# USER:

You are a senior AI researcher.
I am researching the novelty of research ideas generated by a certain new algorithm.
I now want to use the LLM as a judge method, calling the API interface of a third-party LLM (such as OpenAI), to score each method under the same topic.

There are 5 json files in the Qwen3-1.7B_results folder, each containing a list, where each item is the test result for each topic, in the format {'id': '...', 'theme': '...', 'elaboration': '...', "output": ...}
Among them, "output" is the prediction result. This result may contain unwanted thinking processes, and we only need the final result, so the content between " ... " will be removed.

For example, you can ask the LLM like this:
```
You are a top-tier and rigorous scientific review expert.
Here are 5 scientific ideas generated by different AI on a research theme. Please score each of them on a scale of 1-10 from the following three dimensions, and provide brief reasons. Finally, give an overall score and indicate which one is better overall.

You should evaluate:
      Dimension 1: Scientific Plausibility - Is this idea scientifically feasible without obvious factual errors? (1=nonsense, 10=very plausible)
      Dimension 2: Structure & Clarity - Is the writing clear, is the structure complete, and is the logic coherent? (1=chaotic, 10=very clear)
      Dimension 3: Innovation Potential - Does this idea present interesting or novel viewpoints or methods? (1=stale, 10=highly innovative)
      Overall Score: (1=worst, 10=best)

Please note that these are not formal research proposals, so they may not contain complete introduction, methods, experiments, etc., which is acceptable.
We are only evaluating the scientific ideas.

The starting theme is:
```
{theme}

{elaboration}

# Optimized prompt

```
A: [Text generated by the first model]
B: [Text generated by the second model]
...
(Complete for me)

Please output your scores and final choice in JSON format. The evaluation criteria include relevance, accuracy, fluency, creativity, and overall quality. Please provide a score from 1-10 for each dimension, and give an overall score. For your final choice, clearly indicate which model option you consider better.

The JSON output format should be as follows:
(Here needs to be placed a specific JSON example, to agree on the format)
```

You need to refine and optimize this prompt.

Then, by parsing the results in the ```json``` from the API response, obtain the statistical results of all models.

Help me complete the code and write it to llm_judge.py.

# AGENT:


✦ Okay, as a senior AI researcher, I will complete this task for you.

  First, let's optimize the prompt provided to the "LLM Judge". A good prompt needs to have a clear role, well-defined tasks, detailed scoring criteria, contextual information, and strict output format requirements.

  Here is the optimized prompt version I've created for you:

```
    1 You are a professor at a top university, serving as the program chair of top-tier academic conferences, known for being rigorous, fair, and having profound insights.
    2 
3 Your task is: to evaluate scientific ideas generated by five different AI models under a core research theme. Please score each idea from 1-10 based on the following three dimensions, and provide precise and concise comments. Finally, after comprehensive evaluation, select the best idea.
    4 
'5 **Core Assessment Dimensions:**'
    6 
    7 1.  **Scientific Rationality (Plausibility)**: Is the concept based on reliable scientific principles? Are there obvious logical or factual fallacies? (1=Pure speculation, 10=Highly feasible)
    8 2.  **Structure & Clarity**: Is the presentation of the concept well-organized, structurally complete, and fluent in language? (1=Completely chaotic, 10=Crystal clear)
    9 3.  **Innovation Potential**: Does the concept offer novel perspectives, methods, or research approaches? Does it have the potential to challenge existing paradigms? (1=Old and uninteresting, 10=Highly original)
   10 
   11 **Important Notice:**
   *   These ideas are preliminary concepts, not complete Research Proposals. Therefore, it is normal for sections such as introduction, methodology, and experiments to be missing. Please focus on the core value of the ideas themselves.
   *   Your scoring needs to be strict. Please create a significant gap in your scoring to reflect the quality differences between different ideas.
   14 
'   15 **Content to be evaluated:**'.Please translate.
   16 
17 **Core Theme:** {theme}
   18 

19 **Topic Elaboration:** {elaboration}
   20 
   21 ---
   22 
23 **Concept A ({method_A}):**
   24 {output_A}
   25 
26 **Concept B ({method_B}):**
   27 {output_B}
   28 
   29 **Concept C ({method_C}):**
   30 {output_C}
   31 
32 **Concept D ({method_D}):**
   33 {output_D}
   34 
35 **Concept E ({method_E}):**
   36 {output_E}
   37 
   38 ---
   39 
'   40 **Output requirements:**'
   41 
   42 Please strictly follow the JSON format below to return your review results, without including any additional explanations or comments.
  {
    "evaluations": {
      "A": {
        "method": "{method_A}",
        "plausibility": <score_1_to_10>,
        "structure_clarity": <score_1_to_10>,
        "innovation_potential": <score_1_to_10>,
"reason": "<concise and to-the-point comment>"
      },
      "B": {
        "method": "{method_B}",
        "plausibility": <score_1_to_10>,
        "structure_clarity": <score_1_to_10>,
        "innovation_potential": <score_1_to_10>,
"reason": "<a concise and to-the-point comment>"
      },
      "C": {
        "method": "{method_C}",
        "plausibility": <score_1_to_10>,
        "structure_clarity": <score_1_to_10>,
        "innovation_potential": <score_1_to_10>,

"reason": "<concise and to-the-point comment>"
      },
      "D": {
        "method": "{method_D}",
        "plausibility": <score_1_to_10>,
        "structure_clarity": <score_1_to_10>,
        "innovation_potential": <score_1_to_10>,
        "reason": "<Concise and to-the-point comments>"
      },
      "E": {
        "method": "{method_E}",
        "plausibility": <score_1_to_10>,
        "structure_clarity": <score_1_to_10>,
        "innovation_potential": <score_1_to_10>,

        "reason": "<Concise and to-the-point comments>"
      }
    },
    "final_decision": {
      "best_proposal": "<A, B, C, D, or E>",
"justification": "<After comprehensive comparison, explain why it is the best concept>"
    }
  }
   1 
```

Optimization points:
   1. More specific role: "Review Chair" has more authority and contextual relevance than "Expert".
   2. Clearer task: Emphasizing "strict" and "differentiating" guides the LLM to make more discriminating judgments.
   3. More precise dimension descriptions: Each dimension's description has been fine-tuned to better fit the context of scientific research review.
   4. Dynamic tags: Added {method_X} placeholders to the concept tags, making it clear which concept corresponds to which method in the results, facilitating subsequent analysis.
   5. Improved JSON format: Added a "method" field for each model in evaluations, and separated "best_proposal" and "justification" in final_decision, making the structure clearer and easier for program parsing.
 ╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ x  WriteFile {"content":"\nimport os\nimport json\nimport re\nimport time\nfrom openai import OpenAI\nfrom tqdm import tqdm\nimport logging\n\n# --- Configuration…  │
 │                                                                                                                                                             │
 │    File path must be absolute: llm_judge.py                                                                                                                 │
 ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Next, I will write the llm_judge.py script. This script will automatically read five JSON files, aggregate the results generated by five methods for each theme, call the OpenAI API for scoring, and finally summarize all results.

✦ I understand, the tool requires the use of absolute paths. I will correct the path and rewrite the file.

The absolute path of the file is: ${PWD}/experiment/compare_experiment/llm_judge.py

Now, I will create the llm_judge.py file with the correct path.
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to llm_judge.py                                                                                                               │
 │                                                                                                                                                    │
 │    ... first 132 lines hidden ...                                                                                                                  │
 │    133 ```                                                                                                                                         │
 │    134 """                                                                                                                                         │
 │    135                                                                                                                                             │
 │    136 # --- Core Functions ---                                                                                                                      │
 │    137                                                                                                                                             │
 │    138 def clean_output(text: str) -> str:                                                                                                         │
│    139     """Remove ... tags and their content"""                                                                                          │
 │    140     if not isinstance(text, str):                                                                                                           │
 │    141         return ""                                                                                                                           │
 │    142     return re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL).strip()                                                                 │
 │    143                                                                                                                                             │
 │    144 def load_and_group_data() -> dict:                                                                                                          │

│    145     """Load all JSON files and group the data by theme"""                                                                                         │
 │    146     grouped_data = {}                                                                                                                       │
 │    147     for key, info in METHOD_MAPPING.items():                                                                                                │
 │    148         file_path = os.path.join(INPUT_DIR, info["file"])                                                                                   │
 │    149         try:                                                                                                                                │
 │    150             with open(file_path, 'r', encoding='utf-8') as f:                                                                               │
 │    151                 data = json.load(f)                                                                                                         │
 │    152                 for item in data:                                                                                                           │
 │    153                     theme = item.get("theme")                                                                                               │
 │    154                     if theme not in grouped_data:                                                                                           │
 │    155                         grouped_data[theme] = {                                                                                             │
 │    156                             "elaboration": item.get("elaboration"),                                                                         │
 │    157                             "outputs": {}                                                                                                   │
 │    158                         }                                                                                                                   │
 │    159                     grouped_data[theme]["outputs"][key] = {                                                                                 │
 │    160                         "method": info["name"],                                                                                             │
 │    161                         "output": item.get("output", "")                                                                                    │
 │    162                     }                                                                                                                       │
 │    163         except FileNotFoundError:                                                                                                           │
│    164             logging.error(f"File not found: {file_path}")                                                                                       │
 │    165         except json.JSONDecodeError:                                                                                                        │
│    166             logging.error(f"JSON parsing error: {file_path}")                                                                                     │
 │    167     return grouped_data                                                                                                                     │
 │    168                                                                                                                                             │
 │    169 def get_llm_judgment(prompt: str, max_retries=3) -> dict | None:                                                                            │
│    170     """Call LLM API to get review results, and handle retry and JSON parsing"""                                                                                     │
 │    171     for attempt in range(max_retries):                                                                                                      │
 │    172         try:                                                                                                                                │
 │    173             response = client.chat.completions.create(                                                                                      │
 │    174                 model=JUDGE_MODEL,                                                                                                          │
 │    175                 messages=[{"role": "user", "content": prompt}],                                                                             │
```
 │    176                 temperature=0.1, # low temperature to ensure stable and reproducible scoring                                                                             │
```
 │    177                 response_format={"type": "json_object"},                                                                                    │
 │    178             )                                                                                                                               │
 │    179             content = response.choices[0].message.content                                                                                   │
 │    180             # The API with json_object response_format should return a valid JSON string.                                                   │
 │    181             return json.loads(content)                                                                                                      │
 │    182         except Exception as e:                                                                                                              │
 │    183             logging.error(f"API call or JSON parsing failed (attempt {attempt + 1}/{max_retries}): {e}")                                                 │
 │    184             time.sleep(5 * (attempt + 1)) # exponential backoff                                                                                        │
 │    185     return None                                                                                                                             │
 │    186                                                                                                                                             │
 │    187 def main():                                                                                                                                 │
 │    188     """Main execution function"""                                                                                                                        │
 │    189     logging.info("Starting to execute the LLM-as-a-Judge process...")                                                                                           │
 │    190                                                                                                                                             │

 │    191     # 1. Load and aggregate data                                                                                                                     │
 │    192     logging.info("Loading and grouping data from JSON files...")                                                                                         │
 │    193     grouped_data = load_and_group_data()                                                                                                    │
 │    194     if not grouped_data:                                                                                                                    │
 │    195         logging.error("Failed to load any data, program will terminate.")                                                                                       │
 │    196         return                                                                                                                              │
 │    197                                                                                                                                             │
 │    198     all_results = []                                                                                                                        │
 │    199     themes_to_judge = list(grouped_data.items())                                                                                            │
 │    200                                                                                                                                             │
│    201     logging.info(f"Found a total of {len(themes_to_judge)} unique themes to be reviewed.")                                                                     │
 │    202                                                                                                                                             │
 │    203     # 2. Iterate through each theme for evaluation                                                                                       │
 │    204     for i, (theme, data) in enumerate(tqdm(themes_to_judge, desc="Evaluation Progress")):                                                              │
 │    205         logging.info(f"Evaluating theme {i+1}/{len(themes_to_judge)}: '{theme[:50]}...'")                                                       │
 │    206                                                                                                                                             │

 │    207         # Check whether all method outputs have been collected                                                                                                      │
 │    208         if len(data["outputs"]) != len(METHOD_MAPPING):                                                                                     │

logging.warning(f"Topic '{theme}' is missing output for some methods, skipped.")
 │    210             continue                                                                                                                        │
 │    211                                                                                                                                             │
│    212         # 3. Build Prompt                                                                                                                     │
 │    213         prompt_payload = {                                                                                                                  │
 │    214             "theme": theme,                                                                                                                 │
 │    215             "elaboration": data["elaboration"]                                                                                              │
 │    216         }                                                                                                                                   │
 │    217         for key, output_data in data["outputs"].items():                                                                                    │
 │    218             prompt_payload[f"method_{key}"] = output_data["method"]                                                                         │
 │    219             prompt_payload[f"output_{key}"] = clean_output(output_data["output"])                                                           │
 │    220                                                                                                                                             │
 │    221         final_prompt = JUDGE_PROMPT_TEMPLATE.format(**prompt_payload)                                                                       │
 │    222                                                                                                                                             │
│    223         # 4. Obtain LLM's evaluation result                                                                                                              │
 │    224         judgment = get_llm_judgment(final_prompt)                                                                                           │
 │    225                                                                                                                                             │
 │    226         if judgment:                                                                                                                        │
 │    227             result_entry = {                                                                                                                │
 │    228                 "theme": theme,                                                                                                             │
 │    229                 "elaboration": data["elaboration"],                                                                                         │
 │    230                 "judgment": judgment                                                                                                        │
 │    231             }                                                                                                                               │
 │    232             all_results.append(result_entry)                                                                                                │
│    233             logging.info(f"Topic '{theme[:50]}...' review successful.")                                                                              │
 │    234         else:                                                                                                                               │
│    235             logging.error(f"Topic '{theme[:50]}...' review failed, skipped.")                                                                     │
 │    236                                                                                                                                             │
│    237         # To avoid hitting API rate limits, you can add a short delay.                         │
 │    238         time.sleep(1)                                                                                                                       │
 │    239                                                                                                                                             │

 │    240     # 5. Save all results                                                                                                                       │
 │    241     logging.info(f"All topic reviews completed, saving results to {OUTPUT_FILE}...")                                                                    │
 │    242     try:                                                                                                                                    │
 │    243         with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:                                                                                 │
 │    244             json.dump(all_results, f, ensure_ascii=False, indent=4)                                                                         │
│    245         logging.info("Result saved successfully!")                                                                                                      │
 │    246     except Exception as e:                                                                                                                  │

 │    247         logging.error(f"Failed to save result: {e}")                                                                                                 │
 │    248                                                                                                                                             │
 │    249 if __name__ == "__main__":                                                                                                                  │
 │    250     main()                                                                                                                                  │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯


 ✕ [API Error: Model stream ended with an invalid chunk or missing finish reason.]




# USER: 
╭──────────────────╮
'│  > help me rewrite  │'
╰──────────────────╯

# AGENT: 



✦ Okay, I will overwrite and rewrite the llm_judge.py file with the same code content.
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to llm_judge.py                                                                                                               │
 │                                                                                                                                                    │
 │    1 -                                                                                                                                             │
 │    1   import os                                                                                                                                   │
 │    2   import json                                                                                                                                 │
 │    3   import re                                                                                                                                   │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ File llm_judge.py has been successfully written to your directory.

  Before running, please make sure you have completed the following preparations:

   1. Set environment variables: In your terminal, set your OpenAI API key. Please replace 'your_key_here' with your own key.
   1     export OPENAI_API_KEY='your_key_here'

   2. Install dependency libraries: The script requires openai and tqdm libraries. If not already installed, please run the following command:
   1     pip install openai tqdm

3. Run script: After everything is ready, execute the following command in the terminal to start the review process:
   1     python llm_judge.py

The script will read data from the Qwen3-1.7B_results folder, call the LLM to score each topic, and after completion, save all evaluation results in the llm_judge_results.json file. You can monitor the progress through the terminal output.

# USER: 
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Overall, it's very good. 1. I hope to maintain the phrasing "Is this concept scientifically feasible?" and "Is the structure complete and the logic coherent?" to make it more rigorous. 2.                                      │
│    The theme and elaboration I provided are initial concepts, and based on these, certain extensions and innovations can be made, which may even be necessary and would not be considered off-topic. This point needs to be reflected. 3. The json folder may not necessarily have the 'method' field, so please distinguish which model it is based on the file name. 4. I hope that after each scoring, the data can be saved to prevent loss due to network issues. 5. You can write prompts in English. Please make modifications according to the above suggestions.           │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 



✦ Okay, I completely understand your modification suggestions. These are all excellent recommendations that will make the review process more rigorous and robust.

I will make the following modifications to the llm_judge.py script:

1. Prompt wording: Restore the more rigorous wording that you prefer.
2. Encourage innovation: Explicitly state in the Prompt that reasonable extensions and innovations on the topic are encouraged.
3. Model differentiation: The code will continue to rely on filenames to distinguish between models, which aligns with your initial requirements, and I will ensure this is clearly reflected in the code.
4. Incremental saving: Modify the main logic to save results immediately after completing the review of each topic, thereby preventing data loss due to unexpected interruptions.
5. English Prompt: Translate the entire Prompt template into professional English.

This is the modified version of the llm_judge.py script.
   ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to llm_judge.py                                                                                                               │
 │                                                                                                                                                    │
 │    ... first 195 lines hidden ...                                                                                                                  │

 │    175 -                 temperature=0.1, # low temperature to ensure stable and reproducible scoring                                                                           │
 │    174 +                 temperature=0.1,                                                                                                          │
 │    175                   response_format={"type": "json_object"},                                                                                  │
 │    176               )                                                                                                                             │
 │    177               content = response.choices[0].message.content                                                                                 │
 │    179 -             # The API with json_object response_format should return a valid JSON string.                                                 │
 │    178               return json.loads(content)                                                                                                    │
 │    179           except Exception as e:                                                                                                            │
 │    182 -             logging.error(f"API call or JSON parsing failed (attempt {attempt + 1}/{max_retries}): {e}")                                               │
 │    183 -             time.sleep(5 * (attempt + 1)) # exponential backoff                                                                                      │
 │    180 +             logging.error(f"API call or JSON parsing failed (Attempt {attempt + 1}/{max_retries}): {e}")                                  │
 │    181 +             time.sleep(5 * (attempt + 1))                                                                                                 │
 │    182       return None                                                                                                                           │
 │    183                                                                                                                                             │
 │    184   def main():                                                                                                                               │
 │    187 -     """Main execution function"""                                                                                                                      │
 │    188 -     logging.info("Starting to execute LLM-as-a-Judge process...")                                                                                         │
 │    185 +     """Main execution function with incremental saving."""                                                                                │
 │    186 +     logging.info("Starting LLM-as-a-Judge process...")                                                                                    │
 │    187                                                                                                                                             │
 │    190 -     # 1. Load and aggregate data                                                                                                                   │
 │    191 -     logging.info("Loading and grouping data from JSON file...")                                                                                       │
 │    188 +     # Load existing results to support resuming                                                                                           │
 │    189 +     if os.path.exists(OUTPUT_FILE):                                                                                                       │
 │    190 +         try:                                                                                                                              │
 │    191 +             with open(OUTPUT_FILE, 'r', encoding='utf-8') as f:                                                                           │
 │    192 +                 all_results = json.load(f)                                                                                                │
 │    193 +             judged_themes = {res['theme'] for res in all_results}                                                                         │
 │    194 +             logging.info(f"Loaded {len(all_results)} existing results from {OUTPUT_FILE}. Resuming...")                                   │
 │    195 +         except (json.JSONDecodeError, TypeError):                                                                                         │
 │    196 +             logging.warning(f"Could not parse existing results file {OUTPUT_FILE}. Starting fresh.")                                      │
 │    197 +             all_results = []                                                                                                              │
 │    198 +             judged_themes = set()                                                                                                         │
 │    199 +     else:                                                                                                                                 │
 │    200 +         all_results = []                                                                                                                  │
 │    201 +         judged_themes = set()                                                                                                             │
 │    202 +                                                                                                                                           │
 │    203 +     # Load and group data from source files                                                                                               │
 │    204 +     logging.info("Loading and grouping data from source JSON files...")                                                                   │
 │    205       grouped_data = load_and_group_data()                                                                                                  │
 │    206       if not grouped_data:                                                                                                                  │
│    194 -         logging.error("Failed to load any data, program terminated.")                                                                                     │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    207 +         logging.error("No data loaded. Exiting.")                                                                                         │
 │    208           return                                                                                                                            │
 │    209                                                                                                                                             │
 │    197 -     all_results = []                                                                                                                      │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    210       themes_to_judge = list(grouped_data.items())                                                                                          │
 │    211 +     logging.info(f"Found {len(themes_to_judge)} unique themes to process.")                                                               │
 │    212                                                                                                                                             │
 │    200 -     logging.info(f"In total found {len(themes_to_judge)} unique themes for review.")                                                                   │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    213 +     # Main loop for judging each theme                                                                                                    │
 │    214 +     for i, (theme, data) in enumerate(tqdm(themes_to_judge, desc="Judging Progress")):                                                    │
 │    215 +         if theme in judged_themes:                                                                                                        │
 │    216 +             logging.info(f"Theme '{theme[:50]}...' already judged. Skipping.")                                                            │
 │    217 +             continue                                                                                                                      │
 │    218                                                                                                                                             │
```
 │    202 -     # 2. Iterate through each topic for evaluation                                                                                     │
 │    203 -     for i, (theme, data) in enumerate(tqdm(themes_to_judge, desc="Evaluation progress")):                                               │
 │    204 -         logging.info(f"Currently evaluating topic {i+1}/{len(themes_to_judge)}: '{theme[:50]}...'")                                   │
```
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    219 +         logging.info(f"Judging theme {i+1}/{len(themes_to_judge)}: '{theme[:50]}...'")                                                    │
 │    220                                                                                                                                             │
206 -         # Check whether all method outputs have been collected                                                                                                    │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    221           if len(data["outputs"]) != len(METHOD_MAPPING):                                                                                   │

```
logging.warning(f"Topic '{theme}' is missing output from some methods, has been skipped.")
```
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    222 +             logging.warning(f"Theme '{theme}' is missing outputs from some methods. Skipping.")                                           │
 │    223               continue                                                                                                                      │
 │    224                                                                                                                                             │

│    211 -         # 3. Build Prompt                                                                                                                   │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    225           prompt_payload = {                                                                                                                │
 │    226               "theme": theme,                                                                                                               │
 │    227               "elaboration": data["elaboration"]                                                                                            │
 │    232                                                                                                                                             │
 │    233           final_prompt = JUDGE_PROMPT_TEMPLATE.format(**prompt_payload)                                                                     │
 │    234                                                                                                                                             │

222 -         # 4. Obtain LLM's evaluation results                                                                                                            │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    235           judgment = get_llm_judgment(final_prompt)                                                                                         │
 │    236                                                                                                                                             │
 │    237           if judgment:                                                                                                                      │
 │    241                   "judgment": judgment                                                                                                      │
 │    242               }                                                                                                                             │
 │    243               all_results.append(result_entry)                                                                                              │
│    232 -             logging.info(f"Topic '{theme[:50]}...' review successful.")                                                                            │
 │    233 -         else:                                                                                                                             │
 │    234 -             logging.error(f"Topic '{theme[:50]}...' review failed, has been skipped.")                                                                   │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    244 +             judged_themes.add(theme)                                                                                                      │
 │    245                                                                                                                                             │
 │    236 -         # To avoid hitting the API rate limit, a brief delay can be added.                                                                                       │
 │    237 -         time.sleep(1)                                                                                                                     │
 │    238 -                                                                                                                                           │
 │    239 -     # 5. Save all results                                                                                                                     │
 │    240 -     logging.info(f"All topic reviews completed, saving results to {OUTPUT_FILE}...")                                                                  │
 │    246 +             # Incremental save after each successful judgment                                                                             │
 │    247               try:                                                                                                                          │
 │    248                   with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:                                                                       │
 │    249                       json.dump(all_results, f, ensure_ascii=False, indent=4)                                                               │
 │    244 -         logging.info("Result saved successfully!")                                                                                                    │
 │    250 +                 logging.info(f"Progress saved. Total judged themes: {len(all_results)}")                                                  │
 │    251               except Exception as e:                                                                                                        │
 │    246 -         logging.error(f"Result save failed: {e}")                                                                                               │
 │    252 +                 logging.error(f"Failed to save progress after judging theme: {e}")                                                        │
 │    253 +         else:                                                                                                                             │
 │    254 +             logging.error(f"Failed to get judgment for theme '{theme[:50]}...'. It will be retried on next run.")                         │
 │    255                                                                                                                                             │
 │    256 +         time.sleep(1) # Rate limiting                                                                                                     │
 │    257 +                                                                                                                                           │
 │    258 +     logging.info("LLM-as-a-Judge process finished.")                                                                                      │
 │    259 +                                                                                                                                           │
 │    260   if __name__ == "__main__":                                                                                                                │
 │    261       main()                                                                                                                                │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

✦ Okay, I have successfully rewritten the llm_judge.py file according to all your requirements, using the new English Prompt and incremental saving logic.

  Main updates are as follows:

   1. Prompt updated to English version: The entire prompt is now in English, incorporating the rigorous wording you suggested, such as "Is the idea scientifically plausible?".
   2. Encouraging innovation: New rules have been added to the Prompt, clearly stating that reasonable extensions and innovations on the topic are encouraged.
   3. Incremental saving: The script is now more robust.
       * At startup: It will first check and load existing results from llm_judge_results.json.
       * During runtime: After successfully evaluating each topic, it will immediately write the updated complete list of results back to the file. If the script is interrupted, it will automatically skip completed topics and resume from the breakpoint on the next run.
   4. Model identification: The code logic remains unchanged, continuing to accurately identify each method (cgmcts, cot, etc.) through filenames, as per your requirements.

  You can run this script just as before.

  Operation reminders:

   1. Set environment variables:
   1     export OPENAI_API_KEY='your_key_here'

'   2. Install dependencies:'.
   1     pip install openai tqdm

'   3. Run script:'
   1     python llm_judge.py

The script will now execute the review task in a safer and more compliant manner with your requirements.

# USER: 
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Good, there are a few issues to note: 1.TypeError                                 Traceback (most recent call last)                                             │
│    Cell In[2], line 1                                                                                                                                    │
│    ----> 1 def get_llm_judgment(prompt: str, max_retries=3) -> dict | None:                                                                              │
│          2     """Calls the LLM API to get the judgment, with retries and JSON parsing."""                                                               │
│          3     for attempt in range(max_retries):                                                                                                        │
│                                                                                                                                                          │
│    TypeError: unsupported operand type(s) for |: 'type' and 'NoneType'      \n 2.Please clearly specify that when asking an LLM to select the best model, it should choose *one* best model to prevent providing multiple options.\n   │
│    3.You could consider writing some content in the system prompt. \n                                                                                                      │
│    4.Please pay attention to the issue with special characters when formatting strings in your Python code, for example {} sometimes represents a dictionary and other times represents characters to be replaced. Handle this properly.                                      │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 





✦ Your question is very professional and hits the nail on the head. Thank you for your correction! I will make modifications immediately based on your feedback.

  To summarize the modifications:

   1. Fix `TypeError`: For compatibility with older Python versions, I will use typing.Optional instead of the | syntax for type hints.
   2. Emphasize single best choice: I will explicitly require in the Prompt that the LLM must only select ONE best concept.
   3. Introduce System Prompt: Adopting your suggestion, I will move static elements such as role definition, task instructions, and output format into the System Prompt to make instructions clearer and help the model follow them better.
   4. Handle string formatting: I will escape all {} appearing as literals in the Prompt template by changing them to {{}} to avoid conflicts with .format() placeholders.

  Now, I will generate the final version of the llm_judge.py script for you.
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to llm_judge.py                                                                                                                   │
 │                                                                                                                                                        │
 │    ... first 154 lines hidden ...                                                                                                                      │
 │    112 + **Content to Evaluate:**                                                                                                                      │
 │    113 +                                                                                                                                               │
 │    114 + **Core Theme:** {theme}                                                                                                                       │
 │    115 +                                                                                                                                               │
 │    116 + **Elaboration:** {elaboration}                                                                                                                │
 │    117 +                                                                                                                                               │
 │    118 + ---                                                                                                                                           │
 │    119 +                                                                                                                                               │
 │    120 + **Proposal A ({method_A}):**                                                                                                                  │
 │    121 + {output_A}                                                                                                                                    │
 │    122 +                                                                                                                                               │
 │    123 + **Proposal B ({method_B}):**                                                                                                                  │
 │    124 + {output_B}                                                                                                                                    │
 │    125 +                                                                                                                                               │
 │    126 + **Proposal C ({method_C}):**                                                                                                                  │
 │    127 + {output_C}                                                                                                                                    │
 │    128 +                                                                                                                                               │
 │    129 + **Proposal D ({method_D}):**                                                                                                                  │
 │    130 + {output_D}                                                                                                                                    │
 │    131 +                                                                                                                                               │
 │    132 + **Proposal E ({method_E}):**                                                                                                                  │
 │    133 + {output_E}                                                                                                                                    │
 │    134 + """                                                                                                                                           │
 │    135 +                                                                                                                                               │
│    136   # --- Core Functions ---                                                                                                                        │
 │    137                                                                                                                                                 │
 │    138   def clean_output(text: str) -> str:                                                                                                           │
 │    141           return ""                                                                                                                             │
 │    142       return re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL).strip()                                                                   │
 │    143                                                                                                                                                 │
 │    141 - def load_and_group_data() -> dict:                                                                                                            │
 │    144 + def load_and_group_data() -> Dict[str, Any]:                                                                                                  │
 │    145       """Loads all json files and groups data by theme."""                                                                                      │
 │    146       grouped_data = {}                                                                                                                         │
 │    147       for key, info in METHOD_MAPPING.items():                                                                                                  │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    156                               "elaboration": item.get("elaboration"),                                                                           │
 │    157                               "outputs": {}                                                                                                     │
 │    158                           }                                                                                                                     │
 │    156 -                     # The model is identified by the filename from METHOD_MAPPING                                                             │
 │    159                       grouped_data[theme]["outputs"][key] = {                                                                                   │
 │    160                           "method": info["name"],                                                                                               │
 │    161                           "output": item.get("output", "")                                                                                      │
 │    166               logging.error(f"JSON decode error in file: {file_path}")                                                                          │
 │    167       return grouped_data                                                                                                                       │
 │    168                                                                                                                                                 │
 │    167 - def get_llm_judgment(prompt: str, max_retries=3) -> dict | None:                                                                              │
 │    169 + def get_llm_judgment(system_prompt: str, user_prompt: str, max_retries=3) -> Optional[Dict[str, Any]]:                                        │
 │    170       """Calls the LLM API to get the judgment, with retries and JSON parsing."""                                                               │
 │    171       for attempt in range(max_retries):                                                                                                        │
 │    172           try:                                                                                                                                  │
 │    173               response = client.chat.completions.create(                                                                                        │
 │    174                   model=JUDGE_MODEL,                                                                                                            │
 │    173 -                 messages=[{"role": "user", "content": prompt}],                                                                               │
 │    175 +                 messages=[                                                                                                                    │
 │    176 +                     {"role": "system", "content": system_prompt},                                                                             │
 │    177 +                     {"role": "user", "content": user_prompt}                                                                                  │
 │    178 +                 ],                                                                                                                            │
 │    179                   temperature=0.1,                                                                                                              │
 │    180                   response_format={"type": "json_object"},                                                                                      │
 │    181               )                                                                                                                                 │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    190       """Main execution function with incremental saving."""                                                                                    │
 │    191       logging.info("Starting LLM-as-a-Judge process...")                                                                                        │
 │    192                                                                                                                                                 │
 │    188 -     # Load existing results to support resuming                                                                                               │
 │    193       if os.path.exists(OUTPUT_FILE):                                                                                                           │
 │    194           try:                                                                                                                                  │
 │    195               with open(OUTPUT_FILE, 'r', encoding='utf-8') as f:                                                                               │
 │    197               judged_themes = {res['theme'] for res in all_results}                                                                             │
 │    198               logging.info(f"Loaded {len(all_results)} existing results from {OUTPUT_FILE}. Resuming...")                                       │
 │    199           except (json.JSONDecodeError, TypeError):                                                                                             │
 │    196 -             logging.warning(f"Could not parse existing results file {OUTPUT_FILE}. Starting fresh.")                                          │
 │    197 -             all_results = []                                                                                                                  │
 │    198 -             judged_themes = set()                                                                                                             │
 │    200 +             logging.warning(f"Could not parse {OUTPUT_FILE}. Starting fresh.")                                                                │
 │    201 +             all_results, judged_themes = [], set()                                                                                            │
 │    202       else:                                                                                                                                     │
 │    200 -         all_results = []                                                                                                                      │
 │    201 -         judged_themes = set()                                                                                                                 │
 │    203 +         all_results, judged_themes = [], set()                                                                                                │
 │    204                                                                                                                                                 │
 │    203 -     # Load and group data from source files                                                                                                   │
 │    205       logging.info("Loading and grouping data from source JSON files...")                                                                       │
 │    206       grouped_data = load_and_group_data()                                                                                                      │
 │    207       if not grouped_data:                                                                                                                      │
 │    211       themes_to_judge = list(grouped_data.items())                                                                                              │
 │    212       logging.info(f"Found {len(themes_to_judge)} unique themes to process.")                                                                   │
 │    213                                                                                                                                                 │
 │    213 -     # Main loop for judging each theme                                                                                                        │
 │    214       for i, (theme, data) in enumerate(tqdm(themes_to_judge, desc="Judging Progress")):                                                        │
 │    215           if theme in judged_themes:                                                                                                            │
 │    216               logging.info(f"Theme '{theme[:50]}...' already judged. Skipping.")                                                                │
 │    219           logging.info(f"Judging theme {i+1}/{len(themes_to_judge)}: '{theme[:50]}...'")                                                        │
 │    220                                                                                                                                                 │
 │    221           if len(data["outputs"]) != len(METHOD_MAPPING):                                                                                       │
 │    222 -             logging.warning(f"Theme '{theme}' is missing outputs from some methods. Skipping.")                                               │
 │    222 +             logging.warning(f"Theme '{theme}' is missing outputs. Skipping.")                                                                 │
 │    223               continue                                                                                                                          │
 │    224                                                                                                                                                 │
 │    225           prompt_payload = {                                                                                                                    │
 │    226               "theme": theme,                                                                                                                   │
 │    227               "elaboration": data["elaboration"]                                                                                                │
 │    228           }                                                                                                                                     │
 │    229 +         # Populate method names and outputs for the user prompt                                                                               │
 │    230           for key, output_data in data["outputs"].items():                                                                                      │
 │    231               prompt_payload[f"method_{key}"] = output_data["method"]                                                                           │
 │    232               prompt_payload[f"output_{key}"] = clean_output(output_data["output"])                                                             │
 │    233                                                                                                                                                 │
 │    233 -         final_prompt = JUDGE_PROMPT_TEMPLATE.format(**prompt_payload)                                                                         │
 │    234 +         # Also populate method names for the system prompt's JSON example                                                                     │
 │    235 +         for key, info in METHOD_MAPPING.items():                                                                                              │
 │    236 +              prompt_payload[f"method_{key}"] = info["name"]                                                                                   │
 │    237                                                                                                                                                 │
 │    235 -         judgment = get_llm_judgment(final_prompt)                                                                                             │
 │    238 +         final_system_prompt = SYSTEM_PROMPT.format(**prompt_payload)                                                                          │
 │    239 +         final_user_prompt = USER_PROMPT_TEMPLATE.format(**prompt_payload)                                                                     │
 │    240                                                                                                                                                 │
 │    241 +         judgment = get_llm_judgment(final_system_prompt, final_user_prompt)                                                                   │
 │    242 +                                                                                                                                               │
 │    243           if judgment:                                                                                                                          │
 │    238 -             result_entry = {                                                                                                                  │
 │    239 -                 "theme": theme,                                                                                                               │
 │    240 -                 "elaboration": data["elaboration"],                                                                                           │
 │    241 -                 "judgment": judgment                                                                                                          │
 │    242 -             }                                                                                                                                 │
 │    244 +             result_entry = {"theme": theme, "elaboration": data["elaboration"], "judgment": judgment}                                         │
 │    245               all_results.append(result_entry)                                                                                                  │
 │    246               judged_themes.add(theme)                                                                                                          │
 │    247                                                                                                                                                 │
 │    246 -             # Incremental save after each successful judgment                                                                                 │
 │    248               try:                                                                                                                              │
 │    249                   with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:                                                                           │
 │    250                       json.dump(all_results, f, ensure_ascii=False, indent=4)                                                                   │
 │    251                   logging.info(f"Progress saved. Total judged themes: {len(all_results)}")                                                      │
 │    252               except Exception as e:                                                                                                            │
 │    252 -                 logging.error(f"Failed to save progress after judging theme: {e}")                                                            │
 │    253 +                 logging.error(f"Failed to save progress: {e}")                                                                                │
 │    254           else:                                                                                                                                 │
 │    255               logging.error(f"Failed to get judgment for theme '{theme[:50]}...'. It will be retried on next run.")                             │
 │    256                                                                                                                                                 │
 │    256 -         time.sleep(1) # Rate limiting                                                                                                         │
 │    257 +         time.sleep(1)                                                                                                                         │
 │    258                                                                                                                                                 │
 │    259       logging.info("LLM-as-a-Judge process finished.")                                                                                          │
 │    260                                                                                                                                                 │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

✦ The script llm_judge.py has been successfully rewritten according to your latest instructions.

  All modifications have been applied:

   * Type hint compatibility: The typing module has been imported and Optional[Dict[str, Any]] is now used, which resolves the TypeError issue in older versions of Python.
   * System Prompt application: The script has been restructured to place fixed instructions (role, evaluation criteria, output format) in the System Prompt, while using dynamic data (topic, content ideas) as the User Prompt. This typically results in more stable model performance.
   * Emphasis on single selection: The System Prompt now clearly requires the reviewer to select "a single best proposal" to prevent selecting multiple best answers.
   * Formatting safety: All {} used for JSON examples in the Prompt template have been escaped to {{}}, completely resolving potential KeyError issues during string formatting.

  The script is now more complete and robust. You can run it at any time.

  Operation instructions (remain unchanged):

   1. Set environment variables:
   1     export OPENAI_API_KEY='your_key_here'
'   2. Install dependencies:'
   1     pip install openai tqdm
'   3. Run script:'
   1     python llm_judge.py



# USER: 
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Great, I have finished running the experiment, and the results are placed in the llm_judge_results.json file under the Qwen3-1.7B_results folder. This experiment will be part of the experiments in my AI top conference paper. Please help me analyze which metrics I should count and which charts I should draw to present the results?                                                                                                                     │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ Congratulations! Completing the experiment and obtaining results is a crucial step in paper writing. As your research partner, I will design a comprehensive data analysis and visualization plan for you, which will be sufficient to support the experimental section of an AI top-tier conference paper.

  The data we have (llm_judge_results.json) is a treasure trove because it not only contains quantitative scores but also qualitative review reasons. A convincing experimental analysis needs to combine both.

  Analysis Strategy: From macro to micro, combining quantitative and qualitative approaches

  Our core objective is to prove the superiority of your new algorithm (presumably cgmcts) compared to baseline methods. The analysis should be organized around the following three levels:

   1. Overall Performance: Which method is best overall?
   2. Dimensional Abilities: Which specific abilities (reasonableness, clarity, innovation) are different methods stronger or weaker in?
   3. Case Study: Why is cgmcts better? Demonstrate its advantages through specific examples.
  ---

Part 1: Key Quantitative Metrics 

You should first calculate the following core metrics, which will form the basis of your experimental results.

   1. Average Scores:
       * Average scores by dimension: For each method, calculate the average scores it received across the three dimensions: plausibility, structure_clarity, and innovation_potential. This will clearly show the strengths and weaknesses of each method.
       * Overall average score: For each method, calculate the average of its total scores (or average scores) across the three dimensions. This is an intuitive metric for measuring comprehensive strength.

   2. "Best Proposal" Win Rate:
       * Calculate the percentage of times each method is rated as the best_proposal by the LLM Judge. This is a highly persuasive metric that directly reflects the strength of the model's ability to generate the best creative ideas.

   3. Score Distribution:
       * Analyze the distribution of scores for each method across each dimension (e.g., using standard deviation). A method may have a high average score, but if the variance is also large, it indicates inconsistent performance. A method with low variance and high mean is what we want.
  ---

Part 2: Recommended Visualizations

Charts are the lifeline of a paper. Clear and informative charts allow reviewers to see your contributions at a glance.

1. Chart 1: Multi-dimensional Performance Comparison - Grouped Bar Chart
   * Purpose: To visually compare the average performance of five methods across three core dimensions.
   * X-axis: Three dimensions (Plausibility, Structure & Clarity, Innovation Potential).
   * Y-axis: Average score (1-10).
   * Drawing method: Under each dimension, there should be five bars representing the five methods. Your cgmcts method should be distinguished with the most prominent color (e.g., red or blue).

2. Chart 2: Overall Capability Comparison - Radar Chart
   * Purpose: To display the "capability profile" of each method across three dimensions in a single view.
   * Axes: The three corners of the radar chart represent the three dimensions.
   * Drawing method: Each method is represented by a closed colored curve. A larger and more balanced shape typically indicates stronger overall capabilities. This is ideal for showing whether cgmcts outperforms the baselines across all dimensions.

3. Chart 3: "Best Idea" Win Rate - Simple Bar Chart
   * Purpose: To highlight the ultimate "winner".
   * X-axis: Five methods.
   * Y-axis: Win rate (%).
   * Drawing method: This is one of the most direct and impactful charts; be sure to place it in a prominent position in your paper.

4. Chart 4 (Optional but recommended): Score Distribution - Box Plot
   * Purpose: To show the details of score distribution, not just the average values.
   * X-axis: Five methods.
   * Y-axis: Score (1-10).
   * Drawing method: Create a box plot for the scores of each dimension. It can clearly show the median, quartiles, and outliers, strongly demonstrating the stability of your method.
  ---

  Part 3: Qualitative Analysis

  Top conference papers should not only demonstrate "What" (our method is better), but more importantly explain "Why". Qualitative analysis is key to answering "Why".

   * Select Typical Cases (Case Study): Choose 1-2 typical themes where cgmcts was rated as the best from the results.
   * Present in the paper:
       1. Briefly introduce this theme.
       2. Display side by side the outputs generated by cgmcts and a poorly performing baseline method (such as CoT).
       3. Cite the comments given by the LLM Judge in final_decision.justification, explaining why cgmcts's approach is better (for example, "...the method proposed by cgmcts is not only novel, but also takes potential ethical issues into consideration, while the baseline method is merely a simple combination of existing work").

  This specific case analysis can greatly enhance the persuasiveness of your paper.

  Next Action Plan

  To implement the above analysis, I suggest writing a Python script to automate all of this. I can complete this task for you.

  This script will:
   1. Read the llm_judge_results.json file.
   2. Use the pandas library to parse the nested JSON data and convert it into a flat, easy-to-analyze DataFrame.
   3. Automatically calculate all the quantitative metrics mentioned above.
   4. Use matplotlib and seaborn libraries to generate all recommended high-quality charts and save them as image files (such as .pdf or .png) for direct insertion into your paper.
   5. Output a clear summary table that you can directly copy into your paper.

  Would you like me to create this analysis script for you?

# USER: 
╭────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Good, should I just select one of the four charts in the core chart suggestions? Is the information somewhat repetitive? Also, should I use a table?  │
╰────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ You've raised an excellent question, which is exactly the kind of thinking a rigorous researcher should have. In top conference papers, every inch of space for charts and tables is extremely valuable. It's necessary to maximize information content while avoiding unnecessary repetition.

Your intuition is correct: the information in these four charts does overlap partially. You don't need to, and shouldn't, include all of them in the main body of your paper. At the same time, the table is absolutely necessary.

Below, I'll provide you with a more focused and strategic suggestion, which is also the most common and effective combination in top conference papers.

Final presentation plan: 1 core table + 2 core charts

This combination can comprehensively present your experimental results from different angles in the most concise way.

1. Core table: The "data dictionary" for all results (indispensable)

The table is the cornerstone of your experimental results. It provides precise numerical values and serves as the foundation for your charts. Reviewers will use the table to verify the conclusions you draw in the main text and charts.

Suggested table structure:
  ┌──────────────┬──────────────────────────┬────────────────────────────────┬─────────────────────────────────┬────────────────────┬─────────────┐
  │ Method       │ Plausibility (Avg. ± ... │ Structure & Clarity (Avg. ±... │ Innovation Potential (Avg. ±... │ Overall Score (... │ Win Rate... │
  ├──────────────┼──────────────────────────┼────────────────────────────────┼─────────────────────────────────┼────────────────────┼─────────────┤
  │ **cgmcts (o... │ 8.5 ± 1.1                │ 8.2 ± 1.3                      │ 7.9 ± 1.5                       │ 8.20               │ 65%         │
  │ cot          │ 7.2 ± 1.5                │ 7.8 ± 1.2                      │ 6.5 ± 1.8                       │ 7.17               │ 15%         │
  │ react        │ 7.0 ± 1.4                │ 7.5 ± 1.3                      │ 6.8 ± 1.7                       │ 7.10               │ 10%         │
  │ tot          │ 7.5 ± 1.3                │ 8.0 ± 1.1                      │ 7.2 ± 1.6                       │ 7.57               │ 10%         │
  │ simple       │ 6.8 ± 1.6                │ 7.1 ± 1.4                      │ 6.2 ± 1.9                       │ 6.70               │ 0%          │
  └──────────────┴──────────────────────────┴────────────────────────────────┴─────────────────────────────────┴────────────────────┴─────────────┘



* Avg. ± Std.: Simultaneously displays the mean and standard deviation, reflecting the performance and stability of the method.
* Overall Score: Can be the average score across the three dimensions.
* Win Rate: This is one of the most impressive metrics.
* Bold: Bold the results of your cgmcts method to highlight them.
  ---

2. Core Charts: From the perspectives of "Average Performance" and "Final Victory"

You need to select two most representative and information-complementary charts from the previous chart suggestions.

Selection 1: Grouped Bar Chart
* Purpose: To show the average performance of each method across different capability dimensions. This is a visualization of the average score data from the table, making it very intuitive.
* Why choose it:
This is the most standard and clear "multi-dimensional performance comparison" chart. It answers the question "In which specific aspects is our method stronger?" Although radar charts look cool, they sometimes have too high information density and are not as clear as bar charts.

Selection 2: Win Rate Bar Chart
* Purpose: To show who the ultimate winner is.
* Why choose it: The information in this chart is unique; it shows the ability to be "recognized as the best" rather than average performance. A method may not have the highest average score but can consistently produce some "stunning" SOTA-level results. This chart can capture that. Its conclusion is the most direct and impactful.

Why not choose the other two?

* Radar chart: Its function highly overlaps with the grouped bar chart, so choosing one is sufficient. Bar charts are usually easier for precise comparison.
* Box plot: It is very useful, but it also has the highest information density. If your paper has space constraints, the "Standard Deviation (Std.)" in the table already reflects stability numerically to some extent and can partially replace the box plot. If space allows, or if you want to emphasize the "stability" of your method, you can include it in the Appendix.

Summary

The core storyline of your paper's experimental section should be:

1. First, use the win rate bar chart to tell readers: "Look, our method cgmcts has achieved an overwhelming victory!"
2. Then, use the grouped bar chart to explain in depth: "Not only that, our method comprehensively surpasses all baseline methods in scientific validity, clarity, and especially in innovative potential."
3. Finally, use the core results table to provide all precise numbers and tell readers: "All these conclusions are supported by precise, verifiable data."

This "table + two charts" combination is logically clear, progressive, and persuasive, representing the gold standard for top-tier conference papers.

Now, I can write an analysis script for you to generate this core table and two core charts. What do you think of this plan?

# USER: 
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Table + radar chart, I guess. My experimental results are in the llm_judge_results.json under Qwen3-1.7B_results. Each item in the list is the result for a topic, in the form of {{'judgment':{{  │
│      "evaluations": {{                                                                                                               │
│        "A": {{                                                                                                                       │
│          "method": "{method_A}",                                                                                                     │
│          "plausibility": <score_1_to_10>,                                                                                            │
│          "structure_clarity": <score_1_to_10>,                                                                                       │
│          "innovation_potential": <score_1_to_10>,                                                                                    │
│          "reason": "<A concise justification for the scores>"                                                                        │
│        }},                                                                                                                           │
│        "B": {{                                                                                                                       │
│          "method": "{method_B}",                                                                                                     │
│          "plausibility": <score_1_to_10>,                                                                                            │
│          "structure_clarity": <score_1_to_10>,                                                                                       │
│          "innovation_potential": <score_1_to_10>,                                                                                    │
│          "reason": "<A concise justification for the scores>"                                                                        │
│        }},                                                                                                                           │
│        "C": {{                                                                                                                       │
│          "method": "{method_C}",                                                                                                     │
│          "plausibility": <score_1_to_10>,                                                                                            │
│          "structure_clarity": <score_1_to_10>,                                                                                       │
│          "innovation_potential": <score_1_to_10>,                                                                                    │
│          "reason": "<A concise justification for the scores>"                                                                        │
│        }},                                                                                                                           │
│        "D": {{                                                                                                                       │
│          "method": "{method_D}",                                                                                                     │
│          "plausibility": <score_1_to_10>,                                                                                            │
│          "structure_clarity": <score_1_to_10>,                                                                                       │
│          "innovation_potential": <score_1_to_10>,                                                                                    │
│          "reason": "<A concise justification for the scores>"                                                                        │
│        }},                                                                                                                           │
│        "E": {{                                                                                                                       │
│          "method": "{method_E}",                                                                                                     │
│          "plausibility": <score_1_to_10>,                                                                                            │
│          "structure_clarity": <score_1_to_10>,                                                                                       │
│          "innovation_potential": <score_1_to_10>,                                                                                    │
│          "reason": "<A concise justification for the scores>"                                                                        │
│        }}                                                                                                                            │
│      }},                                                                                                                             │
│      "final_decision": {{                                                                                                            │
│        "best_proposal": "<The single best proposal: A, B, C, D, or E>",                                                              │
│        "justification": "<A comprehensive explanation of why this proposal is the best overall>"                                     │
│      }}                                                                                                                              │

│    }} \n}}help me complete the Python code                                                                                                         │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ Okay, we'll go with the combination of "core table + radar chart". The radar chart is perfect for displaying the comprehensive capability profiles of different methods across multiple dimensions, making it immediately clear whether your cgmcts method is balanced or has outstanding advantages in specific dimensions.

I will write a Python script named analyze_results.py for you. It will:
 1. Read the experimental results located at Qwen3-1.7B_results/llm_judge_results.json.
 2. Use pandas and numpy to calculate all core metrics.
 3. Print the formatted core results table in the terminal.
 4. Use matplotlib to generate a radar chart and save it as radar_chart.pdf, ensuring it's a vector graphic that can be scaled losslessly for use in your paper.

Here is your analysis script:
 ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to analyze_results.py                                                                                                       │
 │                                                                                                                                                  │
 │    ... first 33 lines hidden ...                                                                                                                 │
 │     34     for item in data:                                                                                                                     │
 │     35         theme = item.get('theme')                                                                                                         │
 │     36         judgment = item.get('judgment', {})                                                                                               │
 │     37         evaluations = judgment.get('evaluations', {})                                                                                     │
 │     38         final_decision = judgment.get('final_decision', {})                                                                               │
 │     39         winner_letter = final_decision.get('best_proposal')                                                                               │
 │     40                                                                                                                                           │
│     41         # Method name for determining the winner                                                                                                            │
 │     42         winner_method = evaluations.get(winner_letter, {}).get('method')                                                                  │
 │     43                                                                                                                                           │
 │     44         for _, eval_data in evaluations.items():                                                                                          │
 │     45             method = eval_data.get('method')                                                                                              │
 │     46             if not method: continue                                                                                                       │
 │     47                                                                                                                                           │
 │     48             record = {                                                                                                                    │
 │     49                 'theme': theme,                                                                                                           │
 │     50                 'method': method,                                                                                                         │
 │     51                 'is_winner': 1 if method == winner_method else 0                                                                          │
 │     52             }                                                                                                                             │
 │     53             for dim in DIMENSIONS:                                                                                                        │
 │     54                 record[dim] = eval_data.get(dim)                                                                                          │
 │     55                                                                                                                                           │
 │     56             parsed_records.append(record)                                                                                                 │
 │     57                                                                                                                                           │
 │     58     return pd.DataFrame(parsed_records)                                                                                                   │
 │     59                                                                                                                                           │
 │     60 def create_summary_table(df: pd.DataFrame) -> pd.DataFrame:                                                                               │
 │     61     """Calculate metrics based on DataFrame and generate the final summary table."""                                                                                     │
 │     62     # Group by method                                                                                                                          │
 │     63     grouped = df.groupby('method')                                                                                                        │
 │     64                                                                                                                                           │
│     65     # Calculate mean and standard deviation                                                                                                                  │
 │     66     means = grouped[DIMENSIONS].mean()                                                                                                    │
 │     67     stds = grouped[DIMENSIONS].std()                                                                                                      │
 │     68                                                                                                                                           │
│     69     # create formatted Avg. ± Std. column                                                                                                         │
 │     70     summary_df = pd.DataFrame()                                                                                                           │
 │     71     for dim in DIMENSIONS:                                                                                                                │
 │     72         summary_df[f'{dim.replace(" ", " ").title()} (Avg. ± Std.)'] = \                                                                  │
 │     73             means[dim].map('{:.2f}'.format) + ' ± ' + stds[dim].map('{:.2f}'.format)                                                      │
 │     74                                                                                                                                           │

 │     75     # calculate comprehensive score                                                                                                                        │
 │     76     df['overall_score'] = df[DIMENSIONS].mean(axis=1)                                                                                     │
 │     77     overall_scores = df.groupby('method')['overall_score'].mean()                                                                         │
 │     78     summary_df['Overall Score (Avg.)'] = overall_scores.map('{:.2f}'.format)                                                              │
 │     79                                                                                                                                           │
│     80     # Calculate win rate                                                                                                                          │
 │     81     total_themes = df['theme'].nunique()                                                                                                  │
 │     82     win_counts = grouped['is_winner'].sum()                                                                                               │
 │     83     win_rates = (win_counts / total_themes) * 100                                                                                         │
 │     84     summary_df['Win Rate (%)'] = win_rates.map('{:.1f}%'.format)                                                                          │
 │     85                                                                                                                                           │

│     86     # Adjust the order of columns and rows                                                                                                                    │
 │     87     summary_df = summary_df.reindex(METHOD_ORDER)                                                                                         │
 │     88     return summary_df                                                                                                                     │
 │     89                                                                                                                                           │
 │     90 def plot_radar_chart(df: pd.DataFrame, filepath: str):                                                                                    │
│     91     """Generate and save radar chart."""                                                                                                              │
│     92     # Calculate the average score for each method on each dimension                                                                                                      │
 │     93     avg_scores = df.groupby('method')[DIMENSIONS].mean().reindex(METHOD_ORDER)                                                            │
 │     94                                                                                                                                           │
 │     95     labels = [label.replace('_', ' ').title() for label in DIMENSIONS]                                                                    │
 │     96     num_vars = len(labels)                                                                                                                │
 │     97                                                                                                                                           │
98     # calculate angle                                                                                                                            │
 │     99     angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()                                                                 │
│    100     angles += angles[:1] # closed curve                                                                                                       │
 │    101                                                                                                                                           │
 │    102     fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))                                                                   │
 │    103                                                                                                                                           │
 │    104     for i, (method, row) in enumerate(avg_scores.iterrows()):                                                                             │
 │    105         stats = row.tolist()                                                                                                              │
│    106         stats += stats[:1] # closed curve                                                                                                     │
 │    107                                                                                                                                           │
 │    108         # For cgmcts, use more prominent and thicker lines                                                                                                │
 │    109         is_ours = (method == 'cgmcts')                                                                                                    │
 │    110         linewidth = 2.5 if is_ours else 1.5                                                                                               │
 │    111         alpha = 0.8 if is_ours else 0.1                                                                                                   │
 │    112                                                                                                                                           │
 │    113         ax.plot(angles, stats, linewidth=linewidth, linestyle='solid', label=method)                                                      │
 │    114         ax.fill(angles, stats, alpha=alpha)                                                                                               │
 │    115                                                                                                                                           │
 │    116     # Set chart style                                                                                                                        │
 │    117     ax.set_yticklabels([]) # Hide tick labels of radius axis                                                                                         │
 │    118     ax.set_xticks(angles[:-1])                                                                                                            │
 │    119     ax.set_xticklabels(labels, size=12)                                                                                                   │
 │    120     ax.set_rlabel_position(30)                                                                                                            │
 │    121     plt.yticks([2, 4, 6, 8, 10], ["2", "4", "6", "8", "10"], color="grey", size=10)                                                       │
 │    122     plt.ylim(0, 10)                                                                                                                       │
 │    123                                                                                                                                           │
 │    124     ax.set_title('Multi-Dimensional Performance Comparison', size=16, color='black', y=1.1)                                               │
 │    125     ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))                                                                               │
 │    126                                                                                                                                           │
│    127     # Save as high quality PDF file                                                                                                               │
 │    128     plt.savefig(filepath, format='pdf', bbox_inches='tight')                                                                              │
│    129     print(f"\nRadar chart has been saved to: {filepath}")                                                                                                │
 │    130                                                                                                                                           │
 │    131                                                                                                                                           │
│    132 # --- Main Execution Function ---                                                                                                                      │
 │    133                                                                                                                                           │
 │    134 def main():                                                                                                                               │
 │    135     """Main function that coordinates data loading, analysis, and visualization."""                                                                                            │
 │    136     # 1. Load and parse data                                                                                                                   │
 │    137     df = load_and_parse_data(RESULTS_FILE)                                                                                                │
 │    138     if df.empty:                                                                                                                          │
 │    139         return                                                                                                                            │
 │    140                                                                                                                                           │
│    141     # 2. Create and print summary table                                                                                                               │
 │    142     summary_table = create_summary_table(df)                                                                                              │

│    143     print("--- Core Table of Experimental Results ---")                                                                                                     │
 │    144     print(summary_table.to_string())                                                                                                      │
 │    145     print("---------------------------")                                                                                                  │
 │    146                                                                                                                                           │
 │    147     # 3. draw and save radar chart                                                                                                                 │
 │    148     plot_radar_chart(df, OUTPUT_CHART)                                                                                                    │
 │    149                                                                                                                                           │
 │    150 if __name__ == "__main__":                                                                                                                │
 │    151     main()                                                                                                                                │
 ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

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  ⚡ You can choose to authenticate with a paid API key or continue with the fallback model.
  ⚡ To increase your limits, upgrade to a Gemini Code Assist Standard or Enterprise plan with higher limits at https://goo.gle/set-up-gemini-code-assist
  ⚡ Or you can utilize a Gemini API Key. See: https://goo.gle/gemini-cli-docs-auth#gemini-api-key
  ⚡ You can switch authentication methods by typing /auth

 ——————————————————————NEW CHAT FROM gemin-flash ——————————————————————————————————————————————————————————————————————




# USER: 
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Please read @analyze_results.py , 1. Make the background color of the radar chart lighter and improve the overall color scheme of the lines. 2. Adjust the coordinate axis to start from 4. 3. Add a legend. 4.   │
│    Think about what other types of charts could better display our three indicators?                                                                    │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


 ╭─────────────────────────────────╮
 │ ✓  ReadFile analyze_results.py  │
 ╰─────────────────────────────────╯
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  Edit analyze_results.py:     fig, ax = plt.subplots(fig... =>     fig, ax = plt.subplots(fig...                                                     │
 │                                                                                                                                                        │
 │    101                                                                                                                                                 │
 │    102   fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))                                                                           │
 │    103                                                                                                                                                 │
 │    104 + # Set radar chart background color                                                                                                                          │
 │    105 + ax.set_facecolor('#f0f0f0') # Light gray background                                                                                                      │
 │    106 +                                                                                                                                               │

 │    107 + # Define color cycle, ensuring 'cgmcts' has a prominent color                                                                                   │
 │    108 + colors = plt.cm.get_cmap('Set2', len(METHOD_ORDER)) # Use Set2 colormap                                                                      │
 │    109 +                                                                                                                                               │
 │    110   for i, (method, row) in enumerate(avg_scores.iterrows()):                                                                                     │
 │    111       stats = row.tolist()                                                                                                                      │
│    112       stats += stats[:1] # closed curve                                                                                                             │
 │    113                                                                                                                                                 │
│    114       # For cgmcts, use more prominent and thicker lines                                                                                                        │
 │    115       is_ours = (method == 'cgmcts')                                                                                                            │
 │    110 -     linewidth = 2.5 if is_ours else 1.5                                                                                                       │
 │    111 -     alpha = 0.8 if is_ours else 0.1                                                                                                           │
 │    116 +     linewidth = 3 if is_ours else 1.5                                                                                                         │
│    117 +     alpha = 0.2 if is_ours else 0.1 # Overall lighten fill transparency                                                                                      │
 │    118 +     color = 'red' if is_ours else colors(i) # 'cgmcts' is red, others from colormap                                                           │
 │    119                                                                                                                                                 │
 │    113 -     ax.plot(angles, stats, linewidth=linewidth, linestyle='solid', label=method)                                                              │
 │    114 -     ax.fill(angles, stats, alpha=alpha)                                                                                                       │
 │    120 +     ax.plot(angles, stats, linewidth=linewidth, linestyle='solid', label=method, color=color)                                                 │
 │    121 +     ax.fill(angles, stats, alpha=alpha, color=color)                                                                                          │
 │    122                                                                                                                                                 │

│    123   # Set chart style                                                                                                                                │
│    124   ax.set_yticklabels([]) # Hide tick labels of radius axis                                                                                                 │
 │    125   ax.set_xticks(angles[:-1])                                                                                                                    │
 │    126   ax.set_xticklabels(labels, size=12)                                                                                                           │
 │    127   ax.set_rlabel_position(30)                                                                                                                    │
 │    121 - plt.yticks([2, 4, 6, 8, 10], ["2", "4", "6", "8", "10"], color="grey", size=10)                                                               │
 │    122 - plt.ylim(0, 10)                                                                                                                               │
 │    128                                                                                                                                                 │
│    129 + # Adjust the y-axis scale to start from 4                                                                                                                          │
 │    130 + yticks_labels = [4, 5, 6, 7, 8, 9, 10]                                                                                                        │
 │    131 + plt.yticks(yticks_labels, [str(y) for y in yticks_labels], color="grey", size=10)                                                             │

│    132 + plt.ylim(4, 10) # Y-axis range from 4 to 10                                                                                                              │
 │    133 +                                                                                                                                               │
 │    134   ax.set_title('Multi-Dimensional Performance Comparison', size=16, color='black', y=1.1)                                                       │
 │    125 - ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))                                                                                       │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    135 + ax.legend(loc='upper right', bbox_to_anchor=(1.25, 1.1), fancybox=True, shadow=True)                                                          │
 │    136                                                                                                                                                 │
│    137   # Save as a high-quality PDF file                                                                                                                       │
 │    138   plt.savefig(filepath, format='pdf', bbox_inches='tight')                                                                                      │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I have updated the analyze_results.py file to:
   1. Set the radar chart background to a light gray (#f0f0f0).
   2. Use the Set2 colormap for line colors, with 'cgmcts' specifically highlighted in red and a slightly thicker line. The fill alpha has been adjusted to 0.2 for
      'cgmcts' and 0.1 for others for a lighter appearance.
   3. Adjust the y-axis to start from 4, with ticks at 4, 5, 6, 7, 8, 9, 10.
   4. Adjusted the legend position slightly to bbox_to_anchor=(1.25, 1.1) to ensure it's clearly visible.

  Regarding your question about alternative chart types for three metrics, here are some good options:

   1. Grouped Bar Chart: This is often the most straightforward. Each method would have a group of three bars, one for each metric. It's excellent for direct
      comparison of each metric across different methods.
   2. Small Multiples (Facet Grids) of Bar Charts: You could create a separate, smaller bar chart for each method, showing its performance across the three
      metrics. This is good for comparing the profile of performance for each method.
   3. Heatmap: If you have many methods, a heatmap can be effective. Rows would be methods, columns would be metrics, and the color intensity of each cell would
      represent the score.

  For your scenario with three metrics, a Grouped Bar Chart would likely be the most effective and easiest to interpret.



# USER: 
╭────────────────────────────────────────────────────────╮
│  > Please adjust the color scheme, as the background color of the outer lines is covering the inner content.  │
╰────────────────────────────────────────────────────────╯

# AGENT: 


 ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ x  Edit {"old_string":"    # Define color cycle, ensure 'cgmcts' has a prominent color\n    colors = plt.cm.get_cmap('Set2', len(METHOD_ORDER)) # Use Set2 colormap\n    # You… │
 │                                                                                                                                                                  │

│    Failed to edit, 0 occurrences found for old_string in ${PWD}/experiment/compare_experiment/analyze_results.py.              │
 │    No edits made. The exact text in old_string was not found. Ensure you're not escaping content incorrectly and check whitespace, indentation, and              │
 │    context. Use read_file tool to verify.                                                                                                                        │
 ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
 ╭─────────────────────────────────╮
 │ ✓  ReadFile analyze_results.py  │
 ╰─────────────────────────────────╯
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  Edit analyze_results.py:     fig, ax = plt.subplots(fig... =>     fig, ax = plt.subplots(fig...                                                     │
 │                                                                                                                                                        │
 │    101                                                                                                                                                 │
 │    102   fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))                                                                           │
 │    103                                                                                                                                                 │

 │    104 + # Set radar chart background color                                                                                                                          │
 │    105 + ax.set_facecolor('#f0f0f0') # Light gray background                                                                                                      │
 │    106 +                                                                                                                                               │
│    107 + # Define color cycle to ensure 'cgmcts' has a prominent color                                                                                                  │
│    108 + colors = plt.cm.get_cmap('Set2', len(METHOD_ORDER)) # Use Set2 colormap                                                                       │
 │    109 +                                                                                                                                               │
 │    110   for i, (method, row) in enumerate(avg_scores.iterrows()):                                                                                     │
 │    111       stats = row.tolist()                                                                                                                      │
│    112       stats += stats[:1] # closed curve                                                                                                             │
 │    113                                                                                                                                                 │
│    114       # Use more prominent and thicker lines for cgmcts                                                                                                        │
 │    115       is_ours = (method == 'cgmcts')                                                                                                            │
 │    110 -     linewidth = 2.5 if is_ours else 1.5                                                                                                       │
 │    111 -     alpha = 0.8 if is_ours else 0.1                                                                                                           │
 │    116 +     linewidth = 3 if is_ours else 1.5                                                                                                         │
│    117 +     alpha = 0.2 if is_ours else 0.1 # adjust the overall fill transparency to be lighter                                                                                      │
 │    118 +     color = 'red' if is_ours else colors(i) # 'cgmcts' is red, others from colormap                                                           │
 │    119                                                                                                                                                 │
 │    113 -     ax.plot(angles, stats, linewidth=linewidth, linestyle='solid', label=method)                                                              │
 │    114 -     ax.fill(angles, stats, alpha=alpha)                                                                                                       │
 │    120 +     ax.plot(angles, stats, linewidth=linewidth, linestyle='solid', label=method, color=color)                                                 │
 │    121 +     ax.fill(angles, stats, alpha=alpha, color=color)                                                                                          │
 │    122                                                                                                                                                 │
 │    123   # Set chart style                                                                                                                                │
 │    124   ax.set_yticklabels([]) # Hide tick labels of radial axis                                                                                                 │
 │    125   ax.set_xticks(angles[:-1])                                                                                                                    │
 │    126   ax.set_xticklabels(labels, size=12)                                                                                                           │
 │    127   ax.set_rlabel_position(30)                                                                                                                    │
 │    121 - plt.yticks([2, 4, 6, 8, 10], ["2", "4", "6", "8", "10"], color="grey", size=10)                                                               │
 │    122 - plt.ylim(0, 10)                                                                                                                               │
 │    128                                                                                                                                                 │
│    129 + # adjust y-axis scale to start from 4                                                                                                                          │
 │    130 + yticks_labels = [4, 5, 6, 7, 8, 9, 10]                                                                                                        │
 │    131 + plt.yticks(yticks_labels, [str(y) for y in yticks_labels], color="grey", size=10)                                                             │

│    132 + plt.ylim(4, 10) # Y-axis range from 4 to 10                                                                                                              │
 │    133 +                                                                                                                                               │
 │    134   ax.set_title('Multi-Dimensional Performance Comparison', size=16, color='black', y=1.1)                                                       │
 │    125 - ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))                                                                                       │
 │    ══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    135 + ax.legend(loc='upper right', bbox_to_anchor=(1.25, 1.1), fancybox=True, shadow=True)                                                          │
 │    136                                                                                                                                                 │
│    137   # Save as high-quality PDF file                                                                                                                       │
 │    138   plt.savefig(filepath, format='pdf', bbox_inches='tight')                                                                                      │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I have updated the analyze_results.py file to:
   1. Set the radar chart background to a light gray (#f0f0f0).
   2. Use the Set2 colormap for line colors, with 'cgmcts' specifically highlighted in red and a slightly thicker line. The fill alpha has been adjusted to 0.2 for
      'cgmcts' and 0.1 for others for a lighter appearance.
   3. Adjust the y-axis to start from 4, with ticks at 4, 5, 6, 7, 8, 9, 10.
   4. Adjusted the legend position slightly to bbox_to_anchor=(1.25, 1.1) to ensure it's clearly visible.

  Regarding your question about alternative chart types for three metrics, here are some good options:

   1. Grouped Bar Chart: This is often the most straightforward. Each method would have a group of three bars, one for each metric. It's excellent for direct
      comparison of each metric across different methods.
   2. Small Multiples (Facet Grids) of Bar Charts: You could create a separate, smaller bar chart for each method, showing its performance across the three
      metrics. This is good for comparing the profile of performance for each method.
   3. Heatmap: If you have many methods, a heatmap can be effective. Rows would be methods, columns would be metrics, and the color intensity of each cell would
      represent the score.

  For your scenario with three metrics, a Grouped Bar Chart would likely be the most effective and easiest to interpret.



# USER: 
╭────────────────────────────────────────────────────────╮
│  > Please adjust the color scheme, as the background color of the outer lines is covering the inner ones.  │
╰────────────────────────────────────────────────────────╯

# AGENT: 


 ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ x  Edit {"old_string":"    # Define color cycle, ensure 'cgmcts' has a prominent color\n    colors = plt.cm.get_cmap('Set2', len(METHOD_ORDER)) # 使用Set2 colormap\n    # You… │
 │                                                                                                                                                                  │
 │    Failed to edit, 0 occurrences found for old_string in ${PWD}/experiment/compare_experiment/analyze_results.py.              │
 │    No edits made. The exact text in old_string was not found. Ensure you're not escaping content incorrectly and check whitespace, indentation, and              │
 │    context. Use read_file tool to verify.                                                                                                                        │
 ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
 ╭─────────────────────────────────╮
 │ ✓  ReadFile analyze_results.py  │
 ╰─────────────────────────────────╯
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓  Edit analyze_results.py:     # Define color cycle, ensure 'cgmcts' has a prominent... =>     # Define color cycle, ensure 'cgmcts' has a prominent...                         │
 │                                                                                                                                                        │

│    105   ax.set_facecolor('#f0f0f0') # light gray background                                                                                                      │
 │    106                                                                                                                                                 │
 │    107   # Define color cycle, ensure 'cgmcts' has a prominent color                                                                                                  │
 │    108 - colors = plt.cm.get_cmap('Set2', len(METHOD_ORDER)) # Use Set2 colormap                                                                       │
 │    108 + # Using a custom list of distinct colors for better visual separation                                                                         │
 │    109 + custom_colors = ['#E41A1C', '#377EB8', '#4DAF4A', '#984EA3', '#FF7F00'] # Red, Blue, Green, Purple, Orange                                    │
 │    110                                                                                                                                                 │
 │    111   for i, (method, row) in enumerate(avg_scores.iterrows()):                                                                                     │
 │    112       stats = row.tolist()                                                                                                                      │
│    115       # For cgmcts, use more prominent and thicker lines                                                                                                        │
 │    116       is_ours = (method == 'cgmcts')                                                                                                            │
 │    117       linewidth = 3 if is_ours else 1.5                                                                                                         │

│    117 -     alpha = 0.2 if is_ours else 0.1 # Lighten the overall fill transparency                                                                                      │
 │    118 -     color = 'red' if is_ours else colors(i) # 'cgmcts' is red, others from colormap                                                           │
│    118 +     alpha_fill = 0.06 if is_ours else 0.03 # Lower fill transparency to avoid coverage                                                                       │
 │    119 +     color = custom_colors[i % len(custom_colors)] # Assign colors from custom_colors                                                          │
 │    120                                                                                                                                                 │
 │    121       ax.plot(angles, stats, linewidth=linewidth, linestyle='solid', label=method, color=color)                                                 │
 │    121 -     ax.fill(angles, stats, alpha=alpha, color=color)                                                                                          │
 │    122 +     ax.fill(angles, stats, alpha=alpha_fill, color=color)                                                                                     │
 │    123                                                                                                                                                 │
 │    124   # Set chart style                                                                                                                                │
 │    125   ax.set_yticklabels([]) # Hide tick labels of radius axis                                                                                                 │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I have adjusted the color scheme and fill transparency in analyze_results.py. The radar chart should now have more distinct line colors and a lighter fill to
  prevent overlapping.


----------------------------------------------------------------------------------------------------------------------------
 ----------------------------------------------------------------------------------------------------------------------------
python analyze_results.py 

'--- Core Table of Experimental Results ---'
       Plausibility (Avg. ± Std.) Structure_Clarity (Avg. ± Std.) Innovation_Potential (Avg. ± Std.) Overall Score (Avg.) Win Rate (%)
method                                                                                                                                
cgmcts                8.98 ± 0.32                     9.30 ± 0.54                        8.54 ± 0.71                 8.94        92.0%
cot                   8.66 ± 0.48                     9.48 ± 0.54                        7.74 ± 0.49                 8.63         8.0%
react                 4.58 ± 2.33                     4.82 ± 1.65                        4.28 ± 2.37                 4.56         0.0%
tot                   5.48 ± 1.64                     4.30 ± 1.53                        5.02 ± 1.65                 4.93         0.0%
simple                7.98 ± 0.62                     8.48 ± 0.68                        7.14 ± 0.78                 7.87         0.0%
---------------------------


Radar chart has been saved to: radar_chart.pdf





------------------------------------NEW SESSION---------------------------------------------------------------------

# USER:

I am writing a paper for submission to an AI top conference, and the following is one of my experiments:

"purpose": "Calculate the V_novelty score for each generated result in `llm_judge_results.json`."
            "implementation_details": [
              "Load `experiment_results.json`.",
              "Initialize `LLMInterface` for text vectorization and load the FAISS index.",
              "Iterate through the output text of each method for each topic in the results file.",
              "For each output text, call `llm.get_vector()` to get its vector.",
              "Use the FAISS index to search for the nearest neighbors of this vector, and calculate `1 - cosine_similarity` as the novelty score.",
              "Write the calculated novelty score back to the original result object, creating a new evaluation field.",
              "Write the updated complete data to `results_with_novelty.json`."

My experimental results are in `llm_judge_results.json` under `Qwen3-1.7B_results`. Each item in the list is a result for a topic, in the format {{'judgment':{{  │
      "evaluations": {{
        "A": {{
          "method": "{method_A}",
          "plausibility": <score_1_to_10>,
          "structure_clarity": <score_1_to_10>,
          "innovation_potential": <score_1_to_10>,
          "reason": "<A concise justification for the scores>"
        }},
        "B": {{
          "method": "{method_B}",                                                                 
          "plausibility": <score_1_to_10>,                                                        
          "structure_clarity": <score_1_to_10>,                                                   
          "innovation_potential": <score_1_to_10>,                                                
          "reason": "<A concise justification for the scores>"                                    
        }},                                                                                       
        "C": {{                                                                                   
          "method": "{method_C}",                                                                 
          "plausibility": <score_1_to_10>,                                                        
          "structure_clarity": <score_1_to_10>,                                                   
          "innovation_potential": <score_1_to_10>,                                                
          "reason": "<A concise justification for the scores>"                                    
        }},                                                                                       
        "D": {{                                                                                   
          "method": "{method_D}",                                                                 
          "plausibility": <score_1_to_10>,                                                        
          "structure_clarity": <score_1_to_10>,                                                   
          "innovation_potential": <score_1_to_10>,                                                
          "reason": "<A concise justification for the scores>"                                    
        }},                                                                                       
        "E": {{                                                                                   
          "method": "{method_E}",                                                                 
          "plausibility": <score_1_to_10>,                                                        
          "structure_clarity": <score_1_to_10>,                                                   
          "innovation_potential": <score_1_to_10>,                                                
          "reason": "<A concise justification for the scores>"                                    
        }}                                                                                        
      }},                                                                                         
      "final_decision": {{                                                                        
        "best_proposal": "<The single best proposal: A, B, C, D, or E>",                          
        "justification": "<A comprehensive explanation of why this proposal is the best overall>" 
      }}                                                                                          
    }} \n}}

FAISS index is in database/faiss_index_Qwen3-0.6B.bin
You can refer to the implementation of LLMInterface class in cg_mcts_qwen.py.

Please provide the Python code you'd like me to complete.

# AGENT:

✦ Okay, I understand. You need to calculate novelty scores for each generated result in llm_judge_results.json.

Before I start writing the code, I need an important clarification:

In the llm_judge_results.json structure example you provided, it only contains evaluation scores and reasons for methods A, B, C, D, E,
but it doesn't include the actual generated text content from these methods. To calculate novelty scores, I need to know where the original generated text for these methods (A, B, C, D, E) is located.

Please tell me what is the path to the generated text field for each method (A, B, C, D, E) in the llm_judge_results.json file? For example, it might be at judgment.evaluations.A.generated_text
or some other location.

Once I have this information, I can proceed with writing the code.

# USER: 

╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > There are 5 json files in the Qwen3-1.7B_results folder, each containing a list where each item is the test result for a topic, in the form of {'id': '...', 'theme': '...', 'elaboration': '...',    │
│    "output": ...}                                                                                                                                               │
│    where 'output' is the prediction result. This result may contain thinking processes that we don't need, and we only need the final result, so the content between these two identifiers will be removed.                   │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ Alright, I understand now. The generated text is located in the JSON files within the Qwen3-1.7B_results folder, and I need to remove the content within the  ...  tags.

My plan is as follows:

1. Confirm the experimental result files: First, I will list all JSON files in the Qwen3-1.7B_results directory to confirm which 5 files contain the experimental results.
2. Read `cg_mcts_qwen.py`: I need to read the cg_mcts_qwen.py file to get the implementation of the LLMInterface class for text vectorization.
3. Load all experimental results: Read all relevant JSON files in the Qwen3-1.7B_results directory, extract their output field content, and remove the content within the  ...  tags. I will create a mapping to quickly find the corresponding output text by topic and method name.
4. Load `llm_judge_results.json`: Read the Qwen3-1.7B_results/llm_judge_results.json file, which is the target file where we need to add novelty scores.
5. Initialize `LLMInterface` and FAISS index: Initialize LLMInterface according to the implementation in cg_mcts_qwen.py, and load the FAISS index from database/faiss_index_Qwen3-0.6B.bin.
6. Traverse and calculate novelty scores:
   * Traverse each topic result in llm_judge_results.json.
   * For methods A, B, C, D, E in each topic result:
       * Get the method name (e.g., "CoT", "ReAct", etc.).
       * Find the corresponding preprocessed output text from the mapping created in step 3 based on the topic and method name.
       * Vectorize the output text using LLMInterface.
       * Use the FAISS index to search for nearest neighbors, and calculate 1 - cosine_similarity as the v_novelty score.
       * Add the v_novelty score to the evaluation field of the corresponding method in llm_judge_results.json.
7. Save the updated results: Write the updated data to Qwen3-1.7B_results/results_with_novelty.json.

Now, I will first list the files in the Qwen3-1.7B_results directory to confirm the names of the experimental result JSON files.
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  ReadFolder Qwen3-1.7B_results                                                                                                                       │
 │                                                                                                                                                        │
 │    Listed 8 item(s).                                                                                                                                   │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, I have confirmed the files in the Qwen3-1.7B_results directory, which include llm_judge_results.json and 5 experimental result files.

Next, I will read the cg_mcts_qwen.py file to obtain the implementation of the LLMInterface class.
 ╭──────────────────────────────╮
 │ ✓  ReadFile cg_mcts_qwen.py  │
 ╰──────────────────────────────╯

✦ I have read cg_mcts_qwen.py and understood the LLMInterface and its get_vector method. Now I will start writing the Python code.

Before writing the code, I need to confirm the specific structure of llm_judge_results.json, especially the position of the theme field and the exact values of the method field in evaluations, in order to correctly associate experimental results with evaluation results.

I will first read the llm_judge_results.json file to confirm these details.
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  ReadFile ...3-1.7B_results//llm_judge_results.json                                                                                                  │
 │                                                                                                                                                        │
 │    Read lines 1-2000 of 2352 from Qwen3-1.7B_results/llm_judge_results.json                                                                            │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ The file structure of llm_judge_results.json has been clarified. Each entry contains "theme", "elaboration", and "judgment", where "judgment" includes evaluations of methods A, B, C, D, and E, and each evaluation has a "method" field with values of "cgmcts", "cot", "react", "tot", or "simple".

Now, I will create a mapping from experimental result filenames to method names based on these method names:
   1 filename_to_method_name_map = {
   2     "results_cgmcts.json": "cgmcts",
   3     "results_baseline_cot.json": "cot",
   4     "results_baseline_react.json": "react",
   5     "results_baseline_tot.json": "tot",
   6     "results_baseline_simple_top_p.json": "simple"
   7 }

Next, I will write a Python script to execute novelty score calculation.
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to calculate_novelty_scores.py                                                                                                    │
 │                                                                                                                                                        │
 │    ... first 3 lines hidden ...                                                                                                                        │
 │      4 import os                                                                                                                                       │
 │      5 import numpy as np                                                                                                                              │
 │      6 import faiss                                                                                                                                    │
 │      7 import torch                                                                                                                                    │
 │      8 from transformers import AutoModelForCausalLM, AutoTokenizer                                                                                    │
 │      9                                                                                                                                                 │
 │     10 # --- 1. LLM Interface (Copied from cg_mcts_qwen.py) ---                                                                                        │
 │     11 class LLMInterface:                                                                                                                             │
 │     12     def __init__(self, model_name, device):                                                                                                     │
 │     13         print(f"Loading model: {model_name} on {device}...")                                                                                    │
 │     14         self.tokenizer = AutoTokenizer.from_pretrained(model_name)                                                                              │
 │     15         self.model = AutoModelForCausalLM.from_pretrained(                                                                                      │
 │     16             model_name,                                                                                                                         │
 │     17             dtype="auto",                                                                                                                       │
 │     18             device_map="auto"                                                                                                                   │
 │     19         ).eval()                                                                                                                                │
 │     20         self.device = self.model.device                                                                                                         │
 │     21         print("Model loaded successfully.")                                                                                                     │
 │     22                                                                                                                                                 │
 │     23     def get_vector(self, text: str) -> np.ndarray:                                                                                              │
 │     24         with torch.no_grad():                                                                                                                   │
 │     25             inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(self.device)                                 │
 │     26             outputs = self.model(**inputs, output_hidden_states=True)                                                                           │
 │     27             # Use the last hidden state of the last token as the sentence embedding                                                             │
 │     28             vector = outputs.hidden_states[-1][0, -1, :].cpu().to(torch.float32).numpy()                                                        │
 │     29             del inputs, outputs                                                                                                                 │
 │     30             return vector                                                                                                                       │
 │     31                                                                                                                                                 │
 │     32 # --- 2. Helper Functions ---                                                                                                                   │
 │     33 def preprocess_output(text: str) -> str:                                                                                                        │
 │     34     """Removes content between <think> ... </think> tags."""                                                                                    │
 │     35     return re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL).strip()                                                                     │
 │     36                                                                                                                                                 │
 │     37 def calculate_cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:                                                                   │
 │     38     """Calculates cosine similarity between two L2-normalized vectors."""                                                                       │
 │     39     return np.dot(vec1, vec2)                                                                                                                   │
 │     40                                                                                                                                                 │
 │     41 # --- 3. Main Script ---                                                                                                                        │
 │     42 if __name__ == "__main__":                                                                                                                      │
 │     43     # Configuration                                                                                                                             │
 │     44     MODEL_NAME = "../../Qwen3-1.7B" # Model used for vectorization                                                                              │
 │     45     DEVICE = "cuda" if torch.cuda.is_available() else "cpu"                                                                                     │
 │     46                                                                                                                                                 │
 │     47     # File Paths                                                                                                                                │
│     48     project_root = "${PWD}/experiment/compare_experiment"                                                     │
 │     49     llm_judge_results_path = os.path.join(project_root, "Qwen3-1.7B_results", "llm_judge_results.json")                                         │
 │     50     faiss_index_path = os.path.join(project_root, "database", "faiss_index_Qwen3-0.6B.bin")                                                     │
 │     51     output_results_path = os.path.join(project_root, "Qwen3-1.7B_results", "results_with_novelty.json")                                         │
 │     52     experiment_result_files_dir = os.path.join(project_root, "Qwen3-1.7B_results")                                                              │
 │     53                                                                                                                                                 │
 │     54     experiment_result_filenames = [                                                                                                             │
 │     55         "results_baseline_cot.json",                                                                                                            │
 │     56         "results_baseline_react.json",                                                                                                          │
 │     57         "results_baseline_simple_top_p.json",                                                                                                   │
 │     58         "results_baseline_tot.json",                                                                                                            │
 │     59         "results_cgmcts.json"                                                                                                                   │
 │     60     ]                                                                                                                                           │
 │     61                                                                                                                                                 │
 │     62     filename_to_method_name_map = {                                                                                                             │
 │     63         "results_cgmcts.json": "cgmcts",                                                                                                        │
 │     64         "results_baseline_cot.json": "cot",                                                                                                     │
 │     65         "results_baseline_react.json": "react",                                                                                                 │
 │     66         "results_baseline_tot.json": "tot",                                                                                                     │
 │     67         "results_baseline_simple_top_p.json": "simple"                                                                                          │
 │     68     }                                                                                                                                           │
 │     69                                                                                                                                                 │
 │     70     # 1. Load llm_judge_results.json                                                                                                            │
 │     71     print(f"Loading {llm_judge_results_path}...")                                                                                               │
 │     72     with open(llm_judge_results_path, "r", encoding="utf-8") as f:                                                                              │
 │     73         llm_judge_results_data = json.load(f)                                                                                                   │
 │     74     print(f"Loaded {len(llm_judge_results_data)} entries from llm_judge_results.json.")                                                         │
 │     75                                                                                                                                                 │
 │     76     # 2. Initialize LLMInterface for vectorization                                                                                              │
 │     77     llm_interface = LLMInterface(MODEL_NAME, DEVICE)                                                                                            │
 │     78                                                                                                                                                 │
 │     79     # 3. Load FAISS index                                                                                                                       │
 │     80     print(f"Loading FAISS index from {faiss_index_path}...")                                                                                    │
 │     81     faiss_index = faiss.read_index(faiss_index_path)                                                                                            │
 │     82     print("FAISS index loaded successfully.")                                                                                                   │
 │     83                                                                                                                                                 │
 │     84     # 4. Load and preprocess experiment results                                                                                                 │
 │     85     method_outputs = {} # Key: (theme, method_name), Value: preprocessed_output_text                                                            │
 │     86     for filename in experiment_result_filenames:                                                                                                │
 │     87         method_name = filename_to_method_name_map.get(filename)                                                                                 │
 │     88         if not method_name:                                                                                                                     │
 │     89             print(f"Warning: No method name mapping found for {filename}. Skipping.")                                                           │
 │     90             continue                                                                                                                            │
 │     91                                                                                                                                                 │
 │     92         file_path = os.path.join(experiment_result_files_dir, filename)                                                                         │
 │     93         print(f"Loading experiment results from {file_path} for method '{method_name}'...")                                                     │
 │     94         with open(file_path, "r", encoding="utf-8") as f:                                                                                       │
 │     95             experiment_data = json.load(f)                                                                                                      │
 │     96                                                                                                                                                 │
 │     97         for entry in experiment_data:                                                                                                           │
 │     98             theme = entry.get("theme")                                                                                                          │
 │     99             output = entry.get("output")                                                                                                        │
 │    100             if theme and output:                                                                                                                │
 │    101                 preprocessed_output = preprocess_output(output)                                                                                 │
 │    102                 method_outputs[(theme, method_name)] = preprocessed_output                                                                      │
 │    103             else:                                                                                                                               │
 │    104                 print(f"Warning: Missing 'theme' or 'output' in an entry from {filename}. Skipping.")                                           │
 │    105     print("Finished loading and preprocessing experiment results.")                                                                             │
 │    106                                                                                                                                                 │
 │    107     # 5. Iterate through llm_judge_results_data and calculate novelty scores                                                                    │
 │    108     print("Calculating novelty scores...")                                                                                                      │
 │    109     for i, entry in enumerate(llm_judge_results_data):                                                                                          │
 │    110         theme = entry.get("theme")                                                                                                              │
 │    111         if not theme:                                                                                                                           │
 │    112             print(f"Warning: Entry {i} in llm_judge_results.json missing 'theme'. Skipping novelty calculation for this entry.")                │
 │    113             continue                                                                                                                            │
 │    114                                                                                                                                                 │
 │    115         evaluations = entry.get("judgment", {}).get("evaluations", {})                                                                          │
 │    116         for method_key, eval_data in evaluations.items():                                                                                       │
 │    117             method_name = eval_data.get("method")                                                                                               │
 │    118             if not method_name:                                                                                                                 │
 │    119                 print(f"Warning: Method '{method_key}' in entry {i} missing 'method' name. Skipping.")                                          │
 │    120                 continue                                                                                                                        │
 │    121                                                                                                                                                 │
 │    122             text_to_vectorize = method_outputs.get((theme, method_name))                                                                        │
 │    123             if text_to_vectorize:                                                                                                               │
 │    124                 # Get vector for the generated text                                                                                             │
 │    125                 vector = llm_interface.get_vector(text_to_vectorize)                                                                            │
 │    126                                                                                                                                                 │
 │    127                 # Ensure vector is L2 normalized before searching FAISS                                                                         │
 │    128                 faiss.normalize_L2(vector.reshape(1, -1))                                                                                       │
 │    129                                                                                                                                                 │
 │    130                 # Search FAISS index for the nearest neighbor                                                                                   │
 │    131                 # faiss.IndexFlatIP stores inner product, which is cosine similarity for L2 normalized vectors                                  │
 │    132                 distances, _ = faiss_index.search(vector.reshape(1, -1), 1)                                                                     │
 │    133                                                                                                                                                 │
 │    134                 # The distance returned by search is 1 - cosine_similarity if the index is built with IndexFlatL2                               │
 │    135                 # But for IndexFlatIP, it's cosine similarity directly.                                                                         │
 │    136                 # So, novelty = 1 - cosine_similarity                                                                                           │
 │    137                 cosine_similarity = distances[0][0]                                                                                             │
 │    138                 v_novelty = 1.0 - cosine_similarity                                                                                             │
 │    139                                                                                                                                                 │
 │    140                 eval_data["v_novelty"] = round(v_novelty, 4) # Round for cleaner output                                                         │
 │    141             else:                                                                                                                               │
 │    142                 print(f"Warning: Could not find preprocessed output for theme '{theme}' and method '{method_name}'. Novelty score not           │
 │        calculated.")                                                                                                                                   │
 │    143                                                                                                                                                 │
 │    144     # 6. Save updated results                                                                                                                   │
 │    145     print(f"Saving updated results to {output_results_path}...")                                                                                │
 │    146     with open(output_results_path, "w", encoding="utf-8") as f:                                                                                 │
 │    147         json.dump(llm_judge_results_data, f, ensure_ascii=False, indent=4)                                                                      │
 │    148     print("Novelty score calculation complete and results saved.")                                                                              │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I have written the Python script calculate_novelty_scores.py. Now I will run it.

  The script will perform the following operations:
   1. Load llm_judge_results.json.
   2. Initialize LLMInterface (this will load the Qwen3-1.7B model).
   3. Load the FAISS index.
   4. Load and preprocess 5 experimental result JSON files.
   5. Iterate through llm_judge_results_data, get the generated text for each method, vectorize it, search the FAISS index, calculate novelty scores, and add them to the data.
   6. Save the updated data to results_with_novelty.json.

  Now, I will execute the script.
 ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ -  Shell python3 ${PWD}/experiment/compare_experiment/calculate_novelty_scores.py (Calculate novelty scores for generated res… │
 ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯











-----------------------------NEW SESSION - gemini- flash ------------------------------------------------------------------------------------

# USER:

╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  >  Help me write a Python code. I read a log text file containing a list using f = open('noprogress.log','r').
The log contains the following fields: "--- Processing Theme {num}/50" where num is a number identifying the start of processing a theme, with a total of 50 themes.
During each theme processing, "--- Iteration {curr}/10 ---" is printed, where curr is a number indicating how many times this theme has been iteratively processed, up to a maximum of 10 times.
If the current curr doesn't process any information, the distance to the next "--- Iteration {curr+1}/10---" won't exceed 3 lines.
If this distance is greater than 3 lines, it means information is being processed.
Similarly, when encountering the last iteration, if no information is processed, the distance to the appearance of the "Search completed in" log won't exceed 3 lines.
Additionally, if an iteration doesn't process any messages, generally the subsequent iterations for the current theme won't process any messages either, as the model has converged.
Now, please help me write Python code to count the actual number of iterations that process messages for each theme.
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯


# AGENT：

✦ Okay, I'll help you write this Python script.

This script will read the noprogress.log file and count the actual number of iterations that process messages for each theme based on the rules you provided.

Here's the code:

 ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to analyze_log.py                                                                                                                                           │
 │                                                                                                                                                                                  │
 │     1                                                                                                                                                                            │
 │     2 import re                                                                                                                                                                  │
 │     3                                                                                                                                                                            │
 │     4 def analyze_log(log_file_path):                                                                                                                                            │
 │     5     theme_processed_iterations = {}                                                                                                                                        │
 │     6     current_theme = None                                                                                                                                                   │
 │     7     current_iteration = None                                                                                                                                               │
 │     8     lines_since_last_iteration = 0                                                                                                                                         │
 │     9     # Flag to stop counting processed iterations for a theme if an iteration processes nothing                                                                             │
 │    10     processing_this_theme = True                                                                                                                                           │
 │    11                                                                                                                                                                            │
 │    12     try:                                                                                                                                                                   │
 │    13         with open(log_file_path, 'r') as f:                                                                                                                                │
 │    14             for line in f:                                                                                                                                                 │
 │    15                 line = line.strip()                                                                                                                                        │
 │    16                                                                                                                                                                            │
 │    17                 theme_match = re.match(r"--- Processing Theme (\d+)/50", line)                                                                                             │
 │    18                 iteration_match = re.match(r"--- Iteration (\d+)/10 ---", line)                                                                                            │
 │    19                 search_completed_match = re.match(r"Search completed in", line)                                                                                            │
 │    20                                                                                                                                                                            │
 │    21                 if theme_match:                                                                                                                                            │
 │    22                     # If a new theme starts, process the last iteration of the previous theme if any                                                                       │
 │    23                     if current_theme is not None and current_iteration is not None and processing_this_theme:                                                              │
 │    24                         if lines_since_last_iteration > 3:                                                                                                                 │
 │    25                             theme_processed_iterations[current_theme] += 1                                                                                                 │
 │    26                                                                                                                                                                            │
 │    27                     current_theme = int(theme_match.group(1))                                                                                                              │
 │    28                     theme_processed_iterations[current_theme] = 0                                                                                                          │
 │    29                     current_iteration = None  # Reset iteration for new theme                                                                                              │
 │    30                     lines_since_last_iteration = 0                                                                                                                         │
 │    31                     processing_this_theme = True  # Reset flag for new theme                                                                                               │
 │    32                 elif iteration_match:                                                                                                                                      │
 │    33                     # If a new iteration starts, check the line count of the previous iteration                                                                            │
 │    34                     if current_theme is not None and current_iteration is not None and processing_this_theme:                                                              │
 │    35                         if lines_since_last_iteration > 3:                                                                                                                 │
 │    36                             theme_processed_iterations[current_theme] += 1                                                                                                 │
 │    37                         else:                                                                                                                                              │
 │    38                             # If this iteration processed nothing, subsequent iterations for this theme also process nothing                                               │
 │    39                             processing_this_theme = False                                                                                                                  │
 │    40                                                                                                                                                                            │
 │    41                     current_iteration = int(iteration_match.group(1))                                                                                                      │
 │    42                     lines_since_last_iteration = 0  # Reset line counter for new iteration                                                                                 │
 │    43                 elif search_completed_match:                                                                                                                               │
 │    44                     # This marks the end of the last iteration for a theme                                                                                                 │
 │    45                     if current_theme is not None and current_iteration is not None and processing_this_theme:                                                              │
 │    46                         if lines_since_last_iteration > 3:                                                                                                                 │
 │    47                             theme_processed_iterations[current_theme] += 1                                                                                                 │
 │    48                                                                                                                                                                            │
 │    49                     # Reset for next theme (if any)                                                                                                                        │
 │    50                     current_theme = None                                                                                                                                   │
 │    51                     current_iteration = None                                                                                                                               │
 │    52                     lines_since_last_iteration = 0                                                                                                                         │
 │    53                     processing_this_theme = True                                                                                                                           │
 │    54                 else:                                                                                                                                                      │
 │    55                     # Regular log line, increment counter if we are within an iteration and still processing this theme                                                    │
 │    56                     if current_theme is not None and current_iteration is not None:                                                                                        │
 │    57                         lines_since_last_iteration += 1                                                                                                                    │
 │    58                                                                                                                                                                            │
 │    59             # After loop, handle the very last iteration if the file doesn't end with "Search completed in"                                                                │
 │    60             if current_theme is not None and current_iteration is not None and processing_this_theme:                                                                      │
 │    61                 if lines_since_last_iteration > 3:                                                                                                                         │
 │    62                     theme_processed_iterations[current_theme] += 1                                                                                                         │
 │    63                                                                                                                                                                            │
 │    64     except FileNotFoundError:                                                                                                                                              │
 │    65         print(f"Error: Log file '{log_file_path}' not found.")                                                                                                             │
 │    66         return {}                                                                                                                                                          │
 │    67     except Exception as e:                                                                                                                                                 │
 │    68         print(f"An error occurred: {e}")                                                                                                                                   │
 │    69         return {}                                                                                                                                                          │
 │    70                                                                                                                                                                            │
 │    71     return theme_processed_iterations                                                                                                                                      │
 │    72                                                                                                                                                                            │
 │    73 if __name__ == "__main__":                                                                                                                                                 │
 │    74     log_file = 'noprogress.log'  # Make sure this file is in the same directory as the script, or provide the full path                                                    │
 │    75     results = analyze_log(log_file)                                                                                                                                        │
 │    76                                                                                                                                                                            │
 │    77     if results:                                                                                                                                                            │
 │    78         print("Theme Processing Summary:")                                                                                                                                 │
 │    79         for theme, count in sorted(results.items()):                                                                                                                       │
 │    80             print(f"Theme {theme}: Processed {count} iterations")                                                                                                          │
 │    81     else:                                                                                                                                                                  │
 │    82         print("No theme processing data found or an error occurred.")                                                                                                      │
 ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I have written the code to the ${PWD}/analyze_log.py file.

  How to run:

   1. Make sure your log file is named noprogress.log and it is in the same directory as the analyze_log.py file. If not, please modify the line "log_file = 'noprogress.log'"
      in the analyze_log.py file, replacing it with the correct path to your log file.
   2. Open a terminal or command line.
   3. Navigate to the ${PWD} directory.
   4. Run the following command:

   1     python analyze_log.py

The script will print out the number of iterations for each topic actually running and processing messages.