Latest papers
We develop a geometric theory of information processing in the Horizon-brightened acceleration radiation (HBAR) channel, in which the radiative horizon-area change provides an entropy budget for the information carried by the radiation…
Extension types are a concept in dependent type theory that has appeared in various contexts. The idea is to have types whose terms are partially determined, e.g. via a strict boundary condition. Standard examples are path types of cubical…
Diffusion Language Models (DLMs) offer a compelling alternative to autoregressive (AR) generation by enabling bidirectional context and iterative refinement. However, their reliability under natural input noise and adversarial attacks…
Homogenized continuum models are widely used to describe wave propagation and band-gap behavior in mechanical metamaterials without explicitly resolving their microstructure. Their validity, however, typically relies on the classical…
Large language models used for clinical diagnostic reasoning are sensitive to sociolinguistic register, not just clinical content. We term this failure mode Narrative Anchoring: identical clinical facts expressed in different registers…
We establish the first convergence guarantees for the plain vector-form \emph{Adam} optimizer under heavy-tailed stochastic noise. While several Adam variants are known to achieve optimal iteration complexity in bounded-variance nonconvex…
The formation history of polycyclic aromatic hydrocarbons (PAHs) in the interstellar medium remains a topic of active debate, with proposed mechanisms ranging from high-temperature stellar ejecta processes to low-temperature chemistry…
A novel measure of dependence between a continuous random variable and a multinomial random variable is introduced. The proposed measure is based on the Hellinger distance between conditional distributions. It satisfies the desiderata for a…
Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be inferred implicitly from a highly compressed text prompt.…
High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the humanities and social sciences (HSS) are overlooked, and their…
Accurate evaluation of multimodal large language models (MLLMs) in dental panoramic radiography (orthopantomogram, OPG) is limited by the lack of fine-grained, clinically reliable benchmarks that reflect expert interpretation. This work…
Using the recently introduced ensemble variational Monte Carlo (VMC), we study optimized wave functions for strongly correlated point defects, including nitrogen-vacancy and silicon-vacancy centers in diamond and substitutional iron and…
In switchback experiments with unequal cluster sizes, outcome dispersion across randomization units inflates estimator variance and limits statistical power. Standard control-using-prediction-as-covariate (CUPAC) adjustment may be…
Structural materials for fusion reactors undergo neutron irradiation, generating defect populations that govern their mechanical response through irradiation hardening. Physically based mesoscale constitutive models require accurate…
Continuous software engineering in regulated domains requires engineering teams to address security throughout the development lifecycle. Yet making security requirements explicit in backlog items is still problematic. Engineers must…
Large language models (LLMs) are becoming increasingly integrated into mainstream development platforms and daily technological workflows, typically behind moderation and safety controls. Despite these controls, preventing prompt-based…
The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages. Generative modeling, however, has remained the exception-despite generative models being…
Machine learning methods are increasingly used for traffic prediction in applications such as autonomous driving. Such predictions must be both highly accurate and immediately available, making methods with low computational costs and fast…
Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance. Current methods of Sybil actor detection include constructing graphs, which requires token transfers between…
State-of-the-art head-mounted displays (HMDs) enable gaze-based selection in virtual environments. Yet, these HMDs suffer from the vergence-accommodation conflict (VAC), which is known to affect interaction performance. The VAC might…