
  We first had the AI analyze the meaning of the current research topic and explore potential areas for our work.
  Our initial plan was to leverage AI for the following literature review steps:
   1. Generate keywords based on our preliminary ideas to retrieve relevant literature.
   2. Have the AI filter the retrieved literature by title.
   3. Read the abstracts of the filtered papers to select important ones.
   4. Read the full text of the important papers to extract new research ideas.

  However, this process did not prove successful in practice.

  Our experience showed that the keywords generated by the AI were inaccurate, leading to a very limited number of retrieved articles.
  Furthermore, during the paper screening phase, the AI exhibited "hallucinations," making inaccurate or irrelevant judgments.
  (We hypothesize that this might be because many papers on this current topic are analytical or opinion-based; other concrete techique topics might yield better results.)

  Ultimately, we had to manually select some papers and then used AI to generate summaries for them.

  Here is the prompt we used for generating paper summaries (for Gemini CLI):
  ```
  "You are a senior AI researcher. Please use the ReadFile tool to read the '.pdf' file. What is the main content of this paper? What problem does it solve? What are the detailed methods (use formulas if necessary)? What experiments were conducted? Please summarize this paper for me, thinking point by point, outputting all content, and saving the result to a file."
  ```
  ('summary.py' which  generate 'summary.md', and we further renamed it as 'summary.txt')
