The field of artificial intelligence is witnessing significant developments in the areas of large language models (LLMs) and ontology-based process models. Researchers are focusing on enhancing the reasoning capabilities of LLMs, improving their ability to understand and represent real-world entities, and developing more robust evaluation frameworks to assess their performance. Notably, there is a growing interest in addressing the limitations of LLMs, such as their tendency to memorize rather than truly understand concepts, and their vulnerability to biases and perturbations. In the domain of ontology-based process models, researchers are working on developing more consistent and semantically coherent models, enabling the integration of standardized process semantics and formal mathematical constructs.

Some noteworthy papers in this area include: Measuring (a Sufficient) World Model in LLMs: A Variance Decomposition Framework, which proposes a formal framework for evaluating the robustness of LLMs' world models. ReasonGRM: Enhancing Generative Reward Models through Large Reasoning Models, which introduces a novel framework for improving the reasoning capabilities of generative reward models. The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making, which presents a dataset designed to evaluate the clinical robustness of LLMs under controlled perturbations.