<%* const clean = (value) => (value || "").replace(/\r?\n/g, " ").trim(); const yaml = (value) => clean(value).replace(/"/g, '\"'); const fileSafe = (value) => clean(value) .replace(/[\/:*?"<>|#^[]]/g, "") .replace(/\s+/g, " ") .slice(0, 120) .trim();

const paperTitle = clean(await tp.system.prompt("Paper title", tp.file.title)); const topic = clean(await tp.system.prompt("Topic / field", "")); const paperUrl = clean(await tp.system.prompt("Paper URL", "")); const pdf = clean(await tp.system.prompt("PDF wikilink or file name", "")); const venue = clean(await tp.system.prompt("Venue", "")); const year = clean(await tp.system.prompt("Year", "")); const status = await tp.system.suggester(["draft", "reading", "completed"], ["draft", "reading", "completed"], false, "Status"); const noteTitle = ${paperTitle || "Untitled"} Read Deep; await tp.file.rename(fileSafe(noteTitle) || "Read Deep"); -%>

title: "<% yaml(noteTitle) %>" created: <% tp.date.now("YYYY-MM-DD HH:mm") %> type: paper-reading status: <% status %> tags:

• paper-reading
• read-deep paper_title: "<% yaml(paperTitle) %>" paper_url: "<% yaml(paperUrl) %>" pdf: "<% yaml(pdf) %>" venue: "<% yaml(venue) %>" year: "<% yaml(year) %>" topic: "<% yaml(topic) %>" reading_stage: read-deep

<% paperTitle %>

• Paper: <% paperUrl %>
• PDF: 论文.pdf
• Venue / year: <% venue %> <% year %>
• Topic: <% topic %>

1. To Understand List

2. Abstract

• Model / method:
• Problem:
• Method:
• Result:

• Keywords:

3. Problem-Solution Chain

Introduction Notes

• Core problem:
• Why it matters:
• Previous methods:
• Limitations:
• Main contributions:

4. Related Works

What this paper does

Prior work map

5. Key Figure / Pipeline

• Figure:
• Input:
• Output:
• Main modules:
• Difference from prior methods:
• My explanation in plain language:

Questions about the figure

6. Methods

Core idea

Step-by-step process

1. Raw input:
2. Operation:
3. Model / algorithm:
4. Intermediate representation:
5. Training objective:
6. Final output:

Formula / algorithm notes

7. Experiments

Setup

• Datasets:
• Baselines:
• Metrics:
• Training / inference setting:

Main results

Ablation / analysis

• What matters most:
• Failure cases:
• Surprising result:

• Metrics to understand:

8. Conclusion

• Main takeaway:

• Innovation points:

• Reusable methods / experience:

• Problems / limitations:

• Next action: