| name | paper2code-enhanced |
| description | Paper2Code-Enhanced — multi-agent paper-to-code pipeline (planning → analyzing → coding) using PaperCoder. Generates working implementations from ML papers. ~$0.50–0.70 per run, ~1 hour per run. |
| type | skill-manual |
| created | "2026-05-24T00:00:00.000Z" |
| updated | "2026-05-24T00:00:00.000Z" |
| tags | ["paper-to-code","ml","reproduction","papercoder","autonomous-agents"] |
| triggers | ["paper to code","generate from paper","reproduce paper","paper2code","TreeOfThoughts","Yao et al"] |
Paper2Code-Enhanced Skill Manual
Paper2Code-Enhanced transforms ML papers into code repositories via PaperCoder — a three-stage multi-agent pipeline (planning, analysis, coding).
Repo: /home/ty/Repositories/ai_workspace/Paper2Code-Enhanced/
Entry: codes/pipeline.py (Python API)
When to Use
- Paper has an official implementation you want to reproduce or study
- You want a working scaffold from a paper without reading it line-by-line
- Paired with OrCAID commit0 for validation+fixup
- Cost: ~$0.50–0.70 per full run (MiniMax M2.7)
- Runtime: ~1 hour per full run
Do NOT use for:
- Simple papers with obvious implementations (overkill)
- Real-time needs (1 hour per run)
- Papers without clear algorithmic specs
Python API (Recommended)
import sys
sys.path.insert(0, '/home/ty/Repositories/ai_workspace/Paper2Code-Enhanced/codes')
from pipeline import run_pipeline, PipelineConfig
config = PipelineConfig(
paper_name="TreeOfThoughts",
pdf_json_path="/path/to/arxiv_2605.15612_cleaned.json",
output_dir="/home/ty/Repositories/ai_workspace/paper2code-projects/TreeOfThoughts/",
output_repo_dir="/home/ty/Repositories/ai_workspace/paper2code-projects/TreeOfThoughts_repo/",
)
result = run_pipeline(config)
print(result.status, result.output_repo_dir)
Requires PYTHONPATH — always prepend the codes directory:
import sys
sys.path.insert(0, '/home/ty/Repositories/ai_workspace/Paper2Code-Enhanced/codes')
Three Stages
| Stage | Script | What it does |
|---|
| Planning | codes/1_planning.py | Decomposes paper into modules, generates planning_artifacts/ |
| Analyzing | codes/2_analyzing.py | Extracts algorithm details, produces analyzing_artifacts/ |
| Coding | codes/3_coding.py | Generates the full repository in <PaperName>_repo/ |
Each stage writes results to SQLite DB (codes/db.py) and artifacts to output_dir/.
Input Preparation
From arXiv JSON (cleaned format)
cd /home/ty/Repositories/ai_workspace/Paper2Code-Enhanced
uv run python codes/0_pdf_process.py \
--input_json_path examples/Transformer.json \
--output_json_path examples/Transformer_cleaned.json \
--mode auto
From PDF directly
uv run python codes/0_pdf_process.py \
--input_json_path path/to/paper.pdf \
--output_json_path output_cleaned.json \
--mode auto
Modes: auto (default, VLM if key available, else local), vlm (Vision-Language Model), local (pypdf, offline), olmocr (olmOCR pipeline)
From LaTeX source
cd scripts
uv run bash run_latex.sh
Output Structure
outputs/
├── <PaperName>/ # Artifacts (planning, analyzing, coding artifacts)
│ ├── planning_artifacts/
│ ├── analyzing_artifacts/
│ ├── coding_artifacts/
│ └── accumulated_cost.json
└── <PaperName>_repo/ # Generated code repository (THE OUTPUT YOU WANT)
├── config.yaml
├── main.py
├── utils.py
├── tasks/
└── tot/ # (for TreeOfThoughts paper)
Configuration (.env)
Set in /home/ty/Repositories/ai_workspace/Paper2Code-Enhanced/.env:
LLM_API_KEY=your_key_here
LLM_BASE_URL=https://api.minimax.io/v1
LLM_MODEL=MiniMax-M2.7
Base URL must be https://api.minimax.io/v1 (OpenAI-compatible endpoint). The /anthropic/v1/messages endpoint only serves Claude models and returns 404 for MiniMax.
MiniMax M2.7 recommended — supports thinking/reasoning blocks natively via Anthropic SDK.
CLI (Broken — Use Python API)
The CLI scripts (scripts/run.sh, codes/1_planning.py, etc.) do from db import ... but don't set PYTHONPATH, causing import errors.
Always use the Python API for programmatic runs.
If calling stage scripts directly, prepend PYTHONPATH:
PYTHONPATH=/home/ty/Repositories/ai_workspace/Paper2Code-Enhanced/codes uv run python codes/1_planning.py ...
Common Issues
db module import error
CLI scripts do from db import ... but don't set PYTHONPATH. Use Python API instead.
run.sh empty --gpt_version
If LLM_MODEL env var is empty, GPT_VERSION="" gets passed as --gpt_version "" which scripts interpret as a positional argument. Ensure .env has LLM_MODEL=MiniMax-M2.7 set.
Missing _cleaned.json suffix
Pipeline expects <paper_name>_cleaned.json (with the _cleaned suffix). Running 0_pdf_process.py produces this.
OrCAID Integration
Paper2Code-Enhanced generates a repository; OrCAID commit0 validates and fixes it:
- Paper2Code generates →
<PaperName>_repo/ with implementation
- OrCAID commit0 validates → runs pytest, parses JSON report, applies patches
cd /home/ty/Repositories/ai_workspace/OrCAID && \
uv run python -m orcaid.cli \
--task=commit0 \
--model=minimax/MiniMax-M2.7 \
--multi_agent=false \
--max_iterations=5 \
--patch_target /home/ty/Repositories/ai_workspace/paper2code-projects/TreeOfThoughts_repo/
Key Files
| File | Purpose |
|---|
codes/pipeline.py | Python API entry (run_pipeline, PipelineConfig, PipelineResult) |
codes/1_planning.py | Planning stage |
codes/2_analyzing.py | Analyzing stage |
codes/3_coding.py | Coding stage |
codes/0_pdf_process.py | PDF/JSON ingestion + cleaning |
codes/eval.py | Evaluation script |
codes/db.py | SQLite persistence (sqlmodel) |
examples/Transformer_cleaned.json | Example input |
Constraints
- Always use Python API for programmatic runs (CLI broken due to db module path)
- Prepend
PYTHONPATH when calling stage scripts directly
LLM_MODEL must be set in .env before running- ~1 hour per full run — don't use for trivial papers
- Cost ~$0.50–0.70 per run with MiniMax M2.7
