Function llm_judge_should_stop

tests/leetcode_reflection_agent.py:492–594

使用 LLM as a Judge 判断是否应该停止优化 Args: task: 任务 current_code: 当前代码 current_reasoning: 当前推理 eval_result: 评测结果 model: 模型名称 round_num: 当前轮数 Returns: (should_stop, reason): 是否停止及原因

(
    task: Task,
    current_code: str,
    current_reasoning: str,
    eval_result: Dict[str, Any],
    model: str,
    round_num: int,
)

async def llm_judge_should_stop(
    task: Task,
    current_code: str,
    current_reasoning: str,
    eval_result: Dict[str, Any],
    model: str,
    round_num: int,
) -> tuple[bool, str]:
    """
    使用 LLM as a Judge 判断是否应该停止优化
    
    Args:
        task: 任务
        current_code: 当前代码
        current_reasoning: 当前推理
        eval_result: 评测结果
        model: 模型名称
        round_num: 当前轮数
    
    Returns:
        (should_stop, reason): 是否停止及原因
    """
    question_text = task.input
    eval_result_text = format_evaluation_result(eval_result)
    status = eval_result.get("status", "Unknown")
    
    # 构建 LLM Judge 的 prompt
    judge_system_prompt = f"""You are an expert code reviewer and judge for LeetCode solutions.
Your task is to evaluate whether the current solution is satisfactory and whether optimization should stop.

Current Round: {round_num}/{MAX_REFLECTION_ROUNDS}

Evaluation Criteria:
1. **Correctness**: Is the solution accepted by LeetCode? (This is the most important criterion)
2. **Efficiency**: If accepted, is the runtime and memory performance acceptable?
3. **Code Quality**: Is the code clean, readable, and well-structured?
4. **Improvement Potential**: Is there significant room for improvement?

Decision Guidelines:
- If the solution is NOT accepted (Wrong Answer, TLE, Runtime Error, etc.), you should NOT stop - we need to fix the bugs.
- If the solution is Accepted with good performance (beats > 50%), you can consider stopping.
- If the solution is Accepted but with poor performance, consider whether optimization is worth the effort.
- Consider the current round number - if we're at the last round, we might want to stop.

Output format:
The output should be a JSON object with the following fields:
{{
    "should_stop": true/false,
    "reasoning": "Your detailed reasoning for the decision",
    "suggestion": "If should_stop is false, provide a brief suggestion for improvement"
}}
"""

    judge_user_prompt = f"""## Problem
{question_text}

## Current Solution (Round {round_num})
### Reasoning: