arXiv preprints from January 1, 2026 through September 25, 2026 — 15:32:48 EST
Graph Prompt Feature (GPF) learning has been widely used in adapting pre-trained GNN model on the downstream task. GPFs first introduce some prompt atoms and then learns the optimal prompt vector for each graph node using the linear combination of prompt atoms. However, existing GPFs generally conduct prompting over node's all feature...
Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for...
Localizable measurements are joint quantum measurements that can be implemented using only non-adaptive local operations and shared entanglement. We provide a protocol-independent characterization of localizable projection-valued measures (PVMs) by exploiting algebraic structures that any such measurement must satisfy. We first show...
Long context may impose challenges for encoder-only language models in text processing, specifically for automated scoring of essays. This study trained several commonly used encoder-based language models for automated scoring of long essays. The performance of these trained models was evaluated and compared with the ensemble models...
Quantum valley Hall-based topological phases have been attracting attention across diverse fields as a robust platform for wave guidance due to their high compatibility with engineering frameworks. Combining three representative boundary types enables topological waveguides with flexible designability and enhanced functionality....
Constancy of life vitally depends on the internalization of particles through biomembranes. Of particular interest, cellular uptake, including phagocytosis, receptor-mediated endocytosis, and membrane fusion, critically depends on the elasticity of particles. Cellular membranes are strongly linked to a supporting cytoskeleton....
Clustering is a central tool in biomedical research for discovering heterogeneous patient subpopulations, where group boundaries are often diffuse rather than sharply separated. Traditional methods produce hard partitions, whereas soft clustering methods such as fuzzy $c$-means (FCM) allow mixed memberships and better capture...
We construct compact hyperbolic 3-manifolds with totally geodesic boundary, such that the closed 3-pseudomanifolds obtained by coning off the boundary components are negatively curved and contain locally convex subspaces whose fundamental groups have property (T). In particular, the fundamental groups of these 3-pseudomanifolds are...
The Euler buckling of rods is a long-studied mechanical instability, and it remains relevant to this day, as the constituent components in many biological and physical systems are linear polymers, such as microtubules or carbon nanotubes. At finite temperature, if a polymer is shorter than its persistence length, the polymer is...
Few-body physics plays a central role in many branches of physics, such as nuclear physics and atomic physics. Advances in controlling ultra-cold quantum gases provide an ideal testbed for few-body physics theory. In this work, we study few-body systems consisting of two distinct species of non-relativistic fermions in one spatial...
Many program transformations and optimizations require information about the future behavior of the program. A standard way to obtain this information is to build an intermediate program representation, then use a backwards program analysis to propagate relevant information against the flow of control back to the...
This work explores how small molecules sound. Infrared (IR) spectra of HCl, H2O, NH3, and acetone are mapped into the audible range using a simple anharmonic oscillator model and NIST vibrational data. Comparing harmonic and anharmonic sonifications reveals systematic pitch flattening, beating, and the emergence of combination bands,...
Automated vehicles present unique opportunities and challenges, with progress and adoption limited, in part, by policy and regulatory barriers. Underrepresented groups, including individuals with mobility impairments, sensory disabilities, and cognitive conditions, who may benefit most from automation, are often overlooked in crucial...
We propose a derivative-free saddle-search algorithm designed to locate transition states using only function evaluations. The algorithm employs a nested architecture consisting of an inner eigenvector search and an outer saddle-point search. Through rigorous numerical analysis, we prove the almost sure convergence of the inner step...
We present a hybrid framework for adaptive insider-threat detection that tightly integrates multi-agent simulation (MAS), layered Security Information and Event Management (SIEM) correlation, behavioral and communication forensics, trust-aware machine learning, and Theory-of-Mind (ToM) reasoning. Intelligent agents operate in a...
Online 3D Bin Packing (3D-BP) with robotic arms is crucial for reducing transportation and labor costs in modern logistics. While Deep Reinforcement Learning (DRL) has shown strong performance, it often fails to adapt to real-world short-term distribution shifts, which arise as different batches of goods arrive sequentially, causing...
We introduce a problem-level prioritization framework for RL post-training of large language models. Building on insights from prioritized replay in deep RL, as well as prior observations that rollouts with intermediate success rates tend to produce stronger learning signals under methods such as GRPO, our approach selects problems...
The filling of the large van der Waals gap in Transition Metal Dichalcogenides (TMDs) often leads to lattice and electronic instabilities, which prelude the onset of a rich phenomenology. Here, we investigate the electronic structure of the TMDs ZrTe$_2$ and Ni-intercalated ZrTe$_2$ (Ni$_x$ZrTe$_2$, $x\approx 0.05$) employing...
Personal health analytics systems face a persistent cold-start dilemma: users expect meaningful insights early in data collection, while conventional statistical inference requires data volumes that often exceed engagement horizons. Existing approaches either delay inference until fixed statistical thresholds are met -- leading to...
Cinemagraphs, which combine static photographs with selective, looping motion, offer unique artistic appeal. Generating them from a single photograph in a controllable manner is particularly challenging. Existing image-animation techniques are restricted to simple, low-frequency motions and operate only in narrow domains with...
Several planners have been developed to compute dynamically feasible, collision-free robot paths from an initial to a goal configuration. A key assumption in these works is that the goal region is reachable; an assumption that often fails in practice when environments are disconnected. Motivated by this limitation, we consider known...
The transistor transformed not only electronics but everyday life, and the integrated circuit - now simply the "chip" - made computation scalable and ubiquitous. Magnonics has long promised a parallel path to low-energy information processing by using spin waves instead of charge. Progress, however, has been limited by two fundamental...
Tool-calling conversational agents querying structured databases often face two linked failures: underspecification (missing constraints needed for a precise query) andinfeasibility (a fully specified query returns anemptyset). Prior systems often respond with "no results" or apply ad hoc relaxations, which can violate user intent by...
We introduce a notion of quasiconvexity for continuous functions $f$ defined on the vector bundle of linear maps between the tangent spaces of a smooth Riemannian manifold $(M,g)$ and $\mathbb{R}^m$, naturally generalizing the classical Euclidean definition. We prove that this condition characterizes the sequential lower...
Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as the device's CPU utilization and thermal conditions. In this paper, through extensive experiments, we investigate two key issues that must be addressed to deploy on-device models in...