Paper Cluster Survey V2.2#

Overview#

Turn raw paper URLs and PDFs into usable review inputs. Extract structured metadata and text evidence first, then classify the papers, produce a classification table, and write a review that follows common academic survey conventions instead of a rigid fill-in-the-blanks template.

Workflow#

1. Normalize the source set#

• Accept multiple local PDF paths, arXiv URLs, DOI URLs, and general paper URLs.
• Use scripts/normalize-sources.mjs when the source set is mixed or should be stored as a reusable manifest.
• Preserve the original source string for traceability.

2. Extract paper records before reasoning#

• Use scripts/extract-paper-records.mjs to turn PDFs and URLs into structured records before classification.
• The extraction pass should gather as much of the following as possible:
  • title
  • authors
  • year
  • venue
  • abstract
  • task
  • method
  • datasets
  • metrics
  • main_contribution
  • limitations
  • source
  • extraction_notes
• Treat extracted records as the primary context for classification and survey drafting.
• If important fields are missing, only fall back to direct source reading for the specific missing details.

Read extraction-pipeline.md when deciding how much to trust the extracted fields and when to re-open the raw source.

3. Verify evidence quality#

• Do not classify from titles alone when abstract or body text is available.
• Prefer abstract, introduction, and method section.
• Mark uncertain inferences explicitly.
• If the extractor had to fall back to weak methods, keep claims conservative.

4. Design the classification scheme#

• Produce a classification scheme before writing the review.
• Prefer task-based categories first.
• If tasks are too similar, classify by method family.
• Use application-domain categories only when they best explain the corpus.
• Keep the taxonomy shallow unless the corpus is large.
• Assign one primary category to each paper unless the user explicitly wants multi-label grouping.

Read taxonomy-guidelines.md when the category design is ambiguous.

5. Output the classification table#

• Always provide one classification table before the review.
• Include:
  • paper
  • year
  • category
  • rationale
  • evidence used
• Keep rationales brief and evidence-based.

6. Decide the review shape#

Default rule:

• Write one integrated literature review for the entire corpus after the classification table.

Exception:

• If the user explicitly asks for "each category write a survey", "分别写综述", "per-category review", or equivalent, write separate review sections for each category.

7. Write the review in academic survey style#

The review must read like a normal survey paper, not a bullet summary dump.

• Use a concise academic title.
• Include an abstract when the output is formal enough to justify it.
• Include keywords when they help position the review.
• Use an introduction that explains background, significance, scope, source selection, and the organizing logic of the review.
• Organize the main body by the most defensible basis for the corpus:
  • chronology
  • research themes
  • method families
  • viewpoints or schools
• End with a conclusion or concluding discussion.
• Add future directions, outlook, or open problems when the corpus supports them.
• List references in GB/T 7714 style when bibliographic data is available.

Typical sections in a strong review are:

• title
• abstract
• keywords
• introduction
• themed main sections
• discussion, conclusion, or both
• future directions or open problems when useful
• references

Not every output needs every section. Match the structure to the user's request, the corpus size, and the field while staying recognizably review-like.

Read review-paper-style.md when drafting the prose review or choosing section structure.

8. Keep the prose review-like#

• Prefer connected academic prose over bullet dumps.
• Use tables only to support comparison, not replace the review.
• Do not invent datasets, metrics, or reference details.
• If extracted metadata is incomplete, keep partial references and state what is missing.

Output Contract#

Return results in this order unless the user asks otherwise:

1. Corpus summary
2. Classification scheme
3. Classification table
4. Formal review article
5. References

If the user wants structured output, read output-schema.md.

Bundled Scripts#

scripts/normalize-sources.mjs#

• Normalize mixed PDF and URL inputs into a JSON manifest.
• Use when the source set is large, mixed, or should be reused.

scripts/extract-paper-records.mjs#

• Fetch URLs, resolve likely paper metadata, and extract paper text evidence from URLs or PDFs.
• Prefer this script before asking the model to reason over a large source set.
• Use its output as the main context object for classification and review drafting.

scripts/render-formal-review-template.mjs#

• Render a flexible academic-review scaffold from structured paper records.
• Default to one integrated review.
• Use --per-category only when the user explicitly asks for separate category reviews.

Quality Bar#

• Run extraction before classification unless the user already gave structured paper records.
• Keep classification and review consistent with extracted evidence.
• Use raw source re-reading only to fill important gaps.
• If the extractor had to rely on weak fallbacks, say so.