Extract and maintain global and method-specific mathematical-model assumptions from the problem frame, active method cards, data profile, and risk probes, while leaving necessity and impact judgments to the human modeler.
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Read legacy candidate pools only during migration.
assumption_necessity decisions in qx_decisions.jsonl.planning/model_assumptions.md, transcribing settled human labels and impacts with decision IDs.No reviews yet. Tell others what you thought.
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Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.
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Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.
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Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.
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Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
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Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
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