# NavAI: A Generalizable LLM Framework for Navigation Tasks in Virtual Reality Environments

arXiv: https://arxiv.org/abs/2601.03251 · HTML: https://mlsanity.com/paper/2601.03251/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [ExploreAI: Agentic Exploration Knowledge Bases for Reproducible Observable-Regression Testing of Black-Box VR and 3D Applications](https://mlsanity.com/paper/2608.21628/) (2026)

## Abstract (arXiv)

Navigation is one of the fundamental tasks for automated exploration in Virtual Reality (VR). Existing technologies primarily focus on path optimization in 360-degree image datasets and 3D simulators, which cannot be directly applied to immersive VR environments. To address this gap, we present NavAI, a generalizable large language model (LLM)-based navigation framework that supports both basic actions and complex goal-directed tasks across diverse VR applications. We evaluate NavAI in three distinct VR environments through goal-oriented and exploratory tasks. Results show that it achieves high accuracy, with an 89% success rate in goal-oriented tasks. Our analysis also highlights current limitations of relying entirely on LLMs, particularly in scenarios that require dynamic goal assessment. Finally, we discuss the limitations observed during the experiments and offer insights for future research directions.