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Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

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Heavy Heisenberg Algebras, Zero Modes and Modularity

It is known due to a result of Miyamoto that zero mode correlators of weight $1$ vectors of vertex operator algebras are quasi-modular forms, and that their generating series are Jacobi forms. In this article we generalize this to higher weight vectors. We consider weight 2 vectors that generate what we call heavy Heisenberg algebras. We prove that their zero mode correlators are a specific type of quasi-modular forms. As a consequence, they too can be summed up into a generating series with Jacobi-like modular transformation properties. As an application of our results, we sketch an argument that gives the asymptotic behavior of the dimensions of states of our VOAs.

math.QA↗

Large Excursions of Reflected Lévy Processes: Local Asymptotics

This paper establishes uniform local large-deviation asymptotics for excursions of a Lévy process $X$ with negative drift $-β$ and $α$-locally regularly varying Lévy measure $ν$, reflected at its running infimum. Under the excursion measure $\underline n$, we first prove that the lifetime $ζ$ and height $\overlineε$ satisfy \begin{equation*} \underline{n}\big(ζ\in(t,t+δ] \big) \sim \underline{n}(\overlineε\in\big (βt,βt+βδ]\big) \sim\underline{n}(ζ)\cdotν\big((βt,βt+βδ]\big), \quad \mbox{as } t\to\infty, \end{equation*} uniformly in $δ\in[δ_0,\infty]$ for every $δ_0>0$. In contrast to the asymptotic equivalence of the corresponding tail events, fixed-width lifetime and height windows have asymptotically negligible relative overlap. Nevertheless, for any positive function $θ$ satisfying $|X_t+βt|/θ_t\to0$ in probability, \begin{equation*} \underline n\big( |\overlineε-βt| \leq θ_t \,\big|\, ζ\in (t,t+δ] \big) \sim \underline n\big( |ζ-t| \leq θ_t \,\big|\, \overlineε\in (βt,βt+βδ] \big) \to 1 . \end{equation*} For conditioning windows of sublinear width bounded away from zero, both conditional excursion laws converge under spatial rescaling to a single-jump process with deterministic jump size and random arrival time determined by the integrated excursion survival function. An additional time rescaling yields the same deterministic linear descent killed at zero. Finally, we identify the joint limiting distribution of the unscaled pre-terminal position and terminal overshoot, including continuous and discontinuous termination.

math.PR↗

ACTIVE: A Dynamic Pareto-Frontier-Based Unified Index for Dual-Vector Query

Existing approximate nearest neighbor search (ANNS) methods generally assume that each entity has a single vector. In many real-world applications, however, an entity is associated with more than one vector, and queries may jointly consider two attributes. With different queries assigning different weights to each attribute, Pareto-frontier-based indices provide an effective solution for dual-vector retrieval by connecting to approximate nearest neighbors under all possible query preferences. However, existing Pareto-frontier-based approaches suffer from substantial inefficiencies in both index construction and dynamic updates, limiting their practical applicability. To address these issues, we propose ACTIVE, a dynamic Pareto-frontier-based DVQ index that supports efficient construction and updates while maintaining high retrieval performance under varying query preferences. It employs angle-constrained active-range computation and connectivity-loss-driven adaptive repair strategies to accelerate node insertion and deletion, respectively. Extensive experiments show that, at comparable (or slightly below) search accuracy, ACTIVE improves insertion throughput by $1.20$--$1.43\times$ over DEG, and deletion throughput by $1.64$--$2.06\times$ over neighbor-reconnection-based repair and by $22.64$--$39.51\times$ over reinsertion-based repair.

cs.DB↗

A Stochastic Proximal-Gradient Method for Regularized Generally Constrained Continuous Optimization

A stochastic proximal-gradient method is proposed and analyzed for solving regularized optimization problems subject to nonlinear equality constraints and nonnegativity bounds. The objective function's values and gradients are available only through inexact stochastic oracles. The method decomposes each trial step into one component that reduces linearized infeasibility and a second component that seeks optimality while preserving that reduction. Trial steps are accepted or rejected through a relaxed sufficient decrease condition subject to stochastic merit function evaluations. Without assuming unbiasedness of stochastic gradient estimates, it is proved under reasonable assumptions that the method reaches an $ε$-KKT point within $O(ε^{-2})$ iterations with high probability. This guarantee further implies almost-sure termination in finitely many iterations and an $O(ε^{-2})$ bound on the expected iteration complexity.

math.OC↗

CLOSURE: Cross-Channel Closure for Multivariate Time Series Anomaly Detection

Multivariate time series produced by industrial installations and server clusters are under continuous monitoring, and anomaly detection is the first line of defense before a local fault escalates into a system-wide failure. In the absence of labels, the dominant paradigm trains a reconstruction model on normal data and scores the residual to reach a verdict. Recent work has steadily enriched the expressiveness of the recovery map through Transformers, diffusion models, and cross-scale associations. A residual, however, only certifies that an observation can be recovered; it does not certify that the variables still explain one another as they do under normal operation. A coordinated yet unfamiliar operating transition inflates the residual without breaking any relationship, whereas a sensor drifting away from its peers may leave the residual silent. Methods that fold inter-variable dependencies into the reconstruction objective inherit this ambiguity, because their relational evidence is ultimately supervised by the same recovery loss. We therefore propose CLOSURE, which evaluates recoverability and cross-channel consistency as two normal-data prediction tasks learned in separation. The recoverability task is solved by a diffusion reconstructor conditioned on a discrete memory of normal temporal bases. The consistency task is solved by a relation-closure operator that predicts every channel exclusively from the others, where the target's own trajectory is withheld and sparse signed gates decide which peers are actually used. The two scores are reconciled only through calibration on normal data. Extensive experiments show that CLOSURE consistently outperforms recent reconstruction-, frequency-, and topology-based detectors on public benchmark streams, confirming the value of examining consistency alongside recoverability.

cs.AI↗

On explicit formulas for Grassmannian polylogarithms and Gelfand-MacPherson forms

We answer a question of Goncharov by showing that his Grassmannian $n$-logarithm can be recovered by integrating the Borel invariant form on the symmetric space $\mathrm{SL}_{n}(\mathbf{C})/\mathrm{SU}(n)$ over a naturally defined ideal simplex. As corollaries, we recover the finiteness of primitive canonical integrals of Brown and the evaluation for wheel graphs of Brown and Schnetz. We also compute explicitly the Gelfand-MacPherson forms for powers of the first Pontryagin class and give a general integral formula for the Gelfand-MacPherson forms associated to each component of the Pontryagin character.

math.NT↗

The size of an overtwisted disk

We introduce a notion of size for an overtwisted disk, defined in terms of the Shelukhin-Hofer energy required to stabilise Legendrians in its vicinity. We compute this size for the standard $3$-dimensional overtwisted disk, which we use to prove that certain explicit product hyperdisks in higher-dimensional contact manifolds are overtwisted (and their overtwisted size can be computed explicitly as well). We then use the overtwisted size to establish a number of quantitative results on contact flexibility. Concretely, we (1) obtain quantitative $h$--principles for Legendrians in the complement of an overtwisted disk, (2) construct overtwisted-at-infinity contact manifolds whose Legendrian Shelukhin-Hofer metric vanishes, (3) estimate overtwisted size under contact products and under products with Liouville manifolds, and (4) derive action bounds for contractible Reeb orbits in closed overtwisted contact manifolds.

math.SG↗

VidEdit3D: Flexible 3D Gaussian Editing with Video Diffusion

Recent advancements in text-guided 3D editing, particularly with 3D Gaussian Splatting (3DGS), have shown promise but still lag behind the flexibility of 2D editing tools. Existing methods face a critical trade-off between preserving multi-view consistency and allowing structural modifications. Image-based editors can also struggle to apply the intended edit to objects viewed from unusual angles. To address these limitations, we introduce VidEdit3D, a framework that reformulates 3D scene editing as a trajectory-based video editing task. By drawing a parallel between camera movement in static 3D scenes and temporal dynamics in video, we leverage the robust spatiotemporal priors of pre-trained Video Diffusion Models (VDMs) to encourage semantic and structural coherence across views. Specifically, we propose Trajectory-Guided Rendering (TGR) to select informative keyframes and construct smooth video clips aligned with VDM input requirements. To propagate edits from an anchor view to these clips, we develop Flow-Aligned Video Editing (FAVE), a training-free framework incorporating an Editing Consistency Guidance (ECG) mechanism. ECG contrasts source and edited image conditions at a shared latent state, using scheduled source-image amplification to strengthen adherence to the edited reference while maintaining original scene context. Finally, we introduce Region-Selective 3DGS Refinement (RS-GS) to strictly confine geometric and appearance updates to target primitives, reducing background attribute leakage. Experiments demonstrate stronger instruction alignment and better multi-view consistency than the baselines.

cs.CV↗

EEGaze: Multimodal Attention Prediction in Visual Search via EEG-Based Saliency Refinement

Visual search is ubiquitous in daily life, from locating keys in a messy apartment to finding a tool in a cluttered workshop or an application icon on a desktop screen. While visual attention is to a large extent driven by image content, research in psychology indicates that user cognitive states have a significant influence on attention allocation. A promising way to access information about cognitive states is via Electroencephalography (EEG) recordings. Despite this potential, previous works in computational attention modeling have largely disregarded EEG, and related studies in psychology operate with abstract, simplistic stimuli, or even artificially guided search procedures. We bridge this gap with two contributions. First, we present EEGaze 140, the largest publicly available multimodal dataset of free visual search with realistic stimuli. The dataset contains synchronized EEG and eye tracking recordings from 36 participants across 140 naturalistic scenes, covering two scenarios: searching for desktop icons and finding tools in cluttered workshop scenes. Second, we introduce EEGazeNet, the first method for human attention prediction in visual search that integrates EEG signals. Our approach consists of a EEG guided refinement stage that adapts the output of a state of the art saliency prediction method to the user current cognitive state. In comprehensive evaluations on EEGaze 140, we show that EEGazeNet consistently outperforms all baselines and ablation conditions.

cs.HC↗

Competing coherence geometries in positive maps: positivity hierarchies, atomicity, and optimal entanglement witnesses

We introduce a family of positive maps on qudits whose Choi operators combine two competing coherence patterns: independent exchange couplings and a collective set of diagonal couplings. For every mixed member of the family, we derive exact conditions for positivity, complete positivity, and all intermediate levels of k-positivity, and we determine the complete phase structure of decomposability and atomicity. The higher-positivity thresholds depend on the channel multiplicities, whereas the exact decomposability boundary is universal in scale-free coordinates. Its proof combines an explicit positive-plus-copositive decomposition with a matching family of positive-partial-transpose (PPT) states. The latter form PPT edge states whose detection region is an ellipse, or a disk after collective rescaling. An embedded-qutrit certificate shows that indecomposability and atomicity coincide throughout the mixed family. In contrast, the purely collective endpoint is decomposable over its entire positive interval. Finally, at maximal coherence, an explicit positive subtraction produces an atomic optimal entanglement witness with the spanning property and a strictly larger PPT- detection set. The study thus provides an exactly solvable setting in which k-positivity, decomposability, atomicity, PPT-edge-state geometry, and witness optimality can be compared in arbitrary local dimension.

quant-ph↗

Entropic hypocoercivity via the Polchinski flow: an application to the lattice sine-Gordon model

We develop a multiscale criterion for exponential decay of relative entropy in the canonical stochastic quantization of lattice field theories, the underdamped Langevin dynamics of the field and its momentum. For smooth bounded perturbations of Gaussian fields, the criterion requires the uniform operator-norm bound on the smoothed interaction Hessian along the Polchinski flow of Bauerschmidt and Bodineau to have a small $L^2$ norm in the scale variable. The decay constants depend only on this $L^2$ norm and the friction. The proof measures the transfer of momentum dissipation to position on the same scales that decompose entropy. A phase-space extension of the Polchinski flow gives Gaussian coarse-graining that commutes with free kinetic evolution. Integrating position--momentum cross terms over these scales yields a corrector bounded by entropy. A force defect accounts for changes in the averaged interaction force away from equilibrium; its evolution in scale controls the errors in the kinetic estimate. For the massive two-dimensional lattice sine--Gordon model with $0<β<4π$ and small dimensionless local coupling, the criterion gives entropy decay for all finite-entropy initial laws, uniformly in the lattice spacing and the volume. For friction proportional to the mass $m$, the rate has optimal order $m$.

math-ph↗

Endpoint gradient bounds for centered convex maximal operators and probability kernels

We establish the endpoint gradient estimate for centered homothetic maximal averages in every dimension $d\ge2$ over each fixed convex body with $C^{1,θ}$ boundary, $0<θ\le1$. In particular, the centered ball maximal operator satisfies the $W^{1,1}$ gradient bound in every dimension. Neither strict convexity nor a lower bound on the support gap is required. The geometric argument combines concentration in gradient values with nonhomogeneous calibrated rows and a summable radial correction. It extends the scalar refinement, direction-history construction and global assembly of the planar centered-disk proof of OpenAI. A separate transfer theorem gives sharp singular-derivative identities for bounded compact probability kernels once local BV output is known, and gradient continuity under an additional spatial-tail condition. Quantitative contact and translation estimates yield further profile and kernel examples. The estimates retain the fixed body's regularity data; the endpoint for arbitrary $C^1$ and nonsmooth convex bodies remains open here.

math.CA↗

Beyond Physical Illness: Can Clinical Narratives Reveal Emerging Mental Health Risks in Chronically Ill Patients?

Mental-health risks such as depression, dementia, and suicidal ideation can emerge during hospitalization among chronically ill patients and may worsen without timely recognition and intervention. However, most existing mental health studies focus on patients with established psychiatric conditions rather than patients hospitalized primarily for physical illness. In this work, we investigate whether latent mental-health risk in chronically ill patients can be inferred from longitudinal clinical narratives. We construct a weakly supervised cohort around 4,000 patients from MIMIC-III using mental-health evidence extracted from discharge summaries. A hierarchical state-space framework derives mental-health representations from day-wise clinical narratives and models their temporal evolution for risk estimation throughout hospitalization. Preliminary results demonstrate improved binary risk prediction using the proposed representations and show distinct model derived risk trajectories between risk and no-risk cohorts, providing a foundation for studying emerging mental-health risks in chronically ill patients.

cs.AI↗

The Evolution and 'Chineseness' of Urban Screens in China: From Propaganda Posters to Smart City Interfaces

Urban screens have become a global feature of the media city, yet their developmental paths are not universal. This paper argues that the evolution of urban screens in China constitutes a distinct paradigm of media urbanism, termed Chineseness, understood here not as cultural essence but as a relational, historically situated configuration of three linked elements: deep-rooted lineages of public communication; state-steered technological leapfrogging driven by industrial policy and mega-events; and a regulatory paradox that enables spectacular scale while tightening operational control. Drawing on a five-stage genealogy (1990-present), a chronology of representative screens, and five paradigmatic cases, the study shows how this configuration produces temporal-spatial compression, the coexistence of multiple technological generations, and privileges collective witnessing over device-centric interactivity. The paper contributes a documented genealogy of Chinese urban screens, develops temporal-spatial compression and collective witnessing as analytical tools for non-Western media ecologies, and positions Chineseness as a globally relevant alternative to market-led models of media architecture.

cs.HC↗

Existence and uniqueness of solutions for a random PDE-ODE model describing glioma invasion and radiotherapy

We study a stochastic model for the growth and invasion of glioma cells in the anisotropic brain tissue, also incorporating effects of radiotherapeutic interventions and associated uncertainties. The PDE-ODE model couples glioma and healthy tissue dynamics in a nonlinear manner and explicitly includes treatment-related stochasticity through random depletion terms. The macroscopic system has been obtained by an upscaling procedure from a microscopic description in [1]. We prove the existence and uniqueness of local strong solutions to the corresponding stochastic system. The analysis addresses challenges arising from the nonlinear coupling, anisotropic diffusion, taxis mechanisms, and stochastic treatment effects, providing a first rigorous step toward the mathematical understanding of stochastic continuum models for glioma invasion and therapy response.

math.AP↗

Compositional Operational Rank, Smooth Realizability, and Dimensional Reduction

Higher-dimensional theories distinguish the dimensionality of a microscopic description from the number of directions accessible to a physical observer. Relating these notions requires not only dimensional reduction, but also a consistent preservation of operational independence across different levels of description. We establish sufficient conditions under which a compositional operational rank is preserved first as the dimension of a smooth integral leaf and then as an observable Fisher-information rank. The key requirement is closure-faithfulness: the smooth realization must preserve the dependency relations of the operational closure, rather than merely its total rank. Together with real representability, constant rank, involutivity, and observer/scale compatibility, this yields a local integral leaf whose dimension equals the operational rank. A further primary-identification condition is required at the measurement level. If an independently specified observer image coincides locally with the primary integral distribution and the admissible protocols provide local resolution, finite labeled-mixture closure, and locally constant outcome support, then the same rank can be realized by a finite composite Fisher-information protocol. The construction holds for arbitrary finite rank. At r = 4 it provides a conditional operational criterion for dimensional reduction in higher-dimensional theories: a viable reduction must preserve the relevant operational dependency structure, not merely produce four effective coordinates. Quantum-hardware negative controls further show that the same restricted Fisher rank can conceal different larger ranks under a broader pre-specified measurement protocol. The framework therefore constrains the operational outcome of dimensional reduction without imposing a universal four-dimensional bound on microscopic state spaces.

hep-th↗

Do Similarity Measures Measure Similarity? How User Activity Creates Phantom Correlation in Recommender Systems

Item-item similarity is one of the oldest and most foundational signals in recommender systems: two items are assumed to be similar when the same users consume them. Despite the sparsity and scale of interaction data, the implicit assumption across the field is that item co-occurrence reflects genuine shared preference, capturing what users like together. In this paper, we demonstrate that a substantial fraction of observed item-item similarity is an artifact of variance in user activity rather than shared taste. Even if every user consumed items completely independently, differing only in their total consumption volume, standard similarity measures still detect strong associations between items. We term this phenomenon phantom correlation: spurious item association that emerges strictly from heterogeneous user activity in the complete absence of preference structure. Starting from minimal formal assumptions, we show that independent consumption under varying activity is described by a Rasch model, and we derive how the resulting association depends on item popularity for each similarity measure. By comparing our theoretical baseline against real-world benchmark datasets, we reveal the extent to which popular similarity measures reflect phantom correlation, which bears on how item co-occurrence is interpreted and used in collaborative filtering.

cs.IR↗

Improving Signals, Declining Encoder: A Known-Answer Test of Label-Free Monitoring in Self-Supervised Learning

Self-supervised training is monitored without labels, through the loss and statistics of the learned features. We report a case where we know the encoders lost part of what they had learned, so we can check whether these signals noticed. We trained LeJEPA encoders on synthetic images with known generating factors. In nine of ten seeds, the readability of those factors fell abruptly and stayed down, nearly always along the same direction in factor space. Meanwhile the loss kept falling, and most of the eleven label-free signals we tracked kept improving and rated the declining runs better than runs that did not decline. Standard change-point detection rarely flagged the decline near its onset, and selecting checkpoints by these signals mostly chose ones after it.

cs.LG↗

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