Latest papers
Ferroelectric nematic liquid crystals combine fluidity with macroscopic polar order. Although the archetypal NF material RM734 exhibits large nonlinear optical coefficients, most known ferroelectric nematics were designed without…
Recent advances in quantum information and quantum thermodynamics have reshaped the understanding of energy storage at the microscopic scale, paving the way toward protocols for storing and transferring energy in quantum devices. These…
Agentic systems have widened the gap between producing candidate outputs and reviewing them. This paper asks a practical architectural question: should domain specialization be built into an evaluator's weights, or into the rule that…
Natural populations evolve under fluctuating environments and limited resources, yet it is unclear how these factors jointly shape adaptation. Here, we introduce a minimal stochastic model of a population undergoing a birth-death process in…
Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an initial rig and repeatedly edit its skeletal structure and…
We show that gyrokinetics in a spatially uniform equilibrium (or `astrophysical gyrokinetics') possesses a previously unknown quadratic invariant, which we call the gyrokinetic helicity. We derive the limiting forms of the gyrokinetic…
Hierarchical hybrid nanoarchitectures that integrate vertically oriented graphene nanowalls, GNWs, with metal oxide, MeOx, nanotube scaffolds offer versatile platform for smart surfaces, nanoelectronics, and electrochemical technologies.…
Solar $^8\mathrm{B}$ neutrinos offer a unique probe of solar interior dynamics and neutrino electromagnetic properties. We present a systematic, multi-method periodogram analysis of the 22-year Super-Kamiokande solar neutrino dataset…
In spring 2026, an economics professor at Brown University gave a take-home midterm and, after unusually high scores, made the final exam proctored. Among the 59 students who completed the course, average scores fell from 95.7 out of 100 to…
We present results of a search for associated and intervening HI 21 cm absorption in star-forming galaxies and a control sample of radio galaxies at 0.4 < $z$ < 1.0, using ASKAP's FLASH HI 21 cm absorption survey. We report the detection of…
Spatially distributed service systems rely on state-dependent routing to allocate users, tasks, or requests to less-loaded service nodes. In practice, a routing decision does not take effect immediately: the assigned job reaches the…
We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions. Building on the doubly lifted, measure-valued formulation of Transformer dynamics, we view data sets as probability laws on pairs of…
The ASNR-MICCAI BraTS Local Synthesis (Inpainting) task asks for the anatomically plausible completion of healthy brain tissue within a masked region of a T1-weighted MRI, providing a tumor-free anatomical reference for downstream analysis.…
Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents. However, existing methods predominantly rely on sparse task rewards for policy optimization, failing to…
The hybrid intelligent reflecting surface (IRS) architecture is a novel technology that leverages the advantages of both passive and active IRS; the passive IRS offers a large aperture, while the active IRS provides additional power…
Combining Tensor Networks (TNs) and Decision Diagrams (DDs) provides a high-performance framework for the exact simulation of quantum circuits on classical computers by exploiting structural redundancies and topological entanglement.…
The coexistence of quantum and classical signals in optical fiber infrastructures represents a major challenge for large-scale quantum networks, as noise sources such as Raman scattering can significantly impact entanglement distribution,…
Despite substantial progress in remote sensing multi-temporal change detection (MTCD), most existing MTCD methods still represent the dynamic process at each spatial location over the entire observation period using a single change category…
Machine unlearning seeks to selectively remove specific knowledge from trained language models without full retraining, a growing necessity under privacy regulations such as GDPR and the EU AI Act. Recent work has reformulated unlearning as…
Credit assignment is a fundamental challenge in cooperative multi-agent reinforcement learning, particularly in embodied AI settings characterized by limited and delayed feedback as well as dynamically changing numbers of active agents. We…