# SciClaimEval: Cross-modal Claim Verification in Scientific Papers

arXiv: https://arxiv.org/abs/2602.07621 · HTML: https://mlsanity.com/paper/2602.07621/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 7 Feb 2026.

## Builds on (most cited first, 21 of 21)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://mlsanity.com/paper/2011.03088/) (2020)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021)
- [SciFact-Open: Towards open-domain scientific claim verification](https://mlsanity.com/paper/2210.13777/) (2022)
- [SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables](https://mlsanity.com/paper/2305.13186/) (2023)
- [SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)](https://mlsanity.com/paper/2105.13995/) (2021)
- [ChartCheck: Explainable Fact-Checking over Real-World Chart Images](https://mlsanity.com/paper/2311.07453/) (2023)
- [SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification](https://mlsanity.com/paper/2506.15569/) (2025)
- [Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning](https://mlsanity.com/paper/2501.13042/) (2025)
- [MuSciClaims: Multimodal Scientific Claim Verification](https://mlsanity.com/paper/2506.04585/) (2025)
- [Atomic Reasoning for Scientific Table Claim Verification](https://mlsanity.com/paper/2506.06972/) (2025)
- [Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers](https://mlsanity.com/paper/2506.10486/) (2025)

## Built on by (most cited first, 7 of 7)

- [CiteCheck: Retrieval-Grounded Detection of LLM Citation Hallucinations in Scientific Text](https://mlsanity.com/paper/2605.27700/) (2026)
- [SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task](https://mlsanity.com/paper/2609.00654/) (2026)
- [Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification](https://mlsanity.com/paper/2606.01679/) (2026)
- [SciLens: Multi-modal Scientific Claim Verification with Agentic Entailment and Grounding](https://mlsanity.com/paper/2606.20873/) (2026)
- [MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering](https://mlsanity.com/paper/2607.01420/) (2026)
- [ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning](https://mlsanity.com/paper/2607.16131/) (2026)
- [How Robust Is Multimodal Claim Verification to LLM Rewriting?](https://mlsanity.com/paper/2610.02841/) (2026)

## Abstract (arXiv)

We present SciClaimEval, a new scientific dataset for the claim verification task. Unlike existing resources, SciClaimEval features authentic claims, including refuted ones, directly extracted from published papers. To create refuted claims, we introduce a novel approach that modifies the supporting evidence (figures and tables), rather than altering the claims or relying on large language models (LLMs) to fabricate contradictions. The dataset provides cross-modal evidence with diverse representations: figures are available as images, while tables are provided in multiple formats, including images, LaTeX source, HTML, and JSON. SciClaimEval contains 1,664 annotated samples from 180 papers across three domains, machine learning, natural language processing, and medicine, validated through expert annotation. We benchmark 11 multimodal foundation models, both open-source and proprietary, across the dataset. Results show that figure-based verification remains particularly challenging for all models, as a substantial performance gap remains between the best system and human baseline.