Paper Reproduction by Python

This skill should be used when the user asks to "reproduce a paper", "implement paper methods in Python", "extract paper content to Markdown", or works on paper reproduction tasks. Use for TeX-first extraction, modular Python implementation, and bilingual documentation.

Follow this workflow end-to-end unless the user explicitly asks to skip steps

1) Intake and scope

• Confirm input artifacts: TeX source path(s), PDF path, supplementary files, target repository, and expected outputs.
• State assumptions explicitly when information is missing.
• Keep approach adaptable to the specific paper; do not force a fixed dependency stack or rigid project template.
• Check whether the working folder already contains paper source files (.tex, .bib, style files, figures).
• Check whether the working folder contains user-preprocessed documents (.md, .json, images such as .png, .jpg, .svg).
• Source priority rule (read in order, stop when sufficient):
  1. TeX sources (preferred): If usable TeX source files (.tex, .bib, style files) are present, use them as the primary source.
  2. User-preprocessed documents (secondary): If TeX is absent or incomplete, read user-provided documents (.md, .json) and images (.png, .jpg, .svg) that may contain pre-extracted paper content.
  3. PDF fallback (last resort): Only when both TeX and user-preprocessed documents are unavailable or insufficient, fall back to PDF extraction.

2) Source extraction (TeX → preprocessed docs → PDF)

• TeX path (highest priority):

  • Parse and read the main TeX project structure first (main.tex or equivalent entry file and includes).
  • Preserve original scientific wording when converting relevant content to Markdown notes.
  • Resolve equations, theorem blocks, citations, and appendices from source files whenever possible.
  • Record unresolved include/bibliography issues explicitly; do not invent missing content.

• User-preprocessed documents path (secondary):

  • Read Markdown files (.md) that may contain paper content extracted by the user.
  • Read JSON files (.json) that may contain structured paper data (metadata, sections, references).
  • View images (.png, .jpg, .svg) that may contain paper figures, tables, or scanned pages.
  • Preserve original content; do not summarize or paraphrase.
  • Note the source of each piece of information (which file, which section).

• PDF fallback path (lowest priority, when all else fails):

  • Extract paper content page by page into Markdown, preserving the original wording.
  • Do not summarize, paraphrase, or rewrite scientific statements.
  • Preserve structure faithfully:
    • Title, authors, affiliations, abstract, sections, subsections.
    • Equations (LaTeX-friendly when possible), theorem/lemma/proposition blocks.
    • Tables, figure captions, references, appendices, footnotes.
  • If a PDF is scanned or partially unreadable:
    • Run OCR and mark uncertain spans clearly.
    • Never silently invent missing text.
  • Include image references/placeholders when figures cannot be represented as plain text.
  • Produce one primary output file such as paper_fulltext.md.

3) Extraction quality checks

• Validate completeness before moving to reproduction:
  • Section/headings coverage matches the TeX project or PDF source used.
  • Key equations and algorithm blocks are present.
  • References and appendices are included if present in the source.
• Report known extraction limitations and exact affected files/pages/segments.

4) Reproduction planning (paper-specific)

• Build a reproduction plan from the extracted source materials (TeX-derived notes and/or Markdown), not from memory.
• Identify:
  • Problem definition, notation, assumptions, and objective functions.
  • Algorithm steps and required components.
  • Dataset generation/loading, training/optimization, and evaluation protocol.
  • Baselines and ablations required for faithful reproduction.
• If details are missing or ambiguous, call out the gap and provide a conservative implementation choice with rationale.

5) Python implementation principles

• Implement with modular design and clear boundaries:
  • Separate concerns (data, models/algorithms, training/solver loop, evaluation, utils, config).
  • Prefer low coupling and high cohesion.
• Avoid monolithic scripts:
  • Split code into modules whenever responsibilities can be separated.
  • Prefer one clear responsibility per file.
• File size guideline:
  • Keep a single source file under ~200 lines whenever practical.
  • If a file grows beyond ~200 lines, refactor into submodules unless there is a clear reason not to.
• Keep dependencies minimal and paper-driven; choose tools based on the paper's actual needs.
• Avoid over-engineering early; start from the minimum reproducible core, then extend.
• Add tests/checks for critical math or pipeline steps where feasible.
• Preserve reproducibility:
  • deterministic seeds when applicable,
  • explicit config for key hyperparameters,
  • clear experiment entry points.

6) Logging and data persistence

• All reproduction experiments must save execution logs and output data:
  • Logs: Save console output, training progress, and error messages to timestamped log files (e.g., logs/experiment_YYYYMMDD_HHMMSS.log).
  • Data: Save all generated data (metrics, model checkpoints, intermediate results) to structured files (e.g., JSON, CSV, or pickle) in a dedicated outputs/ or results/ directory.
  • Configuration snapshot: Save the exact configuration/hyperparameters used for each run alongside the outputs.
• Logging should capture enough detail to:
  • Trace the execution flow for debugging.
  • Compare results across multiple runs.
  • Identify the exact conditions under which results were produced.

7) Result verification and comparison

• After running reproduction experiments, compare results against the paper's reported data:
  • Extract quantitative metrics (tables, figures) from the paper source.
  • Compute the same metrics from reproduction outputs.
  • Document both paper-reported values and reproduction values side by side.
• Identify discrepancies:
  • If reproduction results deviate significantly from paper results, investigate potential causes:
    • Implementation errors (algorithm bugs, missing steps).
    • Hyperparameter differences.
    • Random seed or initialization differences.
    • Dataset or preprocessing differences.
  • Document all findings and fixes applied.
• Validation criteria:
  • Define acceptable tolerance for numerical differences based on the paper's domain.
  • Flag results outside tolerance for further review.
• The goal of reproduction is accurate reconstruction of paper results; verification is mandatory, not optional.

8) README header requirements (paper metadata)

• Every reproduction project README must start with paper metadata before any other content:

  • Paper title (original title as published)
  • Authors (full names, affiliations, and email addresses if available)
  • Abstract (verbatim copy of the original abstract)

• For README_zh-CN.md:

  • Paper title: provide Chinese translation if original is in English; keep original if paper is in Chinese.
  • Authors: keep original names and affiliations; translate country/region names if needed.
  • Abstract: provide faithful Chinese translation of the abstract.

• Format example (English README):

  markdown

  # [Paper Title]
  
  **Authors:** Author Name¹, Co-Author Name²
  **Affiliations:**
  ¹ Department, University, Country ([e-mail])
  ² Lab, Institution, Country ([e-mail])
  
  ## Abstract
  
  [Verbatim abstract text from the paper]
  
  ---
  
  [Then reproduction project content begins...]
  

9) README update requirements (bilingual + images)

• Generate and maintain two README files after code changes:
  • README.md (English original)
  • README_zh-CN.md (Chinese translation aligned with the English version)
• After the paper metadata header, ensure both files include:
  • paper citation and target claims to reproduce,
  • environment/setup commands,
  • project structure overview and module responsibilities,
  • how to run main experiments,
  • expected outputs/metrics and where artifacts are saved,
  • known deviations from the paper and why.
• Insert generated figures/images into both README files using valid relative Markdown image paths.
• Image output granularity rule: unless multi-panel comparison is explicitly needed, save one chart per image file (one figure per file).
• Keep both README files aligned with actual code paths and commands.
• Keep Chinese content as faithful translation of English technical content (no missing key steps).

10) Output contract

• Deliver:
  • source-derived extraction notes/file(s) (TeX-first, PDF fallback when needed),
  • implemented/updated Python code,
  • README.md and README_zh-CN.md with embedded generated images.
• Clearly separate:
  • exact extracted content (verbatim from source),
  • your implementation notes and engineering decisions.
• Report reproduction status:
  • which claims/experiments were successfully reproduced,
  • known gaps or deviations from paper results, with reasons.