        self._active_client = ("client", str(client_id))
+
+    def _client_key(self):
+        if getattr(self, "_active_client", None) is not None:
+            return self._active_client
+        context = getattr(self, "_request_context", None) or {}
+        return (context.get("evaluation_id"), context.get("trial_id"))
+
+    def _forget_virtual(self, virtual: int) -> None:
+        self._last_infer_step.pop(virtual, None)
+        self._infer_count_by_virtual.pop(virtual, None)
+        self._ensemble_by_virtual.pop(virtual, None)
+        self._last_ensemble = None
+        if self.model is not None and hasattr(self.model, "reset_env"):
+            self.model.reset_env(virtual)
+
+    def _virtual_env_idx(self, env_idx: int) -> int:
+        client = self._client_key()
+        self._client_use_counter += 1
+        self._client_last_use[client] = self._client_use_counter
+        key = (client, int(env_idx))
+        virtual = self._virtual_env_ids.get(key)
+        if virtual is None:
+            virtual = self._next_virtual_env_id
+            self._next_virtual_env_id += 1
+            self._virtual_env_ids[key] = virtual
+            self._evict_stale_clients()
+        return virtual
+
+    def _evict_stale_clients(self) -> None:
+        clients = {key[0] for key in self._virtual_env_ids}
+        excess = len(clients) - self._max_retained_clients
+        if excess <= 0:
+            return
+        current = self._client_key()
+        stale = sorted(
+            (c for c in clients if c != current), key=lambda c: self._client_last_use.get(c, 0)
+        )[:excess]
+        for client in stale:
+            for key in [k for k in self._virtual_env_ids if k[0] == client]:
+                self._forget_virtual(self._virtual_env_ids.pop(key))
+            self._client_last_use.pop(client, None)
+            self._last_obs_by_client.pop(client, None)
+            self._last_instruction_by_client.pop(client, None)
+            self._last_virtual_by_client.pop(client, None)
+
+    def set_request_context(self, context):
+        self._request_context = dict(context or {})
+        if self.model is not None and hasattr(self.model, "set_request_context"):
+            self.model.set_request_context(self._request_context)
+
+    def _remember(self, encoded, raw_obs) -> None:
+        client = self._client_key()
+        self.last_obs = encoded
+        self.last_instruction = _get_instruction(raw_obs, self.default_instruction)
+        self._last_obs_by_client[client] = self.last_obs
+        self._last_instruction_by_client[client] = self.last_instruction
+        self._last_virtual_by_client[client] = encoded["env_idx"]
+
+    def update_obs(self, obs):
+        self._bind_client(obs)
+        observation_history = obs.get("_vpp2_observation_history")
+        if observation_history:
+            step = (self._request_context or {}).get("step")
+            virtual = self._virtual_env_idx(int(obs.get("env_idx", 0)))
+            if step is not None:
+                last = self._last_infer_step.get(virtual)
+                if last is not None and int(step) <= last:
+                    self.history_guard_duplicates += 1
+                    print(
+                        f"[VPP2][history-guard] duplicate infer step={step} last={last} client={self._client_key()} total_duplicates={self.history_guard_duplicates}",
+                        flush=True,
+                    )
+                    current = observation_history[-1]
+                    self._remember(self._encode_observation(current), current)
+                    self._infer_count_by_virtual[virtual] = max(
+                        0, self._infer_count_by_virtual.get(virtual, 0) - 1
+                    )
+                    return
+                self._last_infer_step[virtual] = int(step)
+            for history_obs in observation_history:
+                if not isinstance(history_obs, dict):
+                    continue
+                encoded = self._encode_observation(history_obs)
+                self._remember(encoded, history_obs)
+                if self.model is not None and hasattr(self.model, "observe"):
+                    self.model.observe(encoded)
+            return
+        encoded = self._encode_observation(obs)
+        self._remember(encoded, obs)
+        if self.model is not None and hasattr(self.model, "observe"):
+            self.model.observe(encoded)
+
+    def _check_history_alignment(self, virtual: int) -> None:
+        """Before the k-th inference of an episode exactly replan_steps*k
+        observations must have been recorded after the anchor (step 0)."""
+        count = self._infer_count_by_virtual.get(virtual, 0)
+        self._infer_count_by_virtual[virtual] = count + 1
+        model = self.model
+        if model is None or not getattr(model, "history_condition_frames", 0):
+            return
+        recorded = getattr(model, "_history_step_by_env", {}).get(virtual)
+        expected = int(self.replan_steps) * count
+        if recorded != expected:
+            self.history_guard_mismatches += 1
+            print(
+                f"[VPP2][history-guard] MISMATCH inference={count} recorded_obs_step={recorded} expected={expected} client={self._client_key()} total_mismatches={self.history_guard_mismatches}",
+                flush=True,
+            )
+
+    def _infer_actions(self, obs, instruction):
+        if obs is None:
+            raise ValueError("No observation is available. Call update_obs() before get_action().")
+        action_chunk = self.model._infer_action_chunk(obs, instruction)
+        action_chunk = np.asarray(action_chunk, dtype=np.float32)
+        if action_chunk.ndim == 1:
+            action_chunk = action_chunk[None, :]
+        virtual = int(obs["env_idx"])
+        ensemble = self._ensemble_by_virtual.setdefault(virtual, TemporalEnsemble())
+        action_chunk = ensemble.apply(action_chunk)
+        self._last_ensemble = ensemble.last_info
+        return self._unpack_robodojo_ee16_actions(action_chunk)
+
+    def get_action(self, obs=None):
+        self._bind_client(obs)
+        client = self._client_key()
+        last_obs = self._last_obs_by_client.get(client, self.last_obs)
+        instruction = self._last_instruction_by_client.get(client, self.last_instruction)
+        virtual = self._last_virtual_by_client.get(client)
+        if virtual is not None:
+            self._check_history_alignment(virtual)
+        return self._infer_actions(last_obs, instruction)
+
+    def _reset_client_state(self, client) -> None:
+        for key in [k for k in self._virtual_env_ids if k[0] == client]:
+            self._forget_virtual(self._virtual_env_ids.pop(key))
+        self._last_obs_by_client.pop(client, None)
+        self._last_instruction_by_client.pop(client, None)
+        self._last_virtual_by_client.pop(client, None)
+
+    def reset_client(self, obs=None):
+        """Episode reset for one identified client (upstream per-call protocol)."""
+        self._bind_client(obs)
+        client = self._client_key()
+        self.last_obs = None
+        self.last_instruction = self.default_instruction
+        if self.model is not None and hasattr(self.model, "reset_env"):
+            self._reset_client_state(client)
+        elif self.model is not None:
+            self.model.reset()
+
+    def reset(self):
+        self._active_client = None
+        self._request_context = {}
+        self._seen_client_ids = False
+        self._next_virtual_env_id = 0
+        self._client_use_counter = 0
+        self.last_obs = None
+        self.last_instruction = self.default_instruction
+        for state in (self._virtual_env_ids, self._client_last_use,
+                      self._last_infer_step, self._infer_count_by_virtual,
+                      self._ensemble_by_virtual, self._last_obs_by_client,
+                      self._last_instruction_by_client, self._last_virtual_by_client):
+            state.clear()
+        self._last_ensemble = None
+        self.history_guard_duplicates = 0
+        self.history_guard_mismatches = 0
+        if self.model is not None:
+            self.model.reset()
+
+    def update_obs_batch(self, obs_list):
+        if len(obs_list) != 1:
+            raise NotImplementedError("CP1000 is qualified for one environment per instance")
+        self.update_obs(obs_list[0])
+
+    def get_action_batch(self, env_idx_list=None):
+        if env_idx_list is not None and len(env_idx_list) != 1:
+            raise NotImplementedError("CP1000 is qualified for one environment per instance")
+        return [self.get_action()]

inference/policy/bagel_robotics_1/requirements-transport.txt

@@ -0,0 +1,5 @@
+opencv-python-headless==4.11.0.86
+websockets==16.1.1
+h5py==3.16.0
+PyYAML==6.0.3
+msgpack==1.2.3

inference/policy/bagel_robotics_1/runtime/LICENSE

@@ -0,0 +1,24 @@
+MIT License
+
+Copyright (c) 2026 The FastWAM Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+Except for third-party code and files that retain their original license
+headers, this repository is released under the MIT License.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

inference/policy/bagel_robotics_1/runtime/THIRD_PARTY_NOTICES.md

@@ -0,0 +1,8 @@
+# Runtime source notices
+
+The VPP2/FastWAM-derived source retains the MIT notice in LICENSE.
+Wan2.1/DiffSynth-derived components retain Apache-2.0 terms, included in
+licenses/Apache-2.0.txt. Source attribution is retained in individual modules
+where present. Source hashes and packaging changes accompany the adapter in
+source-provenance.json. Model weights and datasets are separate artifacts with
+their own upstream terms; this code license does not relicense those artifacts.

inference/policy/bagel_robotics_1/runtime/licenses/Apache-2.0.txt

@@ -0,0 +1,201 @@
+                                 Apache License
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+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+      any Contribution intentionally submitted for inclusion in the Work
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+      this License, without any additional terms or conditions.
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+      the terms of any separate license agreement you may have executed
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+
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inference/policy/bagel_robotics_1/runtime/pyproject.toml

@@ -0,0 +1,20 @@
+[build-system]
+requires = ["setuptools>=77", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "bagel-cp1000-inference"
+version = "0.1.0"
+description = "Isolated inference runtime for Bagel-Robotics-1 recovery checkpoint 1000"
+requires-python = ">=3.10"
+license = "MIT AND Apache-2.0"
+license-files = ["LICENSE", "THIRD_PARTY_NOTICES.md", "licenses/*.txt"]
+dependencies = [
+  "torch==2.11.0", "torchvision==0.26.0", "numpy==1.26.4",
+  "transformers==4.49.0", "huggingface-hub==0.36.0", "hydra-core==1.3.2",
+  "omegaconf==2.3.0", "einops==0.8.1", "Pillow==12.0.0", "rich==14.2.0",
+  "safetensors==0.5.3", "ftfy==6.3.1", "regex>=2024.11", "sentencepiece>=0.2"
+]
+
+[tool.setuptools.packages.find]
+where = ["src"]