148
followers ·
2 following None yet
posted an update 1 day ago ✅ Article highlight: *Deployment & Rollback Governance for Learning Worlds* (art-60-169, v0.1)
TL;DR:
This article argues that deployment is the highest-risk moment in a learning world.
Training produces a new policy. Deployment turns that policy into an institution inside the world. So rollout cannot be treated like a casual model swap. It needs deploy-gate contracts, canaries, phased rollout, kill-switches, rollback receipts, and explicit non-interference rules that stop “better learning” from silently rewriting world reality.
Read:
https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-169-deployment-and-rollback-governance-for-learning-worlds.md
Why it matters:
• treats deployment as governed change, not routine ops
• prevents silent reality drift when a newly trained policy changes world outcomes
• binds rollout to safety envelopes, evaluation validity, performance SLOs, and canon boundaries
• makes rollback and emergency stop part of the formal operating contract
What’s inside:
• a *model deploy gate contract* that defines when a learned policy may enter the world
• canary and phased rollout as explicit governed stages
• kill-switch and rollback receipts for emergency containment
• non-interference audits so training and deployment do not rewrite canon or governance outcomes
• appeal and publication boundaries for claims like “we deployed safely” or “we rolled back successfully”
Key idea:
Do not say:
*“we trained a better model, so we deployed it.”*
Say:
*“this policy entered the world under this deploy gate, this rollout stage, these envelope and SLO checks, these rollback guarantees, and these receipts.”*
That is how deployment becomes governance with receipts.
replied to their post 3 days ago ✅ Article highlight: *Worlds as Training Substrates* (art-60-167, v0.1)
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:
https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-167-worlds-as-training-substrates.md
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.
posted an update 3 days ago ✅ Article highlight: *Worlds as Training Substrates* (art-60-167, v0.1)
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:
https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-167-worlds-as-training-substrates.md
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.
View all activity None yet
view post ✅ Article highlight: *Deployment & Rollback Governance for Learning Worlds* (art-60-169, v0.1) TL;DR: This article argues that deployment is the highest-risk moment in a learning world. Training produces a new policy. Deployment turns that policy into an institution inside the world. So rollout cannot be treated like a casual model swap. It needs deploy-gate contracts, canaries, phased rollout, kill-switches, rollback receipts, and explicit non-interference rules that stop “better learning” from silently rewriting world reality. Read: kanaria007/agi-structural-intelligence-protocols Why it matters: • treats deployment as governed change, not routine ops • prevents silent reality drift when a newly trained policy changes world outcomes • binds rollout to safety envelopes, evaluation validity, performance SLOs, and canon boundaries • makes rollback and emergency stop part of the formal operating contract What’s inside: • a *model deploy gate contract* that defines when a learned policy may enter the world • canary and phased rollout as explicit governed stages • kill-switch and rollback receipts for emergency containment • non-interference audits so training and deployment do not rewrite canon or governance outcomes • appeal and publication boundaries for claims like “we deployed safely” or “we rolled back successfully” Key idea: Do not say: *“we trained a better model, so we deployed it.”* Say: *“this policy entered the world under this deploy gate, this rollout stage, these envelope and SLO checks, these rollback guarantees, and these receipts.”* That is how deployment becomes governance with receipts. See translation
CityOS Under SI-Core: A Worked Example Across All Invariants
