Published: 2026-03-18 09:27:27
Authors: Madhav S. Baidya, S. S. Baidya, Chirag Chawla
Categories: cs.CL, cs.AI
Abstract:
The rapid proliferation of large language models (LLMs) has created an urgent need for robust and generalizable detectors of machine-generated text. Existing benchmarks typically evaluate a single detector on a single dataset under ideal conditions, leaving open questions about cross-domain transfer, cross-LLM generalization, and adversarial robustness.
We present a comprehensive benchmark evaluating diverse detection approaches across two corpora: HC3 (23,363 human-ChatGPT pairs) and ELI5 (15,000 human-Mistral-7B pairs). Methods include classical classifiers, fine-tuned transformer encoders (BERT, RoBERTa, ELECTRA, DistilBERT, DeBERTa-v3), a CNN, an XGBoost stylometric model, perplexity-based detectors, and LLM-as-detector prompting.
Results show that transformer models achieve near-perfect in-distribution performance but degrade under domain shift. The XGBoost stylometric model matches performance while remaining interpretable. LLM-based detectors underperform and are affected by generator-detector identity bias. Perplexity-based methods exhibit polarity inversion, with modern LLM outputs showing lower perplexity than human text, but remain effective when corrected. No method generalizes robustly across domains and LLM sources.
Published: 2026-03-18 09:26:55
Authors: Masafumi Hattori, Theodoros Stylianos Papazachariou, Aline Zanardini
Categories: math.AG
Abstract:
A general net of quadric surfaces, together with a choice of a base point, defines a net of plane cubics via the Gale transformation of the remaining seven base points. To both nets, one can also naturally associate the same smooth plane quartic. In this paper, we generalize the cycle of correspondences arising from nets of quadrics that define rational elliptic threefolds and provide a complete criterion for GIT stability of the three underlying geometric objects using birational-geometric techniques.
Published: 2026-03-18 09:25:53
Authors: Wasif H. Syed, Juan E. Machado, Hans Würfel, Ekrem Hanli, Johannes Schiffer
Categories: eess.SY
Abstract:
To reduce CO2 emissions and tackle increasing fuel costs, the aviation industry is swiftly moving towards the electrification of aircraft. From the viewpoint of systems and control, a key challenge brought by this transition corresponds to the management and safe operation of the propulsion system's onboard electrical power distribution network. In this work, for a series-hybrid-electric propulsion system, we propose a distributed adaptive controller for regulating the voltage of a DC bus that energizes the electricity-based propulsion system. The proposed controller -- whose design is based on principles of back-stepping, adaptive, and passivity-based control techniques -- also enables the proportional sharing of the electric load among multiple converter-interfaced sources, which reduces the likelihood of over-stressing individual sources. Compared to existing control strategies, our method ensures stable, convergent, and accurate voltage regulation and load-sharing even if the effects of power lines of unknown resistances and inductances are considered. The performance of the proposed control scheme is experimentally validated and compared to state-of-the-art controllers in a power hardware-in-the-loop (PHIL) environment.
Published: 2026-03-18 09:23:02
Authors: Yanglong Zhang, Mingshuo Zhou
Categories: math.AG
Abstract:
For a classical simple and simply connected group $G$, let $\mathcal{M}_{G,ω}$ be the moduli space of $ω$-semistable parabolic $G$-bundles on a complex smooth projective curve of genus $g$. We prove two results in this article: (1) $\mathcal{M}_{G,ω}$ is of Fano type when $g\geq 3$; (2) the algebra of conformal blocks on any $n$-pointed stable curve for a classical simple Lie algebra is finitely generated.
Published: 2026-03-18 09:19:08
Authors: Mengyu Bu, Yang Feng
Categories: cs.CL
Abstract:
Large language models (LLMs) exhibit strong general intelligence, yet their multilingual performance remains highly imbalanced. Although LLMs encode substantial cross-lingual knowledge in a unified semantic space, they often struggle to reliably interface this knowledge with low-resource or unseen languages. Fortunately, pretrained encoder-decoder translation models already possess balanced multilingual capability, suggesting a natural complement to LLMs. In this work, we propose XBridge, a compositional encoder-LLM-decoder architecture that offloads multilingual understanding and generation to external pretrained translation models, while preserving the LLM as an English-centric core for general knowledge processing. To address the resulting representation misalignment across models, we introduce lightweight cross-model mapping layers and an optimal transport-based alignment objective, enabling fine-grained semantic consistency for multilingual generation. Experiments on four LLMs across multilingual understanding, reasoning, summarization, and generation indicate that XBridge outperforms strong baselines, especially on low-resource and previously unseen languages, without retraining the LLM.
Published: 2026-03-18 09:07:39
Authors: Cem Uluoglakci, Tugba Taskaya Temizel
Categories: cs.CL
Abstract:
Large language models (LLMs) often hallucinate, producing fluent but false information, partly because supervised fine-tuning (SFT) implicitly rewards always responding. We introduce $\textit{HypoTermInstruct}$, an SFT dataset (31,487 responses for 11,151 questions) designed to teach models epistemological humility-the ability to recognize the limits of their own knowledge and admit uncertainty. This is achieved through questions about non-existent "hypothetical" terms. We also release $\textit{HypoTermQA-Enhanced}$, a benchmark for hallucination tendency strengthened through multiple validations. We conducted 800 controlled LoRA SFT runs across $\textit{Llama3.1-8B}$ and $\textit{Gemma3-4B}$ (base and instruct), testing 100 fine-tuning configurations with paired controls. Our results demonstrate that replacing generic instruction data with $\textit{HypoTermInstruct}$ significantly improves the HypoTerm Score (median increases of 0.19% to 25.91%) and FactScore (+0.39% to +0.86%), while maintaining stable performance on MMLU (minimal decreases of 0.26% to 0.35%). Our work demonstrates that targeted, high-quality SFT data teaching meta-cognitive skills can effectively reduce hallucination without preference/RL pipelines, providing mechanistic insights and a practical path toward more reliable AI systems.
Published: 2026-03-18 09:06:13
Authors: Dáire Healy, Isadora Antoniano-Villalobos, Claudia Collarin, Nathan Huet, Ilaria Prosdocimi, Emilia Siviero
Categories: stat.ME, stat.AP
Abstract:
This article summarises the methods used by the team ``Ca' Foscari" for the EVA 2025 Data Challenge. The questions of the challenge concern the estimation of exceedance probabilities across several locations. Rather than modelling the spatial dependence structure, we reduce the problems to univariate ones by considering relevant spatial order statistics across the sites. Within a Peaks over Threshold framework, we model the marginal distributions of exceedances using generalised Pareto distributions. Generalised additive models are employed to allow the parameters to vary as functions of external predictors, which for all questions are reduced to the month. For questions 1 and 2, the required estimates and confidence intervals are obtained by generating samples from our fitted models. Question 3 involves the dependence between two consecutive observed statistics. To account for this temporal dependence, we fit a conditional extreme value model and derive empirical estimates of persistent extreme events by simulating from this model.
Published: 2026-03-18 09:06:04
Authors: Arvin Rasoulzadeh
Categories: math.DG
Abstract:
Alignable nets are grid structures that can collapse to a planar strip, which is in fact the real-world counterpart of a curve. This property simplifies on-site assembly and enables compact transport and storage. These grid structures can then be deployed by a scissor motion at each vertex in a desired location. In this article, we classify all surfaces supporting an alignable net that additionally have the geodesic and conjugate net property, namely, the alignable Voss surfaces. In doing so, we use Cartan's theory of moving-frames and we obtain a coordinate-free classification of these surfaces. In the next step we express our findings in local coordinates and at the level of the fundamental forms. We show that the alignable Voss surfaces consist of two classes where each in turn consists of two two-parameter families of surfaces. A surprising feature of one of these classes is that they admit an isothermal-conjugate geodesic net, thereby providing a counterexample to Eisenhart's earlier classification claim for Voss surfaces of this type. Finally, we derive explicit immersion formulas for one of the classes as functions of the deformation and alignability parameters. Additionally, we show that, upon disregarding certain singularities, the above immersions of alignable Voss surfaces give rise to infinitely many explicit immersions of other Voss surfaces still depending on the deformation parameter. Since explicit immersion formulas for Voss surfaces that include the deformation parameter are seldom obtainable, this provides a rare result in the literature. Finally, we examine several notable subclasses in detail, including the well known example of infinitely many geodesic-conjugate nets on a helicoid, and we give a kinematical explanation for why this phenomenon appears in computations.
Published: 2026-03-18 09:05:11
Authors: Wei Hong, Luca Izzo, Massimo Della Valle, Orlando Luongo, Marco Muccino, Tong-Jie Zhang
Categories: astro-ph.CO, astro-ph.HE, gr-qc
Abstract:
Context. Gamma-ray bursts (GRBs) reach redshifts beyond Type Ia supernovae (SNe Ia) and can extend distance measurements into the early Universe, but their use as distance indicators is limited by the circularity problem in calibrating empirical luminosity relations. Aims. We present a model-independent methodology to overcome this circularity by combining Pantheon$+$ SNe Ia, a distance reconstruction based on artificial neural networks (ANNs), and two GRB correlations (Amati and Combo) into a distance ladder from low to high redshift, with the goal of constraining cosmological parameters in $Λ\mathrm{CDM}$ and $w_0 w_a \mathrm{CDM}$. Methods. We use the ReFANN to reconstruct the luminosity distance $d_L(z)$ and distance modulus $μ(z)$ from the Pantheon$+$ dataset, with hyperparameters optimized via approximate Bayesian computation rejection and a risk function. This model-independent reconstruction calibrates the Amati and Combo relations using a low-redshift ($z<1$) GRB sample from Fermi GBM and Swift-XRT. The calibrated relations then provide distance estimates for GRBs at $z \geq 1$. Finally, a joint Bayesian analysis simultaneously constrains the cosmological and GRB correlation parameters, ensuring self-consistent uncertainty propagation. Results. We obtain consistent cosmological constraints from two independent GRB correlations. The Hubble constant $H_0$ agrees with SNe Ia values, though potentially influenced by Pantheon$+$ dataset. High-redshift GRBs favour a higher matter density $Ω_m$ than the Pantheon$+$ and hint at possible dark energy evolution.Conclusions. We present a framework that mitigates GRB cosmology's circularity problem, extending the distance ladder to $z \sim 9$ and establishing GRBs as a high-redshift probe.
Published: 2026-03-18 08:58:43
Authors: Huansheng Ning, Jianguo Ding
Categories: cs.ET
Abstract:
Human communication is undergoing a fundamental paradigm shift. Physical space, social relations, mental states, and digital information are converging into a unified cyber-physical-social-thinking (CPST) fusion space, rendering them no longer separable domains. However, all existing communication systems, including natural and programming languages, as well as interaction protocols, were designed for a world in which these four dimensions remained distinct. We introduce Cyberlanguage, a theoretically grounded communicative framework that is native to the CPST fusion space. Grounded in the philosophical orientation of cyberism and employing CPST theory as an analytical framework, Cyberlanguage possesses four core characteristics: native four-dimensional fusion, multi-agent universality, dynamic compilability, and contextual adaptability. We have constructed a semiotic model based on the Cybersign unit, a four-dimensional synchronous grammar, a five-layer architectural stack, and a context-driven pragmatic mechanism. We also present testable empirical predictions and a staged implementation roadmap. Cyberlanguage is not intended to replace natural or programming languages, but rather to serve as a meta-communication infrastructure capable of coordinating heterogeneous agents, humans, artificial intelligences, robots, and digital entities, within an increasingly fused cyber-physical-social-cognitive reality.
Published: 2026-03-18 08:55:35
Authors: Shenghui Huang, Menghao Hu, Longkun Zou, Hongyu Chi, Zekai Li, Feng Gao, Fan Yang, Qingyao Wu, Ke Chen
Categories: cs.CV
Abstract:
Detecting Unmanned Aerial Vehicles (UAVs) in low-altitude environments is essential for perception and defense systems but remains highly challenging due to complex backgrounds, camouflage, and multimodal interference. In real-world scenarios, UAVs are frequently visually blended with surrounding structures such as buildings, vegetation, and power lines, resulting in low contrast, weak boundaries, and strong confusion with cluttered background textures. Existing UAV detection datasets, though diverse, are not specifically designed to capture these camouflage and complex-background challenges, which limits progress toward robust real-world perception. To fill this gap, we construct UAV-CB, a new RGB-T UAV detection dataset deliberately curated to emphasize complex low-altitude backgrounds and camouflage characteristics. Furthermore, we propose the Local Frequency Bridge Network (LFBNet), which models features in localized frequency space to bridge both the frequency-spatial fusion gap and the cross-modality discrepancy gap in RGB-T fusion. Extensive experiments on UAV-CB and public benchmarks demonstrate that LFBNet achieves state-of-the-art detection performance and strong robustness under camouflaged and cluttered conditions, offering a frequency-aware perspective on multimodal UAV perception in real-world applications.
Published: 2026-03-18 08:55:17
Authors: Pascal Auscher, Lukas Niebel
Categories: math.AP
Abstract:
We define and study homogeneous kinetic Sobolev spaces adapted to the Kolmogorov equation. We consider both local and non-local diffusion. The spaces are built from the Lebesgue spaces L p for all integrability exponents p $\in$ (1, $\infty$) with regularity assumptions in the transport and diffusive directions according to the scaling of the Kolmogorov equation. The regularity scale accommodates weak and strong solutions. We prove that the proposed spaces satisfy sharp embeddings quantifying the transfer-ofregularity {à} la Bouchut-H{ö}rmander, continuity-in-time in the spirit of Lions and the gainof-integrability of Sobolev and Hardy-Littlewood-Sobolev type. A core tool are mapping properties of the Kolmogorov operator, given by the fundamental solution, established between anisotropic homogeneous Sobolev spaces. To achieve this, we prove L^p boundedness of related singular integral operators, for which we deduce novel kernel estimates by a Littlewood-Paley decomposition and geometric considerations. Moreover, we provide a new uniqueness criterion which allows us to show well-posedness of the Cauchy problem.
Published: 2026-03-18 08:55:02
Authors: Samuel Bismuth, Erel Segal-Halevi
Categories: cs.CC
Abstract:
We present an approximation notion for NP-hard optimization problems represented by binary functions. We prove that (assuming P != NP) the new notion is strictly stronger than FPTAS, but strictly weaker than having a polynomial-time algorithm.
Published: 2026-03-18 08:07:01
Authors: Dailan He, Guanlin Feng, Xingtong Ge, Yi Zhang, Bingqi Ma, Guanglu Song, Yu Liu, Hongsheng Li
Categories: cs.CV
Abstract:
Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis, yet remain difficult to align via reinforcement learning from human feedback (RLHF). Existing SDE-based GRPO methods face challenges in this setting: few-step ODEs and consistency model samplers deviate from standard flow-matching ODEs, and their short, low-stochasticity trajectories are highly sensitive to initialization noise, rendering intermediate SDE exploration ineffective. We propose AR-CoPO (AutoRegressive Contrastive Policy Optimization), a framework that adapts the Neighbor GRPO contrastive perspective to streaming AR generation. AR-CoPO introduces chunk-level alignment via a forking mechanism that constructs neighborhood candidates at a randomly selected chunk, assigns sequence-level rewards, and performs localized GRPO updates. We further propose a semi-on-policy training strategy that complements on-policy exploration with exploitation over a replay buffer of reference rollouts, improving generation quality across domains. Experiments on Self-Forcing demonstrate that AR-CoPO improves both out-of-domain generalization and in-domain human preference alignment over the baseline, providing evidence of genuine alignment rather than reward hacking.
Published: 2026-03-18 07:53:28
Authors: Yijun Sun, Xudong Liao, Songrun Xie, Hao Chen, Han Tian, Wenxue Li, Yiming Zhang, Kai Chen
Categories: cs.NI, cs.DC
Abstract:
Meeting stringent Time-To-First-Token (TTFT) requirements is crucial for LLM applications. To improve efficiency, modern LLM serving systems adopt disaggregated architectures with diverse parallelisms, introducing complex multi-stage workflows involving reusable KV-block retrieval, collective communication, and P2D transfer. Flows from dependent stages overlap within and across requests on shared bottleneck links, making TTFT highly susceptible to network contention and necessitating stage-aware scheduling. Unfortunately, most existing works schedule flows in a stage-agnostic manner, leading to uncoordinated contention that constitutes a primary cause of SLO violations.
In this paper, we present MFS, a holistic multi-stage flow scheduling mechanism designed to maximize TTFT SLO attainment. At its core, MFS approximates the Least-Laxity-First (LLF) scheduling policy without requiring precise knowledge of a request's remaining slack. It achieves this through a Defer-and-Promote principle implemented through a Reverse Multi-Level Queue (RMLQ) structure. By dynamically promoting task precedence as effective laxity diminishes, MFS prioritizes flows with less laxity while preventing requests with loose SLOs from prematurely consuming network bandwidth. We implement MFS as a pluggable module integrated into vLLM, and evaluate it on a 8-server, 32-GPU testbed as well as through large-scale simulations. Our results demonstrate that MFS effectively outperforms state-of-the-art baselines, improving the TTFT SLO attainment by 1.2x--2.4x.
Published: 2026-03-18 07:46:49
Authors: Yong-Cheng Pan, Tommy Kotte, Toni Helm, Motoki Osada, Atsushi Tsukazaki, Yu-Te Hsu
Categories: cond-mat.supr-con
Abstract:
We report a systematic magnetotransport study on high-crystallinity La$_{1-x}$Sr$_{x}$NiO$_2$ (LSNO) thin films with $x=0.20-0.24$. By conducting pulsed-field transport experiment up to 62 T, we reveal two salient features of the normal-state transport in overdoped LSNO thin films: (1) the magnetoresistance does not follow the Kohler's rule but exhibits a $H$-linear behavior in the high $H/T$ limit and (2) the normal-state $ρ(T)$ below 30 K consistently follows a $T^2$ behavior across the overdoped regime. Our results demonstrate a coexistence of $H$-linear magnetoresistance and $T^2$ resistivity in a model unconventional superconductor and provide new information on the transport characteristics of the normal ground state that host superconductivity in infinite-layer nickelates.
Published: 2026-03-18 07:34:17
Authors: Alexander V. Shenderuk-Zhidkov, Alexander E. Hramov
Categories: cs.NE, cs.CY, cs.HC
Abstract:
This article introduces and substantiates the concept of Neuro-Linguistic Integration (NLI), a novel paradigm for human-technology interaction where Large Language Models (LLMs) act as a key semantic interface between raw neural data and their social application. We analyse the dual nature of LLMs in this role: as tools that augment human capabilities in communication, medicine, and education, and as sources of unprecedented ethical risks to mental autonomy and neurorights. By synthesizing insights from AI ethics, neuroethics, and the philosophy of technology, the article critiques the inherent limitations of LLMs as semantic mediators, highlighting core challenges such as the erosion of agency in translation, threats to mental integrity through precision semantic suggestion, and the emergence of a new `neuro-linguistic divide' as a form of biosemantic inequality. Moving beyond a critique of existing regulatory models (e.g., GDPR, EU AI Act), which fail to address the dynamic, meaning-making processes of NLI, we propose a foundational framework for proactive governance. This framework is built on the principles of Semantic Transparency, Mental Informed Consent, and Agency Preservation, supported by practical tools such as NLI-specific ethics sandboxes, bias-aware certification of LLMs, and legal recognition of the neuro-linguistic inference. The article argues for the development of a `second-order neuroethics,' focused not merely on neural data protection but on the ethics of AI-mediated semantic interpretation itself, thereby providing a crucial conceptual basis for steering the responsible development of neuro-digital ecosystems.
Published: 2026-03-18 07:28:17
Authors: Mankeun Jeong, Myungshin Im, Joonho Kim, Seo-Won Chang, Sungho Jung, Chung-Uk Lee, Dong-Jin Kim, Bomi Park, Jaewon Lee, Jiseop Shin, Changwan Kim, Gregory S. H. Paek
Categories: astro-ph.IM
Abstract:
We present a comprehensive pipeline developed for the image processing of the KMTNet Synoptic Survey of the Southern Sky (KS4) Data Release 1. This pipeline encompasses several key processes, including data quality assurance, astrometry, photometric zero-point (ZP) calibration, bad pixel masking, image stacking, and difference image analysis (DIA). The astrometric solutions were validated by cross-matching with the Gaia EDR3 catalog, achieving sub-pixel astrometric accuracy (< 0.4 arcsec). To ensure spatial consistency, we divided each image into multiple subsections and confirmed that astrometric accuracy was maintained even at the edges. We performed a two-stage photometric calibration. Initial ZP solutions were computed for each individual image frame using the APASS DR9 and SkyMapper DR3 catalogs. Subsequently, we corrected residual spatial variations in the stacked images using Gaia XP photometry. This procedure yielded a 5-sigma depth of 22-23 AB mag across the BVRI bands, with root-mean-square errors of approximately 0.03 mag when referenced to Gaia stars in the magnitude range of 14-19 mag. The processed KS4 images span over 4,000 deg^2 of the southern sky, providing reference images suitable for DIA. This publicly available pipeline also supports real-time processing of newly acquired images, enabling prompt transient detection. We demonstrate its effectiveness through successful applications in gravitational-wave follow-up observations.
Published: 2026-03-18 07:24:19
Authors: Linxiao Yang, Xue Jiang, Gezheng Xu, Tian Zhou, Min Yang, ZhaoYang Zhu, Linyuan Geng, Zhipeng Zeng, Qiming Chen, Xinyue Gu, Rong Jin, Liang Sun
Categories: cs.LG, cs.AI
Abstract:
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted features, while end-to-end sequence models lack inference-time adaptation. We bridge this gap with a unified framework, Baguan-TS, which integrates the raw-sequence representation learning with ICL, instantiated by a 3D Transformer that attends jointly over temporal, variable, and context axes. To make this high-capacity model practical, we tackle two key hurdles: (i) calibration and training stability, improved with a feature-agnostic, target-space retrieval-based local calibration; and (ii) output oversmoothing, mitigated via context-overfitting strategy. On public benchmark with covariates, Baguan-TS consistently outperforms established baselines, achieving the highest win rate and significant reductions in both point and probabilistic forecasting metrics. Further evaluations across diverse real-world energy datasets demonstrate its robustness, yielding substantial improvements.
Published: 2026-03-18 07:18:41
Authors: Dibakar Sigdel
Categories: cs.LG, cs.AI
Abstract:
Transformer models have redefined sequence learning, yet dot-product self-attention introduces a quadratic token-mixing bottleneck for long-context time-series. We introduce the \textbf{Phasor Transformer} block, a phase-native alternative representing sequence states on the unit-circle manifold $S^1$. Each block combines lightweight trainable phase-shifts with parameter-free Discrete Fourier Transform (DFT) token coupling, achieving global $\mathcal{O}(N\log N)$ mixing without explicit attention maps. Stacking these blocks defines the \textbf{Large Phasor Model (LPM)}. We validate LPM on autoregressive time-series prediction over synthetic multi-frequency benchmarks. Operating with a highly compact parameter budget, LPM learns stable global dynamics and achieves competitive forecasting behavior compared to conventional self-attention baselines. Our results establish an explicit efficiency-performance frontier, demonstrating that large-model scaling for time-series can emerge from geometry-constrained phase computation with deterministic global coupling, offering a practical path toward scalable temporal modeling in oscillatory domains.
Published: 2026-03-18 07:15:02
Authors: Benjamin Ingimarson, Igor Kukavica
Categories: math.AP
Abstract:
Under the assumption that a solution to the 3D incompressible Euler equations blows up at a time $T_\ast$ and that $T_\ast $ is the first such time, we establish lower bounds on the rate of blow-up of the maximum norm of the vorticity. In particular, when the domain is $\mathbb{R}^3$ or $\mathbb{T}^3$, we provide lower bounds on the accumulation of $\|ω\|_{L^\infty}$ up to time $t$ and the supremum over $[0,t]$ of $\|ω\|_{L^\infty}$ for $t$ sufficiently close to~$T_\ast$. Notably, this gives a quantitative description of the BKM blow-up criterion. Moreover, we provide lower bounds on the supremum over $[0,t]$ of $\|D^k ω\|_{L^\infty}$. When the domain is $\mathbb{T}^3$, we establish pointwise-in-time lower bounds on $\|D^kω\|_{L^\infty}$ for $t$ sufficiently close to~$T_\ast$.
Published: 2026-03-18 07:08:56
Authors: Tomochika Kurita
Categories: quant-ph
Abstract:
As we are entering an early-FTQC era, circuit execution protocols with logical qubits and certain error-correcting codes are being discussed. Here, we propose a circuit execution protocol for the space-time efficient analog rotation (STAR) architecture. Gate operations within the STAR architecture is based on lattice surgery with surface codes, but it allows direct execution of continuous gates $Rz(θ)$ as non-Clifford gates instead of $T = Rz(π/4)$. $Rz(θ)$ operations involve creation of resource states $|m_θ\rangle = \frac{1}{\sqrt{2}} (|0 \rangle + e^{iθ} |1\rangle ) $ followed by ZZ joint measurements with target logical qubits. While employing $Rz(θ)$ enables more efficient circuit execution, both their creations and joint measurements are probabilistic processes and adopt repeat-until-success (RUS) protocols which are likely to result in considerable time overhead. Our circuit execution protocol aims to reduce such time overhead by parallel trials of resource state creations and more frequent trials of joint measurements. By employing quadratic unconstrained binary optimization (QUBO) in determining resource state allocations within the space, we successfully make our protocol efficient. Furthermore, we proposed performance estimators given the target circuit and qubit topology. It successfully predicts the time performance within less time than actual simulations do, and helps find the optimal qubit topology to run the target circuits efficiently.
Published: 2026-03-18 06:59:22
Authors: Aaron Lau, Kouji Yano
Categories: math.PR, math.DS
Abstract:
The weak and strong laws of large numbers for time-inhomogeneous Markov chains are studied under general conditions. First, under Drift Condition and Contraction Condition in total variation, we prove the weak law of large numbers. Then, assuming Drift Condition together with a time-inhomogeneous Doeblin minorization, we develop a Nummelin-type splitting and obtain a strong law of large numbers. Our results utilize the invariant measure family in the sense of Liu--Lu (2025), and extend the classical Harris-ergodic LLN to the time-inhomogeneous setting.
Published: 2026-03-18 06:54:49
Authors: Jie Zheng, Dusit Niyato, Changyuan Zhao, Jiawen Kang, Jiacheng Wang
Categories: cs.AI
Abstract:
The rapid evolution toward 6G and beyond communication systems is accelerating the convergence of digital twins and world models at the network edge. Traditional digital twins provide high-fidelity representations of physical systems and support monitoring, analysis, and offline optimization. However, in highly dynamic edge environments, they face limitations in autonomy, adaptability, and scalability. This paper presents a systematic survey of the transition from digital twins to world models and discusses its role in enabling edge general intelligence (EGI). First, the paper clarifies the conceptual differences between digital twins and world models and highlights the shift from physics-based, centralized, and system-centric replicas to data-driven, decentralized, and agent-centric internal models. This discussion helps readers gain a clear understanding of how this transition enables more adaptive, autonomous, and resource-efficient intelligence at the network edge. The paper reviews the design principles, architectures, and key components of world models, including perception, latent state representation, dynamics learning, imagination-based planning, and memory. In addition, it examines the integration of world models and digital twins in wireless EGI systems and surveys emerging applications in integrated sensing and communications, semantic communication, air-ground networks, and low-altitude wireless networks. Finally, this survey provides a systematic roadmap and practical insights for designing world-model-driven edge intelligence systems in wireless and edge computing environments. It also outlines key research challenges and future directions toward scalable, reliable, and interoperable world models for edge-native agentic AI.
Published: 2026-03-18 06:44:54
Authors: Shiming Chen, Shuhuang Chen, Guo-Sen Xie, Xinge You
Categories: cs.CV, cs.LG
Abstract:
Zero-shot learning (ZSL) aims to recognize the unseen classes in the open-world guided by the side-information (e.g., attributes). Its key task is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus conducting a desirable semantic knowledge transfer from seen classes to unseen ones. Prior works simply utilize unidirectional attention within a weakly-supervised manner to learn the spurious and limited latent semantic representations, which fail to effectively discover the intrinsic semantic knowledge (e.g., attribute semantic) between visual and attribute features. To solve the above challenges, we propose a mutually causal semantic distillation network (termed MSDN++) to distill the intrinsic and sufficient semantic representations for ZSL. MSDN++ consists of an attribute$\rightarrow$visual causal attention sub-net that learns attribute-based visual features, and a visual$\rightarrow$attribute causal attention sub-net that learns visual-based attribute features. The causal attentions encourages the two sub-nets to learn causal vision-attribute associations for representing reliable features with causal visual/attribute learning. With the guidance of semantic distillation loss, the two mutual attention sub-nets learn collaboratively and teach each other throughout the training process. Extensive experiments on three widely-used benchmark datasets (e.g., CUB, SUN, AWA2, and FLO) show that our MSDN++ yields significant improvements over the strong baselines, leading to new state-of-the-art performances.
Published: 2026-03-18 06:23:03
Authors: Keiichi Shigechi
Categories: math.CO
Abstract:
We study four bijections, which are promotion, evacuation, rowmotion, and rowvacuation, on generalized Dyck paths in rational Catalan combinatorics. We define the maps on generalized Dyck paths, which have their origins in maps on Dyck paths and non-crossing partitions. They include rotation, Kreweras complement map, Simion--Ullman involution on non-crossing partitions, and Lalanne--Kreweras involution on Dyck paths. These maps have an expression in terms of the four combinatorial bijections. By extending the bijection studied by D. Armstrong, C. Stump, and H. Thomas on one hand, and the correspondence of RSK type studied by B. Adenbaum and S. Elizalde on the other, we present the equivalence between the two bijections, promotion and rowmotion, on generalized Dyck paths through these bijection and correspondence. For this purpose, we provide an alternative description of the correspondence of RSK type in terms of Dyck tilings.
Published: 2026-03-18 06:14:00
Authors: Qingran Lin, Fengwei Yang, Chaolun Zhu
Categories: cs.CV
Abstract:
Material classification has emerged as a critical task in computer vision and graphics, supporting the assignment of accurate material properties to a wide range of digital and real-world applications. While traditionally framed as an image classification task, this domain faces significant challenges due to the scarcity of annotated data, limiting the accuracy and generalizability of trained models. Recent advances in vision-language foundation models (VLMs) offer promising avenues to address these issues, yet existing solutions leveraging these models still exhibit unsatisfying results in material recognition tasks. In this work, we propose a novel framework that effectively harnesses foundation models to overcome data limitations and enhance classification accuracy. Our method integrates two key innovations: (a) a robust image generation and auto-labeling pipeline that creates a diverse and high-quality training dataset with material-centric images, and automatically assigns labels by fusing object semantics and material attributes in text prompts; (b) a prior incorporation strategy to distill information from VLMs, combined with a joint fine-tuning method that optimizes a pre-trained vision foundation model alongside VLM-derived priors, preserving broad generalizability while adapting to material-specific features.Extensive experiments demonstrate significant improvements on multiple datasets. We show that our synthetic dataset effectively captures the characteristics of real world materials, and the integration of priors from vision-language models significantly enhances the final performance. The source code and dataset will be released.
Published: 2026-03-18 06:08:06
Authors: Guangzhi Wang, Xiaohui Yang, Kai Li, Jiawen He, Kai Yang, Ruixuan Zhang, Zhi Liu
Categories: cs.IR, cs.CL
Abstract:
As retrieval models converge on generic benchmarks, the pressing question is no longer "who scores higher" but rather "where do systems fail, and why?" Person-job matching is a domain that urgently demands such diagnostic capability -- it requires systems not only to verify explicit constraints but also to perform skill-transfer inference and job-competency reasoning, yet existing benchmarks provide no systematic diagnostic support for this task. We introduce PJB (Person-Job Benchmark), a reasoning-aware retrieval evaluation dataset that uses complete job descriptions as queries and complete resumes as documents, defines relevance through job-competency judgment, is grounded in real-world recruitment data spanning six industry domains and nearly 200,000 resumes, and upgrades evaluation from "who scores higher" to "where do systems differ, and why" through domain-family and reasoning-type diagnostic labels. Diagnostic experiments using dense retrieval reveal that performance heterogeneity across industry domains far exceeds the gains from module upgrades for the same model, indicating that aggregate scores alone can severely mislead optimization decisions. At the module level, reranking yields stable improvements while query understanding not only fails to help but actually degrades overall performance when combined with reranking -- the two modules face fundamentally different improvement bottlenecks. The value of PJB lies not in yet another leaderboard of average scores, but in providing recruitment retrieval systems with a capability map that pinpoints where to invest.
Published: 2026-03-18 05:57:46
Authors: Hongbo Lu, Liang Yao, Chenghao He, Fan Liu, Wenlong Liao, Tao He, Pai Peng
Categories: cs.CV
Abstract:
A fundamental bottleneck in Novel View Synthesis (NVS) for autonomous driving is the inherent supervision gap on novel trajectories: models are tasked with synthesizing unseen views during inference, yet lack ground truth images for these shifted poses during training. In this paper, we propose VisionNVS, a camera-only framework that fundamentally reformulates view synthesis from an ill-posed extrapolation problem into a self-supervised inpainting task. By introducing a ``Virtual-Shift'' strategy, we use monocular depth proxies to simulate occlusion patterns and map them onto the original view. This paradigm shift allows the use of raw, recorded images as pixel-perfect supervision, effectively eliminating the domain gap inherent in previous approaches. Furthermore, we address spatial consistency through a Pseudo-3D Seam Synthesis strategy, which integrates visual data from adjacent cameras during training to explicitly model real-world photometric discrepancies and calibration errors. Experiments demonstrate that VisionNVS achieves superior geometric fidelity and visual quality compared to LiDAR-dependent baselines, offering a robust solution for scalable driving simulation.
Published: 2026-03-18 05:25:38
Authors: Umangi Jain, Vladimir Kim, Matheus Gadelha, Igor Gilitschenski, Zhiqin Chen
Categories: cs.CV
Abstract:
We introduce the problem of material-aware part grouping in untextured meshes. Many real-world shapes, such as scales of pinecones or windows of buildings, contain repeated structures that share the same material but exhibit geometric variations. When assigning materials to such meshes, these repeated parts often require piece-by-piece manual identification and selection, which is tedious and time-consuming. To address this, we propose Material Magic Wand, a tool that allows artists to select part groups based on their estimated material properties -- when one part is selected, our algorithm automatically retrieves all other parts likely to share the same material. The key component of our approach is a part encoder that generates a material-aware embedding for each 3D part, accounting for both local geometry and global context. We train our model with a supervised contrastive loss that brings embeddings of material-consistent parts closer while separating those of different materials; therefore, part grouping can be achieved by retrieving embeddings that are close to the embedding of the selected part. To benchmark this task, we introduce a curated dataset of 100 shapes with 241 part-level queries. We verify the effectiveness of our method through extensive experiments and demonstrate its practical value in an interactive material assignment application.
Published: 2026-03-18 05:19:17
Authors: Zihan Yan, Denan Li, Xin Wu, Zhoulin Liu, Chen Hua, Boyi Situ, Hao Yang, Shengjie Tang, Benrui Tang, Ziyang Wang, Shangzhao Yi, Huan Wang, Dian Huang, Ke Li, Qilin Guo, Zherui Chen, Ke Xu, Yanzhou Wang, Ziliang Wang, Gang Tang, Shi Liu, Zheyong Fan, Yizhou Zhu
Categories: cond-mat.mtrl-sci
Abstract:
Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications.
Published: 2026-03-18 04:40:53
Authors: Chaeyoung Huh, Hyunmin Hwang, Jung Hwan Shin, Jinse Park, Jong Chul Ye
Categories: cs.CL
Abstract:
Drug recommendation requires a deep understanding of individual patient context, especially for complex conditions like Parkinson's disease. While LLMs possess broad medical knowledge, they fail to capture the subtle nuances of actual prescribing patterns. Existing RAG methods also struggle with these complexities because guideline-based retrieval remains too generic and similar-patient retrieval often replicates majority patterns without accounting for the unique clinical nuances of individual patients. To bridge this gap, we propose PACE-RAG (Patient-Aware Contextual and Evidence-based Policy RAG), a novel framework designed to synthesize individual patient context with the prescribing tendencies of similar cases. By analyzing treatment patterns tailored to specific clinical signals, PACE-RAG identifies optimal prescriptions and generates an explainable clinical summary. Evaluated on a Parkinson's cohort and the MIMIC-IV benchmark using Llama-3.1-8B and Qwen3-8B, PACE-RAG achieved state-of-the-art performance, reaching F1 scores of 80.84% and 47.22%, respectively. These results validate PACE-RAG as a robust, clinically grounded solution for personalized decision support. Our code is available at: https://github.com/ChaeYoungHuh/PACE-RAG.
Published: 2026-03-18 04:28:19
Authors: Jianquan Ge, Lixin Xiao
Categories: math.DG
Abstract:
Addressing Yau's conjecture (Problem 117) on $S^4$, we investigate the self-duality of weakly stable Yang-Mills fields under the assumption of irreducibility. For structure groups with a simple Lie algebra, we prove that any weakly stable irreducible connection must be either self-dual or anti-self-dual. Furthermore, we demonstrate that if the Lie algebra admits a non-trivial abelian center, no irreducible Yang-Mills fields can exist over $S^4$.
Published: 2026-03-18 04:13:11
Authors: Jeongbin Jo
Categories: quant-ph
Abstract:
Linear response theory and Green's functions provide a universal framework for understanding how macroscopic and strongly correlated systems respond to weak external perturbations. While the theoretical foundation for non-Hermitian linear response theory has been recently established to describe open quantum systems, generalizing these predictions onto practical quantum computers remains a formidable algorithmic challenge due to the non-unitary nature of the dynamics. In this work, we present a systematic algorithmic mapping that transforms the non-unitary multi-time correlation functions into a unitary form viable for quantum hardware. By mapping the vectorization of the Lindblad master equation into a unitary Schrödinger-like equation using the continuous-variable Schrödingerization technique, we show that generalized non-Hermitian Green's functions can be systematically extracted. This approach bridges the gap between the established physical theory of non-Hermitian linear response and quantum simulation, achieving optimal state preparation cost.
Published: 2026-03-18 03:44:37
Authors: Sreelakshmi N, Saparya Suresh, Sudheesh K. Kattumannil
Categories: stat.ME, math.ST
Abstract:
The Sen index and Sen-Shorrocks-Thon (SST) index are widely used measures of poverty indices. Developing reliable inference for these measures enables us to compare these measures in different populations of interest in an effective way. It is important to construct confidence intervals for the Sen index and SST index, which provide better coverage probability and shorter interval length. Motivated by this, we discuss empirical likelihood (EL) and jackknife empirical likelihood (JEL) based inference for the Sen index. To derive a JEL-based confidence interval for the Sen and SST indices, we propose a new estimator for the Sen index using the theory of U-statistics and examine its properties. The large sample properties of the EL and JEL ratio statistics are studied. We also discuss EL and JEL-based inference for the Sen-Shorrocks-Thon (SST) index. The finite sample performance of the EL and JEL-based confidence intervals of both Sen and SST indices is evaluated through a Monte Carlo simulation study. Finally, we illustrate our methods using individual-level data from the Panel Study of Income Dynamics (PSID) survey from the US as well as Indian household level income data for different states sourced from the Consumer Pyramids Household Survey (CPHS).
Published: 2026-03-18 03:37:13
Authors: Alvin Zhu, Mingzhang Zhu, Beom Jun Kim, Jose Victor S. H. Ramos, Yike Shi, Yufeng Wu, Raayan Dhar, Fuyi Yang, Ruochen Hou, Hanzhang Fang, Quanyou Wang, Yuchen Cui, Dennis W. Hong
Categories: cs.RO
Abstract:
Scaling dexterous robot learning is constrained by the difficulty of collecting high-quality demonstrations across diverse operators. Existing wearable interfaces often trade comfort and cross-user adaptability for kinematic fidelity, while embodiment mismatch between demonstration and deployment requires visual post-processing before policy training. We present DexEXO, a wearability-first hand exoskeleton that aligns visual appearance, contact geometry, and kinematics at the hardware level. DexEXO features a pose-tolerant thumb mechanism and a slider-based finger interface analytically modeled to support hand lengths from 140~mm to 217~mm, reducing operator-specific fitting and enabling scalable cross-operator data collection. A passive hand visually matches the deployed robot, allowing direct policy training from raw wrist-mounted RGB observations. User studies demonstrate improved comfort and usability compared to prior wearable systems. Using visually aligned observations alone, we train diffusion policies that achieve competitive performance while substantially simplifying the end-to-end pipeline. These results show that prioritizing wearability and hardware-level embodiment alignment reduces both human and algorithmic bottlenecks without sacrificing task performance. Project Page: https://dexexo-research.github.io/
Published: 2026-03-18 03:16:41
Authors: Hongbing Li, Jiamin Liu, Shuo Zhang, Bo Xiao
Categories: cs.CV
Abstract:
Grounded Multimodal Named Entity Recognition (GMNER) identifies named entities, including their spans and types, in natural language
text and grounds them to the corresponding regions in associated images. Most existing approaches split this task into two steps:
they first detect objects using a pre-trained general-purpose detector and then match named entities to the detected objects.
However, these methods face a major limitation. Because pre-trained general-purpose object detectors operate independently of textual
entities, they tend to detect common objects and frequently overlook specific fine-grained regions required by named entities. This
misalignment between object detectors and entities introduces imprecision and can impair overall system performance. In this paper,
we propose a proposal-free Query-Guided Network (QGN) that unifies multimodal reasoning and decoding through text guidance and cross-
modal interaction. QGN enables accurate grounding and robust performance in open-domain scenarios. Extensive experiments demonstrate
that QGN achieves top performance among compared GMNER models on widely used benchmarks.
Published: 2026-03-18 03:13:06
Authors: Huan Song, Shuyu Tian, Qingfei Zhao, Wenhao Hong, Jiang Liu, Ting Long, Jiawei Shao, Xuelong Li
Categories: cs.CL
Abstract:
We present Ruyi2.5, a multimodal familial model built on the AI Flow framework. Extending Ruyi2's "Train Once, Deploy Many" paradigm to the multimodal domain, Ruyi2.5 constructs a shared-backbone architecture that co-trains models of varying scales within a single unified pipeline, ensuring semantic consistency across all deployment tiers. Built upon Ruyi2.5, Ruyi2.5-Camera model is developed as a privacy-preserving camera service system, which instantiates Ruyi2.5-Camera into a two-stage recognition pipeline: an edge model applies information-bottleneck-guided irreversible feature mapping to de-identify raw frames at the source, while a cloud model performs deep behavior reasoning. To accelerate reinforcement learning fine-tuning, we further propose Binary Prefix Policy Optimization (BPPO), which reduces sample redundancy via binary response selection and focuses gradient updates on response prefixes, achieving a 2 to 3 times training speedup over GRPO. Experiments show Ruyi2.5 matches Qwen3-VL on the general multimodal benchmarks, while Ruyi2.5-Camera substantially outperforms Qwen3-VL on privacy-constrained surveillance tasks.
Published: 2026-03-18 03:04:49
Authors: Haiyang Yan, Hongyun Zhou, Peng Xu, Xiaoxue Feng, Mengyi Liu
Categories: cs.CV, cs.AI
Abstract:
Despite rapid developments and widespread applications of MLLM agents, they still struggle with long-form video understanding (LVU) tasks, which are characterized by high information density and extended temporal spans. Recent research on LVU agents demonstrates that simple task decomposition and collaboration mechanisms are insufficient for long-chain reasoning tasks. Moreover, directly reducing the time context through embedding-based retrieval may lose key information of complex problems. In this paper, we propose Symphony, a multi-agent system, to alleviate these limitations. By emulating human cognition patterns, Symphony decomposes LVU into fine-grained subtasks and incorporates a deep reasoning collaboration mechanism enhanced by reflection, effectively improving the reasoning capability. Additionally, Symphony provides a VLM-based grounding approach to analyze LVU tasks and assess the relevance of video segments, which significantly enhances the ability to locate complex problems with implicit intentions and large temporal spans. Experimental results show that Symphony achieves state-of-the-art performance on LVBench, LongVideoBench, VideoMME, and MLVU, with a 5.0% improvement over the prior state-of-the-art method on LVBench. Code is available at https://github.com/Haiyang0226/Symphony.
Published: 2026-03-18 03:02:10
Authors: Gexin Zhao
Categories: cs.CL, q-bio.NC
Abstract:
A foundational assumption in linguistics holds that the relationship between a word's sound and its meaning is arbitrary. Accumulating evidence from sound symbolism challenges this view, yet no study has systematically mapped the multidimensional semantic profile of every phonological unit within a language. Here we show that individual letter-phonemes in English carry structured, multidimensional semantic signals. Using a minimal-pair paradigm spanning all 220 pairwise letter contrasts, three large language models independently recover consistent phoneme-meaning associations across nine perceptual dimensions. These associations are systematically predicted by articulatory-phonetic features, with manner and place of articulation mapping onto distinct semantic dimensions. Behavioral data from English speakers confirm these patterns at rates well above chance (80.8%), and preliminary cross-linguistic evidence from five typologically diverse languages suggests that core mappings generalize beyond English. Our findings indicate that sound-meaning iconicity is not an occasional curiosity but a pervasive, structured property of the phonological signal, one so systematic that large language models recover it when given only text input, without exposure to speech or articulation during the task.
Published: 2026-03-18 02:55:02
Authors: Zahin Sufiyan, Shadan Golestan, Yoshihiro Mitsuka, Shotaro Miwa, Osmar Zaiane
Categories: cs.LG
Abstract:
Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retrieval network. Despite their demonstrated efficiency compared to state-of-the-art Reinforcement Learning (RL) algorithms, their practical application in robotic control tasks is constrained by the reliance on pre-training the retrieval network. This dependency poses challenges in dynamic robotic environments, where pre-training data may not be readily available or representative of the current environment. This paper introduces WINFlowNets, a novel CFlowNets framework that enables the co-training of flow and retrieval networks. WINFlowNets begins with a warm-up phase for the retrieval network to bootstrap its policy, followed by a shared training architecture and a shared replay buffer for co-training both networks. Experiments in simulated robotic environments demonstrate that WINFlowNets surpasses CFlowNets and state-of-the-art RL algorithms in terms of average reward and training stability. Furthermore, WINFlowNets exhibits strong adaptive capability in fault environments, making it suitable for tasks that demand quick adaptation with limited sample data. These findings highlight WINFlowNets' potential for deployment in dynamic and malfunction-prone robotic systems, where traditional pre-training or sample inefficient data collection may be impractical.
Published: 2026-03-18 02:49:26
Authors: Yu Tao, Jie Tang, Xuan Wei
Categories: astro-ph.GA
Abstract:
Changing-look active galactic nuclei (CL AGNs) show large changes in luminosity and optical spectral state on time-scales of a few years, and provide a valuable probe of time-dependent accretion in the disc-BLR-torus system. We present a systematic statistical study of their optical variability in a well-defined Type-1 phase, using g- and r-band light curves from the Zwicky Transient Facility for 165 CL AGNs. A subsample of 34 objects also has NEOWISE W1 and W2 light curves, which we use to measure optical-mid-infrared time lags. We use structure functions and a damped random-walk model to characterize variability amplitudes and time-scales on rest-frame scales from tens to a few hundred days, and examine their dependence on black hole mass, luminosity, and Eddington ratio. In the Type-1 phase, the short-time-scale optical variability amplitude on about 30-day time-scales shows little dependence on black hole mass, luminosity, or Eddington ratio. By contrast, the longer-term amplitudes on 150-300 day time-scales, as well as the damped random-walk time-scales, increase slowly with black hole mass and luminosity, but still show no clear dependence on Eddington ratio. The sample shows a ubiquitous bluer-when-brighter trend and larger variability at shorter wavelengths, consistent with continuum variability from a multi-temperature accretion disc. For the NEOWISE subsample, the dust lag-luminosity relation inferred from the optical-mid-infrared lags is similar to that of normal Type-1 AGNs. Overall, CL AGNs in the Type-1 phase behave like normal Type-1 AGNs within the standard disc-BLR-dusty torus framework, but are more prone to large continuum reconfigurations on year-like time-scales.
Published: 2026-03-18 02:44:16
Authors: Hua Chen, Linke Yu, Tuo Wu, Maged Elkashlan, Naofal Al-Dhahir, Merouane Debbah, K. C. Ho
Categories: eess.SP
Abstract:
Current reconfigurable intelligent surface (RIS)-aided near-field (NF) localization methods assume the RIS position is known a priori, and it has limited their practical applicability. This paper applies a hybrid RIS (HRIS) at an unknown position to locate non-line-of-sight (NLOS) NF targets. To this end, we first propose a two-stage gridless localization framework for achieving HRIS self-localization, and then determine the positions of the NF targets. In the first stage, we use the NF Fresnel approximation to convert the signal model into a virtual far-field model through delay-based cross-correlation of centrally symmetric HRIS elements. Such a conversion will naturally extend the aperture of the virtual array. A single-snapshot decoupled atomic norm minimization (DANM) algorithm is then proposed to locate an NF target relative to the HRIS, which includes a two-dimensional (2-D) direction of arrival (DOA) estimation with automatic pairing, the multiple signal classification (MUSIC) method for range estimation, and a total least squares (TLS) method to eliminate the Fresnel approximation error. In the second stage, we leverage the unique capability of HRIS in simultaneous sensing and reflection to estimate the HRIS-to-base station (BS) direction vectors using atomic norm minimization (ANM), and derive the three-dimensional (3-D) HRIS position with two BSs via the least squares (LS)-based geometric triangulation. Furthermore, we propose a semidefinite relaxation (SDR)-based HRIS phase optimization method to enhance the received signal power at the BSs, thereby improving the HRIS localization accuracy, which, in turn, enhances NF target positionings. The Cramer-Rao bound (CRB) for the NF target parameters and the position error bound (PEB) for the HRIS coordinates are derived as performance benchmarks.
Published: 2026-03-18 02:40:54
Authors: Jin Xie, Songze Li, Guang Cheng
Categories: cs.CR
Abstract:
Retrieval-Augmented Generation (RAG) systems introduce a critical vulnerability: contextual leakage, where adversaries exploit instruction-following to exfiltrate Personally Identifiable Information (PII) via adaptive extraction. Current defenses force a rigid trade-off between semantic utility and latency. We present SEAL-Tag, a privacy-preserving runtime environment that resolves this via a Verify-then-Route paradigm. SEAL-Tag introduces the SEAL-Probe protocol, transforming auditing into a structured tool-use operation where the model generates a verifiable PII-Evidence Table (PET) alongside its draft. To adjudicate this evidence, we employ a Probabilistic Circuit (PC) that enforces verifiable logical constraints for robust decision-making. To overcome the privacy "Cold Start" problem, we introduce the S0--S6 Anchored Synthesis Pipeline, generating high-fidelity, provenanced RAG interactions. We pair this with a Two-Stage Curriculum that first optimizes for entity detection before aligning the model to the rigorous audit protocol. Our evaluation demonstrates that SEAL-Tag establishes a new Pareto frontier, reducing adaptive leakage by over 8$\times$ while matching the utility and speed of unsafe baselines.
Published: 2026-03-18 02:09:12
Authors: Cindy Tsang
Categories: math.GR, math.QA
Abstract:
It is a simple fact that a group has a trivial automorphism group if and only if it is of order $1$ or $2$. We prove that the same holds for certain families of skew braces, and given any odd prime $p$, we construct a skew brace of order $2p^3$ that has a trivial automorphism group.
Published: 2026-03-18 01:54:32
Authors: Abhinava Chatterjee, Chao-Xing Liu
Categories: cond-mat.mes-hall
Abstract:
The surface phonon Hall viscosity (PHV)-an acoustic analog of axion electrodynamics-emerges from the strain response of magnetic topological insulators and gives rise to novel acoustic phenomena. In this work, we propose a previously unexplored effect: a phonon polarization-filter mechanism induced by the surface PHV, which generates an interface phonon mode with its frequency below the bulk mode frequency. This interface mode possesses a specific circular polarization and therefore acts as a polarization filter, confining only phonons with the matching polarization at the interface. Magnetic topological insulators can thus selectively transmit one type of circularly polarized phonon mode, enabling the manipulation of phonon polarization and angular momentum. In addition, we further develop a generalized scattering framework to study the effect of an injected acoustic wave from a trivial insulator to a magnetic topological insulator with both normal and oblique incidence, and discuss the phenomena of surface acoustic Faraday rotation and longitudinal-transverse mode conversion. Our results establish surface Hall viscosity as a powerful mechanism for engineering axial phonon states and open new avenues for topological phononic devices based on phonon angular momentum.
Published: 2026-03-18 01:51:21
Authors: Hengrui Luo, Xiaoye S. Li, Yang Liu, Marcus Noack, Ji Qiang, Mark D. Risser
Categories: stat.ME, cs.LG
Abstract:
Gaussian process (GP) regression is widely used for uncertainty quantification, yet the standard formulation assumes noise-free covariates. When inputs are measured with error, this errors-in-variables (EIV) setting can lead to optimistically narrow posterior intervals and biased decisions. We study GP regression under input measurement uncertainty by representing each noisy input as a probability measure and defining covariance through Wasserstein distances between these measures. Building on this perspective, we instantiate a deterministic projected Wasserstein ARD (PWA) kernel whose one-dimensional components admit closed-form expressions and whose product structure yields a scalable, positive-definite kernel on distributions. Unlike latent-input GP models, PWA-based GPs (\PWAGPs) handle input noise without introducing unobserved covariates or Monte Carlo projections, making uncertainty quantification more transparent and robust.
Published: 2026-03-18 01:49:58
Authors: Athul S. Rema, Adrián E. Rubio López, Felipe Herrera
Categories: quant-ph, cond-mat.mes-hall, physics.chem-ph
Abstract:
Quantum emitters near the surface of silver nanoparticles undergo Rabi oscillations in electronic population dynamics due to strong coupling with near-field multipole modes that are not radiative. Low-frequency nanoparticle dipole modes are radiative but do not couple strong enough to quantum emitters. These features limit the observation of strong coupling. Using macroscopic quantum electrodynamics theory within a Lorentzian pseudo-mode approximation for the non-Markovian interaction kernel, we demonstrate that by coating spherical silver nanoparticles with a thin molecular J-aggregate layer, the resulting core-shell plexciton resonance restructures the local electromagnetic vacuum at dipole-mode frequencies to enable Rabi oscillations for quantum emitters that otherwise would only undergo exponential population decay. Specifically, we show for quantum dot emitters in the near field of silver nanospheres of 20 nm radius, that weak-to-strong coupling crossovers can be induced using 2 nm J-aggregate shells. Our work demonstrates the potential of molecular aggregates to enable deep sub-wavelength structuring of the vacuum field for the observation of coherent quantum dynamics in optical nanocavities.
Published: 2026-03-18 01:49:57
Authors: Shuang Miao, Siqi Ren, Zhifei Zhang
Categories: math.AP
Abstract:
In this paper, we establish the asymptotic linear stability of a class of Coriolis-driven columnar vortices for the 3-D axisymmetric Euler equations. This result represents a critical step toward proving the nonlinear asymptotic stability of such vortices. The key and widely applicable strategy is to construct a distorted Fourier basis, which is achieved by solving a two-parameter $(c, ξ)$-dependent Schrödinger equation associated with the linearized operator of the system. To capture the precise asymptotic behavior of the solution, we decompose the $c-ξ$ plane into distinct regions, with the partitioning guided by the leading-order profiles of the Schrödinger equation across different parameter regimes.
Published: 2026-03-18 01:42:46
Authors: Ronald L. Kam, Shilong Wang, Gerbrand Ceder
Categories: cond-mat.mtrl-sci
Abstract:
Disordered rock-salt with Li-excess (DRX) cathode phases within the Li-Mn-Ti-O (LMTO) composition space have recently been extensively studied, as they promise to deliver exceptional energy density at low cost in Li-ion batteries. The continued development of LMTO DRX with improved power density and cycling stability requires optimization of the composition and particle size/morphology, which are determined by synthesis conditions such as annealing temperatures and hold times. These challenges motivate our investigation of the phase diagram of the LMTO rock-salt phase space, with a focus on understanding the stability of DRX by quantifying the order-disorder transition temperature ($T_\text{disord}$) as a function of composition. We harness first-principles calculations and X-ray diffraction experiments to establish the LMTO phase diagram, which lies within the LiMnO$_2$ -- Li$_2$MnO$_3$ -- Li$_2$TiO$_3$ pseudo-ternary. Our calculations predict that the LMTO phase diagram at elevated temperature ($700 - 1300$ C) is composed of three phases: DRX, orthorhombic LiMnO$_2$, and layered Li$_2$Mn$_\text{1-y}$Ti$_\text{y}$O$_3$ ($0 < \text{y} < 1$). $T_\text{disord}$ decreases significantly as off-stoichiometry is introduced to the end-point compositions, resulting in a eutectoid phase diagram. Importantly, a significant range of LMTO compositions containing small to moderate fractions of Li-excess and Ti doping (relative to LiMnO$_2$) have $T_\text{disord}$ spanning $700 - 900$ C. These temperatures are substantially lower than conventional DRX synthesis temperatures ($\geq 1000$ C), suggesting the promise of decreasing synthesis temperatures for specific DRX compositions. The compositions containing moderate to high fractions of Mn$^{4+}$ instead have much greater $T_\text{disord}$ and phase separation to layered Li$_2$MnO$_3$ becomes highly favored.