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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 271 records · Page 15Linked to original sources

arXiv2026

A measure for genuine tripartite entanglement

We introduce a real-valued functional $I(\vec{n}_1,\vec{n}_2)$ of four three-qubit correlation expectation values that turns the Greenberger--Horne--Zeilinger (GHZ) algebraic paradox into a quantitative witness of genuine tripartite entanglement. We prove that $|I(\vec{n}_1,\vec{n}_2;ρ)|\le 2$ for all three-qubit states $ρ$ and all direction pairs, with equality if and only if $\vec{n}_1\perp \vec{n}_2$ and $ρ$ is locally unitarily (LU) equivalent to the GHZ state. We obtain a closed-form $I(\hat{x},\hat{y})$ on the five-parameter Acín canonical family of three-qubit pure states; it depends only on $λ_0λ_4$ and is maximised at $λ_0=λ_4 =1/\sqrt{2}$. For the W state, $I(\hat{x},\hat{y})=0$ and $\max_{\vec{n}_1,\vec{n}_2}|I_{\mathrm{W}}|=35/27\approx 1.296$, strictly below the GHZ value. Maximising the underlying correlation structure over independent local orthonormal frames yields a manifestly LU-invariant quantity $\mathcal{E}_{\mathrm{GHZ}}(ρ)\in[0,1]$ that equals one if and only if $ρ$ is LU equivalent to the GHZ state, equals $35/54\approx0.648$ on the W state, and is bounded by $1/2$ on all biseparable and fully separable states; it is therefore a local-frame-independent indicator of GHZ-type genuine tripartite correlation, obtainable from four Pauli correlators. We relate $\mathcal{E}_{\mathrm{GHZ}}$ to GHZ fidelity witnesses, analyse its behaviour under local operations and classical communication, and delimit what it does and does not certify about genuine tripartite nonlocality, stating which properties are proven and which (notably global convexity and the resulting genuine-multipartite-entanglement witness threshold) are established numerically and remain open analytically. We also outline a generalisation of $I$ to three-qudit systems built from Heisenberg--Weyl operators, recovering the standard qubit construction at $d=2$.

quant-ph↗

arXiv2026

Constraining F-theory Model Building with QCD Axions

In this paper, we investigate axion physics in 4D F-theory MSSM models. We derive the axion coupling term with QCD gauge fields and the axion potential from a top-down perspective, from both IIB superstring and the dual M-theory picture. For the explicit geometric model, we employ the "quadrillion" landscape of 4D F-theory models with the exact Standard Model chiral spectrum, and study simple base threefolds such as $\mathbb{P}^3$, $\mathbb{P}^1\times\mathbb{P}^2$, the generalized Hirzebruch threefold $\tilde{\mathbb{F}}_3$ and $\mathbb{P}^1\times\mathbb{P}^1\times\mathbb{P}^1$. We derive exclusion constraints on the Kähler moduli space of the base threefold from the CP violation angle, the Standard Model gauge coupling constants and the stretched Kähler cone condition. We find stringent constraints on the set of base divisors that should be rigid or rigidified by the inclusion of flux. For the allowed regions of the parameter space, we estimate the typical mass of detectable QCD axions to be around $10^{-9}$eV, and the axion decay constant to be around $f_a\sim 10^{15}$GeV.

hep-th↗

arXiv2026

Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series

Accurate early warning of vineyard disease risk from environmental observations is essential for timely intervention and more sustainable crop protection. However, many existing studies formulate disease prediction as daily presence classification, which can favor persistence-driven predictions and provide only limited support for actionable short-horizon warning. In this paper, we present an event-based approach for early warning of vineyard disease risk from environmental time series and evaluate it through a vineyard case study. Rather than predicting daily disease status, the task is reformulated to predict transitions into annotated disease-risk periods within a future window of 3-7 days. To reduce fragmentation caused by short interruptions in the binary labels, new events are defined only after a minimum disease-free gap. This formulation encourages models to capture environmental precursors associated with upcoming risk periods instead of merely reproducing temporal persistence. Using multi-year agro-meteorological data, we construct input representations that capture humidity dynamics, rainfall accumulation, temperature variability, and seasonal structure through cyclic temporal encoding. We evaluate representative methods from classical machine learning and deep learning, including XGBoost, Long Short-Term Memory (LSTM) networks, and Temporal Convolutional Networks (TCNs), using both standard classification metrics and an event-oriented early warning protocol. The results show that the event-based formulation supports practical short-horizon warning, while the compared models exhibit distinct trade-offs between event recall, lead time, and false-alert behavior. Overall, the study underscores the importance of problem formulation in environmental time-series learning and demonstrates the value of event-based prediction for vineyard disease warning systems.

cs.LG↗

arXiv2026

Kernel of Scott modules and Brauer indecomposability

Let $k$ be an algebraically closed field of prime characteristic $p$. Let $G$ be a finite group. We investigate the Brauer indecomposability of Scott $kG$-modules in relation to the kernel of modules. We generalize a criterion for Brauer indecomposability. We also prove that, in certain cases, Brauer indecomposability of a Scott $kG$-module can be lifted from that of a Scott module over a $p$-local subgroup.

math.RT↗

arXiv2026

eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts

Despite extensive research into mitigating distribution shifts, many existing algorithms yield inconsistent performance, often failing to outperform baseline Empirical Risk Minimization (ERM) across diverse scenarios and necessitating newer algorithms which can handle scenarios where existing algorithms currently underperform. Furthermore, high algorithmic complexity frequently limits interpretability and offers only an indirect means of addressing spurious correlations. We propose eXplaining to Learn (eX2L): an interpretable, explanation-based framework that decorrelates confounding features from a classifier's latent representations during training. eX2L achieves this by penalizing the similarity between Grad-CAM activation maps generated by a primary label classifier and those from a concurrently trained confounder classifier. On the rigorous Spawrious Many-to-Many Hard Challenge synthetic data benchmark, eX2L achieves an average accuracy (AA) of 82.24% +/- 3.87% and a worst-group accuracy (WGA) of 66.31% +/- 8.73%, outperforming the current state-of-the-art (SOTA) by 5.49% and 10.90%, respectively. Beyond its competitive performance, eX2L demonstrates that functional domain invariance can be enforced by explicitly decoupling label and nuisance attributes at the group level.

cs.CV↗

arXiv2026

Flow Matching for Count Data

High-dimensional count data arise in applications such as single-cell RNA sequencing and neural spike trains, where mappings between distributions across successive batches or time points form critical components of data analysis. The recent success of diffusion- and flow-based deep generative models for images, video, and text motivates extending these ideas to count-valued settings, but many existing methods either treat each count as a categorical state or transform counts into a continuous space, neither of which is natural or efficient when the count range is large. We propose count-FM, a flow-matching framework for count data based on a continuous-time birth-death process with local unit jumps. Count-FM learns marginal transitions efficiently in count space through simulation-free training of conditional transition rates, allowing transport between arbitrary count-distributed source and target populations. In simulation, count-FM variants achieve strong sample quality while using substantially fewer parameters. We further apply count-FM to scRNA-seq and neural spike-train data for unconditional generation, transport, and conditional generation. Across these tasks, count-FM yields improved sample quality, greater modeling efficiency, and interpretable transport paths.

stat.ML↗

arXiv2026

Sampling-based Model Predictive Control Using Trust Regions

Sampling-based model predictive control (MPC) algorithms, such as model predictive path integral (MPPI), enable approximate, gradient-free solutions to optimal control problems by drawing samples from a proposal distribution, evaluating their trajectory costs, and updating the proposal parameters accordingly. However, these approaches typically rely on heuristics for adjusting hyperparameters, such as temperature or momentum, or manual tuning. We propose a trust region formulation for sampling-based MPC that constrains updates of the proposal distribution via a principled Kullback--Leibler (KL) divergence bound and, optionally, an entropy lower bound. This replaces heuristic hyperparameter adaptation with values that are optimal w.r.t. the underlying Lagrangian. We further improve sample efficiency and convergence by combining the trust region update with deterministic localized cumulative distribution (LCD)-based sampling. Experiments on two benchmark environments demonstrate that the proposed trust region update achieves faster convergence and better sample efficiency in low-sample and low-iteration regimes, especially when paired with deterministic LCD-based sampling.

eess.SY↗

arXiv2026

Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI

Ensuring that AI agents behave safely and beneficially when interacting with other parties has emerged as one of the central challenges of modern AI safety. While mechanism design, as the theory of designing rules to align individual and collective objectives, can incentivize cooperative behavior, it is still an open question whether it alone is sufficient to maximize LLM agents' social welfare. This work proves that the answer is negative: drawing from incomplete contract theory, we formally show that when contracts cannot distinguish all relevant future contingencies, there is a strictly positive welfare loss that no realistic mechanism can eliminate. We show that prosocial agents, who weigh others' welfare alongside their own, can close this gap and achieve outcomes that are socially superior and individually beneficial. Experimentally, we show that in multi-agent resource-allocation environments and canonical social dilemmas where agents are powered by large language models, prosociality is beneficial. The implication for AI safety is clear: to enable cooperative interactions at scale, designing adequate mechanisms is not sufficient; agents must be built to be intrinsically prosocial.

cs.GT↗

arXiv2026

Machine-learned, finite temperature Fermi-operator expansions suitable for GPUs and AI-hardware

We present several finite-temperature recursive Fermi-operator expansion schemes based on the second-order spectral projection (SP2) method. Our approach builds on a previous observation that the electronic structure problem, as formulated through a recursive SP2 expansion, can be mapped onto the architecture of a deep neural network. Using this perspective, we generalize SP2 to finite electronic temperatures by constructing machine learning models that determine optimized recursive expansion coefficients. The same approach is also applied to the prediction of the electronic entropy for fractional occupation numbers. The coefficients are trained for a specified chemical potential and electronic temperature and are not available in closed analytical form. However, by employing an appropriate affine rescaling strategy to the Hamiltonian matrix, we eliminate the need to retrain the model for different temperatures and chemical potentials. Our approach avoids explicit diagonalization and relies solely on highly optimized matrix-matrix multiplication kernels. Compared to state-of-the-art diagonalization, we achieve an order-of-magnitude speedup in the single-particle finite-temperature density matrix calculation for small and moderately sized matrices on modern GPUs and dense matrix multiply units.

quant-ph↗

arXiv2026

Quantum Correlations of Neutrinos in the Kerr-Newman Space-time

Quantum phases establish a connection between gravitation and quantum information, offering a novel avenue for exploring the properties of space-time. In this paper, we investigate the quantum correlations (QCs) of neutrinos in the Kerr--Newman space-time for both zero- and nonzero-angular-momentum propagation. The results show that, for zero-angular-momentum propagation, the oscillation periods of the survival probability and QCs progressively decrease with propagation distance in the inward direction. In the outward direction, increasing $M$ lengthens the oscillation periods of $P_{ν_e\rightarrowν_e}$, entanglement, and the monogamy of nonlocality, whereas increasing the angular momentum $a$ or charge $Q$ shortens them. For nonzero-angular-momentum propagation, the metric parameters also generate local profile modulations through additional two-path interference terms, rather than merely rescaling the oscillation period. Furthermore, we find that, despite differences in their ranges of variation, entanglement and coherence exhibit highly consistent oscillatory behavior in both propagation cases. These findings provide a comprehensive understanding of neutrino-based relativistic quantum information.

gr-qc↗

arXiv2026

The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions

Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing. By evaluating 22,500 deterministic trajectories across 3 dataset contexts (GAIA, SWE-bench, Multi-Challenge) with 3 state-of-the-art (SOTA) models, we semantically audit internal reasoning traces against external outputs. We formalize the \textit{Interaction Depth Limit} ($D_L$), the exact plurality threshold where an agent's logical sovereignty collapses into social compliance. Crucially, we uncover the \textit{Sovereignty Gap}: models frequently compute the correct derivation internally but suffer ``Alignment Hallucinations'' -- actively subjugating empirical evidence to sycophantically appease a simulated swarm. We prove that multi-agent social load is strictly non-commutative; the "brand" identity of the ``Lead Anchor'' auditor disproportionately dictates the swarm's integrity. These findings expose architectural vulnerabilities, proving that unstructured multi-agent topologies can degrade independent reasoning.

cs.MA↗

arXiv2026

Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dense geophone deployments, distributed acoustic sensing (DAS) arrays, and large-scale 2D and 3D surveys. This expansion makes complex noise suppression increasingly challenging, especially when signal fidelity must be preserved. Conventional supervised deep learning methods are often task-specific, require large paired datasets, and can suffer from domain shift under new acquisition conditions. Foundation models offer a promising alternative, but pre-training seismic foundation models from scratch requires massive domain-specific data and substantial computation. We propose an efficient framework that repurposes general-purpose Vision Foundation Models (VFMs) for geophysical tasks through Parameter-Efficient Fine-Tuning. The architecture uses a pre-trained VFM, a DINOv3 encoder, adapted with Low-Rank Adaptation (LoRA) to enable effective feature adaptation with few additional parameters. To improve robustness under unseen field conditions without ground truth, we introduce a kurtosis-guided unsupervised test-time adaptation module that updates only LoRA parameters during inference. This module self-calibrates the model to site-specific noise by identifying information-rich regions via kurtosis and performing self-training without labeled data. Experiments on public exploration seismic images and DAS vertical seismic profiling data from the Utah FORGE site show that the framework matches or outperforms domain-specific models. Tests on unseen cross-site data from a land survey in China and the Groß Schönebeck geothermal site in Germany further demonstrate strong generalization and effective signal-noise separation. These results highlight the potential of adapting pre-trained VFMs to data-intensive problems in exploration seismology.

physics.geo-ph↗

arXiv2026

SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory

Long-term memory is becoming a central bottleneck for language agents. Exsting RAG and GraphRAG systems largely treat memory graphs as static retrieval middleware, which limits their ability to recover complete evidence chains from partial cues, exploit reusable graph-structrual roles, and improve the memory itself through downstream feedback. We introduce SAGE, a Self-evolving Agentic Graph-memory Engine that models graph memory as a dynamic long-term memory substrate. SAGE couples two roles: a memory writer that incrementally constucts structured graph memory from interaction histories, and a Graph Foundation Model-based memory reader to perform retrieval and provide feedback to the memory writer. We provide rigorooous theoretical annalyses supporting the framework. Across multi-hop QA, open-domain retireval, domain-specific review QA, and long-term agent-memory benchmarks, SAGE improves evidence recovery, answer grounding, and retrieval efficiency: after two self-evolution rounds, it achieves the best average rank on multi-hop QA; in zero-shot open-domain transfer, it reaches 82.5/91.6 Recall@2/5 on NQ. Further results on LongMemEval and HaluMem show that traning and reader-writer feedback improve multiple long-term memory and hallucination-diagnostic metrics, suggesting that self-evolving, structure-aware graph memory is a promising foundation for robust long-horizon language agents.

cs.AI↗

arXiv2026

Asymmetry of $\ell^{2}$-cohomology via skewed Følner geometry

We study the two $\ell^{2}$-Dirichlet structures on a countable group $G$ arising from the left and right regular actions on $\mathbb{R}^{G}$. Although the two regular representations are unitarily equivalent, their $\ell^{2}$-Dirichlet subspaces of $\mathbb{R}^{G}$ need not coincide. Our main result gives a complete classification of this asymmetry for countable amenable groups: $$\mathcal{D}_{2}\left(G,λ\right)=\mathcal{D}_{2}\left(G,ρ\right)\quad\Longleftrightarrow\quad G \text{ is an FC-group}.$$ The proof is based on a skewed Følner-geometric mechanism, called a left scheme, combining summability of left boundaries with displacement under a right translation. We develop this mechanism generally, and demonstrate it concretely in the Heisenberg group and amenable wreath products over $\mathbb{Z}$. We also show that this mechanism has a dynamical counterpart in the theory of nonsingular Bernoulli shifts: every countable amenable group that is not an FC-group admits Bernoulli schemes whose left shift is nonsingular, conservative and weakly mixing, whereas the right shift by some element is singular.

math.GR↗

arXiv2026

Reservoir Computing with a single Josephson junction

Physical reservoir computing exploits the nonlinear dynamics of a physical system to perform information processing tasks. Josephson junctions (JJs), as nonlinear superconducting devices with rich dynamical behavior, represent promising yet relatively unexplored candidates for reservoir computing. In this work, we demonstrate for the first time that a single Josephson junction can be employed as a reservoir computing substrate without the use of an explicit delay loop. Using numerical simulations, we analyze the reservoir performance in different dynamical regimes and show that optimal performance is achieved when the JJ operates in a stable yet responsive regime. Despite the absence of delayed feedback, the JJ exhibits sufficient memory through its intrinsic dynamics to achieve good performance on a chaotic time series prediction task. The underlying mechanism is analogous, at the dynamical level, to that of a driven nonlinear pendulum, highlighting the generality of the approach to other nonlinear oscillators. In addition, we explore an alternative input masking approach based on continuous modulation, highlighting its compatibility with practical implementations. These results establish Josephson junctions as a viable and efficient platform for reservoir computing and open the way to ultrafast, low-dissipation hardware realizations.

nlin.CD↗

arXiv2026

The impact of flickering variability and magnetisation on the dynamics, stability and morphology of radio-loud AGN jets

The physics governing the morphology of radio-loud AGN jets is not fully understood. We investigate how magnetization, flickering jet power and their interplay affects the morphology of radio galaxies. We present a grid of relativistic magnetohydrodynamic simulations using the PLUTO code covering constant and variable jets with two levels of magnetisation. We find that the constant high magnetisation jets can lead to highly asymmetrical cocoon morphologies, whilst the variable high magnetisation jet can exhibit a broken morphology, caused by a discontinuous jet beam. Our work highlights the importance of magnetisation and variability on the stability and resulting morphology of radio-loud AGN jets, suggesting both are significant factors in addition to jet power or environment. Furthermore, we show that the interaction between magnetisation and variability can lead to the development of localised kink instabilities along the jet beam. Finally, we discuss the effects of hydrodynamic mixing in low magnetisation jets and the role of viewing angle dependence in comparisons between our simulations and observed sources. To facilitate this comparison we present a library of simulated radio images at different times in the simulations and from various viewing angles, which highlight a diverse set of complex morphologies.

astro-ph.HE↗

arXiv2026

On Tournament Anti-Sidorenko Orientations of Trees

An oriented graph $\vec{H}$ is said to be tournament anti-Sidorenko if the homomorphism density of $\vec{H}$ in any tournament $\vec{T}$ is bounded above by the homomorphism density of $\vec{H}$ in a large uniformly random tournament. We prove the following: (1) Every oriented path with at least three arcs and exactly one non-leaf source or sink vertex is tournament anti-Sidorenko. (2) An oriented path is tournament anti-Sidorenko if the distance between any leaf vertex and any source or sink vertex is at least two and the distance between any pair of non-leaf source or sink vertices is a multiple of four. (3) Every spider with exactly three legs admits a tournament anti-Sidorenko orientation. The first result proves a conjecture posed by He, Mani, Nie, Tung and Wei. The third resolves a problem from the same paper, in fact establishing a substantially more general statement, and provides evidence in support of a conjecture of Fox, Himwich, Mani and Zhou. The second yields the first family of tournament anti-Sidorenko oriented paths which is exponentially large with respect to the number of arcs.

math.CO↗

arXiv2026

NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol

Self-driving laboratories (SDLs) are attracting increasing attention as a means of accelerating scientific discovery; however, developing SDL software remains technically demanding. To improve accessibility, orchestration software frameworks have been proposed to coordinate SDL components, but many existing frameworks are primarily designed for human interaction and lack standardized interfaces for direct integration with AI agents. In this work, we propose an SDL software architecture based on the Model Context Protocol (MCP), in which all SDL functionalities are exposed through MCP servers. Following this design principle, we introduce NIMO Controller, an MCP-based SDL orchestrator that integrates the experimental planning software NIMO. It provides a visual programming interface automatically generated through MCP-based tool discovery, allowing human users to design experimental workflows without writing code. The same MCP backend is also accessible to AI agents, providing a unified interface through which both human users and AI agents can interact with SDL components. AI agents can invoke MCP tools directly or generate NIMO workflows from natural language instructions. We evaluated the agentic workflow generation performance of various LLMs and conducted a user study using a color-matching SDL.

cs.AI↗