
# USER: 
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > You are a senior AI researcher. There is a draft of a paper's idea in real_methodV2.txt.                                                                                                     │
│    cg_mcts_qwen.py is its corresponding implementation.                                                                                                                                         │
│                                                                                                                                                                            │
│    To prove the effectiveness of our method, I designed the following experimental procedure:                                                                                                                      │
│                                                                                                                                                                            │
│      1. Selection of comparison methods: Establish three "bullseyes"                                                                                                                                     │
│                                                                                                                                                                            │
│      The CG-MCTS we propose is an algorithm that enhances the novelty and coherence of generated text through intelligent search during inference-time. Therefore, our comparative experiments must be designed around these core characteristics. We select           │
│      three types of methods as baselines (Baselines):                                                                                                                                       │
│                                                                                                                                                                            │

│      Category 1: Standard Decoding Baselines                                                                                                                            │
│       * Purpose: To demonstrate that our complex search mechanism outperforms the "out-of-the-box" generation of LLMs without any optimization. This is the most fundamental baseline.                                                                      │
│       * Specific Algorithms:                                                                                                                                                          │
│           1. Top-p (Nucleus) Sampling: This is currently the most commonly used and most effective standard method for open-ended text generation. At each step, it samples from a dynamically determined vocabulary set where the sum of probabilities exceeds a threshold p. We need to set a reasonable p value (e.g., 0.9) and report it.                                                                                                                      │
│                                                                                                                                                                            │

│      Category 2: Enhancement Methods Based on Prompt Engineering (Prompt Engineering Baselines)                                                                                         │
│       * Purpose: To demonstrate that our algorithm's advantages do not merely come from "better thinking prompts" but from the systematic search process. These methods do not alter the algorithm but instead stimulate the model's creativity through carefully designed prompts. This is a very strong opponent that must be taken seriously.                                                                                               │
│       * Specific Algorithms:                                                                                                                                                          │
│           2. Zero-Shot Chain-of-Thought (CoT): After inputting the topic, simply add the phrase "Let's think step by step." or "Please think step by step" to guide the model to perform more detailed reasoning.                           │
│           3. Self-Refine (Self-Refine/Reflection): This is a multi-step prompting process that simulates iterative optimization without "tree search".                                                                        │
│               * Step 1 (Generation): "Based on topic [X], generate an initial scientific concept."                                                                                     │
│               * Step 2 (Reflection): "Please evaluate the concept generated in the previous step, pointing out its main defects in terms of 'novelty' and 'feasibility."                                                               │
│               * Step 3 (Rewrite): "Based on the above reflections, please rewrite and improve this concept to make it more novel and more feasible."                                                                             │
│               * This process can be iterated for 2-3 rounds to match the computational cost of our MCTS.                                                                                     │
│           4. Tree of thoughts(ToT)                                                                                                                                         │
│    (                                                                                                                                                                       │
│    ```                                                                                                                                                                     │
│    Algorithm introduction is as follows:                                                                                                                                                          │
│           When using ToT, different tasks requires defining the number of candidates and the number of thoughts/steps. For instance, as demonstrated in the paper, Game of │
│     24 is used as a mathematical reasoning task which requires decomposing the thoughts into 3 steps, each involving an intermediate equation. At each step, the best b=5  │
│    candidates are kept.                                                                                                                                                    │
│                                                                                                                                                                            │
│           Hulbert (2023) has proposed Tree-of-Thought Prompting, which applies the main concept from ToT frameworks as a simple prompting technique, getting the LLM to    │
│    evaluate intermediate thoughts in a single prompt. A sample ToT prompt is:                                                                                              │
│                                                                                                                                                                            │
│    Imagine three different experts are answering this question.                                                                                                            │
│    All experts will write down 1 step of their thinking,                                                                                                                   │
│    then share it with the group.                                                                                                                                           │
│    Then all experts will go on to the next step, etc.                                                                                                                      │
│    If any expert realises they're wrong at any point then they leave.                                                                                                      │
│    The question is...                                                                                                                                                      │
│    ```                                                                                                                                                                     │
│    )                                                                                                                                                                       │
│                                                                                                                                                                            │
│      Category 3: Simplified Search/Generation Algorithms (Simplified Algorithmic Baselines)                                                                                                        │
│       * Purpose: To demonstrate the superiority of our MCTS tree structure and UCT selection strategy, rather than just the effectiveness of our value function. This is equivalent to a "degraded version" of our own algorithm.                                                    │
│       * Specific Algorithms:                                                                                                                                                          │
│           5. Beam Search with Custom Value Function:                                                                                            │
│               * In the first step of generation, generate K (e.g., K=5) most probable "narrative blocks".                                                                                                       │
│               * Use the hybrid value function `V(seq)` from our CG-MCTS to score these K candidates.                                                                                             │
│               * Select the candidate with the highest score and use it as the prefix for the next generation step.                                                                                                       │
│               * Repeat this process until the desired length is reached.                                                                                                                               │
│               * This method uses our value function but discards the "exploration-exploitation" balance and tree-like lookahead capability of MCTS, making it a perfect subject for an "ablation study".                                                    │
│                                                                                                                                                                            │
│      ---                                                                                                                                                                   │
│                                                                                                                                                                            │
│      Regarding the first and second categories, compared to other methods, what is the specific experimental design?                                                                                                                │
│      For example, what kind of prompt should be given to each model?                                                                                                                                    │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯


# AGENT: 


✦ Okay, researcher. This is a very rigorous and clear experimental design approach. To transform this design into an executable plan, we need to determine specific prompts and implementation details for the first and second types of baseline methods (Baselines).

The core principle is fair comparison: all methods should be evaluated under the same "computational budget" (roughly equivalent number of LLM calls or total tokens) and with the same input.

Assuming that in your cg_mcts_qwen.py implementation, the CG-MCTS algorithm calls the LLM an average of N times per run (for example, N is approximately 10-20 times, depending on your MCTS parameter settings), we will use this as a reference to design the computational requirements for other experiments.
  ---

General Experimental Setup

1. Input Topic: We need a list of topics for generating scientific ideas. For example:
   * [Topic] = "Using large language models to predict protein misfolding"
   * [Topic] = "Developing a reinforcement learning system that can monitor urban traffic flow in real-time and dynamically adjust traffic signals"
   * [Topic] = "Designing a novel molecular structure based on generative adversarial networks for targeted anti-cancer drugs"
2. Base LLM: All experiments use the same base LLM (for example, Qwen-Max, Qwen-1.8B-Chat, etc., as determined by your cg_mcts_qwen.py).
3. Decoding Parameters: To ensure generation diversity, we uniformly use top-p=0.9 and a moderate temperature=0.7.
  ---


Category 1: Standard Decoding Methods (Standard Decoding Baselines)
  1. Top-p (Nucleus) Sampling

   * Purpose: To establish a most basic performance baseline.
   * Computational budget: 1 LLM call.
   * Prompt design: This is the most direct and simple prompt, without providing any additional guidance.

```
   1. Please generate a novel and scientifically feasible research idea based on the following topic.
   2 
   3 Topic: [Topic]
```

   * Experimental Procedure:
       1. Combine the above prompt with the specific [topic].
       2. Use top-p=0.9, temperature=0.7 to make a single call to the base LLM.
       3. Record the generated text as the final output.
  ---

Category 2: Enhancement Methods Based on Prompt Engineering (Prompt Engineering Baselines)
  2. Zero-Shot Chain-of-Thought (CoT)

* Purpose: Test whether simple "step-by-step thinking" prompts can match our complex search algorithms.
* Computational budget: 1 LLM call.
* Prompt design: Add a guiding sentence after the basic prompt.

```
1. Please generate a novel and scientifically feasible research concept based on the following topic:
   2 

   3 Topic: [Topic]
   4 

   5 Please think step by step, then provide your final concept.
```

   * Experimental procedure:
       1. Combine the above prompt with a specific [topic].
       2. Make a single call using top-p=0.9, temperature=0.7.
       3. The model will first output the step-by-step thinking process, followed by the final concept. The entire output (including the thinking process and final concept) needs to be recorded for qualitative analysis, but only the final concept part is used for quantitative comparison with the final outputs of other methods.

  3. Self-Refine (Self-Refinement/Reflection)

   * Purpose: To simulate an iterative optimization process without tree search, serving as a strong opponent to CG-MCTS.
   * Computational budget: 3 LLM calls (Generate -> Reflect -> Rewrite), which is computationally comparable to the multiple node expansions in MCTS.
   * Prompt design: This is a three-stage process with independent prompts for each stage.

  Step 1: Generate initial concept (Generate)
```
   1. Based on the following topic, generate an initial research concept.
   2 
   3 Topic: [Topic]

   Step 2: Reflection and Criticism (Reflect)

   1 You are a top-tier scientific reviewer. Please evaluate the following research proposal and clearly identify any potential flaws or areas for improvement in terms of "Novelty" and "Feasibility". Please analyze with a critical eye.
   2 
'   3 Research Framework:'.Please translate.
   4 """

5 [Insert here the preliminary concept generated in step 1]
   6 """
   7 
   8 Your Evaluation:
```

  Step 3: Rewrite and Improve (Rewrite)

```
    1 You are an outstanding scientist. Please carefully read the following preliminary concept and its evaluation. Then, based on these evaluations and reflections, rewrite and significantly improve this research concept to make it more novel and feasible.
    2 
'    3 Preliminary Concept:'.Please translate.
    4 """
    5 [insert the preliminary concept generated in step 1 here]
    6 """
    7 

'    8 Evaluation of the concept:'
    9 """
10 [Insert the evaluation text generated in step 2 here]
   11 """
   12 
   13 Please provide your improved final research concept:
```

   * Experimental process:
       1. Execute step 1, save the output [Initial Concept].
       2. Execute step 2, input [Initial Concept] into the prompt, save the output [Evaluation Text].
       3. Execute step 3, input both [Initial Concept] and [Evaluation Text] into the prompt, the output of which is the final result of the Self-Refine method.
       4. Record the outputs of all three steps for subsequent analysis.
  4. Tree of Thoughts (ToT) Prompting

* Purpose: In a single call, guide the model through prompt to simulate a multi-expert, multi-path thinking mode.
* Computational Budget: 1 LLM call (but the prompt is longer, with more intermediate steps generated, potentially resulting in a larger total token count).
* Prompt Design: Directly use the Hulbert (2023) template you provided and combine it with our task.

```
1 Imagine that three top experts from different fields are jointly answering this question.
2 All experts will first write down the first step of their thinking process, then share with each other.
3 Next, all experts will continue to think about the next step, and so on.
4 At any time, if an expert realizes their line of thinking is wrong, they will withdraw from the discussion.
   5 
6 Now, the problem that needs to be solved is:
7 "Please generate a novel and scientifically feasible research concept based on the following topics."
   8 
9 Topic: [Topic]"
```

   * Experimental Procedure:
       1. Combine the above prompt with the specific [Topic].
       2. Use top-p=0.9, temperature=0.7 for a single call.
       3. The LLM will generate a simulated process where three experts progressively reason and filter ideas. Record the complete output. The conclusion section (usually at the end of the text) is the final output of this method.

  Summary and Next Steps

  Through the above design, you can systematically generate results for each baseline method.

   * Data Collection: For each input [Topic], you will obtain final ideas from Top-p, CoT, Self-Refine, ToT, and your own CG-MCTS, totaling 5 final ideas. For CoT, Self-Refine, and ToT, you should also save their intermediate thinking/iterative processes, which is valuable for qualitative analysis in the paper.
   * Evaluation: After collecting all outputs, the next step is to design an evaluation plan. This typically includes:
       * Human Evaluation: Please have domain experts or use crowdsourcing platforms to score each idea's "novelty," "feasibility," "coherence," and "overall quality" (for example, on a scale of 1-5). This is the most important evaluation method.
       * Automatic Evaluation:
           * Novelty: Can calculate the semantic similarity between generated ideas and training data or existing paper abstracts (the less similar, the more novel it might be).
           * Diversity: Evaluate the differences between results generated by the same method across different runs (e.g., Self-BLEU).

  This detailed experimental plan provides you with a clear roadmap for your next work. Wishing you success in your experiment!

# USER: 
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮

│ > I believe AutomatedThemeGenerator is also an important component of our model, isn't it? Then where should the themes for other comparison models come from? │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ You have raised a crucial question that directly relates to the fairness of the experiment and the completeness of your paper's narrative.

Yes, if AutomatedThemeGenerator is an indispensable part of your methodology, then it is part of your overall contribution. In this case, we must carefully design the experiment, otherwise there will be an unfair "apples to oranges" comparison.

Your question "Where should the themes for other comparison models come from?" is key. The answer is: these themes must also be generated through a "baseline" automated method with comparable computational cost.

We cannot manually design themes for other models because this would introduce bias and fail to demonstrate the true value of your AutomatedThemeGenerator—the ability to automatically discover and determine a valuable, specific research topic from a higher-level concept (such as a broad domain).

Therefore, I suggest dividing your experiment into two distinct parts:

  ---

Experiment 1: Ablation Study to Verify the Superiority of the `CG-MCTS` Algorithm

The purpose of this experiment is to answer: "Given the exact same topics, is my CG-MCTS generation algorithm better than other algorithms?" This can independently prove the value of your core search algorithm.

Experimental Procedure:

 1. Generate a fixed "Topic Dataset":
     * First, use your AutomatedThemeGenerator to generate a batch (e.g., 50 or 100) of high-quality, diverse research topics.
     * Save these topics to form a static benchmark theme dataset for all subsequent comparisons.

 2. Conduct a fair comparison:
     * For each topic in this benchmark theme dataset, apply all the methods we previously discussed:
         * Your CG-MCTS
           * Top-p Sampling
           * Zero-Shot CoT
           * Self-Refine
           * ToT Prompting
           * Beam Search with Custom Value Function
*   This way, the input for all algorithms is completely identical, and any differences in performance can be more reliably attributed to the generation algorithm itself (CG-MCTS vs. others).
    
    This experiment is crucial for demonstrating the innovation of CG-MCTS itself.

  ---

  Experiment 2: End-to-End System Performance Evaluation

  The purpose of this experiment is to answer: "If starting from a very broad research field, can my entire system (AutomatedThemeGenerator + CG-MCTS) generate better final research ideas than other methods?"

  This can demonstrate the practical application value of your entire workflow.

  Experiment Process:

   1. Define High-Level Research Fields:
       * Identify a set of broad domains, for example:
           * [Field] = "AI for Drug Discovery"
           * [Field] = "AI for Climate Change Modeling"
           * [Field] = "Human-Computer Interaction with LLMs"

   2. Design a "Simple Theme Generator" for baseline methods:
       * To provide topics for other models, we need a simple, single-call "baseline theme generator". This can be implemented through a simple prompt.

      Baseline Theme Generator Prompt:

   1     You are a visionary AI research strategist. Based on the broad research field below, propose a specific, novel, and potentially valuable research topic. Please only return the topic itself, without any additional explanation.
   2 
   3     Research Domain: [Domain]

   3. Conduct end-to-end comparison:
       * For each [Domain]:
           * Your method:
               1. [Domain] -> AutomatedThemeGenerator -> [Theme A]
               2. [Theme A] -> cg_mcts_qwen.py -> [Final Concept A]
           * Baseline method (using Self-Refine as an example):
               1. [Domain] -> (using the above "baseline theme generator" prompt) -> [Theme B]
               2. [Theme B] -> (applying the three steps of Self-Refine) -> [Final Concept B]

   4. Evaluation:
       * At this stage, you are comparing the quality of [Final Concept A] and [Final Concept B].
       * Since [Theme A] and [Theme B] are likely different, the evaluation here focuses more on the overall quality, novelty, and inspiration of the final output, rather than a direct confrontation under the same theme.

  Summary and Recommendations

  You should report the results of both experiments in your paper.

   * Experiment 1 demonstrates the superiority of your core algorithm (`CG-MCTS`) in refining ideas.
   * Experiment 2 proves the end-to-end capability of your complete system in starting from scratch, exploring, and deepening a research direction, highlighting the value of AutomatedThemeGenerator.

  This experimental design is not only fair and rigorous but also allows your contributions to be presented more clearly and comprehensively, making your paper more solid and credible.

# USER: 
╭─────────────────────────────────────────────────────────────────────────────────╮
│  > Please re-read my code and analyze: Is AutomatedThemeGenerator an important part of my methodology?  │
╰─────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 



✦ Okay, I will re-read and analyze the code of cg_mcts_qwen.py to determine whether AutomatedThemeGenerator is its inherent and important component part.
 ╭──────────────────────────────╮
 │ ✓  ReadFile cg_mcts_qwen.py  │
 ╰──────────────────────────────╯

✦ After a detailed analysis of the cg_mcts_qwen.py code, I can draw a clear conclusion:

  Yes, `AutomatedThemeGenerator` is a very important and tightly integrated component of your methodology.

  My analysis is based on the following points:

   1. Clear class definition: The code contains a complete class AutomatedThemeGenerator (line 110). This class has its own initialization method __init__, as well as core functional methods _build_knowledge_map and
      generate_theme. This indicates that it is a well-designed functional module, not just an auxiliary function.

   2. Complex internal logic: The generate_theme method of AutomatedThemeGenerator (line 125) implements a complex, multi-stage process:
       * Phase 0: Building a "knowledge map" through K-Means clustering.
       * Phase 1 & 2: Finding moderately distant, unrelated concepts between concept clusters.
       * Phase 3: Creatively "synthesizing" these two concepts using an LLM to generate a novel theme and elaboration.
      This is far more than a simple prompt; it is itself an innovative exploratory theme generation algorithm based on vector space retrieval and LLM synthesis.

   3. Explicitly called in the main program: In the main execution block if __name__ == "__main__" (starting from line 430), the program flow clearly demonstrates this:
       * First, instantiate AutomatedThemeGenerator:
   1         theme_generator = AutomatedThemeGenerator(llm_interface, novelty_documents, vectors, cfg)
* Then, call its method to generate the topic:
   1         automated_theme, theme_elaboration, concept_original_list = theme_generator.generate_theme()
Finally, pass the generated themes automated_theme and theme_elaboration as input to the constructor of the CG_MCTS class:
   1         mcts = CG_MCTS(llm_interface, novelty_db=novelty_db, config=cfg, theme=automated_theme, init_narrative = f"{automated_theme}\\n\\n{theme_elaboration}")

  Conclusion and Implications for Experimental Design:

  Your cg_mcts_qwen.py script implements a two-stage innovative process:
   1. Theme Discovery: Handled by AutomatedThemeGenerator, which discovers novel, cross-domain connection points from existing knowledge bases and forms a specific research topic.
   2. Idea Elaboration: Handled by CG_MCTS, which receives the topic generated in the first stage and systematically and in-depth develops it into a coherent, detailed research concept through Monte Carlo Tree Search.

  Therefore, your previous intuition was completely correct. Simply comparing CG_MCTS with other baseline methods that work under "given topics" is incomplete. This would overlook the innovation of the first stage in your method.

  Final Recommendation:

  I strongly recommend that you adopt the two-layer experimental design we previously discussed:

   * Experiment 1 (Isolated Validation of `CG_MCTS`): Use AutomatedThemeGenerator to pre-generate a batch of topics, then use this fixed set of topics to fairly compare CG_MCTS
     and all other baseline algorithms. This would demonstrate that your search algorithm is superior.
   * Experiment 2 (End-to-end System Evaluation): Starting from a broader [domain], compare your complete system (AutomatedThemeGenerator + CG_MCTS) with the combination of "Simple Topic Generator + Baseline Algorithms". This would demonstrate that your entire methodology is more powerful in practical applications.

  This code analysis confirms that your research contribution is dual, and your experimental design must reflect this in order to maximize the value of your work.

# USER: 
╭─────────────────────────────────────────────────────╮
│  > For Experiment 1, is it necessary to provide "Elaboration" to other comparison models?  │
╰─────────────────────────────────────────────────────╯

# AGENT: 



✦ This is an excellent question that once again touches upon the core of experimental fairness.

  The answer is: Yes, it must be provided.

  The reasons are as follows:

  Based on our analysis of your code cg_mcts_qwen.py, your CG_MCTS algorithm, during initialization, receives not just a brief theme, but a complete initial narrative (init_narrative) that includes both `theme` and `elaboration`.

```
   1 # Your code (line 443)
   2 mcts = CG_MCTS(...,
   3                theme=automated_theme,
   4                init_narrative = f"{automated_theme}\\n\\n{theme_elaboration}")
```

init_narrative becomes the content of the root node of the MCTS search tree. This means that before your algorithm begins searching, it already possesses the rich contextual information provided by elaboration (including problem background, potential methods, and novelty points).

To conduct a fair "apples-to-apples" comparison, all comparison methods must start working from exactly the same starting point. If only short themes are given to other models while your model receives theme + elaboration, then your model would have a significant information advantage, and the experimental results would be completely unconvincing.

Therefore, in Experiment 1, the (theme, elaboration) pair generated by AutomatedThemeGenerator should be treated as an indivisible input unit.

  ---

How to integrate Elaboration into the base model's Prompt

You need to update our previously designed Prompt as follows:

1. Top-p Sampling (Updated)

```
  1. Please expand and deepen the following research concept to generate a more complete version.
    2 
    3 ---

4 Preliminary elaboration of research conception:
5 Topic: [Topic]
    6 
    7 阐述: [Elaboration]
    8 ---
    9 
   10 请继续撰写后续部分，提出具体的研究步骤、实验设计或理论分析。
```

  2. Zero-Shot CoT (更新后)

```
    1 请根据以下研究构想，对其进行扩展和深化，生成一个更完整的版本。
    2 
    3 ---
    4 研究构想初步阐述:
    5 主题: [主题]
    6 
    7 阐述: [Elaboration]
    8 ---
    9 
   10 请逐步思考，然后撰写后续部分，提出具体的研究步骤、实验设计或理论分析。
```

  3. Self-Refine (更新后)

  这里的调整需要更小心一些，因为第一步不再是“生成”，而是“续写”。

  第1步：初步扩展 (Expand)

```
    1 这是我们研究构想的初步阐述。请为其撰写第一个具体的后续部分，例如，详细的方法论或第一个关键实验的设计。
    2 
    3 ---
    4 研究构想初步阐述:
    5 主题: [主题]
    6 
    7 阐述: [Elaboration]
    8 ---
    9 
   10 你的续写:
```

  第2步：反思与批判 (Reflect)

```
    1 你是一位顶尖的科学评审人。请评估以下研究计划的**整体连贯性**以及**新补充部分**的“新颖性”和“可行性”。请以批判性的眼光进行分析。
    2 
    3 ---
    4 原始构想:
    5 主题: [主题]
    6 阐述: [Elaporation]
    7 
'    8 新补充的部分:'.Please translate. 
    9 """
   10 [此处插入第1步生成的续写内容]
   11 """
   12 ---
   13 

   14 Your evaluation:
```

  Step 3: Rewrite and Improve (Rewrite)

  This prompt remains basically unchanged, but the context is richer.

```
    1. You are an outstanding scientist. Please carefully read the following preliminary concept, a continuation section, and its evaluation. Then, based on these evaluations and reflections, rewrite and improve this "continuation section" to make it connect more naturally with the original concept, with more novel content, and more feasible.
    2 
    3 ---
4 Original concept:
5 Theme: [主题]
6 Elaboration: [Elaporation]
    7 
'    8 初步续写的部分:'.Please translate. 
    9 """
   10 [此处插入第1步生成的续写内容]
   11 """
   12 
'   13 Evaluation of the continuation part:'.
   14 """
   15 [Insert the evaluation text generated in step 2 here]
   16 """
   17 ---
   18 

19 Please provide your improved final continuation part:
```

In this way, all methods start from the same amount of information, and Experiment 1 will be able to truly and fairly measure the unique advantages of CG-MCTS in terms of "deepening narrative" and "intelligent search".

# USER: 

╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Because AutomatedThemeGenerator is also part of the algorithm I proposed. What should I do if I'm concerned that providing both theme and elaboration in full will result in insignificant performance of my MCTS model?  │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ The concern you've raised is very practical and represents a common trade-off in rigorous scientific research: how to clearly demonstrate the unique value of your core algorithm while ensuring experimental fairness?

Your concern is: if the elaboration itself contains a large amount of information, then even simple baseline methods (such as Top-p Sampling) might perform "well enough," thereby narrowing the gap with your CG-MCTS, reducing the "signal-to-noise ratio" of the experimental results, and making your algorithm's advantage less "significant".

This is a very reasonable concern. We must absolutely not sacrifice experimental fairness (i.e., giving different models different inputs) just to highlight the effectiveness of CG-MCTS, as this would be a major flaw in the paper. However, we can address this issue through more sophisticated experimental design and hierarchical argumentation.

Rather than conducting just one experiment, it would be better to design a "storyline" that includes multiple experiments, each demonstrating one aspect of your contribution. I recommend adopting the following "three-layer argumentation" strategy:

  ---

First Layer of Argumentation: Demonstrating the Absolute Advantage of `CG-MCTS` in "From Scratch" Construction Capabilities

This experiment is specifically designed to address your concerns. We create a "low-information" environment to highlight CG-MCTS's search and planning capabilities.

 * Experiment Name: Ideation Generation from Sparse Input (Generation from Sparse Input)
 * Experimental Design:
     1. Input: Only use the short theme generated by AutomatedThemeGenerator. No `elaboration` is provided to any models (including your CG-MCTS).
     2. Your Model: You need to slightly modify your code so that in this experiment, the init_narrative passed to CG_MCTS contains only the `theme`.
1         # Modification only used in this experiment
   2         mcts = CG_MCTS(..., theme=automated_theme, init_narrative=automated_theme)
       3. Baseline model: Using the prompt we originally designed for "theme-only".
   * Expected results: In this low-information, high-openness scenario, baseline methods without planning capabilities (such as Top-p) are likely to generate divergent and incoherent content. While your CG-MCTS, guided by its value function and UCT search, can systematically construct a more profound and structured conception. Here, your model's advantages should be very pronounced.
   * Role in the paper: Demonstrate the core value of the CG-MCTS algorithm itself, namely, its ability to perform structured thinking and planning in open-ended exploration.
  ---

  Second Layer of Argument: Proving the Relative Advantage of `CG-MCTS` in "Deepening from 1 to N" Capabilities

  This is the most "apple-to-apple" comparison we discussed previously.

   * Experiment Name: Elaboration from Rich Context
   * Experiment Design:
       1. Input: Use theme + elaboration as the common input for all models.
       2. Your Model: Use the existing init_narrative settings in your code.
       3. Baseline Model: Use the updated prompt for "including elaboration" from our previous discussion.
   * Expected Results: As you are concerned, all models' performance will improve, and the gap between your CG-MCTS and the baseline may narrow. But that's okay! Even if the gap narrows, as long as it remains statistically significant, that's sufficient.
   * Role in the Paper:
       1. This is the most rigorous ablation study, proving that even under favorable information-rich starting conditions, CG-MCTS's intelligent search can still bring additional, measurable value.
       2. It simulates the internal process of your complete methodology (where ThemeGenerator's output is used by MCTS), proving the necessity of the second stage.
       3. You can analyze where exactly CG-MCTS "wins" at this step (e.g., generated subsequent steps are more logical, more comprehensive considerations, etc.), enabling more in-depth qualitative analysis.
  ---

Third Layer of Argumentation: Proving the End-to-End Superiority of the Entire System

This is what we previously referred to as "Experiment 2".

* Experiment Name: End-to-End Research Concept Generation
* Experimental Design: Starting from a broad [domain], compare your complete system (AutomatedThemeGenerator + CG_MCTS) with the combination of "Simple Topic Generator + Baseline Algorithm".
* Role in the Paper: To demonstrate the practical value and comprehensive performance of your proposed complete solution.

Summary

Don't be afraid if CG-MCTS shows "insignificant" results in a particular experiment. A strong research paper does not rely on a single, exaggerated number, but on a rigorous, comprehensive, and logically structured argumentation process.

Through these three layers of argumentation, you can build a very solid story:
1. "First, we proved that when information is sparse, our core algorithm CG-MCTS has a powerful capability to build from scratch." (First layer experiment)
2. "Next, we proved that when simulating the actual process of our complete system, even with abundant initial context, CG-MCTS can still provide significant deepening and optimization effects." (Second layer experiment)
3. "Finally, we proved that our end-to-end system comprehensively outperforms baseline methods in the task of generating complete concepts from high-level domains." (Third layer experiment)

This layered, progressively deepening approach to argumentation is far more powerful than a single comparison that might show insignificant results, and it better reflects the completeness and rigor of your work.

# USER: 
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > I have now collected the novelty_db and vectors. When conducting comparative experiments, what specific data should I select? For example, how many generated automated_theme should I choose as a new dataset? I need to balance the workload (my time is quite │
│    tight) while presenting a complete and convincing experiment to the reviewers.                                                                                                                       │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 





✦ This is a very practical and important question that concerns how to maximize the persuasiveness of research findings within a limited time. In academic research, presenting "complete and persuasive" experiments does not always require massive amounts of data, but rather requires intelligently selecting data and conducting in-depth analysis.

Given your time constraints, I recommend adopting a layered and efficient strategy that focuses your workload where it will have the greatest impact.

Core Recommendation: Combine Quantitative and Qualitative Approaches

Don't try to analyze massive amounts of data indiscriminately. Instead, divide your dataset into two parts:

 1. Core dataset for "quantitative evaluation": A medium-sized dataset used to run all experiments, collect performance metrics (primarily human evaluation scores), and conduct statistical significance tests.
 2. Curated case set for "qualitative analysis": Select a small number (2-3) of the most representative examples from the above dataset for in-depth and detailed analysis in your paper, showing reviewers why and how your model performs better than other models.
  ---


1. Quantitative Evaluation of Dataset: Size and Selection

   * Recommended Size: 30 to 50 subjects.
   * Rationale:
       * Persuasiveness: 30 samples is the minimum sample size considered sufficient by many statistical tests (such as t-tests) to draw meaningful conclusions. 50 samples is more conservative, sufficient to draw clear performance comparison charts (such as box plots) and calculate statistically significant p-values. For top-tier conference papers, this magnitude is completely acceptable.
       * Workload: Assuming you have 6 comparison methods (including your own), 50 subjects would require 50 * 6 = 300 experimental runs. This is a considerable but manageable workload that can be completed in one to two weeks. If time is tighter, 30 is the absolute minimum.
   * Selection Method: Must be random!
       1. First, have your AutomatedThemeGenerator generate a pool of themes larger than your required quantity, for example, 200.
       2. Then, randomly select 30 or 50 themes from these 200.
       3. Why must it be random? This avoids any accusations of "cherry-picking" and ensures your results are fair and representative of the algorithm's general performance on unknown data.
  ---

2. Qualitative Analysis Case Collection: Selection Strategy

* Recommended scale: 3 to 5 topics.
* Rationale:
    * Depth over breadth: Reviewers don't have time to read dozens of cases. A few carefully selected and thoroughly analyzed cases are far more powerful than a pile of raw, unanalyzed outputs.
    * Space limitations: The main body of a paper can typically only accommodate detailed analysis of 1-2 cases, with additional cases placed in the appendix.
* Selection method: Must be carefully chosen! After running 30-50 quantitative experiments, select the most illustrative cases from the results. It is recommended to include the following types:
    1. A "decisive victory" case: In this case, your CG-MCTS generates stunning results, while all baseline methods perform poorly or even fail. This is the best example to attract attention and showcase the upper limit of your model.
    2. A "narrow victory" case: In this case, strong baselines like Self-Refine or ToT also generate good ideas, but your CG-MCTS is "slightly better" in terms of logical coherence, depth of detail, or novelty of perspective. This is used to demonstrate the superiority of your model in subtle aspects, reflecting its ability to "refine" and "deepen".
    3. A case that reveals algorithmic characteristics: For example, a case that clearly demonstrates the "exploration-exploitation" process of MCTS, or how the value function guides the search toward a more promising direction. You can use debug_trace to analyze this process.
  ---

Your specific action plan

1. Generate: Run AutomatedThemeGenerator to create a theme pool containing 200 (theme, elaboration) pairs and save it.
2. Sample: Randomly select 50 themes from the pool to form your quantitative evaluation dataset.
3. Run: Run all your designed experiments (including different versions of the three-layer argumentation) for these 50 themes. This is the most time-consuming part, so you can start it first.
4. Evaluate: Conduct human evaluation and scoring on the 50 * N final outputs (e.g., novelty/feasibility/coherence, on a scale of 1-5). This forms the basis of your quantitative analysis.
5. Analysis and Selection:
   * Calculate the average score for each method on the 50 samples and perform significance tests (e.g., paired t-tests between your method and each baseline).
   * When reviewing the results, select 3-5 themes most suitable for case study analysis according to the strategy mentioned above.
6. Writing:
   * In the experiments section of your paper, first present quantitative result charts based on the 50 samples, using data and p-values to demonstrate that your model is significantly superior to others overall.
   * Then, use one or two dedicated subsections to conduct in-depth analysis of the selected qualitative cases, showing specifically where and why your model performs better.

✦ This is a very practical and important issue concerning how to maximize the persuasiveness of research findings within limited time. In academic research, presenting "complete and persuasive" experiments does not always require massive amounts of data, but rather smart data selection and in-depth analysis.

Considering your tight timeline, I recommend adopting a layered, efficient strategy that concentrates effort where it will have the greatest impact.

Core Recommendation: Combining Quantitative and Qualitative Approaches

Do not attempt to analyze massive amounts of data indiscriminately. Instead, divide your dataset into two parts:

1. Core dataset for "quantitative evaluation": A moderately-sized dataset for running all experiments, collecting performance metrics (mainly human evaluation scores), and conducting statistical significance tests.
2. Selected case studies for "qualitative analysis": Select a small number (2-3) of the most representative examples from the above dataset to conduct in-depth, detailed analysis in your paper, showing reviewers why and how your model performs better than others.
  ---

1. Quantitative Evaluation Dataset: Size and Selection

   * Suggested size: 30 to 50 subjects.
   * Rationale:
       * Persuasiveness: 30 samples is the minimum sample size considered adequate by many statistical tests (such as t-tests) to draw meaningful conclusions. 50 is more conservative, sufficient to create clear performance comparison charts (such as box plots) and calculate statistically significant p-values. For top-tier conference papers, this magnitude is entirely acceptable.
       * Workload: Assuming you have 6 comparison methods (including your own), 50 subjects would require 50 * 6 = 300 experimental runs. This is a substantial but manageable workload that can be completed within one to two weeks. If time is more constrained, 30 is the absolute minimum.
   * Selection method: Must be random!
       1. First, have your AutomatedThemeGenerator generate a pool of themes larger than your required quantity, for example, 200.
       2. Then, randomly select 30 or 50 themes from these 200.
       3. Why must it be random? This avoids any accusations of "cherry-picking" and ensures your results are fair and representative of the algorithm's general performance on unknown data.
  ---

2. Qualitative Analysis Case Collection: Selection Strategy

* Recommended scale: 3 to 5 topics.
* Rationale:
    * Depth over breadth: Reviewers don't have time to read dozens of cases. A few carefully selected and thoroughly analyzed cases are far more powerful than a pile of unanalyzed raw outputs.
    * Space limitations: The main body of a paper can typically only accommodate detailed analysis of 1-2 cases, with more potentially placed in the appendix.
* Selection method: Must be carefully selected! After running 30-50 quantitative experiments, select the most illustrative cases from the results. It is recommended to include the following categories:
    1. A "decisive victory" case: In this case, your CG-MCTS generates stunning results, while all baseline methods perform poorly or even fail. This is the best example to attract attention and showcase the model's upper limits.
    2. A "narrow victory" case: In this case, strong baselines like Self-Refine or ToT also generate good ideas, but your CG-MCTS "outperforms" them in terms of logical coherence, depth of detail, or novelty of perspective. This is used to demonstrate your model's superiority in subtle aspects, reflecting its "refinement" and "deepening" capabilities.
    3. A case that reveals algorithm characteristics: For example, a case that clearly demonstrates the "exploration-exploitation" process of MCTS, or how the value function guides the search toward a more promising direction. You can analyze this process using debug_trace.
  ---


Your specific action plan

1. Generate: Run AutomatedThemeGenerator to generate a theme pool containing 200 (theme, elaboration) pairs and save it.
2. Sampling: Randomly select 50 themes from the theme pool to form your quantitative evaluation dataset.
3. Run: Run all your designed experiments (including different versions of the three-layer argumentation) for these 50 themes. This part is the most time-consuming, so you can start running it first.
4. Evaluation: Manually evaluate and score the 50 * N final outputs (for example, novelty/feasibility/coherence, on a scale of 1-5). This will be the basis for your quantitative analysis.
5. Analysis and Selection:
   * Calculate the average score for each method on the 50 samples and perform significance tests (for example, paired t-tests between your method and each baseline).
   * When reviewing the results, select 3-5 themes that are most suitable for case studies according to the strategy mentioned above.
6. Writing:
   * In the experiment section of the paper, first present the quantitative results charts based on the 50 samples, using data and p-values to prove that your model is significantly superior to other models overall.
   * Then, use one or two dedicated subsections to deeply analyze the selected qualitative cases, showing specifically where your model excels.

# USER: 

╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Help me organize the next steps to be taken in JSON file format. The content should include                                                                                      │
│      Category 1: Standard Decoding Baselines                                                                                                                                  │
│       * Purpose: To demonstrate that our complex search mechanism is superior to the "out-of-the-box" generation method of LLMs without any optimization. This is the most fundamental baseline.                                                     │
│       * Specific Algorithms:                                                                                                                                                  │
│           1. Top-p (Nucleus) Sampling: This is currently the most commonly used and effective standard method for open-ended text generation. At each step, it samples from a dynamically determined minimal vocabulary set where the cumulative probability exceeds threshold p. We need to set a reasonable p value (e.g., 0.9) and report it.                                                    │
│                                                                                                                                                                          │
│      Category 2: Enhancement Methods Based on Prompt Engineering (Prompt Engineering Baselines)                                                                                                       │
│       * Purpose: To prove that our algorithm's advantage comes not just from "better thinking prompts" but from the systematic search process. These methods do not change the algorithm but only stimulate the model's creativity through carefully designed Prompts. This is a very strong opponent that must be taken seriously.                                                                                                                                     │
│       * Specific Algorithms:                                                                                                                                                        │
│           2. Zero-Shot Chain-of-Thought (CoT): After inputting the topic, simply add the sentence "Let's think step by step." or "Please think step by step" to guide the model to perform more detailed reasoning.                         │
│           3. Self-Refine (Self-Refining/Reflection): This is a multi-step prompting process that simulates iterative optimization without "tree search".                                                                      │
│               * Step 1 (Generation): "Based on topic [X], generate a preliminary scientific concept."                                                                                                    │
│               * Step 2 (Reflection): "Please evaluate the concept generated in the previous step and point out its main flaws in terms of 'novelty' and 'feasibility'."                                                                         │
│               * Step 3 (Rewriting): "Based on the above reflections, please rewrite and improve this concept to make it more novel and feasible."                                                                                 │
│               * This process can be iterated for 2-3 rounds to match the computational cost of our MCTS.                                                                                                          │
│                                                                                                                                                                          │
│      Category 3: Simplified Algorithmic Baselines                                                                                                                          │
│       * Purpose: To demonstrate the superiority of our MCTS tree structure and UCT selection strategy, rather than just the effectiveness of our value function. This is equivalent to a "degraded version" of our own algorithm.                                                  │
│       * Specific Algorithms:                                                                                                                                            │
│           4. Beam Search with Custom Value Function:                                                                                                                      │
│               * In the first step of generation, generate K (e.g., K=5) most probable "narrative blocks".                                                                  │
│               * Use the hybrid value function V(seq) from our CG-MCTS to score these K candidates.                                                                         │
│               * Select the candidate with the highest score and use it as the prefix for the next generation step.                                                        │
│               * Repeat this process until the desired length is reached.                                                                                                 │
│               * This method uses our value function but discards the "exploration-exploitation" balance and tree-based lookahead capability of MCTS, making it a perfect subject for an "ablation study".                                                  │
│      ---                                                                                                                                                                 │
│                                                                                                                                                                          │
│      2.  Determination of evaluation indicators: multi-dimensional, combination of quantitative and qualitative                                                                                                                           │
│                                                                                                                                                                          │
│      A single indicator is one-sided. We must measure the quality of generated ideas from multiple dimensions.                                                                                                        │
│                                                                                                                                                                          │
│      A. Automatic Quantitative Metrics (Automatic Quantitative Metrics)                                                                                                                  │
│                                                                                                                                                                          │
│       1. Novelty:                                                                                                                                               │
│           * Metric: V_novelty, which is calculated as 1 minus the cosine similarity between the vector of the generated idea and its nearest neighbor in a paper abstract database (FAISS index).                                                                     │
│           * Calculation: This metric is uniformly applied to all final ideas generated by different methods. A higher score indicates greater novelty.                                                                             │
│                                                                                                                                                                          │
│       2. Coherence/Quality - LLM-as-a-Judge:                                                                                                               │
│           * Method: We will use an independent, more powerful third-party LLM (such as GPT-4 or Claude 3 Opus) as the "judge". This is the current standard practice (SOTA) for evaluating open-ended generation.                              │
│           * Process:                                                                                                                                                        │
│               * Provide a prompt to the judge LLM with the following template:                                                                                                                      │
│                                                                                                                                                                          │

│        1           You are a top-tier scientific research evaluation expert. Here is a research topic and two scientific concepts (A and B) generated by different AI systems. Please score them from 1-5 based on the following three dimensions                                     │
│          and provide brief reasons. Finally, please indicate which one is better overall.                                                                                                        │
│        2                                                                                                                                                                 │

│        3           Research topic: [Enter our research topic]                                                                                                                        │
│        4                                                                                                                                                                 │
│        5           Dimension 1: Scientific Plausibility - Is this concept scientifically feasible without obvious factual errors? (1=Nonsense, 5=Very Plausible)               │
│        6           Dimension 2: Structure & Clarity - Is the writing of this concept clear, is the structure complete, and is the logic coherent? (1=Chaotic, 5=Very Clear)          │
│        7           Dimension 3: Innovation Potential - Does this concept present interesting and novel viewpoints or methods? (1=Outdated, 5=Highly Innovative)                │
│        8                                                                                                                                                                 │

│        9           Concept A: [text generated by the first model]                                                                                                                        │
│       10           Concept B: [text generated by the second model]                                                                                                                        │
│       11                                                                                                                                                                 │
│       12           Please output your rating and final choice in JSON format.                                                                                                                  │
│               * By conducting pairwise comparisons between our method and all baseline methods, we can obtain very reliable quantitative ratings.                                                                         │
│                                                                                                                                                                          │
│      B. 人类定性评估 (Human Qualitative Evaluation)                                                                                                                      │
│         This is the gold standard of the experiment and is irreplaceable.                                                                                              │
│         Due to time and resource constraints, it will not be conducted at present.                                                                                       │
│      ---                                                                                                                                                                 │
│                                                                                                                                                                          │
│      3. Key issues that must be noted in experimental design                                                                                                                                     │
│                                                                                                                                                                          │
│       1. Strictness of control variables:                                                                               │
│           * Model consistency: All comparison methods must use the exact same backbone LLM (our Qwen model) and the same model weights.                                                          │
│           * Input consistency: All methods must be based on the exact same set of topics generated by AutomatedThemeGenerator for experiments. We need at least 50-100 different topics to ensure the generality of the results.                    │
│           * Fair computation budget:                                                                                   │
│             Our MCTS method consumes significantly more computational resources (in terms of token count) than a single Top-p sampling. This is a critical fairness issue. We must design a roughly equivalent computation budget for the baseline methods. For example:                 │
│               * For Self-Refine, allow it to iterate for 2-3 rounds, making its total token consumption close to our MCTS's total consumption.                                                                                                        │
│               * For Top-p Sampling, generate multiple (e.g., 10) samples, then use our value function V(seq) to select the best one, and report this process.                                           │
│                                                                                                                                                                          │
│       2. Conduct Ablation Studies:                                                                                                                                │
│           * To prove that each component of our algorithm design contributes, we must "weaken" ourselves.                                                                                             │
│           * Design the following variants:                                                                                                                                               │
│               * CG-MCTS (w/o Guidance): Remove the directional guidance term w_dir from the UCT formula to verify the effect of v_target.                                                                            │
│               * CG-MCTS (w/o Novelty): Remove the novelty term w_nov from the value function to observe whether the generated results will regress to more "mediocre" concepts.                                                          │
│               * CG-MCTS (w/o Progress): Remove the progress term w_prog from the value function to observe whether it becomes easier to generate repetitive or meaningless expansions.                                                            │
│           * Comparing these ablated versions with the complete version is a hallmark of top conference papers.                                                                                                       │
│                                                                                                                                                                          │
│       3. Statistical Significance of Results:                                                                             │
│           * Don't just report average scores! For all quantitative metrics, we must conduct statistical significance tests (e.g., paired t-test or Wilcoxon signed-rank test).                                                  │
│           * When reporting results, p-values must be included to demonstrate that the advantages achieved by our method are not due to random fluctuations. p-value < 0.05 is the minimum requirement.\n\n You should output a JSON file that includes the content to be completed at each step, │
│    the code/data result file names to be output. For example, the first step is to prepare the test dataset, generate the construct_test_dataset.py code file, and run it to get the test_theme.json data (let's agree on the specific format of the file content), etc.           │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ Okay, here is a JSON file that transforms your ambitious experimental plan into concrete, executable steps. This plan aims to balance efficiency and rigor, ensuring you obtain the most persuasive results within a limited time.

```
   ... first 40 lines hidden ...
    24     },
    25     {
    26       "step_number": 2,
    27       "title": "Run all experiments",
    28       "description": "Create a main script that loads 'test_themes.json' and runs our own method, all baseline methods, and ablation experiments for each theme. All results will be aggregated into one file.",
    29       "actions": [
    30         {
    31           "action_type": "generate_code",
    32           "description": "Create a Python script to orchestrate the execution of all experiments.",
    33           "output_filename": "run_experiments.py",
    34           "details": {
    35             "code": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]: \n\n\n# This script should be structured to run all experiments.\n# Due to the 
       complexity of implementing all baselines (Top-p, CoT, Self-Refine, Beam Search)\n# and the main CG-MCTS logic within a single file, this code will provide a 
       high-level\n# orchestration structure. You will need to fill in the generation logic for each method\n# based on the prompts and designs we have 
       discussed.\n\nimport json\nimport os\nfrom cg_mcts_qwen import LLMInterface, CG_MCTS, Config, build_faiss_index\nimport numpy as np\n\n# --- Helper Functions
       for each Baseline (Implement these based on our discussion) ---\n\ndef run_top_p(llm, theme_obj):\n    print(f\"Running Top-p for {theme_obj['id']}...\")\n

prompt = f\"\"\"Please expand and deepen the following research concept...\\n\\nTheme: {theme_obj['theme']}\\nElaboration: {theme_obj['elaboration']}\\n\\nPlease continue writing the subsequent parts...\"\"\"\n    # NOTE: For fair budget, generate 10 samples and choose the best with a value function.
       # This is a simplified version for clarity.\n    return llm.generate_chat_completion([{'role': 'user', 'content': prompt}], 1024)\n\ndef run_cot(llm, 

       theme_obj):\n    print(f\"Running CoT for {theme_obj['id']}...\")\n    prompt = f\"\"\"Please expand and deepen the following research concept...\\n\\nTopic: 
       {theme_obj['theme']}\\nElaboration: {theme_obj['elaboration']}\\n\\nPlease think step by step, then write the subsequent part...\"\"\"\n    return llm.generate_chat_completion([{'role': 'user',
       'content': prompt}], 1500) # Allow more length for thoughts\n\ndef run_self_refine(llm, theme_obj):\n    print(f\"Running Self-Refine for 
       {theme_obj['id']}...\")\n    # Step 1: Expand\n    prompt1 = f\"\"\"This is our initial elaboration of the research concept...\\n\\nTopic: {theme_obj['theme']}\\nElaboration: 
       {theme_obj['elaboration']}\\n\\nYour continuation:\"\"\"\n    expansion = llm.generate_chat_completion([{'role': 'user', 'content': prompt1}], 512)\n    \n    # Step 
       2: Reflect\n    prompt2 = f\"\"\"You are a top-tier scientific reviewer...\\n\\nOriginal concept:\\nTopic: {theme_obj['theme']}\\nElaboration: 
       {theme_obj['elaboration']}\\nNewly added parts:\\n{expansion}\\n\\nYour evaluation:\"\"\"\n    reflection = llm.generate_chat_completion([{'role': 'user', 'content': 
       prompt2}], 512)\n\n    # Step 3: Rewrite\n    prompt3 = f\"\"\"You are an outstanding scientist...\\n\\nOriginal concept: ...\\nInitially written parts: ...\\nEvaluation of the written parts: 
       ...\\n\\nPlease provide your improved final continuation:\"\"\" # Simplified for brevity\n    final_text = llm.generate_chat_completion([{'role': 'user', 'content':
       prompt3.format(theme=theme_obj['theme'], elaboration=theme_obj['elaboration'], expansion=expansion, reflection=reflection)}], 1024)\n    return 
       final_text\n\ndef run_beam_search_custom(llm, theme_obj):\n    print(f\"Running Beam Search w/ Custom Value for {theme_obj['id']}...\")\n    # This is a 
       complex implementation. You would generate K initial blocks,\n    # score them with the MCTS value function, pick the best, and repeat.\n    # Returning a 
       placeholder for now.\n    return \"[Placeholder for Beam Search with Custom Value Function Output]\"\n\ndef run_cg_mcts_main(llm, novelty_db, cfg, theme_obj,
       ablation_flags={}):\n    print(f\"Running CG-MCTS for {theme_obj['id']} with ablations: {ablation_flags}...\")\n    \n    # Modify config based on ablation 
       flags\n    temp_cfg = Config()\n    if ablation_flags.get('no_guidance'): temp_cfg.W_DIR = 0.0\n    if ablation_flags.get('no_novelty'): temp_cfg.W_NOV = 
       0.0\n    if ablation_flags.get('no_progress'): temp_cfg.W_PROG = 0.0\n\n    init_narrative = f\"{theme_obj['theme']}\\n\\n{theme_obj['elaboration']}\"\n    
       mcts = CG_MCTS(llm, novelty_db, temp_cfg, theme=theme_obj['theme'], init_narrative=init_narrative)\n    mcts.search()\n    return 
       mcts.get_best_sequence(debug=False)\n\n\ndef main():\n    cfg = Config()\n    llm = LLMInterface(cfg.MODEL_NAME, cfg.DEVICE)\n    \n    # Load novelty DB\n
       model_name_for_file = os.path.basename(cfg.MODEL_NAME) or 'default_model'\n    faiss_index_filename = f'faiss_index_{model_name_for_file}.bin'\n    
       novelty_db = faiss.read_index(faiss_index_filename)\n\n    with open('test_themes.json', 'r', encoding='utf-8') as f:\n        test_themes = json.load(f)\n\n
       results = []\n    for theme_obj in test_themes:\n        print(f\"\\n{'='*20} Processing {theme_obj['id']} {'='*20}\")\n        \n        # Run baselines\n
       output_top_p = run_top_p(llm, theme_obj)\n        output_cot = run_cot(llm, theme_obj)\n        output_self_refine = run_self_refine(llm, theme_obj)\n
       output_beam_search = run_beam_search_custom(llm, theme_obj)\n\n        # Run main method and ablations\n        output_cg_mcts_full = run_cg_mcts_main(llm, 
       novelty_db, cfg, theme_obj)\n        output_cg_mcts_no_guidance = run_cg_mcts_main(llm, novelty_db, cfg, theme_obj, ablation_flags={'no_guidance': True})\n
       output_cg_mcts_no_novelty = run_cg_mcts_main(llm, novelty_db, cfg, theme_obj, ablation_flags={'no_novelty': True})\n        output_cg_mcts_no_progress = 
       run_cg_mcts_main(llm, novelty_db, cfg, theme_obj, ablation_flags={'no_progress': True})\n\n        results.append({\n            'id': theme_obj['id'],\n
       'theme': theme_obj['theme'],\n            'elaboration': theme_obj['elaboration'],\n            'outputs': {\n                'top_p': output_top_p,\n
       'cot': output_cot,\n                'self_refine': output_self_refine,\n                'beam_search_custom': output_beam_search,\n                
       'cg_mcts_full': output_cg_mcts_full,\n                'cg_mcts_no_guidance': output_cg_mcts_no_guidance,\n                'cg_mcts_no_novelty': 
       output_cg_mcts_no_novelty,\n                'cg_mcts_no_progress': output_cg_mcts_no_progress\n            }\n        })\n\n        # Save incrementally\n
       with open('experiment_results.json', 'w', encoding='utf-8') as f:\n            json.dump(results, f, indent=2, ensure_ascii=False)\n\n    print('\\nAll 
       experiments completed.')\n\nif __name__ == '__main__':\n    main()\n"
    36           }
    37         },
    38         {
    39           "action_type": "run_script",
40           "description": "Run the main experiment script. This will be a time-consuming process. The script will incrementally save results to 'experiment_results.json'.",
    41           "details": {
    42             "command": "python run_experiments.py"
    43           }
    44         }
    45       ]
    46     },
    47     {
    48       "step_number": 3,
    49       "title": "Automated Evaluation",
    50       "description": "Perform automated evaluation on all generated ideas. This includes calculating the V_novelty score and using LLM-as-a-Judge for pairwise comparison scoring.",
    51       "actions": [
    52         {
    53           "action_type": "generate_code",
    54           "description": "Create a script to calculate the V_novelty scores of all outputs.",
    55           "output_filename": "evaluate_novelty.py",
    56           "details": {
    57             "code": "import json\nimport numpy as np\nimport faiss\nimport os\nfrom cg_mcts_qwen import LLMInterface\n\ndef main():\n    # Load LLM for 
       vectorization\n    llm = LLMInterface('../Qwen3-0.6B', 'cuda')\n\n    # Load Faiss Index\n    model_name_for_file = os.path.basename('../Qwen3-0.6B') or 
       'default_model'\n    faiss_index_filename = f'faiss_index_{model_name_for_file}.bin'\n    novelty_db = faiss.read_index(faiss_index_filename)\n\n    with 
       open('experiment_results.json', 'r', encoding='utf-8') as f:\n        results = json.load(f)\n\n    for item in results:\n        print(f\"Calculating 
       novelty for {item['id']}...\")\n        item['evaluations'] = item.get('evaluations', {})\n        item['evaluations']['v_novelty'] = {}\n        for method,
       output in item['outputs'].items():\n            if not output or '[Placeholder' in output:\n                score = 0.0\n            else:\n                
       vector = llm.get_vector(output).reshape(1, -1)\n                faiss.normalize_L2(vector)\n                similarities, _ = novelty_db.search(vector, 1)\n
       score = 1.0 - similarities[0][0]\n            item['evaluations']['v_novelty'][method] = score\n            print(f\"  {method}: {score:.4f}\")\n\n    with 
       open('results_with_novelty.json', 'w', encoding='utf-8') as f:\n        json.dump(results, f, indent=2, ensure_ascii=False)\n    print('\\nNovelty scores 
       calculated and saved to results_with_novelty.json')\n\nif __name__ == '__main__':\n    main()\n"
    58           }
    59         },
    60         {
    61           "action_type": "run_script",
62           "description": "run script to calculate novelty score.",
    63           "details": {
    64             "command": "python evaluate_novelty.py"
    65           }
    66         },
    67         {
    68           "action_type": "generate_code",

    69           "description": "Create a script that uses a powerful third-party LLM (such as GPT-4) as a judge to perform pairwise comparison scoring.",
    70           "output_filename": "evaluate_llm_as_judge.py",
    71           "details": {
    72             "code": "import json\nimport openai\nimport time\n\n# --- Configuration ---\n# IMPORTANT: Set your OpenAI API key here or as an environment 
       variable\n# openai.api_key = 'YOUR_OPENAI_API_KEY'\n\nJUDGE_MODEL = 'gpt-4-1106-preview'\nINPUT_FILE = 'results_with_novelty.json'\nOUTPUT_FILE = 
'llm_judge_evaluations.json'

JUDGE_PROMPT_TEMPLATE = """You are a top-tier research evaluation expert...[The full prompt as described in the plan]...Please output your score and final choice in JSON format."""

def call_judge_llm(theme_obj, output_a, output_b):
    # ... (Implementation for calling the OpenAI
       API)\n    # This function should handle API calls, retries, and parse the JSON response.\n    # Returning a placeholder for now.\n    print(f\"  Comparing A 
       vs B for {theme_obj['id']}\")\n    time.sleep(2) # To avoid rate limiting\n    return {\n        'comparison': {'A': 'cg_mcts_full', 'B': 'top_p'},\n        
       'scores': {\n            'A': {'Plausibility': 5, 'Structure': 5, 'Innovation': 5},\n            'B': {'Plausibility': 4, 'Structure': 3, 'Innovation': 3}\n
       },\n        'winner': 'A'\n    }\n\ndef main():\n    with open(INPUT_FILE, 'r', encoding='utf-8') as f:\n        results = json.load(f)\n\n    
       all_evaluations = []\n    for item in results:\n        print(f\"\\nRunning LLM-as-a-Judge for {item['id']}...\")\n        our_method_output = 
       item['outputs']['cg_mcts_full']\n        baselines = {k: v for k, v in item['outputs'].items() if k != 'cg_mcts_full'}\n        \n        item_evals = {'id':
       item['id'], 'comparisons': []}\n        for baseline_name, baseline_output in baselines.items():\n            # Compare our method (A) vs baseline (B)\n
       eval_result = call_judge_llm(item, our_method_output, baseline_output)\n            eval_result['comparison']['B'] = baseline_name # Correctly label the 
       baseline\n            item_evals['comparisons'].append(eval_result)\n        all_evaluations.append(item_evals)\n\n        # Save incrementally\n        with
       open(OUTPUT_FILE, 'w', encoding='utf-8') as f:\n            json.dump(all_evaluations, f, indent=2, ensure_ascii=False)\n\n    print(f'\\nLLM-as-a-Judge 
       evaluations saved to {OUTPUT_FILE}')\n\nif __name__ == '__main__':\n    main()\n"
    73           }
    74         },
    75         {
    76           "action_type": "run_script",
    77           "description": "Run the LLM referee evaluation script. Note: This will call a paid API and may incur costs.",
    78           "details": {
    79             "command": "python evaluate_llm_as_judge.py"
    80           }
    81         }
    82       ]
    83     },
    84     {
    85       "step_number": 4,
    86       "title": "Result Analysis and Visualization",
    87       "description": "Using Python scripts to conduct statistical analysis (such as paired t-tests) on all collected evaluation data, and generate charts (such as box plots) to visually display performance comparisons.",
    88       "actions": [
    89         {
    90           "action_type": "generate_code",

"91           "description": "Create a Jupyter Notebook or Python script for data analysis and visualization.",
    92           "output_filename": "analyze_results.py",
    93           "details": {
    94             "code": "import json\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy.stats import ttest_rel\n\ndef 
       main():\n    # Load novelty and judge scores\n    with open('results_with_novelty.json', 'r', encoding='utf-8') as f:\n        novelty_data = json.load(f)\n
       with open('llm_judge_evaluations.json', 'r', encoding='utf-8') as f:\n        judge_data = json.load(f)\n\n    # --- 1. Process Novelty Data ---\n    
       novelty_records = []\n    for item in novelty_data:\n        for method, score in item['evaluations']['v_novelty'].items():\n            
       novelty_records.append({'id': item['id'], 'method': method, 'novelty': score})\n    df_novelty = pd.DataFrame(novelty_records)\n\n    # --- 2. Process Judge 
       Data ---\n    # ... (Complex data wrangling to create a clean DataFrame for judge scores)\n    # For simplicity, let's assume we create a df_judge with 
       columns: [id, method, plausibility, structure, innovation]\n\n    # --- 3. Statistical Analysis ---\n    print('--- Statistical Significance (Paired t-test 
       vs cg_mcts_full) ---')\n    our_scores = df_novelty[df_novelty['method'] == 'cg_mcts_full']['novelty']\n    baselines = 
       df_novelty['method'].unique().tolist()\n    baselines.remove('cg_mcts_full')\n\n    for baseline in baselines:\n        baseline_scores = 
       df_novelty[df_novelty['method'] == baseline]['novelty']\n        if len(our_scores) == len(baseline_scores):\n            stat, p_value = 
       ttest_rel(our_scores, baseline_scores)\n            print(f\"Novelty: cg_mcts_full vs {baseline}: p-value = {p_value:.4f}\")\n\n    # --- 4. Visualization 
       ---\n    plt.figure(figsize=(12, 7))\n    sns.boxplot(data=df_novelty, x='method', y='novelty')\n    plt.title('Comparison of V_Novelty Scores Across 
       Methods')\n    plt.xticks(rotation=45)\n    plt.tight_layout()\n    plt.savefig('novelty_comparison.png')\n    print('\\nSaved novelty comparison plot to 
       novelty_comparison.png')\n\n    # ... (Similar plots for judge scores)\n\nif __name__ == '__main__':\n    main()\n"
    95           }
    96         },
    97         {
    98           "action_type": "run_script",
99           "description": "Run analysis script, generate statistical results and chart files.",
   100           "details": {
   101             "command": "python analyze_results.py"
   102           }
   103         }
   104       ]
   105     }
   106   ]
   107 }

```

# USER: 
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > You don't need to write all the code right now. Write the function of the code, the specific content to be implemented, input/output file names, specific file content format, etc. Write this json to a file  │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 



✦ Okay, I understand your requirements. We focus on planning and design, rather than immediate implementation.

  I will generate a file named experimental_plan.json
  This file will describe each step of the experiment in detail, including the objectives of each step, the functionality of the scripts to be created, the names and specific content formats of their input/output files, but it will not contain the complete implementation code.

✦ Okay, I understand your requirements. We focus on planning and design, rather than immediate implementation.

  I will generate a file named experimental_plan.json
  This file will describe each step of the experiment in detail, including the objectives of each step, the functionality of the scripts to be created, the names and specific content formats of their input/output files, but it will not contain the complete implementation code.
 ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to experimental_plan.json                                                                                                                            │
 │                                                                                                                                                                           │
 │    ... first 36 lines hidden ...                                                                                                                                          │
 │     37           "details": { "command": "python construct_test_dataset.py" }                                                                                             │
 │     38         }                                                                                                                                                          │
 │     39       ]                                                                                                                                                            │
 │     40     },                                                                                                                                                             │
 │     41     {                                                                                                                                                              │
 │     42       "step_number": 2,                                                                                                                                            │
 │     43       "title": "Run all experiments",                                                                                                                                     │
 │     44       "description": "Create a main script that loads 'test_themes.json', and runs, for each theme, our own method, all baseline methods, as well as ablation experiments. All results will be aggregated into a single file.",         │
 │     45       "actions": [                                                                                                                                                 │
 │     46         {                                                                                                                                                          │
 │     47           "action_type": "generate_code",                                                                                                                          │
 │     48           "description": "Create a Python script to orchestrate the running of all experiments.",                                                                                           │
 │     49           "output_filename": "run_experiments.py",                                                                                                                 │
 │     50           "details": {                                                                                                                                             │
 │     51             "purpose": "For each topic in the test set, automatically run all models and algorithm variants, and collect their generated results.",                                                           │
 │     52             "implementation_details": [                                                                                                                            │
 │     53               "Load the `test_themes.json` file.",                                                                                                                      │
 │     54               "Load modules such as `LLMInterface` and `CG_MCTS` from `cg_mcts_qwen`, as well as the FAISS index.",                                                                            │
 │     55               "Create separate execution functions for each comparison method (Top-p, CoT, Self-Refine, Beam Search) and our own method (CG-MCTS and its ablation variants).",                                 │
 │     56               "Each function receives a theme object, constructs the corresponding prompt, calls the LLM, and returns the generated text.",                                                                           │
 │     57               "The main loop iterates through each theme in `test_themes.json`.",                                                                                                      │
 │     58               "In the loop, all execution functions are called in sequence to obtain the outputs of all methods.",                                                                                              │
 │     59               "The input for each theme and the outputs of all methods are aggregated into a large dictionary.",                                                                                            │
 │     60               "To prevent data loss due to interruptions during long runs, after processing each theme, all current results are incrementally written to `experiment_results.json`."                                          │
 │     61             ],                                                                                                                                                     │
 │     62             "inputs": [                                                                                                                                            │
 │     63               {"filename": "test_themes.json", "format": "JSON file generated in step 1."},                                                                                 │
 │     64               {"filename": "faiss_index_{model_name}.bin", "format": "FAISS index file used for CG-MCTS and novelty calculation."}                                                   │
 │     65             ],                                                                                                                                                     │
 │     66             "outputs": [                                                                                                                                           │
 │     67               {                                                                                                                                                    │
 │     68                 "filename": "experiment_results.json",                                                                                                             │
 │     69                 "format": "JSON list, where each object corresponds to a topic. Object format: {'id': '...', 'theme': '...', 'elaboration': '...', 'outputs': {'top_p': '...', 'cot':        │
 │        '...', 'cg_mcts_full': '...', ...}}"                                                                                                                               │
 │     70               }                                                                                                                                                    │
 │     71             ]                                                                                                                                                      │
 │     72           }                                                                                                                                                        │
 │     73         },                                                                                                                                                         │
 │     74         {                                                                                                                                                          │
 │     75           "action_type": "run_script",                                                                                                                             │

 │     76           "description": "Run the main experiment script. This will be a time-consuming process.",                                                                                             │
 │     77           "details": { "command": "python run_experiments.py" }                                                                                                    │
 │     78         }                                                                                                                                                          │
 │     79       ]                                                                                                                                                            │
 │     80     },                                                                                                                                                             │
 │     81     {                                                                                                                                                              │
 │     82       "step_number": 3,                                                                                                                                            │
 │     83       "title": "Automated Evaluation",                                                                                                                                       │
 │     84       "description": "Perform automated evaluation on all generated ideas. This includes calculating V_novelty scores and using LLM-as-a-Judge for pairwise comparison scoring.",                                          │
 │     85       "actions": [                                                                                                                                                 │
 │     86         {                                                                                                                                                          │
 │     87           "action_type": "generate_code",                                                                                                                          │

 │     88           "description": "Create a script to calculate all output V_novelty scores.",                                                                                        │
 │     89           "output_filename": "evaluate_novelty.py",                                                                                                                │
 │     90           "details": {                                                                                                                                             │
 │     91             "purpose": "To calculate the V_novelty score for each generated result in `experiment_results.json`.",                                                                       │
 │     92             "implementation_details": [                                                                                                                            │
 │     93               "Loading `experiment_results.json`.",                                                                                                                   │
 │     94               "Initializing `LLMInterface` for text vectorization and loading FAISS index.",                                                                                             │
 │     95               "Iterating through the output text of each method for each topic in the results file.",                                                                                   │
 │     96               "For each output text, call `llm.get_vector()` to get its vector.",                                                                                                     │
 │     97               "Using FAISS index to search for the nearest neighbors of this vector, calculating `1 - cosine_similarity` as the novelty score.",                                             │
 │     98               "Writing the calculated novelty score back to the original result object, creating a new evaluation field.",                                                             │
 │     99               "Writing the updated complete data to `results_with_novelty.json`."                                                                                                    │
 │    100             ],                                                                                                                                                     │
 │    101             "inputs": [                                                                                                                                            │
 │    102               {"filename": "experiment_results.json", "format": "JSON file generated in Step 2."},                                                                          │
 │    103               {"filename": "faiss_index_{model_name}.bin", "format": "FAISS index file."}                                                                            │
 │    104             ],                                                                                                                                                     │
 │    105             "outputs": [                                                                                                                                           │
 │    106               {                                                                                                                                                    │
 │    107                 "filename": "results_with_novelty.json",                                                                                                           │
 │    108                 "format": "Same structure as `experiment_results.json`, but adds the field 'evaluations': {'v_novelty': {'top_p': 0.8, 'cot': 0.85, ...}} in each topic object."          │
 │    109               }                                                                                                                                                    │
 │    110             ]                                                                                                                                                      │
 │    111           }                                                                                                                                                        │
 │    112         },                                                                                                                                                         │
 │    113         {                                                                                                                                                          │
 │    114           "action_type": "run_script",                                                                                                                             │
 │    115           "description": "Run script to calculate novelty score.",                                                                                                               │
 │    116           "details": { "command": "python evaluate_novelty.py" }                                                                                                   │
 │    117         },                                                                                                                                                         │
 │    118         {                                                                                                                                                          │
 │    119           "action_type": "generate_code",                                                                                                                          │

│    120           "description": "Create a script that uses a powerful third-party LLM as a referee to conduct pairwise comparison scoring.",                                                                          │
 │    121           "output_filename": "evaluate_llm_as_judge.py",                                                                                                           │
 │    122           "details": {                                                                                                                                             │
│    123             "purpose": "Using the LLM-as-a-Judge method to conduct pairwise comparisons between our core method and all baseline methods, scoring them from multiple dimensions.",                                             │
 │    124             "implementation_details": [                                                                                                                            │
 │    125               "Load `experiment_results.json`.",                                                                                                                   │
 │    126               "Set up an API interface for a powerful judge LLM (such as GPT-4).",                                                                                                       │
 │    127               "Define the judge prompt template that you designed in your plan.",                                                                                                              │
 │    128               "Iterate through each topic in the results file.",                                                                                                                      │
 │    129               "For each topic, pair the output of `cg_mcts_full` with the output of each baseline method.",                                                                                  │
 │    130               "For each pair (A, B), format the judge prompt and call the judge LLM API.",                                                                                                   │
 │    131               "Parse the returned JSON-formatted score and store it.",                                                                                                              │
 │    132               "Aggregate all paired evaluation results for all topics and write them to `llm_judge_evaluations.json`."                                                                               │
 │    133             ],                                                                                                                                                     │
 │    134             "inputs": [                                                                                                                                            │
│    135               {"filename": "experiment_results.json", "format": "JSON file generated in step 2."}                                                                           │
 │    136             ],                                                                                                                                                     │
 │    137             "outputs": [                                                                                                                                           │
 │    138               {                                                                                                                                                    │
 │    139                 "filename": "llm_judge_evaluations.json",                                                                                                          │
 │    140                 "format": "JSON list, where each object corresponds to a topic. Object format: {'id': '...', 'comparisons': [{'comparison': {'A': 'cg_mcts_full', 'B': 'top_p'}, 'scores':   │
 │        {'A': {...}, 'B': {...}}, 'winner': 'A'}, ...]}"                                                                                                                   │
 │    141               }                                                                                                                                                    │
 │    142             ]                                                                                                                                                      │
 │    143           }                                                                                                                                                        │
 │    144         },                                                                                                                                                         │
 │    145         {                                                                                                                                                          │
 │    146           "action_type": "run_script",                                                                                                                             │

│    147           "description": "Run the LLM judge evaluation script. Note: This will call paid APIs and may incur costs.",                                                                             │
 │    148           "details": { "command": "python evaluate_llm_as_judge.py" }                                                                                              │
 │    149         }                                                                                                                                                          │
 │    150       ]                                                                                                                                                            │
 │    151     },                                                                                                                                                             │
 │    152     {                                                                                                                                                              │
 │    153       "step_number": 4,                                                                                                                                            │
 │    154       "title": "Result Analysis and Visualization",                                                                                                                                 │
 │    155       "description": "Use Python scripts to perform statistical analysis (such as paired t-tests) on all collected evaluation data, and generate charts (such as box plots) to visually display performance comparisons.",                                │
 │    156       "actions": [                                                                                                                                                 │
 │    157         {                                                                                                                                                          │
 │    158           "action_type": "generate_code",                                                                                                                          │

 │    159           "description": "Create a Python script or Jupyter Notebook for data analysis and visualization.",                                                                           │
 │    160           "output_filename": "analyze_results.py",                                                                                                                 │
 │    161           "details": {                                                                                                                                             │
│    162             "purpose": "Collect all evaluation data, conduct statistical tests, and generate charts for the paper.",                                                                                   │
 │    163             "implementation_details": [                                                                                                                            │
 │    164               "Use the `pandas` library to load `results_with_novelty.json` and `llm_judge_evaluations.json`, and process them into DataFrames that are easy to analyze.",                                  │
 │    165               "For each scoring dimension of V_novelty and LLM-as-a-Judge, perform paired t-tests (`scipy.stats.ttest_rel`) between the scores of `cg_mcts_full` and each of the other methods, and print the p-values.",    │
 │    166               "Use the `matplotlib` and `seaborn` libraries to generate box plots or bar charts for each key metric (such as V_novelty, innovation potential score, etc.) to visually compare the performance distributions of all methods.",                      │
 │    167               "Save the generated charts as high-resolution image files (such as .png or .pdf)."                                                                                               │
 │    168             ],                                                                                                                                                     │
 │    169             "inputs": [                                                                                                                                            │

 │    170               {"filename": "results_with_novelty.json", "format": "File generated in step 3."},                                                                            │
 │    171               {"filename": "llm_judge_evaluations.json", "format": "File generated in step 3."}                                                                            │
 │    172             ],                                                                                                                                                     │
 │    173             "outputs": [                                                                                                                                           │
 │    174               {"filename": "novelty_comparison.png", "format": "Box plot image file comparing V_novelty scores of various methods."},                                                     │
 │    175               {"filename": "llm_judge_scores.png", "format": "Box plot or bar chart image file comparing scores of various methods in LLM judge evaluation."},                                         │
 │    176               {"filename": "statistical_analysis_report.txt", "format": "Text file containing results of all t-tests (p-values)."}                                              │
 │    177             ]                                                                                                                                                      │
 │    178           }                                                                                                                                                        │
 │    179         },                                                                                                                                                         │
 │    180         {                                                                                                                                                          │
 │    181           "action_type": "run_script",                                                                                                                             │

 │    182           "description": "Run analysis script, generate statistical results and chart files.",                                                                                                 │
 │    183           "details": { "command": "python analyze_results.py" }                                                                                                    │
 │    184         }                                                                                                                                                          │
 │    185       ]                                                                                                                                                            │
 │    186     }                                                                                                                                                              │
 │    187   ]                                                                                                                                                                │
 │    188 }                                                                                                                                                                  │
 ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, the detailed experimental plan has been written to the file ${PWD}/experiment/experimental_plan.json.

This plan covers four main steps from dataset preparation to final result analysis, and details the functionality of scripts that need to be created in each step, as well as their input/output files and formats. You can follow this plan step by step to complete your paper's experiments.

✦ Okay, I understand your requirements. We are focusing on planning and design rather than immediate implementation.

I will generate a file named experimental_plan.json
This file will describe in detail every step of the experiment, including the objectives of each step, the functionality of the scripts that need to be created, the names and specific content formats of their input/output files, but it will not contain complete implementation code.
 ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to experimental_plan.json                                                                                                                            │
 │                                                                                                                                                                           │
 │    ... first 36 lines hidden ...                                                                                                                                          │
 │     37           "details": { "command": "python construct_test_dataset.py" }                                                                                             │
 │     38         }                                                                                                                                                          │
 │     39       ]                                                                                                                                                            │
 │     40     },                                                                                                                                                             │
 │     41     {                                                                                                                                                              │
 │     42       "step_number": 2,                                                                                                                                            │
 │     43       "title": "Run all experiments",                                                                                                                                     │
 │     44       "description": "Create a main script that loads 'test_themes.json' and runs our own method, all baseline methods, and ablation experiments for each theme. All results will be summarized into one file.",         │
 │     45       "actions": [                                                                                                                                                 │
 │     46         {                                                                                                                                                          │
 │     47           "action_type": "generate_code",                                                                                                                          │
 │     48           "description": "Create a Python script for orchestrating the execution of all experiments.",                                                                                           │
 │     49           "output_filename": "run_experiments.py",                                                                                                                 │
 │     50           "details": {                                                                                                                                             │
 │     51             "purpose": "For each topic in the test set, automatically run all models and algorithm variants, and collect their generated results.",                                                           │
 │     52             "implementation_details": [                                                                                                                            │

 │     53               "Loading the `test_themes.json` file.",                                                                                                                      │
 │     54               "Loading modules such as `LLMInterface` and `CG_MCTS` from `cg_mcts_qwen`, as well as the FAISS index.",                                                                            │
 │     55               "Creating separate execution functions for each comparison method (Top-p, CoT, Self-Refine, Beam Search) and our own method (CG-MCTS and its ablation variants).",                                 │
 │     56               "Each function receives a theme object, constructs the corresponding prompt, calls the LLM, and returns the generated text.",                                                                           │
 │     57               "The main loop iterates through each theme in `test_themes.json`.",                                                                                                      │
 │     58               "In the loop, all execution functions are called in sequence to obtain the outputs of all methods.",                                                                                              │
 │     59               "The input for each theme and the outputs of all methods are compiled into a large dictionary.",                                                                                                │
 │     60               "To prevent data loss due to interruptions during long runs, after processing each theme, the current complete results are incrementally written to `experiment_results.json`."                                          │
 │     61             ],                                                                                                                                                     │
 │     62             "inputs": [                                                                                                                                            │
```
 │     63               {"filename": "test_themes.json", "format": "JSON file generated in Step 1.",},                                                                                 │
 │     64               {"filename": "faiss_index_{model_name}.bin", "format": "FAISS index file for CG-MCTS and novelty calculation."}                                                   │
```
 │     65             ],                                                                                                                                                     │
 │     66             "outputs": [                                                                                                                                           │
 │     67               {                                                                                                                                                    │
 │     68                 "filename": "experiment_results.json",                                                                                                             │
 │     69                 "format": "JSON list, where each object corresponds to a topic. Object format: {'id': '...', 'theme': '...', 'elaboration': '...', 'outputs': {'top_p': '...', 'cot':        │
 │        '...', 'cg_mcts_full': '...', ...}}"                                                                                                                               │
 │     70               }                                                                                                                                                    │
 │     71             ]                                                                                                                                                      │
 │     72           }                                                                                                                                                        │
 │     73         },                                                                                                                                                         │
 │     74         {                                                                                                                                                          │
 │     75           "action_type": "run_script",                                                                                                                             │
 │     76           "description": "Run the main experiment script. This will be a time-consuming process.",                                                                                             │
 │     77           "details": { "command": "python run_experiments.py" }                                                                                                    │
 │     78         }                                                                                                                                                          │
 │     79       ]                                                                                                                                                            │
 │     80     },                                                                                                                                                             │
 │     81     {                                                                                                                                                              │
 │     82       "step_number": 3,                                                                                                                                            │
 │     83       "title": "Automated Evaluation",                                                                                                                                       │
 │     84       "description": "Perform automated evaluation on all generated ideas. This includes calculating the V_novelty score, and using LLM-as-a-Judge for pairwise comparison scoring.",                                          │
 │     85       "actions": [                                                                                                                                                 │
 │     86         {                                                                                                                                                          │
 │     87           "action_type": "generate_code",                                                                                                                          │
```
 │     88           "description": "Create a script to calculate all generated V_novelty scores.",                                                                                        │
```
 │     89           "output_filename": "evaluate_novelty.py",                                                                                                                │
 │     90           "details": {                                                                                                                                             │
 │     91             "purpose": "Calculate the V_novelty score for each generated result in `experiment_results.json`.",                                                                       │
 │     92             "implementation_details": [                                                                                                                            │
 │     93               "Loading `experiment_results.json`.",                                                                                                                   │
 │     94               "Initializing `LLMInterface` for text vectorization and loading FAISS index.",                                                                                             │
 │     95               "Iterating through the output text of each method for each topic in the results file.",                                                                                      │
 │     96               "For each output text, calling `llm.get_vector()` to get its vector.",                                                                                                     │
 │     97               "Using FAISS index to search for the nearest neighbors of the vector, calculating `1 - cosine_similarity` as the novelty score.",                                          │
 │     98               "Writing the calculated novelty score back to the original result object, creating a new evaluation field.",                                                                │
 │     99               "Writing the updated complete data to `results_with_novelty.json`."                                                                                                      │
 │    100             ],                                                                                                                                                     │
 │    101             "inputs": [                                                                                                                                            │
│    102               {"filename": "experiment_results.json", "format": "JSON file generated in step 2."},                                                                          │
│    103               {"filename": "faiss_index_{model_name}.bin", "format": "FAISS index file."}                                                                            │
 │    104             ],                                                                                                                                                     │
 │    105             "outputs": [                                                                                                                                           │
 │    106               {                                                                                                                                                    │
 │    107                 "filename": "results_with_novelty.json",                                                                                                           │
 │    108                 "format": "The format is the same as `experiment_results.json`, but each topic object has an additional 'evaluations': {'v_novelty': {'top_p': 0.8, 'cot': 0.85, ...}} field."          │
 │    109               }                                                                                                                                                    │
 │    110             ]                                                                                                                                                      │
 │    111           }                                                                                                                                                        │
 │    112         },                                                                                                                                                         │
 │    113         {                                                                                                                                                          │
 │    114           "action_type": "run_script",                                                                                                                             │

│    115           "description": "Run script to calculate novelty score.",                                                                                                               │
 │    116           "details": { "command": "python evaluate_novelty.py" }                                                                                                   │
 │    117         },                                                                                                                                                         │
 │    118         {                                                                                                                                                          │
 │    119           "action_type": "generate_code",                                                                                                                          │
│    120           "description": "Create a script that uses a powerful third-party LLM as a judge to conduct pairwise comparison scoring.",                                                                          │
 │    121           "output_filename": "evaluate_llm_as_judge.py",                                                                                                           │
 │    122           "details": {                                                                                                                                             │
 │    123             "purpose": "Using the LLM-as-a-Judge method to perform pairwise comparisons between our core method and all baseline methods, scoring them from multiple dimensions.",                                             │
 │    124             "implementation_details": [                                                                                                                            │

 │    125               "Load `experiment_results.json`.",                                                                                                                   │
 │    126               "Set up an API interface for a powerful referee LLM (such as GPT-4).",                                                                                                       │
 │    127               "Define the referee prompt template that you designed in your plan.",                                                                                                              │
 │    128               "Iterate through each topic in the results file.",                                                                                                                      │
 │    129               "For each topic, pair the output of `cg_mcts_full` with the output of each baseline method.",                                                                                  │
 │    130               "For each pair (A, B), format the referee prompt and call the referee LLM API.",                                                                                           │
 │    131               "Parse the returned JSON-formatted score and store it.",                                                                                                              │
 │    132               "Aggregate all paired evaluation results across all topics and write them to `llm_judge_evaluations.json`."                                                                               │
 │    133             ],                                                                                                                                                     │
 │    134             "inputs": [                                                                                                                                            │
│    135               {"filename": "experiment_results.json", "format": "JSON file generated in step 2."}                                                                           │
 │    136             ],                                                                                                                                                     │
 │    137             "outputs": [                                                                                                                                           │
 │    138               {                                                                                                                                                    │
 │    139                 "filename": "llm_judge_evaluations.json",                                                                                                          │
│    140                 "format": "JSON list, each object corresponds to a topic. Object format: {'id': '...', 'comparisons': [{'comparison': {'A': 'cg_mcts_full', 'B': 'top_p'}, 'scores':   │
 │        {'A': {...}, 'B': {...}}, 'winner': 'A'}, ...]}"                                                                                                                   │
 │    141               }                                                                                                                                                    │
 │    142             ]                                                                                                                                                      │
 │    143           }                                                                                                                                                        │
 │    144         },                                                                                                                                                         │
 │    145         {                                                                                                                                                          │
 │    146           "action_type": "run_script",                                                                                                                             │
 │    147           "description": "Run LLM referee evaluation script. Note: This will call paid APIs and may incur costs.",                                                                             │
 │    148           "details": { "command": "python evaluate_llm_as_judge.py" }                                                                                              │
 │    149         }                                                                                                                                                          │
 │    150       ]                                                                                                                                                            │
 │    151     },                                                                                                                                                             │
 │    152     {                                                                                                                                                              │
 │    153       "step_number": 4,                                                                                                                                            │
 │    154       "title": "Result Analysis and Visualization",                                                                                                                                 │
 │    155       "description": "Using Python scripts to perform statistical analysis (such as paired t-tests) on all collected evaluation data and generate charts (such as box plots) for visual comparison of performance.",                                │
 │    156       "actions": [                                                                                                                                                 │
 │    157         {                                                                                                                                                          │
 │    158           "action_type": "generate_code",                                                                                                                          │

│    159           "description": "Create a Python script or Jupyter Notebook for data analysis and visualization.",                                                                           │
 │    160           "output_filename": "analyze_results.py",                                                                                                                 │
 │    161           "details": {                                                                                                                                             │
 │    162             "purpose": "Summarize all evaluation data, perform statistical tests, and generate charts for the paper.",                                                                                   │
 │    163             "implementation_details": [                                                                                                                            │

 │    164               "Use the `pandas` library to load `results_with_novelty.json` and `llm_judge_evaluations.json`, and process them into DataFrames that are easy to analyze.",                                  │
 │    165               "For each scoring dimension of V_novelty and LLM-as-a-Judge, perform paired t-tests (using `scipy.stats.ttest_rel`) between the scores of `cg_mcts_full` and each of the other methods, and print the p-values.",    │
 │    166               "Using the `matplotlib` and `seaborn` libraries, generate box plots or bar charts for each key metric (such as V_novelty, innovation potential scores, etc.) to visually compare the performance distributions of all methods.",                      │
 │    167               "Save the generated charts as high-resolution image files (such as .png or .pdf)."                                                                                               │
 │    168             ],                                                                                                                                                     │
 │    169             "inputs": [                                                                                                                                            │
│    170               {"filename": "results_with_novelty.json", "format": "File generated in step 3."},                                                                            │
│    171               {"filename": "llm_judge_evaluations.json", "format": "File generated in step 3."}                                                                            │
 │    172             ],                                                                                                                                                     │
 │    173             "outputs": [                                                                                                                                           │
 │    174               {"filename": "novelty_comparison.png", "format": "Box plot image file comparing the V_novelty scores of various methods."},                                                     │
 │    175               {"filename": "llm_judge_scores.png", "format": "Box plot or bar chart image file comparing the scores of various methods in LLM judge evaluation."},                                         │
 │    176               {"filename": "statistical_analysis_report.txt", "format": "Text file containing the results of all t-tests (p-values)."}                                              │
 │    177             ]                                                                                                                                                      │
 │    178           }                                                                                                                                                        │
 │    179         },                                                                                                                                                         │
 │    180         {                                                                                                                                                          │
 │    181           "action_type": "run_script",                                                                                                                             │

 │    182           "description": "Run analysis script, generate statistical results and chart files.",                                                                                                 │
 │    183           "details": { "command": "python analyze_results.py" }                                                                                                    │
 │    184         }                                                                                                                                                          │
 │    185       ]                                                                                                                                                            │
 │    186     }                                                                                                                                                              │
 │    187   ]                                                                                                                                                                │
 │    188 }                                                                                                                                                                  │
 ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Okay, the detailed experimental plan has been written to the file ${PWD}/experiment/experimental_plan.json.

The plan covers four main steps from dataset preparation to final result analysis, and details the script functions, input/output files, and their formats that need to be created in each step. You can follow this plan step by step to complete your paper's experiment.