Gated Delta Networks: Improving Mamba2 with Delta Rule

Linear Transformers have gained attention as efficient alternatives to standard Transformers, but their performance in retrieval and long-context tasks has been limited. To address these limitations, recent work has explored two distinct mechanisms: gating for adaptive memory control and the delta update rule for precise memory modifications. We observe that these mechanisms are complementary: gating enables rapid memory erasure while the delta rule facilitates targeted updates.

SpecTrack: Learned Multi-Rotation Tracking via Speckle Imaging

Precision pose detection is increasingly demanded in fields such as personal fabrication, Virtual Reality (VR), and robotics due to its critical role in ensuring accurate positioning information. However, conventional vision-based systems used in these systems often struggle with achieving high precision and accuracy, particularly when dealing with complex environments or fast-moving objects. To address these limitations, we investigate Laser Speckle Imaging (LSI), an emerging optical tracking method that offers promising potential for improving pose estimation accuracy.

eXtended Reality and Artificial Intelligence in Medicine and Rehabilitation

This special issue focuses on the application of eXtended Reality (XR) technologies—comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—and Artificial Intelligence (AI) in the fields of medicine and rehabilitation. AR provides support in minimally invasive surgery, where it visualises internal anatomical structures on the patient’s body and provides real-time feedback to improve accuracy, keep the surgeon’s attention and reduce the risk of errors.

Variable Frame Timing Affects Perception of Smoothness in First-Person Gaming

With the advent of variable refresh rate (VRR) monitor technologies, gamers experience variable frame timing (VFT) during their gameplay. Combining VRR with low-latency GPU rendering and increased display refresh rates enables smoother variation of frame presentation sequences. Here, we assess how VFT affects self-reported perceived smoothness of game play by introducing frequent but relatively small ((4−12 ms) variations in frame time around typical refresh rates (30-240 Hz).

The Effects of Network Latency on the Peeker's Advantage in First-person Shooter Games

In first-person shooter (FPS) games, the peeker's advantage is the edge the moving peeker gets when battling a stationary defender at a corner due to network latency. However, confirmation of (the size of) this advantage based on network latency and the distance from the corner has not been studied. This paper assesses the peeker's advantage via two user studies both using an open-source FPS game extended to support two-player networking and a custom map. Users play as both peeker and defender with 3 different corner distances and 3 different network latencies.

High-Precision Benchmarks for the Stochastic Rod

We demonstrate a method to calculate high-precision benchmarks for the reflectance and transmittance of a finite rod with a stochastic cross section, assuming that the attenuation law has a known closed form and both the single-scattering albedo and scattering kernel are deterministic. We introduce new 10-digit values for an existing binary-Markov benchmark (including mean and variance), along with several new benchmarks defined for non-Markov binary mixtures and a continuous-fluctuation model featuring gamma stationary statistics.

A Layered, Heterogeneous Reflectance Model for Acquiring and Rendering Human Skin

We introduce a layered, heterogeneous spectral reflectance model for human skin. The model captures the inter-scattering of light among layers, each of which may have an independent set of spatially-varying absorption and scattering parameters. For greater physical accuracy and control, we introduce an infinitesimally thin absorbing layer between scattering layers. To obtain parameters for our model, we use a novel acquisition method that begins with multi-spectral photographs.

Chong Xiang

Chong is a member of the Security and Privacy Research team, focusing on algorithmic protection for AI models and systems. Chong joined NVIDIA in 2025, after earning his PhD from Princeton University in 2024 and his BSE from Shanghai Jiao Tong University in 2019