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AI Academic Paper Researcher\r

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1. Skill Positioning & Core Objective\r

This skill aims to assist researchers in the AI field by searching for arXiv literature and automating PDF downloads and local file management.\r Core Principle: All download records must rely on the local workspace/paper_list/paper_list.csv for deduplication to prevent repeated downloads.\r \r

2. Tools & Dependencies\r

• Execution Script: python arxiv_tool.py\r
• Target Conference List: The target.csv file located in the same directory as this skill (contains the names of top-tier conferences or journals the user follows, e.g., CVPR, NeurIPS, ICLR).\r \r

3. Sorting Strategy Selection\r

Before executing any search, you must decide which sorting parameter (--sort) to use based on the user's intent:\r

• Searching for Classic Theories / Well-known Algorithms (Classic/Influential): If the user searches for specific well-known algorithms (e.g., "Adam", "ResNet") or foundational papers in core fields, you MUST use --sort relevance. Otherwise, because arXiv defaults to returning a large number of newly submitted papers, classic older papers will be pushed out of the search results.\r
• Tracking Latest Frontiers (Latest Trends): If the user explicitly requests "latest", "this year", or "recent weeks" papers, please use --sort date.\r \r

4. Two Retrieval Modes\r

Infer the required mode based on the user's query:\r \r

Mode A: Broad Search (All Relevant Mode)\r

Trigger Condition: The user only provides a research direction without restricting the papers to be published in top-tier conferences.\r Execution Logic:\r

1. Run python arxiv_tool.py search "[query]" --max 15 --sort [selected sorting strategy].\r
2. Ignore the comment field in the JSON response.\r
3. Exclude papers where is_downloaded: true in the results.\r
4. Select the papers most relevant to the user's needs and proceed directly to the download process.\r \r

Mode B: Top-Tier Conference/Journal Strict Filtering (Top-Tier Verification Mode)\r

Trigger Condition: The user explicitly requests "top-tier conferences", "top journals", or specifies certain conferences (e.g., "Help me find Adam-related papers from past ICLR conferences").\r Execution Logic:\r

1. Read Target List: Use the file reading tool to view the contents of target.csv to get the list of target conferences/journals.\r
2. Initial Search: Run python arxiv_tool.py search "[query]" --max 30 --sort [selected sorting strategy]. (Note: The script automatically fetches the latest version of the paper, so if it has been accepted by a top conference, the comment will contain the relevant information.)\r
3. LLM Semantic Verification (CRITICAL):\r
   • Carefully review the comment field in the JSON of each candidate paper.\r
   • Determine whether any conference listed in target.csv is present in the comment.\r
   • Note on Variations: Be tolerant of abbreviations, year suffixes, or non-standard formatting of conference names when matching (e.g., Accepted to ICLR 2015, NeurIPS'23, Appears in CVPR). As long as it semantically refers to the target conference, consider it a successful match.\r
   • If the comment is empty, or does not contain a publication statement for the target conference, you MUST exclude the paper.\r
4. Exclude already downloaded papers (is_downloaded: true).\r
5. Proceed to the download process for the successfully verified papers.\r \r

5. Download & File Persistence\r

1. For the filtered papers, execute the download command one by one: python arxiv_tool.py download [arxiv_id].\r
2. Collect the script's return results.\r \r

6. Reporting Standard\r

After completing the search and download, report the final results to the user:\r

• Explicitly state which retrieval mode was used (Mode A/B) and which sorting method (Date/Relevance).\r
• List the successfully downloaded papers (Format: [ArXiv ID] Title - (Matched conference, if any)).