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M2-Verify: A Large-Scale Multidomain Benchmark for Checking Multimodal Claim Consistency

Abolfazl Ansari, Delvin Ce Zhang, Zhuoyang Zou, Wenpeng Yin, Dongwon Lee · Apr 1, 2026 · Citations: 0

Automatic Metrics
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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

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

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence. However, existing benchmarks lack the scale, domain diversity, and visual complexity needed to evaluate this alignment realistically. To address this gap, we introduce M2-Verify, a large-scale multimodal dataset for checking scientific claim consistency. Sourced from PubMed and arXiv, M2-Verify provides over 469K instances across 16 domains, rigorously validated through expert audits. Extensive baseline experiments show that state-of-the-art models struggle to maintain robust consistency. While top models achieve up to 85.8\% Micro-F1 on low-complexity medical perturbations, performance drops to 61.6\% on high-complexity challenges like anatomical shifts. Furthermore, expert evaluations expose hallucinations when models generate scientific explanations for their alignment decisions. Finally, we demonstrate our dataset's utility and provide comprehensive usage guidelines.

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.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

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

A secondary eval reference to pair with stronger protocol papers.

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

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 35%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

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.

"Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence."

Reported Metrics

partial

F1, F1 micro

Useful for evaluation criteria comparison.

"Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Sourced from PubMed and arXiv, M2-Verify provides over 469K instances across 16 domains, rigorously validated through expert audits."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: Medicine

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • 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

f1f1 micro

Research Brief

Metadata summary

Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence.

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

Key Takeaways

  • Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence.
  • However, existing benchmarks lack the scale, domain diversity, and visual complexity needed to evaluate this alignment realistically.
  • To address this gap, we introduce M2-Verify, a large-scale multimodal dataset for checking scientific claim consistency.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • 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.

Research Summary

Contribution Summary

  • However, existing benchmarks lack the scale, domain diversity, and visual complexity needed to evaluate this alignment realistically.
  • To address this gap, we introduce M2-Verify, a large-scale multimodal dataset for checking scientific claim consistency.
  • Finally, we demonstrate our dataset's utility and provide comprehensive usage guidelines.

Why It Matters For Eval

  • However, existing benchmarks lack the scale, domain diversity, and visual complexity needed to evaluate this alignment realistically.
  • Furthermore, expert evaluations expose hallucinations when models generate scientific explanations for their alignment decisions.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: f1, f1 micro

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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