Summary: Most stacks βlearnβ by fine-tuning weights and redeploying β powerful, but opaque. SI-Core already produces *structured evidence* (jump logs, ethics traces, effect ledgers, goal vectors, rollback traces), so learning can be *structural* instead:
*Upgrade policies, compensators, SIL code, and goal structures β using runtime evidence.*
> Learning isnβt a model tweak. > *Itβs upgrading the structures that shape behavior.*
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Why It Matters: β’ Makes improvement *localized and explainable* (what changed, where, and why) β’ Keeps βself-improvementβ *governable* (versioned deltas + review + CI/CD) β’ Turns incidents/metric drift into *actionable patches*, not postmortem PDFs β’ Scales to real ops: ethics policies, rollback plans, semantic compression, goal estimators
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Whatβs Inside: β’ What βlearningβ means in SI-Core (and what changes vs. classic ML) β’ The *Pattern-Learning-Bridge*: where it sits between runtime evidence and governed code β’ Safety properties: PLB proposes *versioned deltas*, never edits production directly β’ Validation pipeline: sandbox/simulation β conformance checks β golden diffs β rollout
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π Structured Intelligence Engineering Series A non-normative, implementable design for βlearning from failuresβ without sacrificing auditability.