We introduce MAVERIX (Multimodal Audio-Visual Evaluation and Recognition IndeX), a unified benchmark to probe video understanding in multimodal LLMs, encompassing video, audio, and text inputs with human performance baselines. Although recent advancements in audiovisual models have shown substantial progress, the field lacks a standardized evaluation framework to thoroughly assess their cross-modality comprehension performance. MAVERIX curates 2,556 questions from 700 videos, in the form of both multiple-choice and open-ended formats, explicitly designed to evaluate multimodal models through questions that necessitate tight integration of video and audio information, spanning a broad spectrum of agentic scenarios. MAVERIX uniquely provides models with questions that closely mimic the multimodal understanding experiences available to humans during decision-making processes. To our knowledge, MAVERIX is the first benchmark aimed explicitly at assessing comprehensive audiovisual integration in such granularity. Experiments with state-of-the-art models, including Qwen 2.5 Omni and Gemini 2.5 Flash-Lite, show performance around 64% accuracy, while human experts reach near-ceiling performance of 92.8%, exposing a substantial gap to human-level comprehension. With standardized evaluation protocols, a rigorously annotated pipeline, and a public toolkit, MAVERIX establishes a challenging testbed for advancing audiovisual multimodal intelligence, with the website publicly available below.

This paper demonstrates the methodology and findings of creating an augmented reality navigation app that uses tilesets to create the navigation. It illustrates the method in which the app was created - using vector data and uploading it to MapBox, then accessing that data in Unity through the MapBox API and map editor and then overlaying the camera input with the navigation path layer. The application was tested by creating multiple arbitrary navigation scenarios and checking them for various factors. The main finding of this research is that this navigation solution works better than GPS indoor navigation.

This study discusses the results and findings of an augmented reality navigation app that was created using vector data uploaded to an online mapping software for indoor navigation. The main objective of this research is to determine the current issues with a solution of indoor navigation that relies on the use of GPS signals, as these signals are sparse in buildings. The data was uploaded in the form of GeoJSON files to MapBox which relayed the data to the app using an API in the form of Tilesets. The application converted the tilesets to a miniaturized map and calculated the navigation path, and then overlaid that navigation line onto the floor via the camera.