Scientific Claim Verification

Overview

The field studies how to determine whether scientific claims follow from research papers, datasets, figures, code, and other evidence. Common approaches retrieve relevant passages, break claims into atomic parts, and apply natural-language inference or language models; newer systems add multimodal tools, selective full-text search, and code or experiment checks. Benchmarks span biomedical and climate claims, scientific tables and charts, and evidence chains linking data to conclusions. Strong results on familiar tasks do not always transfer across domains or expose errors in complex claims, leaving open how to evaluate genuine synthesis, uncertainty, and end-to-end evidential support.

Latest papers

CardsList

54 papers7 papers in the last four weeks, up 17% on the four weeks before. 0.0% of all new papers.

  1. From Prose to Properties: Testing Research Claims with ARATest for Agent-Native Science

    Oct 21, 2026Yifeng He, Jiachen Liu, Hao ChenProperty-Based TestingScientific Claim Verification

  2. TCMScience: An Autonomous Scientist for Traditional Chinese Medicine

    Oct 17, 2026Yanlan Kang, Ruiqi Liu, Xinpei Gu +4AI Agents for Scientific DiscoveryScientific Workflow Automation

  3. When Evidence Changes the Subject: Subject-Typed Claim Licensing for Learned Routing

    Sep 27, 2026Jian Chen, Zixuan YuanEvidence-Grounded ReasoningScientific Claim Verification

  4. RINI: Seeing the Prior Is Not Enough

    Sep 27, 2026Hongyi Du, Tianyi Zhang, Heng Wang +7Novelty SearchAI-Assisted Scientific Research

  5. PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress

    Sep 15, 2026Kevin Qinghong Lin, Siyuan Hu, Pan Lu +14AI-Assisted Scientific ResearchScientific Reproducibility

  6. SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task

    Sep 1, 2026Qiming Bao, Neşet Özkan Tan, Siyuan Wang +1Document UnderstandingLLM Evaluation

  7. How Robust Are Automated Fact-Checking Systems? A Cross-Benchmark Evaluation

    Aug 26, 2026Aida Usmanova, Zangir Iklassov, Markus Leippold +1Automated Fact-CheckingScientific Claim Verification

  8. When Does AI for PDEs Yield Scientific Evidence?

    Aug 23, 2026Wenshuo WangScientific Claim VerificationBenchmark Validity

  9. TRACES: A Benchmark for Epistemic Reliability in Scientific Reasoning by LLMs

    Aug 11, 2026Valentin Rodionov, Shamil AssylbekovLLM EvaluationLLM Reliability

  10. HNR-DAC: Hard-Negative Reranking and Distribution-Aligned Classification for Scientific Claim Verification

    Aug 7, 2026Zhenchao Wang, Xin Chen, Luoxi Zhang +2Claim VerificationDistribution Shift

  11. When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verification

    Aug 2, 2026Pritam Deka, Prabhjot SinghExplainable Artificial IntelligenceAutomated Fact-Checking

  12. Auditing Discovery Claims: A Two-Sided Criterion for Agentic Science, with the Negative Side Decidable

    Aug 2, 2026Wenhui Chen, Jianlin Chen, Ziyao Lin +1Reward HackingAI for Science

  13. Opaque Epistemic Mediation: How LLM Deployment Configurations Shape the Validation of Pseudo-Science

    Jul 24, 2026Davide Scarso, Hugo Noronha de Almeida, Joaquim PinaLLM EvaluationLLM Auditing

  14. Grounded verification of chemical and materials reasoning: detection is the bottleneck

    Jul 19, 2026Can Polat, Mustafa Kurban, Erchin Serpedin +1LLM Self-CorrectionLLM Grounding

  15. ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning

    Jul 17, 2026Binglin Zhou, Peng Shi, Ryo Kamoi +2Tool Use in VLMsScientific Claim Verification

  16. VERITAS: Towards a General-Purpose Replication Tool for Scientific Research

    Jul 3, 2026Haokun Liu, Filbert Aurelian Tjiaranata, Chenhao TanAI Coding AgentsScientific Reproducibility

  17. Coding-agents can replicate scientific machine learning papers

    Jul 2, 2026Atharva Hans, Ilias BilionisCoding AgentsLLM Agent Evaluation

  18. SFBench: The SciFy Scientific Feasibility Benchmark

    Jun 28, 2026Cash Costello, James Mayfield, Elsbeth Turcan +7LLM EvaluationBenchmark Design

  19. Position: Correct Answer, Wrong Mechanism -- When AI Scientists Defend General Claims Their Own Data Contradicts

    Jun 22, 2026Steven Young EuligAI Agent ReliabilityReasoning Evaluation

  20. Finetuning with Scientific Data Increases Hallucinations: A Multi-domain Factuality Evaluation of LLMs

    Jun 19, 2026Raia Abu Ahmad, Nikolas Rauscher, Ekaterina Borisova +3LLM EvaluationLLM Fine-Tuning

  21. SciLens: Multi-modal Scientific Claim Verification with Agentic Entailment and Grounding

    Jun 18, 2026Yueming Wang, Tianshi Zheng, Jiaxin Bai +3Multimodal GroundingAI for Science