CC BY 4.0Runewicz, AndréAndréRunewiczRanly, PaulPaulRanlyVogel, InnaInnaVogelSteinebach, MartinMartinSteinebach2025-10-302025-10-302025https://publica.fraunhofer.de/handle/publica/497944https://doi.org/10.24406/publica-595010.24406/publica-59502-s2.0-105019039701The growing spread of misinformation, particularly on social media, has increased the demand for scalable and accurate automated fact-checking systems. This paper presents our approach to Task 3 of the CheckThat! 2025 lab, which focuses on the verification of numerical claims. We propose a three-stage architecture comprising (i) semantic evidence retrieval using dense bi-encoder representations stored in a FAISS index, (ii) contrastive re-ranking with a fine-tuned cross-encoder leveraging weak supervision from gold evidences, and (iii) claim classification using multi-instance learning (MIL) with various evidence pooling strategies. Experiments on the QuanTemp dataset demonstrate that attention and LogSumExp pooling outperform standard concatenation methods, with our best model achieving a macro-F1 score of 0.5213 on the test set. Additionally, ablation studies confirm the effectiveness of contrastive re-ranking and the practical advantages of dense retrieval, which achieves both higher accuracy and significantly faster retrieval than traditional BM25.enfalseClaim verificationContrastive learningCross-encoder re-rankingDense retrievalFact-checkingMisinformation detectionMulti-instance learningNatural language processingNumerical claimsconference paper