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arxiv: 2606.18237 · v1 · pith:UPRPS3TTnew · submitted 2026-06-16 · 💻 cs.CL · cs.AI· cs.LG

ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues

Shanda Li , Qiuhong Anna Wei , Jingwu Tang , Valerie Chen , Nihar B Shah , Tim Dettmers , Yiming Yang , Ameet Talwalkar This is my paper

Pith reviewed 2026-06-27 00:58 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords reproducibilityLLM agentsGitHub issuesmachine learning papersreproduction blockersscalable evaluationauditing
0
0 comments X

The pith

LLM agents surface at least one human-reported reproducibility blocker for roughly 90 percent of machine learning papers from paper and repository text alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ReproRepo as a framework that treats existing GitHub issues on paper repositories as ready-made labels for real reproduction problems. This replaces the manual curation required by prior benchmarks and lets evaluation reach 1,149 recent machine learning papers. Four frontier agent setups are tested; the strongest (Codex with GPT-5.5) matches at least one semantically related human issue in about 90 percent of cases even though the agents never execute code. Agents prove good at locating visible failures and the right semantic area but weaker at pinning down exact details. The same framework can be reused to keep testing new agents on fresh papers.

Core claim

ReproRepo treats human-raised GitHub issues on paper repositories as naturally occurring supervision that marks genuine reproduction blockers. On a corpus of 1,149 recent machine learning papers, LLM agents that receive only the paper text and repository contents (no code execution) identify at least one semantically related blocker for approximately 90 percent of the papers, with the Codex-plus-GPT-5.5 configuration performing best. The agents are especially reliable at surfacing visible failures and identifying the correct semantic region yet remain limited in exact localization.

What carries the argument

ReproRepo, the framework that converts human-raised GitHub issues into scalable, naturally occurring labels for evaluating LLM agents on paper-repository pairs.

If this is right

  • Reproducibility checks can be run at the scale of thousands of papers using only existing issue data.
  • LLM agents supply a practical first filter that catches most visible blockers before any code is run.
  • Evaluation effort can shift from labeling new examples to refining how agents localize issues more precisely.
  • ReproRepo itself becomes a reusable testbed for comparing future agent versions on the same real-world task.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Combining the current text-only agents with lightweight code-execution steps could close the remaining gap in exact localization.
  • The same GitHub-issue approach might transfer to other fields that maintain public code repositories with issue trackers.
  • Patterns in the issues that agents consistently miss could guide targeted improvements in agent prompting or retrieval.
  • Over time the growing set of agent outputs could itself become a dataset for training more specialized reproduction checkers.

Load-bearing premise

Human-raised GitHub issues accurately represent the true reproducibility blockers and semantic relatedness between agent output and those issues is a sufficient signal that the agent has found the problem.

What would settle it

An independent expert review on a fresh sample of papers showing that the issues agents flag are not the actual blockers that prevent reproduction, or a new run on held-out papers where the semantic-match rate falls well below 90 percent.

read the original abstract

Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist with reproducibility, but they are difficult to scale due to their reliance on substantial manual effort for data curation and evaluation. We introduce ReproRepo, a scalable framework for reproducibility evaluation that leverages human-raised GitHub issues as naturally occurring supervision on realistic reproduction blockers. We instantiate ReproRepo on 1,149 recent machine learning papers from major conferences and evaluate four frontier model-agent configurations. Our results show that LLM agents, even without executing code, can identify many real-world reproducibility problems from paper-repository pairs: the best agent in our study, namely Codex with GPT-5.5, surfaces at least one semantically related human-reported blocker for ~90% of papers in the study. Further analysis shows that agents are particularly effective for surfacing visible failures and identifying the right semantic region, but may still be insufficient in exact localization. ReproRepo can serve as a reusable, scalable framework for future evaluations of LLM agents on real-world reproducibility auditing. Our code is released at https://github.com/LithiumDA/ReproRepo.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper introduces ReproRepo, a scalable framework that treats human-raised GitHub issues on paper repositories as naturally occurring labels for reproducibility blockers. It evaluates four LLM agent configurations on 1,149 recent ML papers and reports that the strongest configuration (Codex with GPT-5.5) surfaces at least one semantically related human-reported blocker for ~90% of papers, even without code execution. The work positions this approach as a reusable alternative to manually curated reproducibility benchmarks and releases the associated code.

Significance. If the assumptions about issue validity and semantic relatedness hold, the framework provides a low-cost, scalable method for auditing LLM agents on realistic reproducibility tasks, which could accelerate evaluation beyond the small-scale manual benchmarks common in the field. The public code release is a concrete strength that supports future reuse and extension.

major comments (3)
  1. [Abstract] Abstract and results paragraph: The central quantitative claim (~90% of papers have at least one semantically related blocker surfaced) rests on an unvalidated proxy; the manuscript provides no description of how semantic relatedness is operationalized (e.g., embedding similarity threshold, LLM judge prompt, or human annotation protocol) nor any inter-annotator agreement or manual validation that the matched issues actually describe reproducibility failures rather than installation queries or feature requests.
  2. [Abstract] Dataset construction (implied in abstract and methods): The 1,149-paper corpus is restricted to repositories that already contain GitHub issues; no statistics or filtering criteria are reported to confirm that the retained issues predominantly concern reproducibility blockers, which directly affects whether the 90% figure can be interpreted as evidence that agents identify real-world reproducibility problems.
  3. [Results paragraph] Evaluation design: The claim that agents are 'particularly effective for surfacing visible failures' but 'insufficient in exact localization' is presented without accompanying quantitative breakdowns, example agent outputs, or error analysis that would allow readers to assess the distinction between semantic-region identification and actionable diagnosis.
minor comments (1)
  1. [Abstract] The model name 'Codex with GPT-5.5' is non-standard and should be clarified with exact API identifiers or version numbers used.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and detailed comments. We address each major point below and commit to revisions that strengthen the clarity and rigor of the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract and results paragraph: The central quantitative claim (~90% of papers have at least one semantically related blocker surfaced) rests on an unvalidated proxy; the manuscript provides no description of how semantic relatedness is operationalized (e.g., embedding similarity threshold, LLM judge prompt, or human annotation protocol) nor any inter-annotator agreement or manual validation that the matched issues actually describe reproducibility failures rather than installation queries or feature requests.

    Authors: We agree that additional detail is required. The current manuscript describes the matching procedure at a high level but does not provide the precise operationalization or validation statistics. In the revision we will expand the Methods section with the exact procedure (embedding model and threshold or LLM judge prompt), report inter-annotator agreement from a human validation study on a sampled subset, and clarify the criteria used to confirm that matched issues describe reproducibility blockers. revision: yes

  2. Referee: [Abstract] Dataset construction (implied in abstract and methods): The 1,149-paper corpus is restricted to repositories that already contain GitHub issues; no statistics or filtering criteria are reported to confirm that the retained issues predominantly concern reproducibility blockers, which directly affects whether the 90% figure can be interpreted as evidence that agents identify real-world reproducibility problems.

    Authors: We will add an explicit subsection on dataset construction that reports the repository and issue selection criteria together with summary statistics (e.g., proportion of issues manually categorized as reproducibility-related versus installation queries or feature requests) on a representative sample. This will allow readers to assess the composition of the supervision signal. revision: yes

  3. Referee: [Results paragraph] Evaluation design: The claim that agents are 'particularly effective for surfacing visible failures' but 'insufficient in exact localization' is presented without accompanying quantitative breakdowns, example agent outputs, or error analysis that would allow readers to assess the distinction between semantic-region identification and actionable diagnosis.

    Authors: We accept that the current presentation lacks supporting detail. The revision will include (i) quantitative breakdowns of success rates stratified by failure visibility and localization granularity, (ii) representative agent output examples, and (iii) a dedicated error-analysis subsection that distinguishes semantic-region matches from precise localization failures. revision: yes

Circularity Check

0 steps flagged

No significant circularity; evaluation uses external human-generated labels as independent ground truth

full rationale

The paper's central evaluation compares LLM agent outputs against pre-existing human-raised GitHub issues on paper repositories, treating those issues as naturally occurring external supervision. No derivation step reduces a claimed result to a fitted parameter, self-citation chain, or input by construction; the reported ~90% figure is a direct empirical match rate against an independent dataset. The framework is self-contained against these external benchmarks, with no load-bearing self-citations or ansatzes that collapse the claim into its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the contribution is an empirical framework built on existing public data.

pith-pipeline@v0.9.1-grok · 5764 in / 1152 out tokens · 50612 ms · 2026-06-27T00:58:28.008852+00:00 · methodology

discussion (0)

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Reference graph

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