Architectural design relies on 3D modeling procedures, generally carried out in Building Information Modeling (BIM) formats. In this setting, architects and designers collaborate on building designs, iterating over many possible versions until a final design is agreed upon. However, this iteration is complicated by the fact that any changes need to be made by manually introducing changes to the complex BIM files, which lengthens the design process and makes it difficult to quickly prototype changes. To speed up prototyping, we propose VR-Arch, a virtual assistant that allows users to interact with the BIM file in a virtual reality (VR) environment. This framework enables users to 1) make changes directly in the VR environment, 2) make complex queries about the BIM, and 3) combine these to perform more complex actions. All of this is done via voice commands and processed through a ReAct-based agentic system that selects appropriate tools depending on the query context.This multi-tool approach enables real-time, contextualized interaction through natural language, allowing for a faster and more natural prototyping experience.

In this paper we present our submission for the NorSID Shared Task as part of the 2025 VarDial Workshop, consisting of three tasks: Intent Detection, Slot Filling and Dialect Identification, evaluated using data in different dialects of the Norwegian language. For Intent Detection and Slot Filling, we have fine-tuned a multitask model in a cross-lingual setting, to leverage the xSID dataset available in 17 languages. In the case of Dialect Identification, our final submission consists of a model fine-tuned on the provided development set, which has obtained the highest scores within our experiments. Our final results on the test set show that our models do not drop in performance compared to the development set, likely due to the domain-specificity of the dataset and the similar distribution of both subsets. Finally, we also report an in-depth analysis of the provided datasets and their artifacts, as well as other sets of experiments that have been carried out but did not yield the best results. Additionally, we present an analysis on the reasons why some methods have been more successful than others; mainly the impact of the combination of languages and domain-specificity of the training data on the results.