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6 results for “reproducibility” in keywordSearch & filters ↓

PreprintsTechnical ReportNot peer reviewed

Reproducibility-Checklist Compliance in Machine-Learning Papers: A technical report on what authors state and what they release

Ingrid Novak, Kofi Mensah

Version 1 · 27 July 2026 · 10.xxxx/ctm.pp.2026.0002

Many machine-learning venues now require authors to complete a reproducibility checklist. We audited 240 papers that ticked the item “code is provided” and checked whether the linked repositories existed, ran and reproduced the headline table. 87% had a reachable repository, 58% contained instructions sufficient to run training or evaluation, and 31% reproduced the headline result within the reported variance. Compliance was higher for papers reporting variance across seeds. We recommend checks that can be automated at submission time.

Abstract PDF 2,455 9314 cited
ProceedingsFull paperBest paper

Reproducibility Checklists in Practice: A Three-Year Audit

Ingrid Novak, Carlos Medina

OPENAIR 2026 · pp. 1–12 · 9 November 2026 · 10.xxxx/ctm.pr.2026.0011

We audit 2,400 checklist responses submitted over three years and compare them against the papers they accompany. Checks for compute reporting and variance were answered affirmatively in 78% of cases but supported by the paper in only 41%, suggesting that checklists work best when responses must point to a location in the text.

Abstract PDF 610 2300 cited
PublishingReview ArticlePeer reviewed

A Survey of Evaluation Practices for Language-Model Agents: What is measured, how it is measured, and what is missing

Kofi Mensah, Ingrid Novak, Lena Hartwell

Version 2 · 4 reviewer reports · 22 May 2026 · 10.xxxx/ctm.pb.2026.0003

As language-model agents move from demonstrations to deployments, the way they are evaluated determines which claims can be trusted. We review how agents are evaluated in the literature, organising practice along four axes: the task environment, the success criterion, the treatment of variance, and the reporting of cost. We identify recurring weaknesses: success is often judged by another model without validation, runs are rarely repeated, cost and latency are reported in under a third of studies, and environment versions are seldom pinned. We close with a checklist for evaluation reports and open problems, including measurement of safety-relevant side effects.

Abstract PDF 6,210 2,87011 cited