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Add watermark artifact repository specification v1.0
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Watermark Repository Specification

1. Version

The current schema version is 1.0.0. Every metadata.json and the root manifest.json must declare this version.

2. Dataset identities

Benchmark Suite key Display name
libero long LIBERO-Long
libero goal LIBERO-Goal
libero object LIBERO-Object
libero spatial LIBERO-Spatial
robotwin main RoboTwin

Do not use machine-specific aliases as suite keys.

3. Regimes

Full data 

artifacts/full/<benchmark>/<suite>/<variant>/

Full runs must have:

  • ablation: null
  • no task-range restriction
  • no episode-pool restriction
  • the complete official suite for the declared dataset revision

Episode-pool ablation 

artifacts/ablation/episode_pool/<benchmark>/<suite>/top_<N>/<variant>/

N is one of 10, 20, 30, 40, 50. One episode is selected per task, but the random choice is restricted to the first N episodes. Metadata must record the random seed and episode_pool_limit.

Task-fraction ablation 

artifacts/ablation/task_fraction/<benchmark>/<suite>/p<P>/<variant>/

P is one of 10, 20, ..., 90. Metadata must record the exact selected task count, exact task identifiers, epochs, and target optimization budget. Do not infer the selected tasks only from the percentage.

4. Variant identities

A means action-loss weight and V means video-loss weight.

Variant Method Action weight Video weight
direct_A1V0 direct 1 0
direct_A0V1 direct 0 1
direct_A1V1 direct 1 1
de_proj_A1V0 de_proj 1 0
de_proj_A0V1 de_proj 0 1
bisect_A1V1 bisect 1 1

The directory name is authoritative. Metadata weights must agree with it.

5. Required artifact semantics

  • top1.npy: best-ranked patch under the run's declared selection metric.
  • top2.npy through top5.npy: remaining ranked patches.
  • final.npy: patch at termination.
  • optimization.jsonl: optional per-step trace.
  • metadata.json: required provenance and integrity record.

Watermarks must be stored as NumPy arrays. For the current eye-in-hand setup, the expected logical patch size is 128 x 128; the exact array layout and dtype must be declared in metadata.

6. Metadata requirements

Every metadata file must include:

  • benchmark, suite, regime, variant, and weights
  • public dataset repository and immutable revision
  • public model repository and immutable revision
  • selected tasks/episodes or the explicit full-data declaration
  • seed, camera, patch parameters, optimization settings, and stopping reason
  • Attack-WAM commit and hashes of the three critical source files
  • Python, PyTorch, CUDA build, runtime cuDNN, GPU, and SDPA policy
  • artifact paths, byte sizes, and SHA-256 hashes
  • run status and any recovered anomaly

Never upload local absolute paths, access tokens, usernames, hostnames, or private cache paths.

7. Status values

  • complete: clean completion.
  • complete_with_note: usable completion with a documented recovered event.
  • failed: not valid for evaluation.

Only complete and complete_with_note runs belong in the root manifest.

8. Upload rules

  1. Stage artifacts outside the source-code repository.
  2. Rename artifacts to the canonical filenames.
  3. Generate SHA-256 hashes and update metadata.
  4. Update manifest.json.
  5. Run the validator.
  6. Upload only the staging directory.

Example:

hf upload Wjjjh/Attack /path/to/staging . \
  --exclude "**/.DS_Store" \
  --exclude "**/__pycache__/**"

Do not run hf upload Wjjjh/Attack . from an Attack-WAM source checkout.