Jinhui Ye, Fangjing Wang, Ning Gao, Junqiu Yu, Yangkun Zhu, Bin Wang, Jinyu Zhang, Weiyang Jin, Yanwei Fu, Feng Zheng, Yilun Chen, Jiangmiao Pang • Published: 2026-02-10
Large vision-language models (VLMs) excel at multimodal understanding but fall short when extended to embodied tasks, where instructions must be transformed into low-level motor actions. We introduce ST4VLA, a dual-system Vision-Language-Action framework that leverages Spatial Guided Training to align action learning with spatial priors in VLMs. ST4VLA includes two stages: (i) spatial grounding pr...
Amlan Datta, Kamal R. Joshi, Sunil Ghimire, Makariy A. Tanatar, Cameron J. Kopas, Jayss Marshall, Josh Y. Mutus, David P. Pappas, Matthew J. Kramer, Ruslan Prozorov • Published: 2026-02-10
Among the recognized sources of decoherence in superconducting qubits, the spatial inhomogeneity of the superconducting state and the possible presence of magnetic-flux vortices remain comparatively underexplored. Niobium is commonly used as a structural material in transmon qubits that host Josephson junctions, and excess dissipation anywhere in the transmon can become a bottleneck that limits ov...
L. Sommer, I. Seidler, F. J. Schupp, S. Paredes, N. W. Hendrickx, L. Massai, S. W. Bedell, G. Salis, M. Mergenthaler, P. Harvey-Collard, A. Fuhrer, T. Ihn • Published: 2026-02-10
Spin qubits are typically operated in the lowest orbital of a quantum dot to minimize interference from nearby states. In valence-band hole systems, strong spin-orbit coupling links spin and orbital degrees of freedom, strongly influencing the hole $g$-factor, a key parameter for qubit control. We investigate the out-of-plane $g$-factor in Ge quantum dots using excitation (single-particle) and add...
Vinicius F. Lisboa, Pedro R. Dieguez, Kyrylo Simonov, Roberto M. Serra • Published: 2025-10-30
Allowing the order of quantum operations to exist in superposition is known to open new routes for thermodynamic tasks. We investigate a quantum heat engine where energy exchanges are driven by generalized measurements, and the sequence of these operations is coherently controlled in a superposition of causal orders. Our analysis explores how initial correlations between the working medium and the...
Omer Hofman, Jonathan Brokman, Oren Rachmil, Shamik Bose, Vikas Pahuja, Toshiya Shimizu, Trisha Starostina, Kelly Marchisio, Seraphina Goldfarb-Tarrant, Roman Vainshtein • Published: 2025-05-21
Agentic AI systems, which build on Large Language Models (LLMs) and interact with tools and memory, have rapidly advanced in capability and scope. Yet, since LLMs have been shown to struggle in multilingual settings, typically resulting in lower performance and reduced safety, agentic systems risk inheriting these limitations. This raises concerns about the accessibility of such systems, as users ...
Giulio Caldarelli • Published: 2026-02-10
Unlike Ethereum, which was conceived as a general-purpose smart-contract platform, Bitcoin was designed primarily as a transaction ledger for its native currency, which limits programmability for conditional applications. This constraint is particularly evident when considering oracles, mechanisms that enable Bitcoin contracts to depend on exogenous events. This paper investigates whether new orac...
Luyao Sun, Sitian Li, Huan Huang, Hongliang Zhang, Weidong Mei, Dongdong Zou, Jun Li, Gangxiang Shen, Yi Cai • Published: 2026-02-10
Covert communications, also known as low probability of detection (LPD) communications, offer a higher level of privacy protection compared to cryptography and physical-layer security (PLS) by hiding the transmission within ambient environments. Here, we investigate covert communications in the presence of a disco reconfigurable intelligent surface (DRIS) deployed by the warden Willie, which simul...
Younes Bouhadjar, Emre Neftci • Published: 2026-02-10
Training transmission delays in spiking neural networks (SNNs) has been shown to substantially improve their performance on complex temporal tasks. In this work, we show that learning either axonal or dendritic delays enables deep feedforward SNNs composed of leaky integrate-and-fire (LIF) neurons to reach accuracy comparable to existing synaptic delay learning approaches, while significantly redu...