source/SOURCE_LOCK.json

{
  "schema_version": 1,
  "captured_utc": "2026-07-18T19:30:00Z",
  "openreview_id": "7FG30Wld9Q",
  "arxiv_id": "2510.22579",
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    "arxiv_source": {
      "url": "https://arxiv.org/e-print/2510.22579",
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    "main_tex_sha256": "41e1f44b86405263f081e830594ca95ef46d3df4f2bff1cb1257d2b6e4a11e55"
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  "challenge_snapshots": {
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    "official_index_sha256": "cc56683faf8b20730d40c8821eaecb737125c510d83474793ca5e9410b7e9a6b",
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  "code_availability": {
    "official_repository_found": false,
    "note": "No author implementation or repository is linked in the paper source; the reproduction is an independent NumPy CPU transcription of Algorithms 2-5 and the displayed theorem envelopes."
  },
  "full_score_precedent": {
    "space_id": "rdubwiley/optimal-anytime-algorithms-for-online-convex-optimization-with-adversarial-repro",
    "source": "official verdict snapshot",
    "quality": "medium",
    "score": "6/6",
    "use_policy": "Feasibility boundary only; do not copy code, measurements, prose, or thresholds."
  }
}