Feasibility Check

可行性检查

检查建模方案在给定数据和时间约束下是否可行: $ARGUMENTS

Constants

• REVIEWER_MODEL = gpt-5.4 — Model used via Codex MCP. Must be an OpenAI model (e.g., gpt-5.4, o3, gpt-4o)

Instructions

Given a modeling approach description, systematically verify its feasibility:

Phase A: 提取关键要素

1. Read the user's modeling approach description
2. Identify 3-5 core feasibility dimensions that need verification:
   • What mathematical method is proposed?
   • What data does it require? Does the available data match?
   • What is the computational complexity?
   • What is the implementation difficulty and time estimate?

Phase B: 多源可行性检索

For EACH feasibility dimension, search using ALL available sources:

1. Web Search (via WebSearch):

   • Search for similar competition problems that used this method
   • Use specific technical terms from the approach
   • Try at least 3 different query formulations per dimension
   • Focus on: 数模竞赛优秀论文, 经典教材案例, 方法适用条件

2. Known method references: Check against:

   • Past CUMCM/MCM winning papers using similar methods
   • Classic textbook examples and applicable conditions

3. Read details: For each relevant reference, WebFetch its method section and results to assess real-world performance

Phase C: 交叉模型验证

Call REVIEWER_MODEL via Codex MCP (mcp__codex__codex) with xhigh reasoning:

config: {"model_reasoning_effort": "xhigh"}

Prompt should include:

• The proposed modeling approach
• All references found in Phase B
• Ask: "这个建模方案在给定数据和时间约束下可行吗？数据是否匹配？计算复杂度是否可控？实现时间是否合理？"

Phase D: 可行性报告

Output a structured report:

## 可行性检查报告

### 建模方案
[1-2 sentence description]

### 可行性评估维度
1. [数据匹配度] — 可行性: HIGH/MEDIUM/LOW — 依据: [reference]
2. [计算复杂度] — 可行性: HIGH/MEDIUM/LOW — 依据: [reference]
3. [实现时间] — 可行性: HIGH/MEDIUM/LOW — 依据: [reference]
4. [方法适用性] — 可行性: HIGH/MEDIUM/LOW — 依据: [reference]
...

### 类似赛题案例
| 赛题 | 年份 | 竞赛 | 方法 | 效果 |
|------|------|------|------|------|

### 综合可行性评估
- Score: X/10
- Recommendation: PROCEED / PROCEED WITH CAUTION / ABANDON
- 关键优势: [what makes this approach feasible]
- 风险点: [what could go wrong and how to mitigate]

### 改进建议
[How to adjust the approach to improve feasibility if needed]

Important Rules

• Be BRUTALLY honest — an infeasible approach wastes precious competition time
• "Standard method X applied directly" is feasible but may not score high — note the trade-off
• Check both the method suitability AND the data compatibility
• If the method is feasible but unlikely to score well, say so explicitly and suggest improvements
• Always check whether the competition data actually supports the proposed method