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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 1,531 records · Page 85Linked to original sources
arXiv2026

Fréchet Kriging for Metric Space-Valued Spatial Data

Spatial prediction of non-Euclidean data, such as probability distributions, symmetric positive definite matrices, and networks, arises in many modern applications. Such data are naturally modeled as elements of general metric spaces, where classical kriging methods that rely on vector space structure are no longer directly applicable. We introduce Fréchet kriging, a general framework for spatial prediction of metric space-valued data. Fréchet kriging replaces the weighted average in classical ordinary kriging with a weighted Fréchet mean, while the weights are determined by characterizing the spatial dependence structure through a generalized variogram. This construction allows the proposed method to preserve the intrinsic geometry of the response space without requiring an embedding into a linear space. We establish theoretical properties of the proposed predictor, including a surrogate optimality property and consistency under variogram misspecification for a fixed working variogram under suitable regularity conditions. Simulation studies for univariate distributions and symmetric positive definite matrices demonstrate favorable predictive performance compared with existing approaches. We further illustrate the practical utility of the proposed framework through applications to exceedance probabilities of daily average temperatures, joint probability densities of daily minimum and maximum temperatures, and inter-industry monetary flow networks.

stat.ME↗
arXiv2026

Separating Thinking from Spelling for Efficient Generative Recommendation

Generative recommenders retrieve items by writing their semantic IDs (SIDs) code by code under trie-constrained beam search, an approach now deployed on large web platforms. Standard autoregressive interfaces pay for the SID length K twice: a history of T items enters the backbone as TK code tokens, and every beam-search step runs through the backbone, so returning B candidates costs O(BK) backbone decode tokens per user. The second cost often dominates, and it grows with the backbone, the SID depth and the candidate budget. We introduce Deco (decoding from concepts), which separates reading the history from writing the identifier. The backbone reads each history item as one fused token and condenses the history once into a compact next-item concept; a light KV-cached decoder conditioned on the concept and the generated prefix runs the entire beam search, while the identifiers, the trie and the autoregressive factorization stay unchanged. The decoder approaches full-backbone accuracy with cross-entropy alone, and distillation improves it further. On seven datasets from e-commerce, movies and music streaming, Deco achieves comparable or better accuracy than both a standard generative recommender and the full-backbone teacher with fused inputs, while delivering 3.2--8.6$\times$ their throughput under a 50 ms p95 latency budget in a production-style serving pipeline. Delegating beam search makes larger candidate pools and models practical: Deco generates 1000 candidates faster than its teacher generates 100, and with a 1.5B-parameter LLM backbone, it matches the 69M-parameter teacher's throughput while achieving higher accuracy.

cs.IR↗
arXiv2026

SEMAC: Spherical Energy Matching Ambisonics Codec for Efficient Higher-Order Ambisonics Compression

Higher-order Ambisonics (HOA) is an attractive format for immersive spatial audio, but its high channel count and directional properties present significant audio coding challenges. The widely deployed Opus audio codec supports HOA through Channel Mapping Families 2 and 3 (RFC 8486), which code and transmit every Ambisonics channel, so the bitrate grows quadratically with the spherical harmonic order. We introduce SEMAC, which instead represents an Ambisonics signal by a quantized full-order directional energy envelope together with fewer CELT-coded transport channels. CELT separates a per-band energy envelope from the signal shape and restores it after quantization, and SEMAC generalizes this principle to directional signals. Since the reduced transport spans only a lower-dimensional subspace, the decoder re-imposes the envelope observed at the encoder input, restoring the directional energy distribution at full order. A MUSHRA test on fifth-order material compares SEMAC against Opus CMF~3 in matched configurations at 512 and 768 kbit/s. SEMAC with 16 of 36 transport channels is rated above full-order CMF~3 at both rates, and at equal transport channel count above order truncation. The proposed SEMAC strategy of coding fewer transport channels with the directional envelope is found perceptually preferable to coding all HOA channels at low precision.

eess.AS↗
arXiv2026

The Language of Instruction: Genre and Generative AI

This article argues that instruction-fine-tuned language models should be understood as implementing a genre, what is proposed as the instructional-conversational genre. This genre is an amalgamation of discursive language from prior conversations between generative models and humans recursively run through these massive neural networks. It is instantiated in applications through instruction fine-tuning, the inclusion of a pretext known as a system prompt, and the formalization of inputs in mark-up languages characterized by the use of special tokens and role assignment in turntaking structures.

cs.CL↗
arXiv2026

The degree distribution in bipartite planar maps: applications to the Ising model

We characterize the generating function of bipartite planar maps counted according to the degree distribution of their black and white vertices. This result is applied to the solution of the hard particle and Ising models on random planar lattices. We thus recover and extend some results previously obtained by means of matrix integrals. Proofs are purely combinatorial and rely on the idea that planar maps are conjugacy classes of trees. In particular, these trees explain why the solutions of the Ising and hard particle models on maps of bounded degree are always algebraic.

math.CO↗
arXiv2026

Quantum-aware Transformer model for state classification

Entanglement is a fundamental feature of quantum mechanics, playing a crucial role in quantum information processing. However, classifying entangled states, particularly in the mixed-state regime, remains a challenging problem, especially as system dimensions increase. In this work, we focus on bipartite quantum states and present a data-driven approach to entanglement classification using transformer-based neural networks. Our dataset consists of a diverse set of bipartite states, including pure separable states, Werner entangled states, general entangled states, and maximally entangled states. We pretrain the transformer in an unsupervised fashion by masking elements of vectorized Hermitian matrix representations of quantum states, allowing the model to learn structural properties of quantum density matrices. This approach enables the model to generalize entanglement characteristics across different classes of states. Once trained, our method achieves near-perfect classification accuracy, effectively distinguishing between separable and entangled states. Compared to previous Machine Learning, our method successfully adapts transformers for quantum state analysis, demonstrating their ability to systematically identify entanglement in bipartite systems. These results highlight the potential of modern machine learning techniques in automating entanglement detection and classification, bridging the gap between quantum information theory and artificial intelligence.

quant-ph↗
arXiv2026

Scaling the Wavelength to Optimize Ultrafast Laser Processing of Bandgap Materials

The spectral characteristics of ultrashort laser pulses offer unique possibilities to control interactions in space and time as well as the subsequent material responses. In particular, selecting the wavelength predefines the fundamental mechanisms governing laser-matter interaction, and thus, how energy is deposited. This Chapter provides an overview on how the essential physical effects in ultrafast laser-matter interaction scale with the wavelength, and the consequences for bandgap material processing applications. First, we recall the main spectral properties of bandgap materials and discuss the important question of the expected processing resolutions depending on wavelength. Then, recent studies on the impact of the wavelength in ultrafast laser-dielectric interaction are presented. Finally, we present how the latest developments in infrared ultrafast laser sources can be advantageously utilized to internally process narrow-gap materials despite challenges related to increased nonlinearities.

physics.optics↗
arXiv2026

Developing Security Metrics for Automated Compliance Checking

To automatically check for compliance with security-related standards, prose requirements must be translated to technical properties to make them measurable. A multitude of metrics have been proposed in past literature, but they are lacking a systematic methodology for their elicitation. Without such a methodology, metrics remain inconsistent in their data models and abstraction levels, they exhibit considerable overlaps and are difficult to combine to comprehensively cover a standard's requirement. In this paper, we develop a data model for security metrics based on a systematization of compliance requirements, and we propose a methodology for their elicitation. The method is based on an ontology that enables a systematic development of metrics with a comparable degree of abstraction. We also present our initial work on an open-source effort that demonstrates that our approach also supports a collaborative development of metrics.

cs.CR↗
arXiv2026

Hair Segmentation and Skin Characterization on Macroscopic Dermatological Images for Laser Treatment Parameterization

Current light-based devices, such as laser hair removal, intense pulsed light (IPL), vascular laser therapy, and dermatologic phototherapy, are widely used in clinical dermatology but rely heavily on subjective manual parameterization or uncalibrated point-based colorimeters. To overcome these limitations, this work presents a unified system for hair segmentation and skin characterization in macroscopic dermatological images. Unlike most existing literature that relies on dermoscopic datasets captured under highly controlled conditions, our approach is specifically designed for macroscopic images that replicate real clinical laser treatments. The proposed pipeline integrates a custom-trained variant of the YOLOv9 deep learning architecture for instance segmentation and classical image processing within a single workflow to extract objective skin and hair parameters such as phototype, hair density, color, and thickness. A color calibration stage using spectrophotometric measurements ensures accurate feature estimation regardless of illumination conditions. Experimental results show competitive segmentation metrics (mAP@50 $\approx$ 0.80) on a custom dataset featuring short, shaved hairs, typical of pre-treatment scenarios. These findings demonstrate the feasibility of the proposed pipeline in providing the objective, foundational skin and hair features required for future automated laser parameterization, paving the way for its extension to broader light-based medical therapies.

cs.CV↗
arXiv2026

When disclosure rules are not enough: authorial warrant and judgment in ai-supported scholarship

Editorial policies increasingly require authors to disclose substantive uses of generative AI, but such requirements commonly focus on whether, where, and how a model was used. Even function-specific disclosure may locate a model in the research or writing process without showing how its outputs entered scholarly judgment, how they were evaluated, or whether contributors can reconstruct the relevant reasoning and defend resulting claims without treating those outputs as sufficient justification. Drawing on research ethics, AI disclosure policy, human-model interaction, and researcher formation, this article argues that disclosure documents model use, whereas responsible use requires claim-level authorial warrant: a contributor's capacity to understand, justify, and defend claims within their contribution. The article distinguishes cognitive delegation, in which model outputs shape substantive choices, interpretations, or justifications, from routine delegation, which does not ordinarily bear on the substance of judgment. Its conceptual contribution is to link the epistemic role of outputs that shape judgment to the function-appropriate evaluation required to justify decisions and retain warrant for resulting claims. It then repurposes disclosure categories as a claim-sensitive ex ante heuristic, mapping six recurrent research functions to risks, forms of oversight, and formative questions for recognising cognitive delegation and selecting function-appropriate evaluation. The article contributes to the ethics of information technology by clarifying when AI-supported scholarship remains under responsible human judgment and when cognitive delegation threatens the epistemic basis of authorship.

cs.CY↗
arXiv2026

Corrections and additions to "A note on the hit problem for the polynomial algebra in the case of odd primes and its application" [Rev. R. Acad. Cienc. Exactas Fís. Nat. Ser. A Mat. 118 (2024), article 22]

In this work, we correct results in the published paper [Rev. R. Acad. Cienc. Exactas Fís. Nat. Ser. A Mat. 118 (2024), article 22]. We determine the non-hit kernel of the first reduced power in polynomial degree $p+1$, identify the published Frobenius construction as zero on indecomposables, and replace the multiplier assertion by a theorem with explicit degree hypotheses. We also classify the monomial multipliers of Frobenius that preserve hits in one variable. For the third algebraic transfer, we correct the target dimensions and establish surjectivity throughout the four principal degree families. Explicit invariant calculations and Minami's degree reduction give isomorphisms in seven infinite families at $p=3$ and $p=5$, including a family with an initial change from dimension one to dimension two. For every odd prime, compatibility with the Frobenius operation on $\operatorname{Ext}$ gives an exact sequence comparing successive transfer kernels and forces a nonzero invariant extraction kernel at every second-family dimension jump. We compute the first cohomology of a nonzero extraction kernel and determine the exact obstruction to injectivity from rank-two products in the first and fourth families. Coefficient extraction and changes of coordinates give an additional infinite isomorphism family and a spanning result for annihilated polynomial homology. At each extraction isomorphism, the quotient by all rank-two products is preserved as a general linear group module. We distinguish these results from the unresolved invariant dimensions at arbitrary normalized index differences, and give the precise limitations of composition-factor counts and Dickson multiplication. An appendix supplies explicit representatives for all the nonzero rank-two invariant lines in Crossley's dimension classification.

math.AT↗
arXiv2026

A Randomized Permutation Whole-Model Test Heuristic for Self-Validated Ensemble Models (SVEM)

We introduce a heuristic to test the significance of fit of Self-Validated Ensemble Models (SVEM) against the null hypothesis of a constant response. A SVEM model averages predictions from nBoot fits of a model, applied to fractionally weighted bootstraps of the target dataset. It tunes each fit on a validation copy of the training data, utilizing anti-correlated weights for training and validation. The proposed test computes SVEM predictions centered by the response column mean and normalized by the ensemble variability at each of nPoint points spaced throughout the factor space. A reference distribution is constructed by refitting the SVEM model to nPerm randomized permutations of the response column and recording the corresponding standardized predictions at the nPoint points. A reduced-rank singular value decomposition applied to the centered and scaled nPerm x nPoint reference matrix is used to calculate the Mahalanobis distance for each of the nPerm permutation results as well as the jackknife (holdout) Mahalanobis distance of the original response column. The process is repeated independently for each response in the experiment, producing a joint graphical summary. We present a simulation driven power analysis and discuss limitations of the test relating to model flexibility and design adequacy. The test maintains the nominal Type I error rate even when the base SVEM model contains more parameters than observations.

stat.ME↗
arXiv2026

SVEMnet: An R package for Self-Validated Elastic-Net Ensembles and Multi-Response Optimization in Small-Sample Mixture-Process Experiments

SVEMnet is an R package for fitting Self-Validated Ensemble Models (SVEM) with elastic-net base learners and performing multi-response optimization in small-sample mixture-process design-of-experiments (DOE) studies with numeric, categorical, and mixture factors. SVEMnet wraps elastic-net and relaxed elastic-net models for Gaussian and binomial responses from glmnet in a fractional random-weight (FRW) resampling scheme with anti-correlated train/validation weights; penalties are selected by validation-weighted AIC- and BIC-type criteria, and predictions are averaged across replicates to stabilize fits near the interpolation boundary. In addition to the core SVEM engine, the package provides deterministic high-order formula expansion, a permutation-based whole-model test heuristic, and a mixture-constrained random-search optimizer that combines Derringer-Suich desirability functions, bootstrap-based uncertainty summaries, and optional mean-level specification-limit probabilities to generate scored candidate tables and diverse exploitation and exploration medoids for sequential fit-score-run-refit workflows. A simulated lipid nanoparticle (LNP) formulation study illustrates these tools in a small-sample mixture-process DOE setting, and simulation experiments based on sparse quadratic response surfaces benchmark SVEMnet against repeated cross-validated elastic-net baselines.

stat.CO↗
arXiv2026

Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra

Natural Gradient Descent (NGD) has emerged as a promising optimization algorithm for training neural network-based solvers for partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs). However, its practical use is often limited by the high computational cost of solving linear systems involving the Gramian matrix. While matrix-free NGD methods based on the conjugate gradient (CG) method avoid explicit matrix inversion, the ill-conditioning of the Gramian significantly slows the convergence of CG. In this work, we extend matrix-free NGD to broader classes of problems than previously considered and propose the use of Randomized Numerical Linear Algebra (RandNLA) techniques for efficient preconditioning of the inner CG solver. The resulting algorithms demonstrate substantial performance improvements over existing NGD-based methods on a range of PDE problems discretized using neural networks, and offer competitive results compared to other state-of-the-art optimizers.

math.NA↗
arXiv2026

Efficient Maximum Likelihood Estimation of Multiple Membership Linear Mixed Models, with an Application to Educational Value-Added Assessments

The generalized persistence (GP) model, developed in the context of estimating ``value added'' by individual teachers to their students' current and future test scores, is one of the most flexible value-added models in the literature. Although developed in the educational setting, the GP model can potentially be applied to any structure where each sequential response of a lower-level unit may be associated with a different higher-level unit, and the effects of the higher-level units may persist over time. The flexibility of the GP model, however, and its multiple membership random effects structure lead to computational challenges that have limited the model's availability. We develop an EM algorithm to compute maximum likelihood estimates efficiently for the GP model, making use of the sparse structure of the random effects and error covariance matrices. The algorithm is implemented in the package GPvam in R statistical software. We give examples of the computations and illustrate the gains in computational efficiency achieved by our estimation procedure.

stat.AP↗
arXiv2026

Anisotropic flow measurements in OO and Ne-Ne collisions with ALICE

The LHC results have demonstrated that small collision systems can exhibit collective behavior reminiscent of that~observed in heavy-ion collisions, opening a new frontier for exploring the connection between nuclear structure and collective dynamics at ultra-relativistic energies. However, the interpretation of collectivity in small collision systems is limited by understanding the proton subnucleonic structure. The ${}^{16}$O and ${}^{20}$Ne collisions delivered by the LHC offer an opportunity to study the influence of nuclear geometry on anisotropic flow in systems where the nuclear structure is well-constrained. In this proceeding, measurements of the anisotropic flow of charged particles, $v_n$, in OO and Ne-Ne collisions involving up to eight-particle correlations are presented, using the multi-particle cumulant method. Furthermore, the first reconstruction of the probability distribution function of elliptic flow $P(v_2)$, and the first measurement of the ratio $v_3\{4\}/v_3\{2\}$ in light-ion collision systems are reported. These measurements provide deeper insight into the role of nuclear geometry-driven flow in light-ion collisions.

nucl-ex↗
arXiv2026

A method for calculating entropy production without knowing transition rates or dwell times

The entropy production rate is central to the study of non-equilibrium systems. This parameter is closely connected to violation of time-reversal symmetry, energy consumption, efficiency, and other properties of interest; in short, it quantifies how far a system is from thermodynamic equilibrium. Standard formulas for the entropy production require knowledge of the system's underlying dynamics, but this knowledge may be hard to acquire in practice. Here, I present a method for inferring the entropy production rate of a Markovian system from the sequence of states that the system occupies on a single long trajectory or many shorter trajectories, and the total time of the trajectory or trajectories. The method does not require knowledge of the dwell times in the various states, the times at which transitions occur, or of the transition rates characterizing the Markov process.

cond-mat.stat-mech↗