
# AI:

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.
Experiment-design.txt contains the general idea of the designed experiment,
and experimental_plan.json is the specific steps of the preliminary design.

So far, I have only conducted:
comparison experiments of baseline / cot / tot / ReAct
ablation study w/o Directional Guidance/ Novelty Reward/ Progress Reward


What experimental results do you think I should add?
For example:
Comparison results of LLMs with different specifications (0.6B/1.7B/4B/8B)?
Visualization of clusters in the Automatic Theme Generator?
A few sample demonstrations?
Which ones are necessary? And what others?

# AGENT:

✦ Hello! As a senior AI researcher, I have reviewed your project progress. The experiments you have completed so far are very solid, covering performance comparisons with mainstream methods and ablation studies of your own key modules, which lays a solid foundation for the core argumentation of your paper.

Regarding the questions you raised, I believe your ideas are very valuable. Below, I will combine your ideas with some of my suggestions and prioritize them.

Evaluation of Your Ideas

1. Comparison results of LLMs with different specifications?
   * Evaluation: This is a very important and almost necessary experiment. It demonstrates the scalability and generality of your proposed method (the core idea of cg_mcts_qwen.py). A good method should not work only on a specific model size.
   * Suggestion: If resources allow, select at least 3 models of different magnitudes for comparison, such as small/base/large (as you mentioned 0.6B/1.7B/8B). Ideally, you would want to see that as model capabilities increase, the performance improvement brought by your method (compared to Baseline) is stable or more significant.

2. Visualization of clusters in the Automatic Theme Generator?
   * Evaluation: This is a highly recommended addition. It serves the interpretability and intuitiveness of your paper. Visualizing topic vectors through dimensionality reduction methods like t-SNE or UMAP can intuitively demonstrate that your clustering algorithm indeed groups semantically similar topics together.
   * Suggestion: In the visualization, use different colors to mark different clusters and label representative keywords for each cluster. This will be a very persuasive figure that can prove the effectiveness of a key component in your method in a small amount of space.

3. Several sample showcases (Case Studies)?
   * Evaluation: This is absolutely necessary. Quantitative metrics (such as BLEU, ROUGE, or model scores) show "good or bad", but qualitative samples show "how good" and "why good".
   * Suggestion: Carefully select 2-3 representative cases. The best cases would be:
       * Show your strengths: Choose a case where your model generates high-quality, novel, and logical ideas, while the Baseline method (such as CoT or ReAct) fails (e.g., getting stuck in repetition, divergent thinking, or premature convergence).
       * Compare MCTS process: You can visually show how the MCTS search tree progressively prunes bad branches and eventually moves toward a good node. This can vividly explain the working mechanism of your method.
       * Show diversity: If your method can generate multiple different high-quality ideas for the same topic, you can showcase this to demonstrate its divergent thinking capabilities.

Additional Suggested Experiments

Besides what you mentioned, I also suggest adding experiments in the following dimensions to further enhance the depth and completeness of your research.

4. Efficiency and Cost Analysis
   * Why: MCTS-based methods typically increase computational overhead (more LLM calls). Reviewers will almost certainly be concerned about how much extra cost your method brings.
   * How to do: Record and compare the average number of LLM API calls, total token consumption, or wall-clock time for different methods (yours, ToT, ReAct, etc.) during the process of generating a complete idea. If your method achieves performance improvement with little additional cost, that will be a major highlight. If the cost increases significantly, you need to argue that the performance improvement is worth the cost.

5. Generalization to Other Domains
   * Why: Current experiments may be focused on a specific domain (e.g., AI papers). If you can prove that your method is equally effective in completely different domains (e.g., business strategy generation, novel plot creation, drug molecule design, etc.), it will greatly enhance the impact of your paper.
   * How to do: Select 1-2 new tasks that are significantly different from your current domain, design appropriate prompts and evaluation criteria, and reproduce your experiments. It doesn't need to be as detailed as the main experiments, but it should be sufficient to demonstrate its generalization capabilities.

6. Parameter Sensitivity Analysis
   * Why: Your cg_mcts_qwen.py implementation likely contains some key hyperparameters (such as the exploration factor c_puct in MCTS, the weights of different terms in the reward function, etc.). Analyzing how changes in these parameters affect the results can prove that your model is robust rather than requiring fine-tuning to achieve good results.
   * How to do: Select 1-2 of the most critical hyperparameters, adjust their values within a certain range, observe changes in core performance metrics, and present them in the form of line graphs.

Priority Summary

Overall, I would categorize these experiments into three priority levels:
  ---


First Priority (Necessary Experiments)
These are the cornerstone of a top conference paper, and the absence of any one of them would significantly weaken the paper's persuasiveness.

1. LLM Scalability Experiments: To demonstrate the generality and scalability of the method.
2. Case Studies: To provide qualitative evidence and intuitively demonstrate the advantages of the method.
  ---

  Second Priority (Highly Recommended)
  These experiments can significantly improve the quality and depth of your paper, making your work stand out among many papers.

   3. Efficiency and Cost Analysis: Address reviewers' concerns about the practical application feasibility.
   4. Clustering Visualization: Provide intuitive and clear explanations for key designs in your method.
   5. Generalization Capability Experiments: Demonstrate the universality of your method and expand the potential impact of your research.
  ---

  Third Priority (Icing on the cake - Good to Have)
  If time and resources permit, completing these experiments will make your research more rigorous and comprehensive.

   6. Parameter Sensitivity Analysis: Demonstrate the robustness of the method.
   7. Human Evaluation: For generative tasks, introducing human preferences as the gold standard is very persuasive, but it has high implementation costs. You can design a simple A/B test, letting evaluators choose between your model's output and the Basel
      ine output.

  We hope these suggestions will help you build a more comprehensive and impactful research project. If you want to discuss the design of a particular experiment in depth, feel free to bring it up anytime.