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HFEPX · Eval paper review

AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage

Xuanle Zhao, Zilin Sang, Yuxuan Li, Qi Shi +6 more

Published

May 27, 2025

Citations

0

Trust level

Moderate

Usefulness score

50/100 (Medium)

Extraction confidence

60% (Moderate)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Apr 24, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Coding Expert Verification Multi Agent

Should you rely on this paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Use this for comparison and orientation, not as your only source.

Best use

Secondary protocol comparison source

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

No major weakness surfaced.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
50/100
Moderate-confidence candidate

Useful as a secondary reference; validate protocol details against neighboring papers.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Efficient reproduction of research papers is pivotal to accelerating scientific progress. However, the increasing complexity of proposed methods often renders reproduction a labor-intensive endeavor, necessitating profound domain expertise. To address this, we introduce the paper lineage, which systematically mines implicit knowledge from the cited literature. This algorithm serves as the backbone of our proposed \ours, a multi-agent framework designed to autonomously reproduce experimental code in a complete, end-to-end manner. To ensure code executability, \ours incorporates a sampling-based unit testing strategy for rapid validation. To assess reproduction capabilities, we introduce \ourbench, a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity. Extensive evaluations on PaperBench and \ourbench demonstrate that \ours consistently surpasses existing baselines across all metrics. Notably, it yields substantial improvements in reproduction fidelity and final execution performance. The code is available at https://github.com/AI9Stars/AutoReproduce.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Expert Verification

Directly usable for protocol triage.

"Efficient reproduction of research papers is pivotal to accelerating scientific progress."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Efficient reproduction of research papers is pivotal to accelerating scientific progress."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Efficient reproduction of research papers is pivotal to accelerating scientific progress."

Benchmarks / Datasets

strong

Ourbench, Paperbench

Useful for quick benchmark comparison.

"To assess reproduction capabilities, we introduce \ourbench, a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Efficient reproduction of research papers is pivotal to accelerating scientific progress."

Rater Population

strong

Domain Experts

Helpful for staffing comparability.

"However, the increasing complexity of proposed methods often renders reproduction a labor-intensive endeavor, necessitating profound domain expertise."

Benchmarks and datasets

OurbenchPaperbench

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Expert Verification
Rater population
Domain Experts
Expertise required
Coding
Evaluation details
Evaluation modes
None
Agentic eval
Multi Agent
Quality controls
Not reported
Evidence quality
Moderate
Use this page as
Secondary protocol comparison source

Research brief

Metadata summary

Efficient reproduction of research papers is pivotal to accelerating scientific progress.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Efficient reproduction of research papers is pivotal to accelerating scientific progress.
  • However, the increasing complexity of proposed methods often renders reproduction a labor-intensive endeavor, necessitating profound domain expertise.
  • To address this, we introduce the paper lineage, which systematically mines implicit knowledge from the cited literature.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • To address this, we introduce the paper lineage, which systematically mines implicit knowledge from the cited literature.
  • This algorithm serves as the backbone of our proposed \ours, a multi-agent framework designed to autonomously reproduce experimental code in a complete, end-to-end manner.
  • To assess reproduction capabilities, we introduce \ourbench, a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity.

Why it matters for eval

  • This algorithm serves as the backbone of our proposed \ours, a multi-agent framework designed to autonomously reproduce experimental code in a complete, end-to-end manner.
  • To assess reproduction capabilities, we introduce \ourbench, a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Expert Verification

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: Ourbench, Paperbench

  • Metric reporting is present

    No metric terms extracted.