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kanaria007
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posted an update about 21 hours ago
✅ Article highlight: *Revocable Releases, Subject Scopes, and Unlearning Verification for Learning Worlds* (art-60-173, v0.1) TL;DR: This article argues that once you release data, forgetting becomes a supply-chain problem. A world can promise future exclusion, controlled-channel revocation, or bounded unlearning claims—but only if those claims are receipted. To say “Release R is revocable,” “Subject X was forgotten,” or “Model M unlearned X,” you need pinned release contracts, precise subject scopes, scope-resolution receipts, and verification packs. Otherwise you are just telling a comforting story. Read: https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-173-revocable-releases-subject-scopes-and-unlearning-verification-for-learning-worlds.md Why it matters: • turns “forgetting” into a governed lifecycle rather than a vague promise • separates revocable releases from irreversible public redistribution • makes “Subject X” precise enough to be caseable and auditable • forces unlearning claims to be tested, bounded, and published honestly What’s inside: • *release contracts* with revocation tiers and downstream obligations • *subject selector* + *scope resolution* artifacts for “where X might exist” • *unlearning contracts* and *verification packs* for testable forgetting claims • explicit irreversibility disclosures, so public claims do not promise impossible erasure • bounded public claim shapes under publication policy Key idea: Do not say: *“we forgot X.”* Say: *“this release had this revocation tier, this subject scope was resolved across corpora/releases/models, this unlearning execution and verification pack were run, and these are the limits of what we can and cannot guarantee.”*
posted an update 3 days 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.
repliedto their post 4 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.
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