Large Language Models (LLMs) have introduced reasoning capabilities through multi-step problem-solving processes. These models predominantly perform reasoning in English, limiting their effectiveness in other languages. This paper introduces Bode Reasoning, a Portuguese-language reasoning approach built upon fine-tuned Qwen3-4B and Qwen3-4B-Thinking models, and the Bode Reasoning Portuguese Dataset, comprising 13,961 instances from Brazilian examinations and translated datasets. Through supervised fine-tuning, the proposed approach successfully shifts the reasoning process to Brazilian Portuguese while reducing output verbosity. Experimental evaluation demonstrates that fine-tuned models generate Portuguese reasoning in 86-98.7% of outputs and achieve superior lexical alignment with reference answers. However, this specialization results in moderate mean G-Eval and accuracy degradation across diverse multiple-choice question types, highlighting inherent trade-offs in adapting multilingual reasoning models.

The evaluation of Large Language Models (LLMs) in medicine has predominantly relied on English-language benchmarks aligned with North American clinical guidelines, limiting their applicability to other healthcare systems. In this paper, we evaluate twenty-two proprietary and open-weight LLMs on the 2025 National Examination for the Evaluation of Medical Training (ENAMED), a high-stakes, government-standardized assessment used to evaluate medical graduates in Brazil. The benchmark comprises 90 multiple-choice questions grounded in Brazilian public health policy, clinical practice, and Portuguese medical terminology, and is released as an open dataset. Model performance is measured using both standard accuracy and the official Item Response Theory (IRT) framework employed by ENAMED, enabling direct comparison with human proficiency thresholds. Results reveal a clear stratification of model capabilities: proprietary frontier models achieve the highest performance, whereas many open-weight and smaller-domain-adapted models fail to meet the minimum proficiency criterion. Across comparable scales, large generalist models consistently outperform specialized medical fine-tunes, suggesting that general reasoning capacity is a stronger predictor of success than narrow domain adaptation in this setting. These findings establish ENAMED as a rigorous benchmark for evaluating medical LLMs in Portuguese and highlight both the potential and current limitations of such models for educational assessment.

Retrieval-Augmented Generation (RAG) is proposed to reduce hallucination and improve grounding in clinical language models, yet its effectiveness across different levels of clinical reasoning remains unclear. We conducted a controlled evaluation of medication-related question answering in Portuguese using over 7,000 Brazilian regulatory drug leaflets and a complementary clinical benchmark derived from national medical licensing examinations (Revalida and Fuvest). Retrieval substantially improved factual recall and clinical coherence in medication-specific queries, increasing F1 from 0.276 to 0.412. However, naive retrieval did not consistently improve complex clinical reasoning and sometimes reduced accuracy compared to a parametric-only baseline. We identify retrieval-induced anchoring bias, where partially relevant evidence shifts model decisions toward clinically incorrect conclusions. Critique-based and adaptive retrieval mitigated this effect and achieved the highest clinical benchmark accuracy (54.25%). Clinically grounded evaluation dimensions revealed safety-relevant differences beyond traditional NLP metrics. These results show that retrieval augmentation is effective in regulatory settings but requires adaptive control for higher-level clinical reasoning.