Retrieval-Augmented Generation (RAG) enables grounding of model outputs in external evidence, but its impact on European Portuguese (pt-PT) scientific question answering (QA) remains unclear. We present a controlled evaluation of RAG on pt-PT knowledge QA across different scientific domains using the Portuguese test split of the Global MMLU Lite dataset. As external evidence, we use a Portuguese scientific literature knowledge base containing over 32,000 documents converted to Markdown. We benchmark five instruction-tuned small language models (4-12B) and compare closed-book baselines against 16 RAG configurations that vary by: (i) dense retriever specialization (multilingual vs. Portuguese-specific), (ii) reranking (on/off), and (iii) number of retrieved chunks (k ∈ 1, 3, 5, 10). Results suggest that RAG gains are model-dependent. Some models improve consistently, others are highly sensitive to retrieval choices, and some degrade under retrieval noise, especially at larger values of k. Findings highlight the importance of model-specific retrieval tuning and ensuring that the retriever and reranker languages and domains align when deploying RAG systems for Portuguese natural scientific language processing.

This paper describes the creation of a large-scale corpus of academic texts in Portuguese, dubbed CorEGe-PT, extracted from the institutional repository of a Portuguese university. Its compilation methodology, which combined automatic and manual procedures, is detailed, together with challenges faced and proposed solutions. The process included a thorough analysis of the metadata, which will be publicly released together with the documents, extracted in a markdown format. CorEGe-PT covers five areas of knowledge and, with over 34,000 documents and 1B tokens, is the largest of corpus of its kind in Portuguese, which will enable in-depth linguistic studies while providing data for adapting Large Language Models to academic Portuguese and related tasks.

The ability of large language models (LLMs) to reason effectively is crucial for a wide range of applications, from complex decision-making to scientific research. However, it remains unclear how well reasoning capabilities are transferred or preserved when LLMs undergo Knowledge Distillation (KD), a process that typically reduces model size while attempting to retain performance. In this study, we explore the effects of model distillation on the reasoning abilities of various reasoning language models (RLMs). We introduce Cognitive Flow, a novel framework that systematically extracts meaning and map states in Chain-of-Thought (CoT) processes, offering new insights on model reasoning and enabling quantitative comparisons across RLMs. Using this framework, we investigate the impact of KD on CoTs produced by RLMs. We target DeepSeek-R1-671B and its distilled 70B, 32B and 14B versions, as well as QwenQwQ-32B from the Qwen series. We evaluate the models on three subsets of mathematical reasoning tasks with varying complexity from the MMLU benchmark. Our findings demonstrate that while distillation can effectively replicate a similar reasoning style under specific conditions, it struggles with simpler problems, revealing a significant divergence in the observable thought process and a potential limitation in the transfer of a robust and adaptable problem-solving capability.