We introduce an intuitionistic modal logic strictly contained in the intuitionistic modal logic IK and being an appropriate candidate for the title of “minimal normal intuitionistic modal logic”.
Medical large language models (LLMs) have demonstrated remarkable performance in answering medical examinations. However, the extent to which this high performance is transferable to medical questions in Spanish and from a Latin American country remains unexplored. This knowledge is crucial as LLM-based medical applications gain traction in Latin America. To build a dataset of questions from medical examinations taken by Peruvian physicians pursuing specialty training; to fine-tune a LLM on this dataset; to evaluate and compare the performance in terms of accuracy between vanilla LLMs and the fine-tuned LLM. We curated PeruMedQA, a multiple-choice question-answering (MCQA) dataset containing 8,380 questions spanning 12 specialties (2018–2025). We selected ten medical LLMs, including medgemma-4b-it and medgemma-27b-text-it, and developed zero-shot task-specific prompts to answer the questions. We employed parameter-efficient fine tuning (PEFT) and low-rank adaptation (LoRA) to fine-tune medgemma-4b-it utilizing all questions except those from 2025 (test set). Medgemma-27b showed the highest accuracy across all specialties, achieving the highest score of 89.29
Large Language Models (LLMs) are typically deployed using a fixed architecture, despite growing evidence that not all layers contribute equally to every downstream task. In this work, we introduce TALE (Task-Aware Layer Elimination), an inference-time method that improves task performance by selectively removing layers that are irrelevant or detrimental for a given task. TALE optimizes task-specific validation performance, yielding a task-adapted architecture without retraining or modifying model weights. Across 9 tasks and 5 model families, under both zero-shot and few-shot settings, we show that TALE consistently matches or surpasses baseline performance while simultaneously reducing computational cost, outperforming general and layer-wise pruning approaches such as SLEB. Beyond inference-time gains, TALE synergizes with fine-tuning and few-shot learning, where task-adapted architectures lead to additional performance improvements. Computing TALE for a new task requires modest resources (1-2 GPU hours on an A100), making it a practical and deployable solution for task-specialized LLM inference.
The use of artificial neural networks is now becoming widespread across a wide variety of tasks. In this context of very rapid development issues related to the storage and computational performance of these models emerge, since networks are sometimes very deep and comprise up to trillions of parameters. For all these reasons the use of reduced precision is increasingly being considered, although, until now, its accuracy and robustness had been approached mostly from a practical standpoint or verified by software. The aim of this work is to provide formal tools to better understand, explain and predict the accuracy and stability of neural networks when using floating-point arithmetic. To this end, we apply a rounding error analysis based on existing tools in numerical linear algebra to obtain both forward and backward error bounds. This includes both deterministic worst-case bounds as well as probabilistic bounds that are sharper on average. Since the exact backward error is not directly computable the backward error bounds are validated experimentally through a linearized backward error. These bounds both ensure the proper functioning of neural networks once trained and provide recommendations on architectures and training methods to enhance the robustness of neural networks.
Unsupervised anomaly detection is a challenging problem due to the diversity of data distributions and the lack of labels. Ensemble methods are often adopted to mitigate these challenges by combining multiple detectors, which can reduce individual biases and increase robustness. Yet building an ensemble that is genuinely complementary remains challenging, since many detectors rely on similar decision cues and end up producing redundant anomaly scores. As a result, the potential of ensemble learning is often limited by the difficulty of identifying models that truly capture different types of irregularities. To address this, we propose a methodology for characterizing anomaly detectors through their decision mechanisms. Using SHapley Additive exPlanations, we quantify how each model attributes importance to input features, and we use these attribution profiles to measure similarity between detectors. We show that detectors with similar explanations tend to produce correlated anomaly scores and identify largely overlapping anomalies. Conversely, explanation divergence reliably indicates complementary detection behavior. Our results demonstrate that explanation-driven metrics offer a different criterion than raw outputs for selecting models in an ensemble. However, we also demonstrate that diversity alone is insufficient; high individual model performance remains a prerequisite for effective ensembles. By explicitly targeting explanation diversity while maintaining model quality, we are able to construct ensembles that are more diverse, more complementary, and ultimately more effective for unsupervised anomaly detection.
