Reproducing paper methods
Break a paper into implementation, data processing, and experiment settings. Run controlled experiments and identify differences between published and reproduced results.
Inputs
Paper, official code, dataset documentation, runtime environment, and target metrics.
Deliverables
Runnable code, environment specification, experiment logs, result comparisons, and explanations of differences.
Step-by-step workflow
Break the work into verifiable stages, then choose models and supporting tools.
- 01
Align the method and implementation
Extract formulas, algorithms, and processing steps, noting parameters the paper does not specify.
- 02
Prepare the environment
Use coding assistance to configure dependencies, entry points, and data paths. Fix versions and random seeds.
- 03
Run a minimal experiment
Test a small sample end to end, then run baselines and full experiments, keeping logs.
- 04
Compare and explain differences
Check metrics, data splits, and resource limits. Distinguish implementation errors from experimental differences.
Required model capabilities
Combine models for the actual stages. The directory includes candidates matching one or more of these capabilities.
View matching modelsSupporting tools
Selection & delivery checks
- Choose models that understand repository code and figures in papers.
- A successful reproduction requires actual execution and recorded metrics.
- Check data leakage, test-set use, and third-party code licenses.
An example request
Read a paper's figures and methods, implement the reproduction in Python, run experiments, and explain differences from the published metrics.Find models for this request