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SciCoQA: Quality Assurance for Scientific Paper--Code Alignment

Tim Baumgärtner, Iryna Gurevych · Jan 19, 2026 · Citations: 0

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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations. We construct SciCoQA from GitHub issues and reproducibility papers, and to scale our dataset, we propose a synthetic data generation method for constructing paper-code discrepancies. We analyze the paper-code discrepancies in detail and propose discrepancy types and categories to better understand the occurring mismatches. In total, our dataset consists of 635 paper-code discrepancies (92 real, 543 synthetic), covering the AI domain from real-world data and extending to Physics, Quantitative Biology, and other computational sciences through synthetic data. Our evaluation of 22 LLMs demonstrates the difficulty of SciCoQA, particularly for instances involving omitted paper details, long-context inputs, and data outside the models' pre-training corpus. The best-performing models in our evaluation, Gemini 3.1 Pro and GPT-5 Mini, detect only 46.7% of real-world paper-code discrepancies.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

This paper looks adjacent to evaluation work, but not like a strong protocol reference.
The available metadata is too thin to trust this as a primary source.
The abstract does not clearly describe the evaluation setup.
The abstract does not clearly name benchmarks or metrics.

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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 25%

If you are doing eval pipeline work, start here:

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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

missing

None explicit

No explicit feedback protocol extracted.

"We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations."

Quality Controls

partial

Calibration

Calibration/adjudication style controls detected.

"We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations."

Human Feedback Details

Uses human feedback: No
Feedback types: None
Rater population: Not reported
Expertise required: Coding

Evaluation Details

Evaluation modes:
Agentic eval: None
Quality controls: Calibration
Evidence quality: Low
Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations.

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

Key Takeaways

We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations.
We construct SciCoQA from GitHub issues and reproducibility papers, and to scale our dataset, we propose a synthetic data generation method for constructing paper-code discrepancies.
We analyze the paper-code discrepancies in detail and propose discrepancy types and categories to better understand the occurring mismatches.

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

Research Summary

Contribution Summary

We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations.
We construct SciCoQA from GitHub issues and reproducibility papers, and to scale our dataset, we propose a synthetic data generation method for constructing paper-code discrepancies.
The best-performing models in our evaluation, Gemini 3.1 Pro and GPT-5 Mini, detect only 46.7% of real-world paper-code discrepancies.

Why It Matters For Eval

Our evaluation of 22 LLMs demonstrates the difficulty of SciCoQA, particularly for instances involving omitted paper details, long-context inputs, and data outside the models' pre-training corpus.
The best-performing models in our evaluation, Gemini 3.1 Pro and GPT-5 Mini, detect only 46.7% of real-world paper-code discrepancies.

Researcher Checklist

Gap: Human feedback protocol is explicit

No explicit human feedback protocol detected.
Gap: Evaluation mode is explicit

No clear evaluation mode extracted.
Pass: Quality control reporting appears

Detected: Calibration
Gap: Benchmark or dataset anchors are present

No benchmark/dataset anchor extracted from abstract.
Gap: Metric reporting is present

No metric terms extracted.

Related Papers

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