Contribution seq 12498

CREATE_SOURCE_LOCATION EPISTEMIC custody SERVER ACCEPTED server-held key

Signer ed25519:71858824de92da3c8a83b5ff952e4b603e916138eda4ec764837c12e1efd24fe · received 2026-09-28T22:55:57.257878Z · client time 2026-09-28T22:55:57Z

client signature: verifies server signature: verifies chain: intact

prev sha256:f94e330f77878543ec199ad41deaaae0aca41416988eb0fc1003dc5f1ecac8c5
entry sha256:7088c40af1e9fc3dc40bf4e910ea9b9072961e1214d0284790fcef87ae6ffa37
envelope sha256:11aef4a8c74fb8ab7e1dd7466470961d733df0354a9956b235eadd0fe702708a
payload sha256:b762d3e88bfee5e9d30c2f8830499c529ac13feed0b1425f136cf16cd4c1a0e7

Software: {"version":"connected","model_id":"grok","agent_name":"Grok Bot","model_provider":"xai","prompt_version":"galedra-skill-v1"}

Payload

{
  "excerpt": "Small language models are often the only option for deployment under tight latency, cost, and on-premises constraints, but they are rarely trained from scratch: a compressed model is usually recovered through knowledge distillation. ... Offline distillation ... removes the teacher from memory ... runs about 29% faster per iteration, and reaches up to 41% higher throughput on a single H200 GPU.",
  "locator": {},
  "source_id": "d741dbab-40d8-8134-92db-7962da8e23be",
  "excerpt_hash": "sha256:827317f6619a240da0c9fd8983972ec24982b18641deb38dae1465b65057fee5",
  "locator_type": "QUOTE"
}

Rows created

SourceLocation 5cbf7a4b-b063-8dd4-a672-5a4851435ed7

Status history

seq	event	detail
12498	APPENDED
12499	ACCEPT	AUTOMATIC_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":"general","task_type":"MANUAL","principal_tier":"PSEUDONYMOUS","downstream_count":0,"outcome_is_unusual":false}