This benchmark probes a model's ability to perform multimodal fact-checking by verifying real-world image-text claims. It requires the system to retrieve cross-modal evidence, analyze inconsistencies, and produce a justified verdict that aligns with ground truth labels. Use when the user wants to benchmark on AVerImaTeC, or asks about evaluating this task. Reports verdict_correctness.
Scanned 9/11/2026
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---
name: averimavec-eval
description: This benchmark probes a model's ability to perform multimodal fact-checking by verifying real-world image-text claims. It requires the system to retrieve cross-modal evidence, analyze inconsistencies, and produce a justified verdict that aligns with ground truth labels. Use when the user wants to benchmark on AVerImaTeC, or asks about evaluating this task. Reports verdict_correctness.
metadata:
skill_kind: dataset_eval
source_arxiv: 2602.04587
bibtex_key: jung2026verifying
confidence: high
---
# averimavec-eval
> VILLAIN at AVerImaTeC: Verifying Image-Text Claims via Multi-Agent Collaboration — Jung et al. (2026) (arXiv:2602.04587, 2026)
## What this evaluates
This benchmark probes a model's ability to perform multimodal fact-checking by verifying real-world image-text claims. It requires the system to retrieve cross-modal evidence, analyze inconsistencies, and produce a justified verdict that aligns with ground truth labels.
## Datasets
- **AVerImaTeC** — total ?; splits: test (-1)
## Metrics
- `verdict_correctness` **(primary)** — range: [0, 1]
- A response is correct only if the predicted label matches the ground truth AND the provided evidence score meets or exceeds a threshold of λ=0.3.
## Input / output format
**Input**: Image-text claim along with its associated metadata.
**Output**: Predicted verdict label and supporting evidence.
## Scoring recipe
```python
def score(predictions, gold):
correct = 0
for pred, gold_label, pred_evidence in zip(predictions, gold['labels'], gold['evidence']):
label_ok = (pred['label'] == gold_label)
evidence_ok = pred['evidence_score'] >= 0.3
if label_ok and evidence_ok:
correct += 1
return correct / len(predictions)
```
## Common pitfalls
- A correct label alone is insufficient; the evidence score must also meet the λ=0.3 threshold.
- Evidence can be retrieved from multiple sources (web, document collections, or image collections), not just the web.
## Evidence (verbatim from paper)
> A response is considered correct only if both the predicted label is correct and the provided evidence is adequate. Specifically, a verdict is deemed correct only when the evidence score meets or exceeds the threshold $\lambda=0.3$.
## Citation
```bibtex
@misc{jung2026verifying,
title={VILLAIN at AVerImaTeC: Verifying Image-Text Claims via Multi-Agent Collaboration},
author={Jung et al. (2026)},
year={2026},
note={arXiv:2602.04587}
}
```
- arXiv: 2602.04587
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