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WAMProbe v0.1.0rc1

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@myheart521 myheart521 released this 15 Jul 15:43
v0.1.0rc1
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Learn about vigilant mode.

Changelog

All notable changes to WAMProbe will be documented here.

[0.1.0rc1] - 2026-07-15

This candidate is prepared for reproducibility review and is not yet published to PyPI or
as a final GitHub Release.

Added

  • Strict MkDocs documentation, GitHub Pages deployment, repository-local Markdown link
    validation, and Draft 2020-12 validation of canonical public-schema instances.
  • Typed alpha WAMAdapter protocol and capability declaration.
  • Analytic PointMass-2D paired counterfactual benchmark.
  • Oracle, copy-last-frame, wrong-direction, and action-agnostic reference baselines.
  • Action Dependence, Counterfactual Direction Accuracy, No-op Stability, state ADE,
    and Top-1 Regret metrics.
  • JSON/Markdown reports, schemas, CLI, CI, and contributor documentation.
  • Phase 0 evidence map covering 15 reported WAM/VLA failure modes.
  • Version-pinned audit selecting StarWAM as the first real adapter target.
  • Provider-scoped local model store with pinned revisions, expected paths, sizes, and
    checksums for the first StarWAM integration.
  • wamprobe doctor with safe manifest parsing, revision/size/hash validation, and JSON
    output for local model artifacts.
  • Reproducible StarWAM environment preflight and a narrow patch that restricts verified
    inference checkpoint loads to PyTorch's weights-only deserializer.
  • Checksummed intervention JSONL, corruption-detecting content-addressed prediction cache,
    exact-context comparison, and report-only regeneration commands.
  • Contact-aware BlockPush-2D and attachment-aware Gripper-Catch analytic benchmarks.
  • Four-family LIBERO-CF-Mini generation with exact snapshot restore, repeat, branch-order,
    provenance, and cache validation.
  • A pinned StarWAM 36-prediction matrix and 36 action-chunk simulator executions with
    explicit unsupported-metric skips and retained zero-success results.
  • Dependency-free PSNR/global-SSIM diagnostics and a traditional-video/control-value
    counterexample study.
  • Minimal score-execute-observe closed-loop replanning with context-block intervals and
    offline-ranking/return association analysis.
  • Release artifact audit, candidate workflow, clean-wheel smoke test, evidence manifest,
    and technical-report draft.

Changed

  • GitHub workflows now use the current Node 24-compatible major releases of official
    checkout, Python setup, artifact, Pages, provenance, and uv setup actions.
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