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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.

At least 55 records · Page 3Linked to original sources
arXiv2026

Computational quantum transport: a scattering approach perspective

This review is devoted to the different techniques that have been developed to compute the phase-coherent transport properties of quantum nanoelectronic systems connected to electrodes. Beside a review of the different algorithms proposed in the literature, we provide a comprehensive and pedagogical derivation of the two formalisms on which these techniques are based: the scattering approach and the (nonequilibrium) Green's function approach. We show that the scattering problem can be formulated as a system of linear equations and that different existing algorithms for solving this scattering problem amount to different sequences of Gaussian elimination. We explicitly prove the equivalence of the two formalisms. We discuss the stability and numerical complexity of the existing methods. The review ends with a selection of a few applications where numerical calculations were instrumental in shaping our understanding of the physics.

cond-mat.mes-hall↗
arXiv2026

Simple Models of Randomization and Preservation Theorems

The main purpose of this paper is to provide new, more uniform model-theoretic and combinatorial proofs of the preservation theorems established in [5] and [8]. More precisely, we show that randomization preserves both $NIP$ and stability: if a first-order theory $T$ has $NIP$, then so does $T^R$, and if $T$ is stable, then so is $T^R$. Our proofs rely on the systematic use of simple models of $T^R$ and quantitative combinatorial methods. In the $NIP$ case, we use indiscernible arrays and Rademacher mean width, whereas in the stable case, we use a Ramsey lemma for cross-blocks and sequential Rademacher mean width. Using simple models of $T^R$ has the advantage of allowing us to re-prove these theorems in a simpler and quantitative manner. We finally turn our attention to $NSOP$ in randomization. We show that based on the definition of $NSOP$ given in [14], $T^R$ is stable if and only if it is $NIP$ and $NSOP$.

math.LO↗
arXiv2026

Transferable Multiform Attack for Cross-Modal Person Re-Identification

Cross-modal person re-identification (ReID) adversarial attacks face a transferability challenge: existing methods may exhibit source-pair overfitting, where learned perturbations become overly dependent on the feature statistics of the source modality pair, thereby limiting their transferability under heterogeneous domain shifts. We argue that a key challenge is reducing source-pair-specific bias while maintaining attack effectiveness. To address this, we propose Multiform Attack (MA). The framework first learns a universal perturbation via Mahalanobis-guided gradient optimization. Since this perturbation may still retain source-pair-specific bias, the second stage performs sparse refinement through attention-shift-filtered multi-objective evolutionary search, leveraging heterogeneous source distributions in transfer settings. By separating universal perturbation learning from sparse evolutionary refinement, MA aims to reduce reliance on source-pair-specific patterns and improve transferability to unseen domains. Extensive experiments demonstrate strong attack effectiveness on cross-modal ReID benchmarks and improved transferability in the evaluated held-out-domain settings, with additional evaluations across model architectures.

cs.CV↗
arXiv2026

Differentially Private Covariate Balancing Causal Inference

Differential privacy is the leading mathematical framework for privacy protection, providing a probabilistic guarantee that safeguards individuals' private information when publishing statistics from a dataset. This guarantee is achieved by applying a randomized algorithm to the original data, which introduces unique challenges in data analysis by distorting inherent patterns. In particular, causal inference using observational data while preserving privacy is challenging because it requires a good covariate balance between treatment groups, but checking the covariate balance is challenging when privacy is a primary concern because releasing any statistic (e.g., t-statistic) for the balance check compromises privacy. Additionally, the performance of the privatized estimator critically depends on the choice of differential privacy mechanisms, and it remains unexplored how privacy-protecting causal inference could be conducted with observational data. In this article, we present a differentially private two-stage covariate balancing weighting estimator to infer causal effects from observational data. Our algorithm produces both point and interval estimators with statistical guarantees, such as covariate balance, rate optimality, and oracle budget allocation, under a given privacy budget.

stat.ME↗
arXiv2026

Conformalized Credal Regions for Classification with Ambiguous Ground Truth

An open question in \emph{Imprecise Probabilistic Machine Learning} is how to empirically derive a credal region (i.e., a closed and convex family of probabilities on the output space) from the available data, without any prior knowledge or assumption. In classification problems, credal regions are a tool that is able to provide provable guarantees under realistic assumptions by characterizing the uncertainty about the distribution of the labels. Building on previous work, we show that credal regions can be directly constructed using conformal methods. This allows us to provide a novel extension of classical conformal prediction to problems with ambiguous ground truth, that is, when the exact labels for given inputs are not exactly known. The resulting construction enjoys desirable practical and theoretical properties: (i) conformal coverage guarantees, (ii) smaller prediction sets (compared to classical conformal prediction regions) and (iii) disentanglement of uncertainty sources (epistemic, aleatoric). We empirically verify our findings on both synthetic and real datasets.

stat.ML↗
arXiv2026

From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language

Automatic Speech Recognition (ASR) technology has witnessed significant advancements in recent years, revolutionizing human-computer interactions. While major languages have benefited from these developments, lesser-resourced languages like Urdu face unique challenges. This paper provides an extensive exploration of the dynamic landscape of ASR research, focusing particularly on the resource-constrained Urdu language, which is widely spoken across South Asian nations. It outlines current research trends, technological advancements, and potential directions for future studies in Urdu ASR, aiming to pave the way for forthcoming researchers interested in this domain. By leveraging contemporary technologies, analyzing existing datasets, and evaluating effective algorithms and tools, the paper seeks to shed light on the unique challenges and opportunities associated with Urdu language processing and its integration into the broader field of speech research.

cs.CL↗
arXiv2026

The Price of Failure Risk: Clipped Barrier Policy Gradients with Convex Regularization

In many control problems, actions consume a resource budget, such as a wear allowance, and the probability of exhausting it within a given horizon must stay below a tolerance. A simple, widely used approach is to augment the state with the remaining budget, subtract an interior barrier from the utility and penalize exhaustion, so that policy-gradient methods can be applied unchanged. We clip the combined one-step reward from below at a level m to keep the returns of exploratory policies finite. The clip level is a price of failure risk, since the clipped value decomposes exactly into the utility before failure, the cost of the interior margin and m times a geometric failure risk. Consequently, an approximately optimal policy maximizes utility net of margin cost among policies with no greater risk. Its risk exceeds that of any comparison policy by at most order 1/m and can be certified from its value or from validation data. Our main result proves exponential convergence of the population policy-gradient flow for every parameter regularizer that is convex along generalized geodesics and sufficiently strong relative to the price. This extends the mean-field analysis from relative entropy to weight decay and to finite networks. Relative entropy adds parameter noise and a pull toward a prior, whereas weight decay provides only the pull. Action regularization bounds changes in failure risk by the relative entropy to a reference policy. The bounds are evaluated on an exact maintenance benchmark, a fuel-tank controller, a solvable flow and a benchmark-suite task.

cs.LG↗
arXiv2026

Test-time Correction: An Online 3D Detection System via Visual Prompting

This paper introduces Test-time Correction (TTC), an online 3D detection system designed to rectify test-time errors using various auxiliary feedback, aiming to enhance the safety of deployed autonomous driving systems. Unlike conventional offline 3D detectors that remain fixed during inference, TTC enables immediate online error correction without retraining, allowing autonomous vehicles to adapt to new scenarios and reduce deployment risks. To achieve this, we equip existing 3D detectors with an Online Adapter (OA) module -- a prompt-driven query generator for real-time correction. At the core of OA module are visual prompts: image-based descriptions of objects of interest derived from auxiliary feedback such as mismatches with 2D detections, road descriptions, or user clicks. These visual prompts, collected from risky objects during inference, are maintained in a visual prompt buffer to enable continuous correction in future frames. By leveraging this mechanism, TTC consistently detects risky objects, achieving reliable, adaptive, and versatile driving autonomy. Extensive experiments show that TTC significantly improves instant error rectification over frozen 3D detectors, even under limited labels, zero-shot settings, and adverse conditions. We hope this work inspires future research on post-deployment online rectification systems for autonomous driving.

cs.CV↗
arXiv2026

A particle system approach towards the global well-posedness of master equations for potential mean field games of control

This paper studies the $N$-particle systems as well as the HJB/master equations for a class of generalized mean field control (MFC) problems and the corresponding potential mean field games of control (MFGC). A local in time classical solution for the HJB equation is generated via a probabilistic approach based on the mean field maximum principle. Given an extension of the so called displacement convexity condition, we obtain the uniform estimates on the HJB equation for the $N$-particle system. Such estimates imply the displacement convexity/semi-concavity and thus the prior estimates on the solution to the HJB equation for generalized MFC problems. The global well-posedness of HJB/master equation for generalized MFC/potential MFGC is then proved thanks to the local well-posedness and the prior estimates. In view of the nature of the displacement convexity condition, such well-posedness is also true for the degenerated case. Our analysis on the $N$-particle system also induces an Lipschitz approximator to the optimal feedback function in generalized MFC/potential MFGC where an algebraic convergence rate is obtained. Furthermore, an alternative approximate Nash equilibrium is proposed based on the $N$-particle system, where the approximation error is quantified thanks to the aforementioned uniform estimates.

math.OC↗
arXiv2026

Archimedean period relations for Rankin-Selberg convolutions

We formulate and prove the archimedean period relations for Rankin-Selberg convolutions of $\text{GL}(n)\times \text{GL}(n)$ and $\text{GL}(n)\times \text{GL}(n-1)$, for all generic cohomological representations. As a consequence, we prove the non-vanishing of the archimedean modular symbols. This extends the earlier results in [LLS24] for essentially tempered representations of $\text{GL}(n)\times\text{GL}(n-1)$.

math.RT↗
arXiv2026

The distribution of violent event and interevent times in conflicts

Ongoing violent conflicts at all scales are interrupted by lulls without violence. Studies of interevent times found power-law distributions, based on coarse-grained data with a resolution of days or longer. Fine-grained data with a resolution of seconds or shorter is rare. Here, interevent times in violent conflicts are represented as multiplicative processes, and video-recorded street fights are used as fine grained data. For multiplicative processes, a mathematical theorem predicts that coarse grained data are power-law distributed, but fine grained data are lognormal. To the street violence data, however, the lognormal does not fit better than the power law (and vice versa), although the lognormal covers more data. Violent events, by contrast, take much energy and their durations are shorter, hence their distribution is better approximated by an exponential.

physics.soc-ph↗
arXiv2026

Quasinormal Ringing of de Sitter Braneworlds

Compared with the Poincaré braneworld, the de Sitter (dS) braneworld aligns more closely with the present universe characterized by a small but finite cosmological constant; to explore the quasinormal ringing properties within the dS brane scenario, we investigate gravitational perturbations in both thin and thick dS brane configurations. Analysis of the perturbation equations reveals that the effective potential along the extra dimension exhibits the shape of a Pöschl-Teller potential, asymptotically approaching a constant value (mass gap) at infinity, and in particular this mass gap can be reduced. Analytical calculations further indicate that the gravitational perturbations, apart from the zero mode, possess a series of discrete, purely imaginary quasinormal modes in the late stages, implying that these perturbations decay without oscillation over time; the analytical findings also demonstrate that the brane structure primarily determines the distribution of the quasinormal spectrum while preserving the purely imaginary nature of the quasinormal frequencies. Subsequently, we further simulate the gravitational wave signal by numerically evolving the perturbation equations, which yields late-stage results consistent with the analytical predictions. Interestingly, these quasinormal modes carry information about the cosmological constant on the brane, which provides a potential new pathway for the study of cosmology in the dS brane scenario.

gr-qc↗
arXiv2026

Full-conformal novelty detection

This paper presents a powerful methodology for flexible full-data nonparametric novelty detection that offers distribution-free false discovery rate (FDR) control guarantees. Building on the full conformal inference framework and the concept of e-values, we introduce full conformal e-values to quantify evidence for novelty relative to a given reference dataset. These e-values are then utilized by carefully crafted multiple testing procedures to identify a set of novel units out-of-sample with provable finite-sample FDR control. We showcase several instantiations of e-values, including those which employ a data-driven model selection strategy to amplify power. Furthermore, our framework is extended to address distribution shift, accommodating scenarios where novelty detection must be performed on data drawn from a shifted distribution relative to the reference dataset. In all settings, our method can perform powerfully -- outperforming existing novelty detection methods -- even with limited amounts of reference data; this is illustrated by empirical evaluations on synthetic data and an application to a malicious LLM prompts dataset.

stat.ME↗
arXiv2026

A continuous scale space of diffeomorphisms

In this paper, we define and study a nested family of reproducing kernel Hilbert spaces of vector fields that is indexed by a range of scales, from which we construct a reproducing kernel Hilbert space of scale-dependent vector fields. We provide a characterization of the reproducing kernel of that space, with numerical approximations ensuring quick evaluations when this kernel does not have a closed form. We then introduce a multiscale version of the large deformation diffeomorphic metric mapping (LDDMM) problem and prove the existence of solutions. Finally, we provide numerical experiments performing landmark matching using multiscale LDDMM.

math.NA↗
arXiv2026

From signaling to interviews in random matching markets

In two-sided labor markets, interviews precede matches. Rising interview volumes in medical residency markets have motivated signaling mechanisms that let applicants express interest in a limited number of partners. We study how signaling reduces interviews in centralized random matching markets with post-interview shocks. We focus on interim stability, which ensures that agents do not regret not interviewing with each other. A matching is almost interim stable if it becomes interim stable after removing a vanishing fraction of agents. We consider markets with $n$ agents in which agents on the short side, long side, or both sides signal their top $d$ preferred partners. An interview occurs whenever at least one party signals the other. When the probability of a nonnegative interview shock is bounded away from zero and $d=ω(1)$, short-side signaling leads to almost interim stable matchings. Long-side signaling can fail in strongly imbalanced markets when pre-interview scores outweigh interview shocks. In balanced markets without interview shocks, both-side signaling fails to achieve almost interim stability when $d=o(\log n)$. For larger $d \ge Ω(\log^2 n)$, under the same probability condition, short-side signaling achieves perfect interim stability, while long-side signaling can fail in imbalanced markets. We extend these findings to propose a signaling mechanism for multi-tiered random markets and identify conditions for incentive compatibility. A technical contribution is the analysis of a message-passing algorithm for stable matchings on trees, together with local availability certificates used to study interim stability on general graphs.

cs.GT↗
arXiv2026

A Possible Mass Ratio and Spin-Orbit Misalignment Correlation for Mergers of Binary Black Holes in Nuclear Star Clusters

Despite a decade's worth of gravitational wave observations, the origin of the binary black hole (BBH) mergers detected by the LIGO-Virgo-KAGRA (LVK) collaboration remains an open question. Towards assessing the feasibility and prevalence of the many proposed BBH formation channels, the spin properties of the merging black holes (BHs) hold significant promise, particularly their orientations. Moderate preferential alignment of BH spins with their orbit normals, as well as an apparent anti-correlation between the BBH effective spin parameters $χ_{\rm eff}$ and their mass ratios, would appear to disfavor purely dynamical BBH formation mechanisms, as they lack a preferred orientation to the system. However, alternatives are filled with physical and modeling uncertainties. In this paper, we highlight a dynamical route to easily characterizable spin evolution that results in analytically predictable spin distributions. We show that, when a stellar binary forms a BBH through two phases of stable mass transfer, and the BBH is subsequently driven to merger by the gravitational perturbation of a distant massive object (such as a supermassive black hole), the resulting $χ_{\rm eff}$ may be anti-correlated with the binary mass ratio. While the mechanism as proposed only operates in a modest region of parameter space, it also predicts significantly tighter correlations than are seen in the GWTC-4.0 systems. We discuss avenues for future work that may significantly expand the parameter space of our mechanism while still remaining broadly consistent with observations.

astro-ph.HE↗
arXiv2026

Neutralino dark matter in gauge mediation

We explore the potential of neutralino dark matter within the framework of gauge-mediated supersymmetry (SUSY) breaking. Gauge mediation offers an advantage over gravity mediation, as SUSY CP/flavor problems are avoided more easily, leading to significantly milder fine-tuning for electroweak symmetry-breaking compared to gravity mediation. In our models, the lightest neutralino, as the lightest SUSY particle (LSP), is a viable dark matter candidate, assuming a gravitino mass of $\mathcal{O}(100)~\mathrm{TeV}$. The models are formulated in five-dimensional space-time, where the SUSY breaking field and the matter fields are placed on separate branes to avoid issues related to flavor and CP violation, and the radion is stabilized by a supersymmetric Goldberger--Wise mechanism. Four distinct neutralino dark matter scenarios are studied: bino-wino coannihilation, higgsino dark matter, wino dark matter, and entropy-diluted bino dark matter. For each case, we determine the allowed parameter spaces and evaluate their consistency with existing experimental limits. Additionally, we examine the potential for testing these models through future investigations at the High-Luminosity Large Hadron Collider (HL-LHC) and through dark matter direct detection experiments.

hep-ph↗