Platform
Aether ™ Local Learning Enablement
Local Learning Enablement
This pack is the developer drilldown for how reviewed human corrections become governed local updates, evaluation-gated releases, and rollback-ready lineage artifacts.
This page should explain the local-learning pipeline itself, not just repeat the packaging summary. It shows how correction events from Aether Panes enter a queue, get classified, update different learning surfaces, and then pass through evaluation and lineage controls.
The core value of Pack 03 is not generic fine-tuning. It is controlled adaptation: corrections are transformed into specific update paths, measured against gates, versioned, and only then made eligible for broader propagation.
Turns reviewed corrections into explicit learning-data flows
Separates handler paths for KG, retrieval, model refinement, and policy override
Requires evaluation, versioning, and rollback before downstream promotion
Workflow Architecture
Reduced local-learning WA chunks
These simplified SVG diagrams reduce the attached pipeline into three clean technical chunks so the doc stays readable while still showing the learning flow.
Correction queue and classifier
The learning pipeline begins with the staged correction queue emitted by the Expert Console Pack and a classifier that determines which learning surface should be updated.
- Aether Panes supplies reviewed correction events rather than raw feedback fragments.
- A correction classifier reads both the queue and the correction-semantics document to route each change into the right handler path.
- That routing step prevents every correction from being treated as the same type of model update.
Different correction classes fan out into dedicated handlers for graph amendment, retrieval patching, model-refinement signals, and policy-triple overrides.
- Factual or relational corrections can update the knowledge graph through amendment handlers.
- Retrieval-specific corrections can patch the RAG index without being mistaken for model refinement.
- Model-refinement packaging and policy overrides stay distinct so each update path preserves the right operational semantics.
Evaluation gate, lineage, and rollback store
Every amendment path converges on evaluation gates and a lineage store so local learning remains measurable, reversible, and evidence-ready.
- Regression and delta checks measure whether the update improves behavior or should be held for review.
- Passing changes are versioned into a lineage and rollback store with signed, versioned delta context.
- That store creates the handoff from Pack 03 into Pack 04 for controlled propagation of approved learning.
Usage Paths
What clients should expect in practice
Pack 03 should clarify both the self-hosted learning loop and a generic managed-learning path without prematurely locking down cloud-specific details.
Self-hosted local learning
Open source scenario
For self-hosted teams, Pack 03 provides the controlled-learning pipeline that converts reviewed corrections into local graph, retrieval, model, or policy updates without losing traceability.
Inputs
- Reviewed correction queue from Aether Panes
- Correction-semantics document and workspace rules from Aether Discovery
- Local evaluation suites, version stores, and rollback controls
What gets configured
- Classify each reviewed correction into the correct handler path.
- Apply KG, retrieval, model-refinement, or policy updates locally.
- Run evaluation gates and store passing updates in lineage and rollback state.
Expected outcome
- A local-first learning loop that does not collapse all feedback into one opaque training path
- Evaluation-backed updates with explicit pass or fail outcomes
- Rollback-ready lineage artifacts before any wider propagation is attempted
Generic managed learning path
Platform as a Service scenario
For managed use, the client should be able to submit reviewed corrections into a hosted learning pipeline, receive evaluation results, and pull back signed lineage artifacts without tying the doc to a finalized cloud contract.
Inputs
- Hosted correction queues and classification payloads
- Managed evaluation hooks, threshold settings, and rollback policies
- Remote lineage storage for approved learning deltas
What gets configured
- Submit reviewed corrections into a managed classifier and handler pipeline.
- Run hosted evaluation gates against pre/post correction behavior.
- Retrieve approved versioned deltas, evidence, and rollback-ready lineage outputs.
Expected outcome
- A generic hosted-learning model that stays open on auth, transport, and sync details
- The same handler separation and gate discipline as self-hosted mode
- A direct bridge into Pack 04 once approved deltas are ready for distribution
Outputs
Expected artifacts and stored state
Pack 03 should emit versioned learning artifacts, evaluation results, and rollback-ready lineage state rather than untracked model changes.
.json
Correction classification records
Structured records showing how reviewed corrections were classified and routed into handler paths.
.patch / .ttl
KG and retrieval amendments
Localized amendment artifacts for knowledge-graph updates and retrieval-index patches.
.yaml / .json
Evaluation and threshold results
Gate outcomes, delta measurements, and approval decisions for each local-learning run.
.sig / .bundle
Lineage and rollback package
Signed, versioned learning bundles with rollback metadata and downstream propagation readiness.
Handoff
How Pack 03 connects to the rest of Aether
Pack 03 depends on Aether Panes’ reviewed correction signals, and its approved outputs become the direct input to Pack 04’s propagation model.
Expert Console Pack
Supplies the staged human-review corrections and governed event trail that local learning consumes.
Knowledge Delta Mesh
Consumes approved, versioned learning bundles once they are ready for trusted distribution.
Domain Architecture Sprint
Supplies the correction semantics and architecture contract that tell the learning pipeline how to route and evaluate changes.