The ability to navigate outdoors safely and independently is crucial yet challenging for people with low vision (PLV). While various augmented reality (AR) systems for low vision have been designed and evaluated in ideal lab environments, no research has investigated their real-world feasibility and challenges. We present NavSight, a mobile AR application that assists PLV in outdoor navigation by recognizing important outdoor objects (e.g., curb, vehicle) and rendering real-time visual augmentations. Through a seven-day diary study with 12 PLV in real-world settings, we characterize the impact of NavSight on scene perception, users' configuration strategies on what objects to augment and how to augment them across scenarios, how users made sense of and responded to recognition errors, and the social acceptability of using NavSight in public. We further identify environmental factors affecting recognition, such as weather conditions, lighting and shadows, and nonstandard road markings and textures, as well as usability issues in daily use. We discuss these real-world challenges and derive design implications for future AI-powered assistive AR systems for outdoor use.
Yaxun Dai Soochow University, Wenxuan Xie, Xialie Zhuang, Tianyu Yang Meituan, Ziyi Liu Soochow University, Haiqin Yang, Yiying Yang, Yuhang Zhao 等 10 位作者
Current Text-to-SQL reasoning models often lack integrated execution feedback during generation, and most existing approaches utilize feedback only for post-hoc correction. This separation not only limits real-time error correction, but may also introduce mistakes by altering otherwise correct SQL queries. To address these challenges, we present ReEx-SQL (Reasoning with Execution-Aware Reinforcement Learning), a Text-to-SQL framework that interacts with the SQL execution engine during decoding and dynamically adjusts reasoning based on execution feedback, thereby enabling context-sensitive query refinement and improved accuracy. ReEx-SQL achieves this through structured prompts with markup tags and a stepwise rollout strategy that incorporates execution feedback at each generation stage. For policy supervision, we design a composite reward function—featuring an exploration reward—to explicitly encourage effective interaction with the database. Furthermore, ReEx-SQL adopts a tree-based decoding strategy to facilitate exploratory reasoning and primarily aims to enhance parallel decoding efficiency. Notably, ReEx-SQL achieves 89.1% accuracy on Spider and 65.3% on BIRD at the 7B scale, surpassing baseline models by 2.7% and 2.6%, respectively. In addition, its tree-based decoding accelerates inference by 51.9% compared to linear decoding during sampling.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表