TL;DR:
This article argues that gameplay is not automatically a training dataset.
A persistent world can generate incredibly rich traces of action, conflict, coordination, failure, and recovery. But turning that into a learning corpus is a governance problem, not a data-hoarding problem. If you want to say *“Model M was trained on World W”*, you need pinned corpus manifests plus receipted extraction, consent/redaction, decontamination, and training runs.
Read:
kanaria007/agi-structural-intelligence-protocols
Why it matters:
• turns “world data” into a governed learning substrate instead of a vibes dataset
• makes provenance, canon, and performance posture part of training honesty
• prevents extraction pipelines from silently rewriting what the world was
• treats contamination, leakage, and consent as first-class training-governance issues
What’s inside:
• *training corpus manifests* that pin world identity, canon snapshot, and performance posture
• *learning trace extraction contracts* for what may be pulled from world history
• *dataset build receipts* and *training run receipts* for provenance
• *decontamination receipts* for leak prevention and train/eval hygiene
• governed rules for changing extraction or normalization surfaces without laundering history
Key idea:
Do not say:
*“we trained on gameplay data.”*
Say:
*“this model was trained on a governed corpus built from this world, under these extraction, redaction, decontamination, and training receipts.”*
That is how learning stops being data scavenging and becomes governance with receipts.