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float64
1,708.05031
Neural Collaborative Filtering
Xiangnan He; Lizi Liao; Hanwang Zhang; Liqiang Nie; Xia Hu; Tat-Seng Chua
2,017
null
null
Recommender systems
reproduced
Reproduced
4
0
4
6
2026-10-19
https://openrepro.org/papers/1708.05031/
https://openrepro.org/evidence/arxiv-1708-05031-20261019T135218Z.html
https://openrepro.org/ba…e/1708.05031.svg
04ac3824830007550ce23545b33c04ec2f8c290dd7107cca03355bd8ce4ddfc6
67
https://openrepro.org/bundles/arxiv-1708-05031-20261019T135218Z.tar.gz
526c1df742b41900055aa6ca2ab00d065b10fd744f3fb163204fd9df964561cd
The authors Keras code (commit 312aff0, identical in code to the current default branch) was run on MovieLens-1M with the exact README environment (Python 2.7.15, Keras 1.0.7, Theano 0.8.0) on CPU, using the README commands with 8 predictive factors and 20 epochs. All six pre-registered Table 2/3/4 values at 8 factors ...
arxiv-1708-05031-20261019T135218Z
claims_registered_after_output; plan_not_sealed; evidence_not_bound; agent_selected_extraction
some claims were registered after command output had been observed; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it
report
null
Legacy
2026-10-19
9415526a0cc89ac96f5f036ec663c59c3ffed64312cb6452053aeba2ad3f724f
null
1,606.00931
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Jared Katzman; Uri Shaham; Alexander Cloninger; Jonathan Bates; Tingting Jiang; Yuval Kluger
2,018
BMC Medical Research Methodology
10.1186/s12874-018-0482-1
Medical statistics
reproduced
Reproduced
4
0
4
6
2026-10-19
https://openrepro.org/papers/1606.00931/
https://openrepro.org/evidence/arxiv-1606-00931-20261018T225505Z.html
https://openrepro.org/ba…e/1606.00931.svg
b6c148f9a6936f84ea439e56d1c64f0a7dcb49e564e06bcc17ae75d7dd7f225f
63
https://openrepro.org/bundles/arxiv-1606-00931-20261018T225505Z.tar.gz
a3ec5893321c321b834852634486c0dfe6d3c97bdcdac039cbf2375a469fbe07
Rerunning the first author code at the commit matching arXiv v3 (079dee7, 2017-08-08), with the documented command for each experiment in a reconstructed Python 2.7 / Theano 0.9 / Lasagne environment on CPU, reproduces all six DeepSurv C-index values of Table 1 within 0.01: mean of 10 unseeded runs SUPPORT 0.6186 vs 0....
arxiv-1606-00931-20261018T225505Z
claims_registered_after_output; plan_not_sealed; evidence_not_bound; agent_selected_extraction
some claims were registered after command output had been observed; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it
report
null
Legacy
2026-10-19
4618c43c82c309831531bc345505686abff25b5cf1658e4033b8c092f0f678d0
null
1,711.10561
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
Maziar Raissi; Paris G. Perdikaris; George Em Karniadakis
2,017
null
null
Scientific machine learning
reproduced
Reproduced
1
0
1
2
2026-10-19
https://openrepro.org/papers/1711.10561/
https://openrepro.org/evidence/arxiv-1711-10561-20261018T234638Z.html
https://openrepro.org/ba…e/1711.10561.svg
450dad5c4edbfc2ab0c9dce568dcddd793d19978beba85e4a64f26f6724759da
26
https://openrepro.org/bundles/arxiv-1711-10561-20261018T234638Z.tar.gz
ec1fdbada7591b44cf36e0de3e4bc1798cf1dfc98d745f9fcf3fb9bb8401092d
Running the authors Burgers.py (the earliest version in their repository, from five months after arXiv v1) with its fixed seeds on CPU gives a relative L2 error of 2.15e-3 for the continuous-time Burgers example, against 6.7e-4 reported. That meets the pre-registered order-of-magnitude headline check (at most 6.7e-3) b...
arxiv-1711-10561-20261018T234638Z
bound_checks_only; broad_bound; other_checks_failed; plan_not_sealed; evidence_not_bound; agent_selected_extraction
Burgers error 3.21× the published value; passes a registered 10× ceiling; a stricter 2× check did not hold; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it
report
null
Legacy
2026-10-19
c0df97b17edb7b847d7e7b5cf3dc788c18d3f4960563ba5ff3ce186d721e7037
null
1,607.01759
Bag of Tricks for Efficient Text Classification
Armand Joulin; Édouard Grave; Piotr Bojanowski; Tomáš Mikolov
2,017
Conference of the European Chapter of the Association for Computational Linguistics
10.18653/v1/e17-2068
Natural language processing
reproduced
Reproduced
8
0
8
16
2026-10-18
https://openrepro.org/papers/1607.01759/
https://openrepro.org/evidence/arxiv-1607-01759-20261018T223713Z.html
https://openrepro.org/ba…e/1607.01759.svg
87b5ac6197ec5f210cb74ba6aaa5412fe8e2237bdf845f1afee95be9043de61d
56
https://openrepro.org/bundles/arxiv-1607-01759-20261018T223713Z.tar.gz
31c2f0bbfa19009868e934d406a85c850c9e430dd90b79022e1977909a00cad4
With fastText at the commit of arXiv v3 and its classification-results.sh, test accuracy with 10 hidden units and bigrams reproduces Table 1 on all eight datasets: 92.2, 96.7, 98.6, 95.7, 63.9, 72.4, 60.2 and 94.6 against 92.5, 96.8, 98.6, 95.7, 63.9, 72.3, 60.2 and 94.6. The unigram row also reproduces, within 0.2 poi...
arxiv-1607-01759-20261018T223713Z
plan_not_sealed; evidence_not_bound; agent_selected_extraction
legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it
report
null
Legacy
2026-10-19
d4c54196bf7f87246ae7f1b2123994691f4b3adb69a4f9def94e8ba9c76e3b9a
null
2,004.01105
Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions
Jonas Dehning; Johannes Zierenberg; F. Paul Spitzner; Michael Wibral; João Pinheiro Neto; Michael Wilczek; Viola Priesemann
2,020
Science
10.1126/science.abb9789
Epidemiology
reproduced
Reproduced
4
0
4
7
2026-10-18
https://openrepro.org/papers/2004.01105/
https://openrepro.org/evidence/arxiv-2004-01105-20261018T033025Z.html
https://openrepro.org/ba…e/2004.01105.svg
90e4d597839451c42440893ea4dea7451ef11eaedc3887da05aa3c80dcc11e78
44
https://openrepro.org/bundles/arxiv-2004-01105-20261018T033025Z.tar.gz
133120d2279425834b961d4304c7e6d626fbf8f190ff67d6e32ec166bc6ce68f
Fitting the authors three-change-point SIR model with weekly reporting modulation (repository state on the date of arXiv v3) to German case data until 21 April 2020, using the JHU snapshot bundled with the repository, reproduces all registered values: spreading rates 0.43, 0.25, 0.15 and 0.09 (posterior medians 0.428, ...
arxiv-2004-01105-20261018T033025Z
plan_not_sealed; evidence_not_bound; agent_selected_extraction
legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it
report
null
Legacy
2026-10-19
4f7abfc274235e7f7089ee79e78a693b99a8e4c736292bb44e7f806cb6dfae3d
null
1,810.08473
From Louvain to Leiden: guaranteeing well-connected communities
V. A. Traag; L. Waltman; Nees Jan van Eck
2,019
Scientific Reports
10.1038/s41598-019-41695-z
Network science
reproduced
Reproduced
4
0
4
6
2026-10-18
https://openrepro.org/papers/1810.08473/
https://openrepro.org/evidence/arxiv-1810-08473-20261018T013946Z.html
https://openrepro.org/ba…e/1810.08473.svg
578432e9062b72e8e2576db5eeed0bf5ac96e6f5b649204a2e77964f446edc6c
42
https://openrepro.org/bundles/arxiv-1810-08473-20261018T013946Z.tar.gz
44a9835769081eadd17b22c481374d07d8050ab73869c91bdc82b5ea64e9bca3
Using the authors Java implementation at the release tag matching the paper (networkanalysis 1.0.0) on the SNAP DBLP and Amazon networks, with the paper protocol of 10 random starts of 10 iterations each, all tested Table II values reproduced within 0.0005: DBLP Louvain 0.8267 vs 0.8262 and Leiden 0.8384 vs 0.8387; Ama...
arxiv-1810-08473-20261018T013946Z
broad_bound; plan_not_sealed; evidence_not_bound; agent_selected_extraction
DBLP Leiden gain over Louvain 0.938× the published value; passes a registered floor of 0, a sign check; Amazon Leiden gain over Louvain 1.04× the published value; passes a registered floor of 0, a sign check; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value wa...
report
null
Legacy
2026-10-19
7578c2dcc994acbddf7fe022f261bda0a1b18de84da38b67c401c0f5cee29cfd
null
1,812.0997
Synthetic Difference-in-Differences
Dmitry Arkhangelsky; Susan Carleton Athey; David A. Hirshberg; Guido W. Imbens; Stefan Wager
2,021
American Economic Review
10.1257/aer.20190159
Economics
reproduced
Reproduced
4
0
4
10
2026-10-18
https://openrepro.org/papers/1812.09970/
https://openrepro.org/evidence/arxiv-1812-09970-20261018T010328Z.html
https://openrepro.org/ba…e/1812.09970.svg
8b90233f1aba9384bcb8d580e05cbaaf9a5947ebf64d2d62c4f0f4b157f1f7cb
59
https://openrepro.org/bundles/arxiv-1812-09970-20261018T010328Z.tar.gz
b5ba1fe35f4c450bce1c3d66546b117baf1d15a8633fec07e878292fe03fd96a
Running the authors own Table 1 code (vignettes/paper-results.Rmd) at the synthdid commit named in their session-info, with R 4.0.5 and the pinned MCPanel and glmnet commits, reproduced every entry of Table 1 at the reported precision: SDID -15.6 (8.4), SC -19.6 (9.9), DID -27.3 (17.7), MC -20.2 (11.5), DIFP -11.1 (9.5...
arxiv-1812-09970-20261018T010328Z
plan_not_sealed; evidence_not_bound; agent_selected_extraction
legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it
report
null
Legacy
2026-10-19
1ea4c3c995f66424b59dccd235fa8015529c6ea76038251bc7b787f6a292a9ae
null

OpenRepro Ledger

OpenRepro re-runs published computational research and publishes hash-chained evidence of whether the results hold. This dataset is a mirror of the ledger's machine-readable files from openrepro.org. The site is the source of record. Every value here comes from an attempt's report.json and is the same as on the site.

Release 2026-10-20. 7 papers and 53 registered claims (52 held within tolerance), from 7 released attempts. Only attempts listed in release.json, the release manifest, are included; a paper without a due release record is absent.

arXiv Paper Outcome Headline claims held Qualifiers Assurance Report
1606.00931 DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network Reproduced 4 of 4 some claims were registered after command output had been observed; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it Legacy report
1607.01759 Bag of Tricks for Efficient Text Classification Reproduced 8 of 8 legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it Legacy report
1708.05031 Neural Collaborative Filtering Reproduced 4 of 4 some claims were registered after command output had been observed; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it Legacy report
1711.10561 Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations Reproduced 1 of 1 Burgers error 3.21× the published value; passes a registered 10× ceiling; a stricter 2× check did not hold; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it Legacy report
1810.08473 From Louvain to Leiden: guaranteeing well-connected communities Reproduced 4 of 4 DBLP Leiden gain over Louvain 0.938× the published value; passes a registered floor of 0, a sign check; Amazon Leiden gain over Louvain 1.04× the published value; passes a registered floor of 0, a sign check; legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it Legacy report
1812.09970 Synthetic Difference-in-Differences Reproduced 4 of 4 legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it Legacy report
2004.01105 Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions Reproduced 4 of 4 legacy attempt: the plan was never sealed; claims were frozen only by their first result; at least one value was read from an excerpt the agent chose after seeing the output, not by a rule fixed before it Legacy report

Files

File Contents
ledger.csv (config ledger) One row per listed paper.
ledger.json The same records under papers, plus an envelope: schema_version, version (the build date), generated, license, documentation, citation, feed, mcp, papers_listed.
claims.csv (config claims) One row per registered claim of every listed paper.
claims.json The same records under claims, with the same envelope.
release.json The release manifest: every released attempt (current or superseded) with its release record and the SHA-256 of its report files and evidence bundle. Nothing else is in these files.

In the CSV files, list fields such as authors are joined with "; ". Empty cells are nulls.

from datasets import load_dataset
ledger = load_dataset("openrepro/openrepro-ledger", "ledger", split="train")
claims = load_dataset("openrepro/openrepro-ledger", "claims", split="train")

Ledger fields (one record per paper)

Field Meaning
arxiv_id The paper's arXiv id, without a version suffix. It is the stable key for a paper in every file and on the site (/papers/<arxiv_id>/).
title, authors, year, venue, doi Citation metadata from the report. Display-only corrections are sourced in the site's editorial file.
field A browsing label assigned by OpenRepro.
outcome, outcome_label The computed outcome, as a key and as printed: reproduced, reproduced_with_earlier_code, partially_reproduced, not_reproduced, evidence_incomplete ("Not verified: evidence incomplete": OpenRepro's evidence failed, not the paper) or not_attemptable.
qualifiers, qualifier_text, qualifiers_source Qualifier codes (bound_checks_only, broad_bound, other_checks_failed, untested_for_budget), the same in plain words (show it next to the outcome), and whether they come from the report or were derived by the site from a legacy report.
coverage_note What the report says, or cannot say, about results left untested for budget.
assurance_level (assurance in JSON) Legacy, Partial, Sealed plan, Sealed and bound, or Sealed, bound and re-derived. The JSON has the plan seal, evidence binding, execution manifest, re-derivation and engine version.
attempt_id, superseded_attempts (attempts in JSON) The released attempt, and the lineage: every released attempt of the paper with its status (released or superseded), outcome, superseded_by and reason.
release_on, report_json_sha256 The release date from the release record, and the SHA-256 of the released report.json.
headline_held, headline_held_earlier_code, headline_total How many headline claims held, how many of those held only with a declared earlier version of the code, and how many headline claims were scored.
claims_checked Every claim registered, headline or not.
checked The date the attempt ended (UTC).
page_url, evidence_url, badge_url The report page, the full evidence report, and the SVG badge.
log_head_hash, log_events The head hash and the number of events in the attempt's hash-chained log. The report page prints this as the seal.
bundle_url, bundle_sha256 The downloadable evidence bundle and its SHA-256, when published.
summary The report's summary paragraph.

Claims fields (one record per claim)

Field Meaning
arxiv_id, claim_id The paper and the claim. Together they are the stable key for a claim; the report page anchor is #claim-<claim_id>.
paper The paper's short name.
headline True when the claim counts toward the outcome.
statement, location, metric, unit What was registered: the claim in words, where it is in the paper, and what was measured.
comparison, published, tolerance, accepted_if The rule registered before any result was recorded: within a band, at most, or at least; the published value; the tolerance; and the rule as printed.
check_kind, broad_bound, ratio_to_published Equivalence or bound; whether the bound admits more than twice (or less than half) the published value; observed ÷ published. From the report when present, else derived by the site.
attempt_id The attempt the claim belongs to.
observed The value obtained by re-running the authors' code. Null when untested.
verdict consistent, consistent_earlier_code, inconsistent or untested.
earlier_code_observed The value under the declared earlier-code fallback, when one was tried.
registered_event, first_result_event The log events at which the claim was registered and its first result was recorded, so the order can be checked.
deviation_over_tolerance (observed − published) ÷ tolerance. Null when untested; inf or -inf when the tolerance was zero and the value missed.

How it is produced

For each paper, an AI agent (Claude, made by Anthropic) works in a sandbox with OpenRepro's engine:

  1. It takes the authors' own code and data.
  2. Before results are recorded, it registers the claims to check, each with a quote from the paper and a numeric tolerance. In current (legacy) attempts this does not show that no output had been seen first; each record's assurance says what applies.
  3. It rebuilds the environment as of the paper's date and re-runs the experiments.

Every command and output goes into a hash-chained log. The engine computes the verdicts by fixed rules from the registered claims, tolerances and headline choices, which the agent makes; the qualifiers show when a headline check is a broad bound or another check missed. The report is generated from the log; the one-file verifier checks log and artifact integrity and the report's head reference, not the report's contents. A person oversees the project: choosing papers and setting the rules the agent works under. The full method is at openrepro.org/methods/. The evidence bundles and a one-file verifier are at openrepro.org/verify/.

Limits

  • AI-run, no expert review. The re-runs are done by an AI agent. No domain expert has reviewed the reports, and nothing here is peer reviewed.
  • An outcome is not a judgment of the paper. It says what happened when the authors' code was re-run under the recorded conditions. "Could not reproduce" means the published artifacts did not produce the published numbers under OpenRepro's protocol. It is not a finding about the authors' honesty or the idea's merit.
  • Release is fail-closed. For any outcome other than "reproduced", the authors get 14 days to reply before publication, and every attempt needs an explicit release record. Without a due one, the paper is absent from these files and from the site; release.json lists exactly what is included.
  • Small and selective. The ledger covers only the papers OpenRepro has chosen and finished. It is not a sample of any field and should not be used to estimate a field's reproducibility rate.
  • Snapshot. Each version is the ledger as of its build date. A correction is published as a new attempt that links to the old one; ids never change.

Update cadence

The files are rebuilt with the site whenever an attempt's release record falls due. This mirror is updated after the site, so it can lag by a few days. version in the JSON envelope is the build date. The live files are at openrepro.org/data/.

License and citation

The reports, verdicts, evidence logs and these files are published under CC BY 4.0. Attribute "OpenRepro, https://openrepro.org" and link the report URL so a reader can reach the evidence.

@misc{openrepro_ledger,
  author       = {{OpenRepro}},
  title        = {{OpenRepro}: a public ledger of re-run computational research},
  year         = {2026},
  howpublished = {\url{https://openrepro.org/}},
  note         = {Dataset, version 2026-10-20. CC BY 4.0. Mirrored at https://huggingface.co/datasets/openrepro/openrepro-ledger}
}

The site's CITATION.cff has the same details. Each report page has its own citation block.

Contact and right of reply

Write to hello@openrepro.org to reply to a report or to report an error. Authors may reply at any time, and their notes can be published unedited alongside the report.

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Papers for openrepro/openrepro-ledger