| # Generation Algorithm |
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| ## Design principle |
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| The generator starts from a behavioral archetype and a structured scenario blueprint. |
| It does not begin from a prompt template or a programming language. |
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| ## Pipeline |
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| 1. Select an underrepresented behavioral family. |
| 2. Select a narrow archetype within that family. |
| 3. Assign a group-aware dataset split at the archetype level. |
| 4. Construct a compatible environment: |
| - domain; |
| - architecture; |
| - programming stack; |
| - component interaction; |
| - operational modifier; |
| - constraints. |
| 5. Produce evidence from the intended causal mechanism. |
| 6. Build repository context and relevant files. |
| 7. Produce the user request. |
| 8. Produce ideal agent behavior: |
| - assessment; |
| - assumptions; |
| - plan; |
| - tool trace; |
| - observations; |
| - decision; |
| - implementation strategy; |
| - verification; |
| - stopping criteria; |
| - final communication. |
| 9. Produce contrasting bad behavior. |
| 10. Validate against JSON Schema and consistency rules. |
| 11. Compute exact, normalized, structural, and lexical deduplication reports. |
| 12. Export JSONL files and audit artifacts. |
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| ## Structural uniqueness key |
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| Each record fingerprint includes: |
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| - family |
| - archetype |
| - domain |
| - architecture |
| - stack |
| - interacting component pair |
| - operational modifier |
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| This prevents records from being treated as unique merely because a project name or language changed. |
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| ## Split policy |
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| All 25 scenarios for one archetype remain in the same split. This reduces direct archetype leakage. |
| The test set therefore measures transfer to held-out behavioral archetypes rather than random rows. |
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