Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translated benchmarks likely missing the variant’s linguistic and cultural nuances. We introduce AMALIA, a fully open LLM that prioritizes pt-PT by using more high-quality pt-PT data during both the mid- and post-training stages. To evaluate pt-PT more faithfully, we release a suite of pt-PT benchmarks that includes translated standard tasks and four new datasets targeting pt-PT generation, linguistic competence, and pt-PT/pt-BR bias. Experiments show that AMALIA matches strong baselines on translated benchmarks while substantially improving performance on pt-PT-specific evaluations, supporting the case for targeted training and native benchmarking for European Portuguese.

The use of large language models (LLMs) for complex mathematical reasoning is an emergent area of research, with fast progress in methods, models, and benchmark datasets. However, most mathematical reasoning evaluations exhibit a significant linguistic bias, with the vast majority of benchmark datasets being exclusively in English or (at best) translated from English. We address this limitation by introducing MATH-PT, a novel dataset comprising 1,729 mathematical problems written in European and Brazilian Portuguese. MATH-PT is curated from a variety of high-quality native sources, including mathematical Olympiads, competitions, and exams from Portugal and Brazil. We present a comprehensive benchmark of current state-of-the-art LLMs on MATHPT, revealing that frontier reasoning models achieve strong performance in multiple choice questions compared to open weight models, but that their performance decreases for questions with figures or open-ended questions. To facilitate future research, we release the benchmark dataset and model outputs

As large language models (LLMs) are adopted worldwide, evaluating their alignment across diverse cultural contexts is essential, especially when handling sensitive, politically charged requests. Yet what counts as “sensitive” is shaped by social, historical, and cultural factors, leaving model robustness across cultures poorly understood. We investigate this in the context of Portugal, a country whose layered history surfaces many culturally sensitive topics absent from existing benchmarks. We introduce Lusopol, a benchmark of 1,400 English and Portuguese prompts spanning seven thematic categories—from colonial history and the Estado Novo dictatorship to contemporary political issues—designed to stress-test models under deliberately controversial framings. Testing 16 state-of-the-art LLMs on Lusopol, we find substantial performance variance: frontier closed models and leading open-weight families set the pace, while European-focused open models lag behind. To close this gap, we synthesize a preference corpus of more than 7.5K samples. Targeted alignment training on this resource significantly reduces skewed responses and makes European open models competitive, demonstrating that culturally situated preference data is a practical path toward closing the alignment gap for underrepresented societies.