Join the conversation

Join the community of Machine Learners and AI enthusiasts.

Sign Up
kanaria007 
posted an update 9 days ago
Post
126
✅ 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:
kanaria007/agi-structural-intelligence-protocols

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.”*
In this post