This system paper presents AMU’s submission to RAG4Reports 2026 Task B: a practical multilingual retrieval-augmented generation pipeline for evidence-supported report generation. The system combines full-query retrieval, optional query rewriting, dense retrieval with Qdrant, cross-encoder reranking, diversity-aware context selection, and structured generation. The best submitted run uses BAAI/bge-m3 embeddings, BAAI/bge-reranker-v2-m3 reranking, and gpt-5.1 generation with medium reasoning effort, using a partial-coverage prompt strategy. On the official leaderboard, it achieved F1=0.4351, sentence_support=0.8280, and nugget_coverage=0.3403, indicating that the generated reports were well grounded but only partially comprehensive.

This paper introduces the first shared task on machine-generated text (MGT) detection for Polish, organised as part of the PolEval 2025 evaluation campaign. The task evaluates participating systems under three scenarios – unsupervised, constrained, and open – designed to reflect different levels of access to training data. In total, seven systems were submitted. The results indicate that MGT detection for Polish is feasible, with the best-performing constrained systems achieving over 90% accuracy on the main evaluation set. However, performance drops when models are tested on unseen domains or generator models, revealing substantial limitations in generalisation. In the most challenging settings, unsupervised approaches beat the supervised ones. This shared task establishes a new benchmark for MGT detection in Polish. The publicly released Śmigiel dataset is intended to support future research on robust and generalisable MGT detection.