analyze-results

# 分析求解结果 Analyze: $ARGUMENTS ## Workflow ### Step 1: 定位结果文件 Find all relevant result files: - Check `results/`, `output/`, `figures/`, or project-specific output directories - Parse JSON/CSV/Excel results into structured data - Read solver logs for convergence information ### Step 2: 构建对比表 Organize results by: - **子问题**: which sub-problem each result belongs to - **方法**: which mathematical method was used - **关键指标**: R², RMSE, 目标函数值, 残差, 误差率 etc. - **与预期对比**: compare against expected values, theoretical bounds, or baseline methods ### Step 3: 统计分析 - If multiple runs: report mean +/- std, check robustness - If sweeping a parameter: identify sensitivity trends (monotonic, U-shaped, plateau) - Perform error analysis: residual distribution, prediction vs actual - Check model fit: R², adjusted R², F-statistic, p-values - Flag outliers or suspicious results (unreasonable values, non-convergence) ### Step 4: 生成洞察 For each finding, structure as: 1. **观察**: what the data shows (with numbers) 2. **解读**: why this result makes sense (or doesn't) 3. **意义**: what this means for the competition problem 4. **下一步**: what analysis or improvement would strengthen the result ### Step 5: 生成图表建议 Based on the analysis, suggest figures for the paper: - 拟合曲线 (predicted vs actual) - 残差分布图 - 灵敏度分析图 (parameter sensitivity) - 误差随参数变化图 - 结果对比柱状图/表格 ### Step 6: 更新文档 If findings are significant: - Propose updates to MODEL_REPORT.md or SOLVE_PLAN.md - Draft a concise finding statement (1-2 sentences) - Save analysis to `RESULTS_ANALYSIS.md` ## Output Format Always include: 1. Raw data table (数值结果) 2. Key findings (numbered, concise) 3. Statistical validation (误差分析、模型检验) 4. Suggested figures for paper 5. Suggested next steps (if any)