Kun Luo, Hongjin Qian, Zheng Liu, Ziyi Xia, Shitao Xiao, Zhao Cao, Siqi Bao, Jun Zhao, Kang Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Sensing touch on arbitrary surfaces has long been a goal of ubiquitous computing, but often requires instrumenting the surface.Depth camera-based systems have emerged as a promising solution for minimizing instrumentation, but at the cost of high touch-down detection error rates, high touch latency, and high minimum hover distance, limiting them to basic tasks.We developed HaloTouch, a vision-based system which exploits a multipath interference effect from an off-the-shelf time-of-flight depth camera to enable fast, accurate touch interactions on general surfaces.HaloTouch achieves a 99.2% touch-down detection accuracy across various materials, with a motion-to-photon latency of 150 ms.With a brief (20s) userspecific calibration, HaloTouch supports millimeter-accurate hover sensing as well as continuous pressure sensing.We conducted a user study with 12 participants, including a typing task demonstrating text input at 26.3 AWPM.HaloTouch shows promise for more robust, dynamic touch interactions without instrumenting surfaces or adding hardware to users.

Asymmetric AR/VR collaboration systems bring a remote VR user to a local AR user’s physical environment, allowing them to communicate and work within a shared virtual/physical space. Such systems often display the remote environment through 3D reconstructions or 360° videos. While 360° cameras stream an environment in higher quality, they lack spatial information, making them less interactable. We present VirtualNexus, an AR/VR collaboration system that enhances 360° video AR/VR collaboration with environment cutouts and virtual replicas. VR users can define cutouts of the remote environment to interact with as a world-in-miniature, and their interactions are synchronized to the local AR perspective. Furthermore, AR users can rapidly scan and share 3D virtual replicas of physical objects using neural rendering. We demonstrated our system’s utility through 3 example applications and evaluated our system in a dyadic usability test. VirtualNexus extends the interaction space of 360° telepresence systems, offering improved physical presence, versatility, and clarity in interactions.

Finance is a particularly challenging playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and backtesting overfitting. In this paper, we present an openly accessible FinRL-Meta library that has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we will provide hundreds of market environments through an automatic data curation pipeline that processes dynamic datasets from real-world markets into gym-style market environments. Second, we reproduce popular papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, FinRL-Meta provides tens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. FinRL-Meta is available at: \url{https://github.com/AI4Finance-Foundation/FinRL-Meta}