
# USER: 
╭────────────────────────────────────────────────────────────────────────────────────────────╮
│  > You are a senior AI researcher.                                                                │
│                                                                                            │
│    real_methodV2.txt contains a draft of the paper's approach.                                             │
│    cg_mcts_qwen.py is its corresponding implementation.                                                         │
│    Experiment-design.txt contains the general idea of the designed experiment,                                         │
│    experimental_plan.json is the specific decomposed steps.                                                │
│                                                                                            │
│                                                                                            │
│    Now, please read the above material and analyze how the **ablation study** for the experiment part should be conducted? (excluding comparison with other algorithms)  │
│    What comparison methods should be included?                                                                                                        │
╰────────────────────────────────────────────────────────────────────────────────────────────╯


# AGENT: 



✦ Okay, as a senior AI researcher, let's analyze together how the ablation study (Ablation Study) for this project should be designed.

First, I will read the core materials you provided to ensure I fully understand the essence of our method (which I tentatively call CG-MCTS, Creative Generation based on Monte Carlo Tree Search).
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  ReadManyFiles Will attempt to read and concatenate files using patterns:                                                                        │
 │    real_methodV2.txt`, `cg_mcts_qwen.py`, `Experiment-design.txt`, `experimental_plan.json                                                         │
 │     (within target directory:                                                                                                                      │

│    ${PWD}/experiment                                                                                             │
 │    ) . Excluding: patterns like                                                                                                                    │
 │    **/node_modules/**`, `**/.git/**...`. File encoding: utf-8. Separator: "--- path/to/file.ext ---".                                              │
 │                                                                                                                                                    │
│    ### ReadManyFiles Result (Target Dir: `${PWD}/experiment`)                                                    │
 │                                                                                                                                                    │
 │    Successfully read and concatenated content from **4 file(s)**.                                                                                  │
 │                                                                                                                                                    │
 │    **Processed Files:**                                                                                                                            │
 │    - `Experiment-design.txt`                                                                                                                       │
 │    - `cg_mcts_qwen.py`                                                                                                                             │
 │    - `experimental_plan.json`                                                                                                                      │
 │    - `real_methodV2.txt`                                                                                                                           │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦
Okay, I have carefully read all the relevant materials. real_methodV2.txt clearly describes the four core stages of CG-MCTS, cg_mcts_qwen.py shows its specific implementation, and Experiment-design.txt has already provided a good starting point for the ablation study.

Based on the above information, I believe that a rigorous and persuasive ablation study should aim to break down the core contribution modules of our method one by one to prove that each module is indispensable.

Below is the ablation experiment plan I designed, which will be compared with your complete version of CG-MCTS (Full Method):
  ---

Ablation Experiment Design Plan

Core Objective: Verify the effectiveness of Guidance, Value Components, and the MCTS framework itself in the CG-MCTS framework.

We will design the following comparative methods (all are "degraded" versions of the complete version):

1. Method Variant: w/o Directional Guidance (No Directional Guidance)

   * Modification:
      Remove the "directional guidance" term from the UCT calculation formula in the select function. Specifically, set config.W_DIR to 0.
   1     # in select function
   2     # ...
   3     direction_guidance = 0 # self.config.W_DIR * np.dot(child_vec, self.v_target)
   4     return exploitation + exploration + direction_guidance
   * Experimental Objective:
      This is the most important ablation experiment. It aims to answer the core question: "Is our carefully designed v_target direction vector really effective?" If the innovation and topic relevance of generated concepts decrease significantly after removing this component, it would strongly prove the value of our "target-guided" mechanism. This directly validates Phase 1 (Target Setting) and Phase 2 (Guidance in Selection) of the algorithm.

  2. Method Variant: w/o Novelty Reward 

   * Modification Details:
      In the value calculation of the simulate_and_evaluate function, remove the novelty reward term V_novelty. Specifically, set config.W_NOV to 0.
   1     # in simulate_and_evaluate function
   2     # ...
   3     value = (self.config.W_COH * v_coherence_norm +
   4              0 * v_novelty + # self.config.W_NOV * v_novelty
   5              self.config.W_PROG * v_progress)
* Experiment Purpose:
   To verify the effect of the "encourage innovation" component in the value function. We expect that after removing this component, the algorithm may tend to generate more "safe" and "mediocre" but highly coherent ideas, as it loses the motivation to avoid already dense areas in the knowledge base. This validates the necessity of Phase 2 (V_novelty in Evaluation).

3. Method Variant: w/o Progress Reward 

   * Modifications:
      Remove the progress reward term V_progress from the value calculation in the simulate_and_evaluate function. Specifically, set config.W_PROG to 0. At the same time, it is also necessary to disable the hard pruning based on MIN_PROGRESS_THRESHOLD to observe its pure effect.
   1     # in simulate_and_evaluate function
   2     # ...
   3     # if node.parent and v_progress < self.config.MIN_PROGRESS_THRESHOLD:
   4     #     node.is_terminal = True # Disable this check
   5 
   6     value = (self.config.W_COH * v_coherence_norm +
   7              self.config.W_NOV * v_novelty +
   8              0 * v_progress) # self.config.W_PROG * v_progress

* Objective: 
      To validate the effectiveness of the "Encourage Substantial Progress" reward item. We anticipate that after removing this item, the model may be more prone to generating repetitive, redundant, or "circling in place" text, resulting in insufficient depth and information in the final concept. This validates the necessity of Phase Two (V_progress in Evaluation).

  4. Method Variant: w/o MCTS (Using Iterative Optimization as Alternative)

   * Modifications:
      This variant is more radical as it completely abandons the MCTS framework, utilizing our other designed components in a simpler manner. This can be implemented as an iterative self-optimization (Iterative Refinement):
       1. Start with the init_narrative from the root node.
       2. Call the expand function to generate K_EXPAND candidate next paragraphs.
       3. For these K candidates, calculate their comprehensive value respectively (using the simulate_and_evaluate function).
       4. Select the candidate with the highest value and append it to the current text.
       5. Repeat steps 2-4 until reaching a depth similar to MCTS.
   * Objective:
      To demonstrate that MCTS's "exploration-exploitation" mechanism and tree-like lookahead capability are superior to simple greedy iterative optimization. Although this variant also uses our value function, it lacks the systematic exploration of the entire search space that MCTS provides. If its performance is inferior to the complete version, it proves that the MCTS framework itself (i.e., the overall structure of Phase Two) is key to the algorithm's success, rather than just the well-designed value function.
  ---

Summary

Your ablation experiments should include the following 4 methods compared to the full version of CG-MCTS:

1. CG-MCTS w/o Directional Guidance: Verify the value of v_target guidance.
2. CG-MCTS w/o Novelty Reward: Verify the value of V_novelty reward.
3. CG-MCTS w/o Progress Reward: Verify the value of V_progress reward.
4. CG-MCTS w/o MCTS (Iterative Refinement): Verify the value of the MCTS search framework itself.

Through this set of designs, you can systematically and convincingly demonstrate the specific contributions brought by each carefully designed component in your method, which will constitute a very solid part of your paper.

# USER: 
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > I feel we can skip 4 for now, as it's quite similar to the comparison experiments with ReAct and ToT. For the other ablation studies, please help me create separate code, written in the ablation_experiment directory. Similar to what was done in run_experiments_cgmcts.py, but this time we'll only test the first 20 themes from test_themes_qwen1.7b-50.json.                                                                        │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯


# AGENT: 


✦ Okay, I understand. We will skip the fourth, more macroscopic ablation experiment and focus on implementing the first three ablation experiments targeting the guidance and reward terms at the code level.

First, I will create a new directory named ablation_experiment to store these experiment scripts.
 ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  Shell mkdir -p ablation_experiment (Create a directory named `ablation_experiment` to store the ablation study scripts.)  │
 ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Good, the directory has been created.

  Now, I will use compare_experiment/run_experiments_cgmcts.py as a template to create three new scripts, each corresponding to an ablation variant. All scripts will read
  test_themes_qwen1.7b-50.json and only process the first 20 themes.

  I will generate and write these three files at once.
 ╭────────────────────────────────────────────────────────╮
 │ ✓  ReadFile ..._experiment//run_experiments_cgmcts.py  │
 ╰────────────────────────────────────────────────────────╯
 ╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to ...xperiment//run_ablation_no_guidance.py                                                                                  │
 │                                                                                                                                                    │
 │    ... first 57 lines hidden ...                                                                                                                   │
 │    58                                                                                                                                              │
 │    59     llm = LLMInterface(model_name=absolute_model_path, device=mcts_cfg.DEVICE)                                                               │
 │    60                                                                                                                                              │
 │    61     print(f"Loading FAISS index from: {exp_cfg.FAISS_INDEX_PATH}")                                                                           │
 │    62     novelty_db = faiss.read_index(exp_cfg.FAISS_INDEX_PATH)                                                                                  │
 │    63                                                                                                                                              │
 │    64     print(f"Loading test themes from: {exp_cfg.TEST_THEMES_FILE}")                                                                           │
 │    65     with open(exp_cfg.TEST_THEMES_FILE, 'r', encoding='utf-8') as f:                                                                         │
 │    66         test_themes = json.load(f)[:20] # Process only the first 20 themes                                                                   │
 │    67                                                                                                                                              │
 │    68     all_results = []                                                                                                                         │
 │    69                                                                                                                                              │
 │    70     print(f"\n--- Starting Ablation Experiment: No Directional Guidance ---")                                                                │
 │    71                                                                                                                                              │
 │    72     for i, theme_obj in enumerate(test_themes):                                                                                              │
 │    73         print(f"\n--- Processing Theme {i+1}/{len(test_themes)} (ID: {theme_obj.get('id', 'N/A')}) ---")                                     │
 │    74                                                                                                                                              │
 │    75         final_output = run_cgmcts_search(theme_obj, llm, novelty_db, mcts_cfg)                                                               │
 │    76                                                                                                                                              │
 │    77         result = {                                                                                                                           │
 │    78             "id": theme_obj.get('id'),                                                                                                       │
 │    79             "theme": theme_obj.get('theme'),                                                                                                 │
 │    80             "elaboration": theme_obj.get('elaboration'),                                                                                     │
 │    81             "method": "ablation_no_guidance",                                                                                                │
 │    82             "output": final_output                                                                                                           │
 │    83         }                                                                                                                                    │
 │    84         all_results.append(result)                                                                                                           │
 │    85                                                                                                                                              │
 │    86         print(f"Saving intermediate results to {exp_cfg.OUTPUT_FILE}...")                                                                    │
 │    87         with open(exp_cfg.OUTPUT_FILE, 'w', encoding='utf-8') as f_out:                                                                      │
 │    88             json.dump(all_results, f_out, indent=2, ensure_ascii=False)                                                                      │
 │    89                                                                                                                                              │
 │    90     print("\n--- Ablation experiment finished successfully! ---")                                                                            │
 │    91     print(f"All results saved to {exp_cfg.OUTPUT_FILE}")                                                                                     │
 │                                                                                                                                                    │
 │ ✓  WriteFile Writing to ...experiment//run_ablation_no_novelty.py                                                                                  │
 │                                                                                                                                                    │
 │    ... first 57 lines hidden ...                                                                                                                   │
 │    58                                                                                                                                              │
 │    59     llm = LLMInterface(model_name=absolute_model_path, device=mcts_cfg.DEVICE)                                                               │
 │    60                                                                                                                                              │
 │    61     print(f"Loading FAISS index from: {exp_cfg.FAISS_INDEX_PATH}")                                                                           │
 │    62     novelty_db = faiss.read_index(exp_cfg.FAISS_INDEX_PATH)                                                                                  │
 │    63                                                                                                                                              │
 │    64     print(f"Loading test themes from: {exp_cfg.TEST_THEMES_FILE}")                                                                           │
 │    65     with open(exp_cfg.TEST_THEMES_FILE, 'r', encoding='utf-8') as f:                                                                         │
 │    66         test_themes = json.load(f)[:20] # Process only the first 20 themes                                                                   │
 │    67                                                                                                                                              │
 │    68     all_results = []                                                                                                                         │
 │    69                                                                                                                                              │
 │    70     print(f"\n--- Starting Ablation Experiment: No Novelty Reward ---")                                                                      │
 │    71                                                                                                                                              │
 │    72     for i, theme_obj in enumerate(test_themes):                                                                                              │
 │    73         print(f"\n--- Processing Theme {i+1}/{len(test_themes)} (ID: {theme_obj.get('id', 'N/A')}) ---")                                     │
 │    74                                                                                                                                              │
 │    75         final_output = run_cgmcts_search(theme_obj, llm, novelty_db, mcts_cfg)                                                               │
 │    76                                                                                                                                              │
 │    77         result = {                                                                                                                           │
 │    78             "id": theme_obj.get('id'),                                                                                                       │
 │    79             "theme": theme_obj.get('theme'),                                                                                                 │
 │    80             "elaboration": theme_obj.get('elaboration'),                                                                                     │
 │    81             "method": "ablation_no_novelty",                                                                                                 │
 │    82             "output": final_output                                                                                                           │
 │    83         }                                                                                                                                    │
 │    84         all_results.append(result)                                                                                                           │
 │    85                                                                                                                                              │
 │    86         print(f"Saving intermediate results to {exp_cfg.OUTPUT_FILE}...")                                                                    │
 │    87         with open(exp_cfg.OUTPUT_FILE, 'w', encoding='utf-8') as f_out:                                                                      │
 │    88             json.dump(all_results, f_out, indent=2, ensure_ascii=False)                                                                      │
 │    89                                                                                                                                              │
 │    90     print("\n--- Ablation experiment finished successfully! ---")                                                                            │
 │    91     print(f"All results saved to {exp_cfg.OUTPUT_FILE}")                                                                                     │
 │                                                                                                                                                    │
 │ ✓  WriteFile Writing to ...xperiment//run_ablation_no_progress.py                                                                                  │
 │                                                                                                                                                    │
 │    ... first 58 lines hidden ...                                                                                                                   │
 │    59                                                                                                                                              │
 │    60     llm = LLMInterface(model_name=absolute_model_path, device=mcts_cfg.DEVICE)                                                               │
 │    61                                                                                                                                              │
 │    62     print(f"Loading FAISS index from: {exp_cfg.FAISS_INDEX_PATH}")                                                                           │
 │    63     novelty_db = faiss.read_index(exp_cfg.FAISS_INDEX_PATH)                                                                                  │
 │    64                                                                                                                                              │
 │    65     print(f"Loading test themes from: {exp_cfg.TEST_THEMES_FILE}")                                                                           │
 │    66     with open(exp_cfg.TEST_THEMES_FILE, 'r', encoding='utf-8') as f:                                                                         │
 │    67         test_themes = json.load(f)[:20] # Process only the first 20 themes                                                                   │
 │    68                                                                                                                                              │
 │    69     all_results = []                                                                                                                         │
 │    70                                                                                                                                              │
 │    71     print(f"\n--- Starting Ablation Experiment: No Progress Reward ---")                                                                     │
 │    72                                                                                                                                              │
 │    73     for i, theme_obj in enumerate(test_themes):                                                                                              │
 │    74         print(f"\n--- Processing Theme {i+1}/{len(test_themes)} (ID: {theme_obj.get('id', 'N/A')}) ---")                                     │
 │    75                                                                                                                                              │
 │    76         final_output = run_cgmcts_search(theme_obj, llm, novelty_db, mcts_cfg)                                                               │
 │    77                                                                                                                                              │
 │    78         result = {                                                                                                                           │
 │    79             "id": theme_obj.get('id'),                                                                                                       │
 │    80             "theme": theme_obj.get('theme'),                                                                                                 │
 │    81             "elaboration": theme_obj.get('elaboration'),                                                                                     │
 │    82             "method": "ablation_no_progress",                                                                                                │
 │    83             "output": final_output                                                                                                           │
 │    84         }                                                                                                                                    │
 │    85         all_results.append(result)                                                                                                           │
 │    86                                                                                                                                              │
 │    87         print(f"Saving intermediate results to {exp_cfg.OUTPUT_FILE}...")                                                                    │
 │    88         with open(exp_cfg.OUTPUT_FILE, 'w', encoding='utf-8') as f_out:                                                                      │
 │    89             json.dump(all_results, f_out, indent=2, ensure_ascii=False)                                                                      │
 │    90                                                                                                                                              │
 │    91     print("\n--- Ablation experiment finished successfully! ---")                                                                            │
 │    92     print(f"All results saved to {exp_cfg.OUTPUT_FILE}")                                                                                     │
 ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

✦ Task completed. I have created three scripts in the ablation_experiment/ directory:
   1. run_ablation_no_guidance.py:
       * Ablation point: mcts_cfg.W_DIR = 0.0
       * Output: results_ablation_no_guidance.json
   2. run_ablation_no_novelty.py:
* Ablation point: mcts_cfg.W_NOV = 0.0
* Output: results_ablation_no_novelty.json
   3. run_ablation_no_progress.py:
       * Ablation points: mcts_cfg.W_PROG = 0.0 and mcts_cfg.MIN_PROGRESS_THRESHOLD = 0.0
       * Output: results_ablation_no_progress.json

  Each script will load test_themes_qwen1.7b-50.json, but only process the first 20 themes, and save their respective results to corresponding JSON files. You can enter the ablation_experiment directory and run them sequentially.