arXiv preprints from January 1, 2026 through September 8, 2026 — 00:15:30 EST
Spatial textures of magnetic order, such as domain walls and skyrmions, are fundamental objects in magnetism. In rhombohedral multilayer graphene, magnetic order involves spin and valley degrees of freedom, opening the possibility of qualitatively new spatial textures. Here, we explore this possibility through a microscopic study of a...
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task can be performed simply by specifying it in the context, without any parameter update. This form of in-context learning (ICL) turns...
We predict a new \textit{viscochiral regime} of electronic transport in which spatially varying Hall viscosity selects vortical flow patterns. Although uniform Hall viscosity cannot alter incompressible bulk flow, its spatial gradient redistributes vorticity, amplifying vortices in one chamber while suppressing the vortex in the...
Recent advances in video editing have been largely driven by large-scale instruction-based datasets. However, existing datasets still suffer from two critical limitations. First, target videos are commonly produced by automatic editing models, which may introduce visible artifacts and unreliable supervision signals. Second, most...
Ising machines are non-von Neumann machines designed to solve combinatorial optimization problems (COP) by searching for the ground state, or the lowest energy configuration, within the Ising model. However, Ising machines often face the challenges of getting trapped in local minima due to the complex energy landscapes. Hardware...
Quantum geometry has emerged as a guiding principle across atomic and condensed-matter physics, shaping the topological responses of Bloch bands and the stability of the correlated phases they host. Beyond two-band models, however, closed-form expressions for both the spectrum and the quantum geometry are rare. Here we provide such...
The Local Bubble (LB) contains significant internal structure, with many interstellar clouds lying within or on its walls. By observing time variability of atomic neutral hydrogen (HI) absorption profiles in the direction of pulsars, it was suggested that some of the HI structures exhibit variations in optical depth representative of...
Elemental and isotopic abundances are key tracers of planet formation, stellar evolution, and Galactic chemical evolution. Very low-mass stars are particularly interesting in this regard, because unlike more massive or evolved stars their photospheric abundances retain the star's natal composition. Cool dwarf spectra have historically...
Calibrated torsion-balance spectra constrain deterministic and stochastic deviations from standard Newtonian gravity. Using one-sided spectra, we derive a calibrated angle-equivalent noise budget and finite-time matched-filter/Cramér-Rao bounds for known torque templates. For stochastic models, subtracting the calibrated standard...
In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from streaming unlabeled data. Existing unsupervised post-training methods for MLLMs typically optimize target tokens uniformly, overlooking their heterogeneous visual dependence (VD)....
Existing action quality assessment (AQA) datasets and methods rely primarily on visual inputs such as RGB and pose, overlooking physiological dynamics such as muscle mechanics and often modeling actions as monolithic patterns. These limitations hinder fine-grained, biomechanically grounded feedback. We introduce MyoMechanix, a...
Designing machine learning algorithms for wireless resource management is labour-intensive: the architecture, the loss function and the training recipe are all specified by hand. We demonstrate that this design layer can be surrendered to an autonomous agent in its entirety. We adopt the autoresearch protocol, in which an AI coding...
We prove optimality of the differentiability bound for isotropic positive definite functions on every even-dimensional sphere. If the even continuation of such a function on the $d$-dimensional sphere is $2k$ times differentiable at zero, then the function has $2k+\lfloor(d-1)/2\rfloor$ continuous interior derivatives; previously,...
Civil infrastructure compliance checking has long relied on engineers manually reading legacy 2D plans; however, OCR-based automation strips away the geometry and layout essential for interpreting these plans. We present a Visual-First Multimodal Retrieval-Augmented Generation (RAG) framework called PlanSightRAG. It indexes and...
We present a first application of sparse-autoencoder-based mechanistic interpretability to particle physics. Studying a neutrino foundation model pretrained on IceCube data and fine-tuned for direction reconstruction, we identify a validated atlas of physical concepts in the model representation, using a strict validation protocol...
Generative AI is promoted as a way to enhance knowledge work, yet its benefits and drawbacks fall unevenly across the workforce. Freelance and gig workers, who commonly lack the upskilling pathways available to traditional employees, face heightened risks to both skill development and job security as AI adoption advances. We review...
Addressing critical global challenges, from food security and disaster risk to disease outbreaks and socio-economic vulnerability, demands high-fidelity geospatial modeling. However, building predictive planetary models remains bottlenecked by a fragmented data ecosystem, requiring manual data retrieval, multimodal data curation and...
Let $F=(f_1,\ldots,f_r)$ be a tuple of holomorphic germs on a smooth complex germ, and let $B_{F,0}$ be its Bernstein--Sato ideal. We develop an iterated-residue obstruction showing that a nonzero coefficient-valued residue class on an SNC stratum forces the corresponding exact affine parameter to lie in $Z(B_{F,0})$. As applications,...
Large language models write correct code for isolated problems but remain far weaker at autonomous machine-learning development, where an agent must revise data pipelines, models, and validation over hours of feedback, and on most competitions still finishes below strong human competitors. Outcome-based benchmarks record this gap but...
We investigate the problem of confinement of massive ghost, which violates the unitarity of the physical S-matrix, in quadratic gravity on the basis of a manifestly covariant and local canonical operator formalism. First, we reconsider the manifestly covariant quantization of quadratic gravity in the de Donder gauge (the harmonic...
Esakia duality is the dual equivalence between Heyting algebras and Esakia spaces. However, the traditional proof uses the Prime Ideal Theorem to recover the algebra from its spectrum, a choice principle that is not constructively valid. We build on Townsend's localic Priestley duality to describe a fully constructive, localic...
Deep neural networks often exploit spurious associations in their training data, a failure known as shortcut learning. Concept-based explainability methods screen for shortcuts by testing whether concepts such as a patient's sex or scanner settings can be decoded from a network layer. Because each concept is evaluated in isolation,...
We show that the $d+0$-dimensional Yang-Lee theory describing classical Ising spins in an imaginary magnetic field can be realized, without post-selection, within a $(d-1)+1$ open quantum system whose dynamics consist of local unitaries and engineered dissipation. Competition between the coherent unitary and dissipative dynamics...
Collective intelligence can emerge when individuals coordinate through a shared environment, allowing local actions to accumulate into durable social organization. Language-model agents offer a new substrate for this process, yet most multi-agent systems rely on direct conversation, predefined roles, or centralized workflows. It...
Many empirical investigations of long-run relations are based on cross-section regressions in averaged or long differenced data that effectively have the time dimension of a $T\times N$ panel compressed. We analyze a class of time-compressed I(1) data and show that they have magnified variability stemming from the fact that the...