# Harnessing Vision-Language Models for Perceptual Quality Assessment and Autonomous Content Adjustment in Augmented Reality

arXiv: https://arxiv.org/abs/2610.00677 · HTML: https://mlsanity.com/paper/2610.00677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Placed in

- Multimodal LLMs / vision-language models

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [Confusing Image Quality Assessment: Towards Better Augmented Reality Experience](https://mlsanity.com/paper/2204.04900/) (2022)
- [IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models](https://mlsanity.com/paper/2312.15663/) (2023)
- [ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality](https://mlsanity.com/paper/2501.12553/) (2025)
- [HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment](https://mlsanity.com/paper/2501.01116/) (2025)
- [ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment](https://mlsanity.com/paper/2407.11496/) (2024)
- [The Impact of Spatial Misalignment and Time Delay on Collaborative Presence in Augmented Reality](https://mlsanity.com/paper/2512.07363/) (2025)

## Abstract (arXiv)

Advancements in augmented reality (AR) continue to foster innovative solutions, facilitating novel methodologies within educational systems, healthcare delivery, and risk-mitigation protocols. However, optimizing for end-user immersion and comfort remains challenging, as AR head-mounted displays contend with constrained scene geometry, spatial jitter, and temporal instability. User studies are the standard AR evaluation method for visual quality, but their cost, diminishing scalability, and inflexibility pose bottlenecks during iterative application design. To address this problem, we present an automated framework for AR content evaluation and refinement, built on vision-language models (VLMs), to evaluate and predict the visual fidelity of AR scenes as perceived by users. First, we introduce RateAR, a benchmark of AR images and videos collected across diverse scenes and environmental conditions, with good-to-excellent reliability (ICC(2,5) >= .90) across perceptual factors, including object placement, scale, and shadow consistency. Subsequently, we evaluate eleven commercial VLMs on the crafted benchmark. Results support that VLM-based quality predictions strongly correlate with human subjective judgments, achieving Spearman's rank-order correlations of up to 0.8695. An ablation study further suggests that, compared to other prompting strategies, our contextual prompting yields better alignment with human ratings while balancing introduced complexity cues. Building on these findings, we construct an automated AR content adjustment system and conduct a 21-participant user study. More than 90% of participants found that the system improved placement and size coherence of virtual content.