Design simple, defensible baseline models for CUMCM prediction, classification, evaluation, optimization, simulation, and ranking tasks before using stronger methods. Use when a team needs a minimum viable model, method comparison, ablation baseline, or fallback path under contest time pressure.
Enforce contest-time safety for CUMCM work, including no public problem discussion browsing, no outside solution sharing, source citation discipline, AI-use logging, instructor-contact boundaries, and submission-risk warnings. Use during the active CUMCM contest window or when handling current contest problem statements.
Audit CUMCM datasets for missing values, units, duplicates, outliers, inconsistent identifiers, impossible ranges, time/order problems, leakage, and preprocessing decisions. Use when reviewing raw or processed data before modeling, writing data cleaning notes, or deciding whether results are trustworthy.
Maintain CUMCM experiment records for datasets, preprocessing versions, model parameters, random seeds, metrics, generated figures, result tables, and paper claims. Use when tracking runs, comparing model variants, reproducing outputs, or preparing appendix and supporting-material logs.
Parse and decompose 2026 CUMCM problem statements into subquestions, data requirements, deliverables, hidden constraints, scoring signals, assumptions to verify, and first-day action plans. Use when reading a new CUMCM problem, extracting tasks, clarifying what must be modeled, or building the initial problem brief before choosing methods.
Check consistency between CUMCM paper claims, formulas, tables, figures, units, code outputs, appendix records, and subproblem conclusions. Use when reviewing a draft for mismatched numbers, stale figures, inconsistent symbols, unsupported conclusions, or broken result narrative.
Audit CUMCM supporting materials for required code, data, generated outputs, AI-use details, appendix file lists, package size, cache files, identity leaks, and paper-to-file traceability. Use when preparing ZIP/RAR support packages or checking whether materials can reproduce paper results.
Design and review validation, robustness, sensitivity analysis, error analysis, and uncertainty checks for CUMCM models. Use when deciding how to prove model credibility, comparing parameter choices, checking stability, or writing validation and sensitivity sections.