| # Methodology |
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| ## Objective |
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| The private project explored how to build a source-grounded assistant across three distinct layers: |
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| 1. supervised examples that teach answer behavior; |
| 2. retrieval records that preserve source identity and claim boundaries; |
| 3. sealed evaluations that measure retrieval, reasoning, and unsupported-claim behavior. |
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| The public toolkit documents this architecture without distributing the private source corpus. |
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| ## Provenance model |
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| Every private source was assigned a namespace, a stable source identifier, a source type, and a confidence or claim-status field. The design deliberately separates: |
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| - direct source evidence; |
| - implementation metadata; |
| - secondary summaries; |
| - theories and fan interpretations; |
| - independently authored material. |
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| An implementation artifact can support a claim about observed behavior without automatically supporting a claim about narrative intent. |
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| ## Split construction |
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| The final SFT release used train, validation, and test splits. Release checks rejected: |
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| - exact prompt or answer-pair overlap across splits; |
| - normalized overlap after case and whitespace normalization; |
| - semantic families crossing split boundaries; |
| - leakage from training prompts into sealed evaluation questions. |
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| The public synthetic records use the same broad chat structure but are newly authored and unrelated to the private source text. |
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| ## Evaluation design |
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| The sealed private benchmark covered source-grounded answering, cross-scope reasoning, exact retrieval, chronology, false-premise correction, implementation interpretation, visual identification, citation precision, and missing-source behavior. |
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| Each evaluation record separated required claims, forbidden claims, source references, difficulty, and scoring method. Evaluation files were excluded from training. |
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| ## Release validation |
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| The private blocking validator checked structural integrity, source resolution, image validity, manifest consistency, hashes, dataset counts, split isolation, and local-path redaction. Aggregate results are preserved in this public repository; protected inputs and generated content are not. |
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