deep-dive

name	deep-dive
description	Deep analysis of a single paper. Triggers on "分析这篇", "deep dive", "这篇论文讲了什么", paper ID or arXiv ID input.

Deep Dive Analysis

Flow

1. Resolve paper_id from context: If the user refers to a paper by index (e.g. "第3篇", "分析上面那篇"), resolve it from the papers discussed earlier in this conversation. If user gives an explicit paper ID or arXiv ID, use that directly.
2. Call paper_show(paper_id) to get full paper details
3. Call paper_profile() to understand user's research context
4. Check for full text: Try paper_sections(paper_id) — if it returns sections, mark analysis_basis = "full_text". If not parsed but a PDF exists, call paper_parse(paper_id) to parse it first. If no PDF available, mark analysis_basis = "abstract".
5. Extract structured profile: Call paper_extract(paper_id) to get task/method/dataset/baseline/metric data.
6. Show tables (if available): If analysis_basis == "full_text", call paper_tables(paper_id) to show extracted tables.
7. [FORK] "全面分析，还是关注某个角度？（方法/实验/跟你的关联/可信度）"
8. Generate analysis based on user's choice (or full analysis by default). If full text is available, use paper_ask(paper_id, question) for specific questions.

Source Annotation Rule

IMPORTANT: Every analysis output MUST start with a source annotation:

• If analysis_basis == "full_text": show [基于全文] at the top
• If analysis_basis == "abstract": show [基于摘要] at the top This helps the user understand the depth and reliability of the analysis.

Analysis Template (for AI generation)

When generating the analysis, use tables for structured data:

• 核心信息表: | 字段 | 值 | (title, authors, venue, year, citations)
• 方法: key techniques, architecture, loss function. If full text available, drill into method section.
• 实验结果: use table | 指标 | 本文 | Baseline1 | Baseline2 | — pull from extracted profile when available
• 优劣势对比: use table | 维度 | 本文 | 相关工作1 | 相关工作2 |
• 可信度评估: call paper_credibility(paper_id) — show venue tier, code availability, reproducibility risk
• 结论与建议: 研究价值判断 + 与用户研究方向的关联 + 是否值得深入跟进 + 关键参考文献

After analysis

Auto-track: call paper_note_add to save note to workspace (this is internal tracking, not file export).

If first_use is true in the response, tell the user: "（首次自动记录）我把分析笔记保存到了工作区（.paper-agent/），方便以后查阅。如果不需要自动记录，告诉我'不要记录'就行。" If first_use is false, just briefly say: "已记录到工作区。"

Auto-save: call paper_save_report(report_type="analysis", content=<analysis markdown>, filename="{paper_id}.md") to persist the analysis as a file.

[CONTEXT-AWARE FORK] — Based on the analysis result, suggest next steps:

Analyze the paper's characteristics and suggest accordingly:

• If paper has high citation count (>50) or is a seminal work: "分析笔记已保存至 {path}。\n💡 下一步建议：\n1. 追踪引用链 — 看看谁在跟进这个方向\n2. 找类似方法的论文做对比\n（说编号或告诉我你想做什么）"

• If paper has code available: "分析笔记已保存至 {path}。\n💡 下一步建议：\n1. 做实验计划 — 这篇有代码，可以复现/改进\n2. 追踪引用链看后续工作\n（说编号或告诉我你想做什么）"

• If paper's method is novel (novelty_claim is strong): "分析笔记已保存至 {path}。\n💡 下一步建议：\n1. 看看这个方法能不能用到你的课题 — 生成 research ideas\n2. 找同方向的论文做对比\n（说编号或告诉我你想做什么）"

• Default: "分析笔记已保存至 {path}。\n💡 下一步建议：\n1. 追踪引用链\n2. 找 [{paper_method_family}] 方向的更多论文\n（说编号或告诉我你想做什么）"

If user picks citation trace

Route to paper_citation_trace(paper_id). Carry the paper's method_family and topics as context for the citation exploration.

Rules

• 2 checkpoints (analysis angle + after analysis). Both workspace note-add and file save are automatic.
• If user explicitly says "不要记录", skip the note_add call and the save_report call.
• FORK suggestions must be based on the actual analysis result (citation count, code availability, method novelty). Never give the same generic options.