Large language models (LLMs) generate text step by step, which limits their ability to plan for tasks requiring multiple reasoning steps, such as structured writing or problem-solving. This lack of long-term planning affects their coherence and decision-making in complex scenarios. Some approaches evaluate various alternatives before making a choice, which improves prediction precision. However, they… →

Modern bioinformatics research is characterized by the constant emergence of complex data sources and analytical challenges. Researchers routinely confront tasks that require the synthesis of diverse datasets, the execution of iterative analyses, and the interpretation of subtle biological signals. High-throughput sequencing, multi-dimensional imaging, and other advanced data collection techniques contribute to an environment where traditional,… →

Object-centric learning (OCL) is an area of computer vision that aims to decompose visual scenes into distinct objects, enabling advanced vision tasks such as prediction, reasoning, and decision-making. Traditional methods in visual recognition often rely on feature extraction without explicitly segmenting objects, which limits their ability to understand object relationships. In contrast, OCL models break… →

Personalizing LLMs is essential for applications such as virtual assistants and content recommendations, ensuring responses align with individual user preferences. Unlike traditional approaches that optimize models based on aggregated user feedback, personalization aims to capture the diversity of individual perspectives shaped by culture, experiences, and values. Current optimization methods, such as reinforcement learning from human… →