Model Review

数模方案评审 via Codex MCP (xhigh reasoning)

通过外部 LLM 以最高推理深度对建模方案进行多轮评审，模拟数模竞赛评委视角。

Constants

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

Context: $ARGUMENTS

Prerequisites

• Codex MCP Server configured in Claude Code:

  claude mcp add codex -s user -- codex mcp-server
  

• This gives Claude Code access to mcp__codex__codex and mcp__codex__codex-reply tools

Workflow

Step 1: 收集建模上下文

Before calling the external reviewer, compile a comprehensive briefing:

1. Read project documents (e.g., PROBLEM_BRIEF.md, MODEL_REPORT.md, PROBLEM_ANALYSIS.md, paper drafts)
2. Read any result files for key findings and solving history
3. Identify: core modeling approach, mathematical methods, key results, known weaknesses

Step 2: Initial Review (Round 1)

Send a detailed prompt with xhigh reasoning:

mcp__codex__codex:
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    [完整建模方案 + 赛题描述 + 具体问题]
    请以数模竞赛资深评委/阅卷专家的身份评审。按以下维度打分和评价:
    1. 数学建模严谨性（模型假设是否合理、推导是否正确）
    2. 方法创新性（是否有亮点、是否超越简单套用）
    3. 结果可靠性（计算结果是否合理、是否有验证）
    4. 论文规范性（结构是否完整、表述是否清晰）
    请以国赛一等奖/美赛O奖标准严格评审。

Step 3: Iterative Dialogue (Rounds 2-N)

Use mcp__codex__codex-reply with the returned threadId to continue the conversation:

For each round:

1. Respond to criticisms with evidence/counterarguments
2. Ask targeted follow-ups on the most actionable points
3. Request specific deliverables: model improvements, analysis suggestions, problem-method-result mapping

Key follow-up patterns:

• "If we change model assumption X to Y, does that change your assessment?"
• "What's the minimum additional analysis to satisfy concern Z?"
• "Please suggest the highest-impact improvements within the remaining competition time"
• "Please write a mock competition review with scores for each dimension"
• "Give me a problem-method-result mapping for each sub-problem"

Step 4: Convergence

Stop iterating when:

• Both sides agree on the modeling approach and its validation requirements
• A concrete improvement plan is established
• The paper structure and narrative are settled

Step 5: Document Everything

Save the full interaction and conclusions to a review document in the project root:

• Round-by-round summary of criticisms and responses
• Final consensus on modeling approach, validation, and improvements
• Problem-method-result mapping (问题-方法-结果对应表)
• Prioritized TODO list with estimated time costs
• Paper outline if discussed

Update project memory/notes with key review conclusions.

Key Rules

• ALWAYS use config: {"model_reasoning_effort": "xhigh"} for reviews
• Send comprehensive context in Round 1 — the external model cannot read your files
• Be honest about weaknesses — hiding them leads to worse feedback
• Push back on criticisms you disagree with, but accept valid ones
• Focus on ACTIONABLE feedback — "what analysis or improvement would fix this?"
• Document the threadId for potential future resumption
• The review document should be self-contained (readable without the conversation)

Prompt Templates

For initial review:

"我将展示一个完整的数学建模方案，请以数模竞赛资深评委的身份进行严格评审。按国赛一等奖/美赛O奖标准..."

For improvement design:

"请设计在剩余竞赛时间内能最大幅度提分的改进方案。当前时间余量: [describe]。请给出具体的改进步骤。"

For paper structure:

"请将建模方案转化为数模论文大纲，包含各章节的核心论点和图表规划。"

For problem-method-result mapping:

"请给出问题-方法-结果对应表：每个子问题使用什么方法、预期什么结果、实际得到什么结果。"

For mock review:

"请模拟数模竞赛评审，给出: 总体评价、数学严谨性评分、创新性评分、结果可靠性评分、论文规范性评分、改进建议、获奖预估等级。"