Large Language Models (LLMs) often generate hallucinations, a critical issue in domains like scientific communication where factual accuracy and fluency are essential. The SHROOM-CAP shared task addresses this challenge by evaluating Factual Mistakes and Fluency Mistakes across diverse languages, extending earlier SHROOM editions to the scientific domain. We present Smurfcat, our system for SHROOM-CAP, which integrates three complementary approaches: uncertainty estimation (white-box and black-box signals), encoder-based classifiers (Multilingual Modern BERT), and decoder-based judges (instruction-tuned LLMs with classification heads). Results show that decoder-based judges achieve the strongest overall performance, while uncertainty methods and encoders provide complementary strengths. Our findings highlight the value of combining uncertainty signals with encoder and decoder architectures for robust, multilingual detection of hallucinations and related errors in scientific publications.

Regulatory Natural Language Processing (RegNLP) is a multidisciplinary domain focused on facilitating access to and comprehension of regulatory regulations and requirements. This paper outlines our strategy for creating a system to address the Regulatory Information Retrieval and Answer Generation (RIRAG) challenge, which was conducted during the RegNLP 2025 Workshop. The objective of this competition is to design a system capable of efficiently extracting pertinent passages from regulatory texts (ObliQA) and subsequently generating accurate, cohesive responses to inquiries related to compliance and obligations. Our proposed method employs a lightweight BM25 pre-filtering in retrieving relevant passages. This technique efficiently shortlisting candidates for subsequent processing with Transformer-based embeddings, thereby optimizing the use of resources.

The proliferation of multilingual misinformation demands robust systems for crosslingual fact-checked claim retrieval. This paper addresses SemEval-2025 Shared Task 7, which challenges participants to retrieve fact-checks for social media posts across 14 languages, even when posts and fact-checks are in different languages. We propose a hybrid retrieval pipeline that combines sparse lexical matching (BM25, BGE-m3) and dense semantic retrieval (pretrained and fine-tuned BGE-m3) with dynamic fusion and curriculum-trained rerankers. Our system achieves 67.2% crosslingual and 86.01% monolingual accuracy on the Shared Task MultiClaim dataset.