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ff0ca4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # Methodology
## Objective
The private project explored how to build a source-grounded assistant across three distinct layers:
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.
The public toolkit documents this architecture without distributing the private source corpus.
## Provenance model
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:
- direct source evidence;
- implementation metadata;
- secondary summaries;
- theories and fan interpretations;
- independently authored material.
An implementation artifact can support a claim about observed behavior without automatically supporting a claim about narrative intent.
## Split construction
The final SFT release used train, validation, and test splits. Release checks rejected:
- 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.
The public synthetic records use the same broad chat structure but are newly authored and unrelated to the private source text.
## Evaluation design
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.
Each evaluation record separated required claims, forbidden claims, source references, difficulty, and scoring method. Evaluation files were excluded from training.
## Release validation
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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