version: "1.0.0" name: paper-scout description: > Discover and recommend latest arXiv papers matching user research interests. Use when user says: 推荐今日论文, paper scout, 每日论文, daily papers, paper recommendation.

Paper Scout — Daily Paper Discovery

⚙️ Step 0 — Read the Research Profile (Always First)

Before running any capability, load the user's research profile.

Location: ~/.openclaw/workspace/research-claw-config.md

If this file does not exist, use these defaults silently and mention at the end:

💡 想定制推荐兴趣？试试说「更新我的研究画像」

# Default profile (used when no config found)
research_direction: "Large language models, reinforcement learning, agentic AI"
seed_papers: []
keywords:
  - large language models
  - reinforcement learning
  - agentic AI / AI agents
  - retrieval-augmented generation
  - multimodal models
whitelist_authors: []
learned_preferences:
  accept: []
  reject: []

Config fields reference:

• research_direction — free-text description of the user's research focus
• seed_papers — list of arXiv IDs the user considers gold-standard references
• keywords — interest topics used for Paper Scout search queries
• whitelist_authors — researcher names to prioritize in recommendations
• learned_preferences.accept — keywords/topics user has explicitly liked
• learned_preferences.reject — keywords/topics user has skipped or disliked

Goal: Find today's top arXiv papers matching user interests. Output Top 5–10 with relevance scoring.

Triggers: 推荐今日论文 · 每日论文 · paper scout · daily cron job

Step 1 — Build search queries

From the loaded profile, extract keywords and construct arXiv query URLs:

http://export.arxiv.org/api/query?search_query=all:{KEYWORD}&sortBy=submittedDate&sortOrder=descending&max_results=25&start=0

• Replace spaces in keywords with + (e.g., large+language+models)
• Run 2–4 queries covering different interest areas
• If the user has seed_papers, also fetch their metadata via:

`` https://export.arxiv.org/abs/{ARXIV_ID} `` Use these to calibrate what "relevant" means (topics, methods, problem framing).

Step 2 — Fetch and parse

Use web_fetch for each query URL. Parse the XML Atom response:

<entry>
  <title>...</title>           <!-- paper title -->
  <author><name>...</name></author>   <!-- first/all authors -->
  <summary>...</summary>       <!-- abstract -->
  <id>http://arxiv.org/abs/XXXX.XXXXX</id>   <!-- canonical URL -->
  <published>2026-03-26T...</published>       <!-- submission date -->
  <arxiv:primary_category term="cs.LG"/>     <!-- category -->
</entry>

Filter: Only keep papers published within the last 3 days (compare <published> to today's date in Asia/Shanghai timezone). If fewer than 5 papers remain, extend to 7 days and note it.

Step 3 — Score and rank

Score each paper 1–5 on relevance:

Sort descending by score. Keep Top 5 (or Top 10 if user asks for more).

Step 4 — Format and output

📡 今日论文推荐 | Daily Paper Scout
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🗓️ {DATE} | 匹配兴趣: {COMMA_SEPARATED_KEYWORDS}
 
1️⃣ **{Title}**
   👤 {First Author} et al. ({Year})
   🏷️ {category, e.g. cs.LG · cs.AI}
   💡 {One-sentence summary — Chinese or English, whichever matches user preference}
   🎯 相关原因: {1 sentence — why this matches user's profile}
   🔗 {arXiv URL}
 
2️⃣ **{Title}**
   ... (repeat)
 
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📝 想深读某篇？发链接说 "帮我读一下" | 加入待读说 "加入待读 [链接]"

Step 5 — Auto-learn from feedback

If the user reacts to a recommended paper with:

• "不错" / "这个好" / "有意思" / "精读" → extract keywords from that paper's title/abstract, add to learned_preferences.accept in config
• "skip" / "没意思" / "不相关" → extract keywords, add to learned_preferences.reject

Update ~/.openclaw/workspace/research-claw-config.md immediately.

Cron setup

If user wants daily delivery:

• Time: 9:00 AM (Asia/Shanghai)
• Prompt: 推荐今日论文
• Channel: user's preferred channel (Discord, Telegram, etc.)

⚠️ Error Handling