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Language-protocol substrate

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c0f7adb0 · PointSav Digital Systems ·

docs(2g): corpus-wide Doctrine claim vocabulary removal (227 files)

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@@ -11,8 +11,8 @@ bcsc_class: public-disclosure-safe
last_edited: 2026-04-30
editor: pointsav-engineering
cites:
  - ni-51-102
  - osc-sn-51-721
 - ni-51-102
 - osc-sn-51-721
paired_with: language-protocol-substrate.es.md
---

@@ -51,9 +51,9 @@ Three adapters compose at request time:

```
composed_weights =
    base_model
    ⊕ tenant_adapter[<tenant_id>]      // brand voice
    ⊕ protocol_adapter[PROSE | COMMS | LEGAL | TRANSLATE]
 base_model
 ⊕ tenant_adapter[<tenant_id>] // brand voice
 ⊕ protocol_adapter[PROSE | COMMS | LEGAL | TRANSLATE]
```

Five or more adapters per request crosses into multi-task interference per the 2025 LoRA literature (LoRAX, S-LoRA, TC-LoRA, LoRI). Foundry stays at three.
@@ -85,7 +85,7 @@ This is the meaning of "tenant escalation happens at the deployment boundary, no

## Configuration

Eight editorial task-types are defined in the project-language cluster manifest: `prose-edit`, `comms-edit`, `frontmatter-normalize`, `citation-insert`, `register-tighten`, `cross-link-verify`, `schema-validate`, `template-author`. Each generates verdict-signed training tuples through the apprenticeship-substrate pipeline (Doctrine claim #32). The tuples feed continued pretraining on the customer's adapter when corpus volume warrants.
Eight editorial task-types are defined in the project-language cluster manifest: `prose-edit`, `comms-edit`, `frontmatter-normalize`, `citation-insert`, `register-tighten`, `cross-link-verify`, `schema-validate`, `template-author`. Each generates verdict-signed training tuples through the apprenticeship-substrate pipeline. The tuples feed continued pretraining on the customer's adapter when corpus volume warrants.

Per `[ni-51-102]` continuous-disclosure language and in accordance with the forward-looking information principles of `[osc-sn-51-721]`, the substrate's training pipeline is described in planned terms. The shape is in place; the operational throughput is what matures over time. The pipeline target: every editorial action a Foundry-shaped deployment performs is one tuple of training data for the customer's adapter. The customer's voice deepens over time without their text leaving their substrate.
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