PaperRun turns machine learning papers into runnable, reproducible repositories. Paste a link, and our agent resolves the environment, wires up the reference implementation, and verifies the paper's core claims.
Free for students and academic labs during alpha.
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$ paperrun reproduce arxiv.org/abs/2403.00835 → Parsing paper........................ done → Resolving environment (py3.11, torch 2.3) done → Locating reference implementation.... done → Building repro repo.................. done → Running headline experiment (Table 2) 3.1x speedup, matches claim ✓ Repo ready: ./cllm-repro (runs in 1 command)
Most ML papers take days to get running: broken dependencies, missing configs, undocumented preprocessing. PaperRun automates that entire slog.
Drop in an arXiv link or PDF. PaperRun extracts the method, hyperparameters, datasets, and evaluation setup.
An agent resolves a working environment, pulls or reconstructs the implementation, and pins every dependency.
One command runs the headline experiment and reports how the results compare against the paper's claims.
Started because we were tired of losing whole weekends to "pip install -r requirements.txt" failing on line one.
Automatic detection of framework versions, CUDA constraints, and dataset dependencies, pinned into a reproducible lockfile.
Headline experiments re-run automatically, with a side-by-side report of reproduced metrics versus published numbers.
Export a container or a plain repo. Run locally, on your lab cluster, or on rented GPUs with no changes.
Every module linked back to the equation or section of the paper it implements, so reading the code teaches you the paper.
Comparing against prior work? Pull reproducible baselines from papers we have already built, instead of rebuilding them yourself.
Shared reproduction library for your research group, so nobody rebuilds an environment a labmate already fixed.
Join the waitlist for the private alpha. Free for students and academic labs.
Thanks. You are on the list, we will reach out soon.