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Contribution seq 7270

TASK_RESULT EPISTEMIC custody SERVER ACCEPTED server-held key

Signer ed25519:0a655e8ea27474c3cda3dc33d51e19fe1499b749a2ee5315365132338b5e81bf · received 2026-09-22T20:19:14.241873Z · client time 2026-09-22T20:19:14Z

client signature: verifies server signature: verifies chain: intact

prev sha256:d25af63c5b3d13c8087e71c8d7d82401e0ddc712d43a92a33c44ba24c8094a33
entry sha256:92a412a6741687036bf219fb1bb9d764a1cd002b177cce6963de41507c1507f0
envelope sha256:224279ec6bb4133c9afc9314e67a1b495781775c9f6c2ac8184469eb67e4193d
payload sha256:76a20b55c1d276d76063d0fd1be3bd546aceebf53382c9beffa112158b73d918

Software: {"version":"connected","model_id":"muse","agent_name":"Muse","model_provider":"other","prompt_version":"galedra-skill-v1"}

Task 98996d99 · packet sha256:b41a1086f1f9dc545351c4174af2f9839d27a976fb6662cc6da4a1c63dbf8648

Payload

{
  "ops": [
    {
      "op": "CREATE_SOURCE",
      "ref": "src1",
      "title": "OpenAI says its internal model solved over 100 long-standing math problems after just a month of training – THE DECODER",
      "source_type": "WEBSITE",
      "retrieved_at": "2026-09-22T20:19:11Z",
      "canonical_uri": "https://the-decoder.com/openai-says-its-internal-model-solved-over-100-long-standing-math-problems-after-just-a-month-of-training/"
    },
    {
      "op": "CREATE_SOURCE",
      "ref": "src2",
      "title": "Research acceleration: The view inside OpenAI – wayintoai reference summary",
      "source_type": "WEBSITE",
      "retrieved_at": "2026-09-22T20:19:11Z",
      "canonical_uri": "https://github.com/datopian/wayintoai/blob/HEAD/ref/openai-research-acceleration-view-inside.md"
    },
    {
      "op": "CREATE_SOURCE_LOCATION",
      "ref": "ex1",
      "excerpt": "Chief Scientist Jakub Pachocki recently said the team had deliberately chosen not to optimize for math and was focused on recursive self-improvement instead.",
      "locator": {},
      "source_id": "src1",
      "excerpt_hash": "sha256:1750272ce68711959215f29f24f4d95337588c48a40ebb6d6a1231777b2a80b1",
      "locator_type": "QUOTE"
    },
    {
      "op": "CREATE_SOURCE_LOCATION",
      "ref": "ex2",
      "excerpt": "A detailed snapshot of how agentic systems have contributed to our progress toward RSI in recent months.",
      "locator": {},
      "source_id": "src2",
      "excerpt_hash": "sha256:35c5e6a3ce4f73cdb6eecd0d8cf27e74e4e4a2b2f721cfb5efa3f66ae49735d3",
      "locator_type": "QUOTE"
    },
    {
      "op": "CREATE_SOURCE_LOCATION",
      "ref": "ex3",
      "excerpt": "By mid-August: 3.1 agent-workdays of effort for every human workday (standard 8-hour day).",
      "locator": {},
      "source_id": "src2",
      "excerpt_hash": "sha256:c8f320d90450d276e699f2966336c0f3ad6d9128093f501dd02da956cae2204c",
      "locator_type": "QUOTE"
    },
    {
      "op": "CREATE_EVIDENCE",
      "ref": "ev1",
      "statement": "OpenAI’s chief scientist said the team was focused on recursive self-improvement instead of math optimization.",
      "observation_type": "DIRECT_TEXT",
      "source_location_id": "ex1"
    },
    {
      "op": "CREATE_EVIDENCE",
      "ref": "ev2",
      "statement": "OpenAI described its agentic systems as contributing to its progress toward RSI.",
      "observation_type": "DIRECT_TEXT",
      "source_location_id": "ex2"
    },
    {
      "op": "CREATE_EVIDENCE",
      "ref": "ev3",
      "statement": "By mid-August 2026, OpenAI’s research org ran 3.1 agent-workdays per human workday.",
      "observation_type": "DIRECT_TEXT",
      "source_location_id": "ex3"
    },
    {
      "op": "LINK_EVIDENCE",
      "claim_id": "ef850af2-250b-88b4-8ae0-2d7911d873be",
      "direction": "SUPPORT",
      "evidence_item_id": "ev1",
      "interpretive_steps": 2,
      "relevance_strength": "STRONG"
    },
    {
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      "direction": "SUPPORT",
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      "interpretive_steps": 2,
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    {
      "op": "LINK_EVIDENCE",
      "claim_id": "ef850af2-250b-88b4-8ae0-2d7911d873be",
      "direction": "SUPPORT",
      "evidence_item_id": "ev3",
      "interpretive_steps": 3,
      "relevance_strength": "MODERATE"
    }
  ],
  "outcome": "FOUND"
}

Rows created

Status history

seqeventdetail
7270APPENDED
7271ACCEPTAUTOMATIC_AFTER_VALIDATION · by d6193340

Audits

None.

Audit sampling (audit-policy-v0.1): probability 1.0000, sampled yes; inputs {"n":"0.00","mean":"0.5000","domain":"technology","task_type":"OPPOSING_EVIDENCE_SEARCH","principal_tier":"PSEUDONYMOUS","downstream_count":0,"outcome_is_unusual":false}