ai-paper-researcher

Search and download arXiv AI papers with broad or top-tier filtering, sorting by relevance or date, avoiding duplicates via local CSV management.

AI Academic Paper Researcher

1. Skill Positioning & Core Objective

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

2. Tools & Dependencies

• Execution Script: python arxiv_tool.py
• 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).

3. Sorting Strategy Selection

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

• 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.
• Tracking Latest Frontiers (Latest Trends): If the user explicitly requests "latest", "this year", or "recent weeks" papers, please use --sort date.

4. Two Retrieval Modes

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

Mode A: Broad Search (All Relevant Mode)

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

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

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

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"). Execution Logic:

1. Read Target List: Use the file reading tool to view the contents of target.csv to get the list of target conferences/journals.
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.)
3. LLM Semantic Verification (CRITICAL):
   • Carefully review the comment field in the JSON of each candidate paper.
   • Determine whether any conference listed in target.csv is present in the comment.
   • 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.
   • If the comment is empty, or does not contain a publication statement for the target conference, you MUST exclude the paper.
4. Exclude already downloaded papers (is_downloaded: true).
5. Proceed to the download process for the successfully verified papers.

5. Download & File Persistence

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

6. Reporting Standard

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

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