Published: 2026-05-07 10:31:12
Authors: Yuntai Bao, Qinfeng Li, Xinyan Yu, Xuhong Zhang, Ge Su, Wenqi Zhang, Liu Yan, Haiqin Weng, Jianwei Yin
Categories: cs.LG
Abstract:
Recently, steering vectors (SVs) have emerged as an effective and lightweight approach to steer behaviors of large language models (LLMs), among which fine-tuned SVs are more effective than optimization-free ones. However, current approaches to fine-tuned SVs suffer from two limitations. First, they require careful selection of steering factors on a per-SV basis to balance steering effectiveness and generation quality at inference time. Second, they operate as full-sequence SVs (FSSVs), which can sacrifice generation quality regardless of factor selection due to excessive intervention on the model generation process. To address the first limitation, we propose joint training of steering factors and directions, such that post-hoc factor selection is no longer required. Using neural network scaling theory, we find that moderately large initialization sizes and learning rates for steering factors are essential for stability and efficiency of joint training. To tackle the second limitation, we draw inspiration from representation fine-tuning and introduce Prompt-only SV (PrOSV), an SV that intervenes only on a few prompt tokens. Our empirical results show that PrOSV outperforms traditional FSSVs on AxBench when using our joint training scheme. We also find that PrOSV achieves a better tradeoff between general model utility and adversarial robustness than FSSV.
Published: 2026-05-06 17:59:42
Authors: Xiaoyu Wu, Yifei Wang, Tsu-Jui Fu, Liang-Chieh Chen, Zhe Gan, Chen Wei
Categories: cs.CV, cs.AI, cs.LG
Abstract:
We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small number of high-norm tokens that attract disproportionate attention while carrying limited local information, but their role in generative models remains underexplored. We show that this phenomenon appears in both the encoder and denoiser of modern Representation Autoencoder (RAE)-DiT pipelines: pretrained ViT encoders can produce outlier representations, and DiTs themselves can develop internal outlier tokens, especially in intermediate layers. Moreover, simply masking high-norm tokens does not improve performance, indicating that the problem is not only caused by a few extreme values, but is more closely related to corrupted local patch semantics. To address this issue, we introduce Dual-Stage Registers (DSR), a register-based intervention for both components: trained registers when available, recursive test-time registers otherwise, and diffusion registers for the denoiser. Across ImageNet and large-scale text-to-image generation, these interventions consistently reduce outlier artifacts and improve generation quality. Our results highlight outlier-token control as an important ingredient in building stronger DiTs.
Published: 2026-05-06 17:56:19
Authors: Ivan Dutta, Aayush Vijayvargia, Anamitra Mukherjee, Onur Erten, Kush Saha
Categories: cond-mat.str-el
Abstract:
Spin-orbital generalization of Kitaev model provides a robust extension to the original Kitaev model. However, real materials often exhibit competing interactions that break exact solvability which can give rise to new phases. Motivated by recent microscopic proposals of coexisting Yao-Lee and Kitaev couplings, we investigate the fate of the ground state when two independent exactly solvable spin liquid Hamiltonians each originally formulated on different lattice geometries are combined on a common lattice environment. We first focus on the hybrid Kitaev's honeycomb and square-lattice model. Using self-consistent mean-field analysis and perturbative calculation, we show that the strong-Kitaev regime yields magnetic order in the spin sector, while the orbital sector retains its topological order. We further analyze the hybridization of the Yao-Lee and square-lattice models and find that the model exhibits a rich evolution of Majorana Dirac bands and Lifshitz transitions. Remarkably, when the Yao-Lee and square-lattice couplings are equal and opposite, the model restores its exact solvability with a single itinerant Majorana flavor. These results demonstrate that hybrid spin liquid platforms may host various emergent phases beyond conventional exactly solvable limits.
Published: 2026-05-06 17:55:30
Authors: Benedikt Remlein, Massimiliano Esposito
Categories: cond-mat.stat-mech
Abstract:
Hopf bifurcations are a universal route to self-sustained oscillations in driven systems. Despite the absence of any singular stationary state, we show that time-averaged observables generically exhibit singularities at the onset of oscillations. The origin of this behavior is geometric: phase averaging over the emergent periodic attractor eliminates odd powers of the oscillation amplitude, while the squared amplitude varies smoothly with the distance from the bifurcation. Consequently, the excess of any smooth time-averaged observable admits an integer-power expansion; observables remain finite but display discontinuities in finite-order derivatives. This yields an Ehrenfest-like hierarchy of Hopf singularities, in which the first nonanalytic derivative is determined by the lowest-order coupling between the observable and the limit-cycle waveform that survives phase averaging. Generic observables therefore exhibit kink singularities, while symmetry or geometric cancellations can suppress lower-order couplings and shift nonanalyticity to higher derivatives. We demonstrate this mechanism in chemical, electronic, and climate oscillators. Our results identify supercritical Hopf bifurcations as a universal mechanism for nonanalytic observable behavior, where singular features arise without any underlying singular stationary state. They thus provide a generic setting for singular behavior without divergence.
Published: 2026-05-06 17:55:15
Authors: Paata Ivanisvili, Xinyuan Xie
Categories: math.PR, cs.AI, math.AP, math.CA, math.FA
Abstract:
In this note, we report five mathematical discoveries made in collaboration with Grok, all of which have been subsequently verified by the authors. These include an improved lower bound on the maximal Gaussian perimeter of convex sets in $\mathbb{R}^n$, sharper $L_2$-$L_1$ moment comparison inequalities on the Hamming cube $\{-1,1\}^n$, a strengthened autoconvolution inequality, improved asymptotic bounds on the size of the largest $g$-Sidon sets in $\{1,\dots,n\}$, and an optimal balanced Szarek's inequality.
Published: 2026-05-06 17:54:16
Authors: Yijun Lu, Rui Ye, Yuwen Du, Jiajun Wang, Songhua Liu, Siheng Chen
Categories: cs.AI
Abstract:
Long-horizon search agents must manage a rapidly growing working context as they reason, call tools, and observe information. Naively accumulating all intermediate content can overwhelm the agent, increasing costs and the risk of errors. We propose that effective context management should be adaptive: parts of the agent's trajectory are maintained at different levels of detail depending on their current relevance to the task. To operationalize this principle, we introduce Context-ReAct, a general agentic paradigm for elastic context orchestration that integrates reasoning, context management, and tool use in a unified loop. Context-ReAct provides five atomic operations: Skip, Compress, Rollback, Snippet and Delete, which allow the agent to dynamically reshape its working context, preserving important evidence, summarizing resolved information, discarding unhelpful branches, and controlling context size. We prove that the Compress operator is expressively complete, while the other specialized operators provide efficiency and fidelity guarantees that reduce generation cost and hallucination risk. Building on this paradigm, we develop LongSeeker, a long-horizon search agent fine-tuned from Qwen3-30B-A3B on 10k synthesized trajectories. Across four representative search benchmarks, LongSeeker achieves 61.5% on BrowseComp and 62.5% on BrowseComp-ZH, substantially outperforming Tongyi DeepResearch (43.2% and 46.7%) and AgentFold (36.2% and 47.3%). These results highlight the potential of adaptive context management, showing that agents can achieve more reliable and efficient long-horizon reasoning by actively shaping their working memory.
Published: 2026-05-06 17:52:39
Authors: Wei Luo, Yiting Lu, Xin Li, Haoran Li, Fengbin Guan, Chen Gao, Xin Jin, Yong Li, Zhibo Chen, Sijing Wu, Kang Fu, Yunhao Li, Ziang Xiao, Huiyu Duan, Jing Liu, Qiang Hu, Xiongkuo Min, Guangtao Zhai, Manxi Sun, Zixuan Guo, Yun Li, Ziyang Chen, Manabu Tsukada, Zhengyang Li, Zhenglin Du, Yi Wen, Licheng Jiao, Fang Liu, Lingling Li, Yiwen Ren, Zhilong Song, Dubing Chen, Yucheng Zhou, Tianyi Yan, Huan Zheng
Categories: cs.CV
Abstract:
This paper reports on the LoViF 2026 PhyScore challenge, a competition on holistic quality assessment of world-model-generated videos across both 2D and 4D generation settings. The challenge is motivated by a central gap in current evaluation practice: perceptual quality alone is insufficient to judge whether generated dynamics are physically plausible, temporally coherent, and consistent with input conditions. Participants are required to build a metric that jointly predicts four dimensions, i.e., Video Quality, Physical Realism, Condition-Video Alignment, and Temporal Consistency. Depart from that, participants also need to localize physical anomaly timestamps for fine-grained diagnosis.
The benchmark dataset contains 1,554 videos generated by seven representative world generative models, organized into three tracks (text-2D, image-to-4D, and video-to-4D) and spanning 26 categories. These categories explicitly cover physics-relevant scenarios, including dynamics, optics, and thermodynamics, together with diverse real-world and creative content. To ensure label reliability, scores and anomaly timestamps are produced through trained human annotation with an additional automated quality-control pass.
Evaluation is based on both score prediction and anomaly localization, with a composite protocol that combines TimeStamp_IOU and SRCC/PLCC. This report summarizes the challenge design and provides method-level insights from submitted solutions.
Published: 2026-05-06 17:49:17
Authors: Himanshu Paudel, Basanta Joshi, Dhirendra Raj Madai, Alina Bartaula, Biman Rimal, Sanjay Neupane
Categories: cs.RO, eess.SY
Abstract:
We present a dual-barrier control barrier function (CBF) safety filter for real-time, safety-critical velocity control of holonomic robots operating in incrementally built occupancy grid maps. As a robot explores an unknown environment, unmapped regions introduce irreducible uncertainty, since obstacle geometry beyond the explored frontier is unknown, making entry into such regions a source of collision risk, especially with front-facing sensors. To address this, we enforce two constraints: avoidance of mapped obstacles and restriction from unexplored regions. Both constraints are derived analytically from the occupancy grid's signed distance field, yielding a closed-form safety filter that requires only a small linear system solve per cycle. On resource-constrained platforms such as the Raspberry Pi, where SLAM and planning already consume significant compute, the low overhead of the proposed filter preserves resources. An adaptive gain schedule relaxes the frontier constraint in information-rich regions and tightens it in well-mapped areas, improving exploration efficiency while maintaining safety. The filter operates in velocity space as a minimally invasive correction and composes with arbitrary nominal controllers, including learning-based methods. Hardware flight experiments on a PX4-controlled quadrotor demonstrate zero collisions across multiple indoor runs.
Published: 2026-05-06 17:44:15
Authors: Tomás Ortín
Categories: hep-th, gr-qc
Abstract:
We consider the supersymmetric solutions of several supergravity theories (ungauged $\mathcal{N}=2,d=4$ and $\mathcal{N}=1,d=5$ supergravities coupled to vector supermultiplets and pure $\mathcal{N}=1,d=10$ supergravity) and show that their generalized Komar charges vanish identically for the supersymmetric Killing vector which is constructed as a bilinear of the Killing spinors of those solutions. This property can be used to prove the supersymmetry (``BPS'') bounds satisfied by some of those solutions in a rigorous, coordinate-independent way.
Published: 2026-05-06 17:42:07
Authors: Alexander Hsu, Zhaiming Shen, Wenjing Liao, Rongjie Lai
Categories: cs.LG, math.NA
Abstract:
Pre-trained transformers are able to learn from examples provided as part of the prompt without any weight updates, a remarkable ability known as in-context learning (ICL). Despite its demonstrated efficacy across various domains, the theoretical understanding of ICL is still developing. Whereas most existing theory has focused on linear models, we study ICL in the nonlinear regression setting. Through the interaction mechanism in attention, we explicitly construct transformer networks to realize nonlinear features, such as polynomial or spline bases, which span a wide class of functions. Based on this construction, we establish a framework to analyze end-to-end in-context nonlinear regression with the constructed features. Our theory provides finite-sample generalization error bounds in terms of context length and training set size. We numerically validate the theory on synthetic regression tasks.
Published: 2026-05-06 17:42:01
Authors: Perry E. Radau
Categories: eess.IV, cs.CL, physics.med-ph
Abstract:
Background: Existing MRI LLM benchmarks rely mainly on review-book multiple-choice questions, where top proprietary models already score highly, limiting discrimination. No systematic benchmark has evaluated vendor-specific scanner operational knowledge central to research MRI practice. Purpose: We developed MRI-Eval, a tiered benchmark for relative model comparison on MRI physics and GE scanner operations knowledge using primary multiple-choice questions (MCQ), with stem-only and primed diagnostic conditions as complementary analyses. Methods: MRI-Eval includes 1365 scored items across nine categories and three difficulty tiers from textbooks, GE scanner manuals, programming course materials, and expert-generated questions. Five model families were evaluated (GPT-5.4, Claude Opus 4.6, Claude Sonnet 4.6, Gemini 2.5 Pro, Llama 3.3 70B). MCQ was primary; stem-only removed options and used an independent LLM judge; primed stem-only tested responses to incorrect user claims. Results: Overall MCQ accuracy was 93.2% to 97.1%. GE scanner operations was the lowest category for every model (88.2% to 94.6%). In stem-only, frontier-model accuracy fell to 58.4% to 61.1%, and Llama 3.3 70B fell to 37.1%; GE scanner operations stem-only accuracy was 13.8% to 29.8%. Conclusion: High MCQ performance can mask weak free-text recall, especially for vendor-specific operational knowledge. MRI-Eval is most informative as a relative comparison benchmark rather than an absolute competency measure and supports caution in using raw LLM outputs for GE-specific protocol guidance.
Published: 2026-05-06 17:40:11
Authors: Lakshita Dodeja, Ondrej Biza, Shivam Vats, Stephen Hart, Stefanie Tellex, Robin Walters, Karl Schmeckpeper, Thomas Weng
Categories: cs.RO, cs.AI
Abstract:
Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning. However, BC lacks a self-guided mechanism for online improvement after demonstrations have been collected. Existing offline-to-online learning methods often cause policies to replace previously learned good actions due to a distribution mismatch between offline data and online learning. In this work, we propose Q2RL, Q-Estimation and Q-Gating from BC for Reinforcement Learning, an algorithm for efficient offline-to-online learning. Our method consists of two parts: (1) Q-Estimation extracts a Q-function from a BC policy using a few interaction steps with the environment, followed by online RL with (2) Q-Gating, which switches between BC and RL policy actions based on their respective Q-values to collect samples for RL policy training. Across manipulation tasks from D4RL and robomimic benchmarks, Q2RL outperforms SOTA offline-to-online learning baselines on success rate and time to convergence. Q2RL is efficient enough to be applied in an on-robot RL setting, learning robust policies for contact-rich and high precision manipulation tasks such as pipe assembly and kitting, in 1-2 hours of online interaction, achieving success rates of up to 100% and up to 3.75x improvement against the original BC policy. Code and video are available at https://pages.rai-inst.com/q2rl_website/
Published: 2026-05-06 17:34:42
Authors: Zakaria Dahbi, Amelle Zair
Categories: quant-ph
Abstract:
Absolutely Maximally Entangled (AME) states are important resources in quantum information processing; however, a general systematic approach for constructing these states remains a formidable challenge. We identify a finite-field rank structure underlying multipartite entanglement in a class of quadratic-phase quantum states defined by symmetric matrices over $\mathbb{F}_p$. We prove an exact Rank-Purity Duality: the Rényi-2 purity of any subsystem is determined solely by the rank of the phase matrix. Within this ansatz, the existence of an AME state is equivalent to the existence of a generating phase matrix $P$ whose bipartition submatrices are of full rank, reducing the condition for maximal entanglement to a rank constraint on $P$. This establishes a direct correspondence between entanglement and cut-rank geometry in finite-field matrices. Furthermore, for square-free local dimensions, we show that the entanglement structure factorises via the Chinese Remainder Theorem into independent prime-field contributions, yielding an exact additive decomposition of Rényi-2 entropies. These results provide an algebraic characterisation of entanglement in the quadratic phase formalism and enable the systematic construction of highly entangled states.
Published: 2026-05-06 17:33:23
Authors: Yunhan Yang, Chunshi Wang, Junliang Ye, Yang Li, Zanxin Chen, Zehuan Huang, Yao Mu, Zhuo Chen, Chunchao Guo, Xihui Liu
Categories: cs.CV
Abstract:
Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlooking the functional properties essential for interaction. We propose that interactive asset generation must be rooted in functional logic and hierarchical physics. To bridge this gap, we introduce PhysForge, a decoupled two-stage framework supported by PhysDB, a large-scale dataset of 150,000 assets with four-tier physical annotations. First, a VLM acts as a "physical architect" to plan a "Hierarchical Physical Blueprint" defining material, functional, and kinematic constraints. Second, a physics-grounded diffusion model realizes this blueprint by synthesizing high-fidelity geometry alongside precise kinematic parameters via a novel KineVoxel Injection (KVI) mechanism. Experiments demonstrate that PhysForge produces functionally plausible, simulation-ready assets, providing a robust data engine for interactive 3D content and embodied agents.
Published: 2026-05-06 17:32:49
Authors: Cătălin Paşcu Moca, Doru Sticlet, Ovidiu I. Pâţu, Balazs Dóra
Categories: cond-mat.str-el
Abstract:
We study dynamical spin correlations in a dissipative XXZ spin chain subject to uniform local spin-loss and pumping. Starting from a mixed steady state that is featureless albeit possessing finite magnetization, rich dynamics emerges in time-dependent two-point correlators evaluated on top of it. For unitary evolution in which the reservoir is absent, the longitudinal correlators reproduce the established hierarchy of spin-transport universality classes - ballistic, Kardar-Parisi-Zhang (KPZ) superdiffusive, and diffusive - across the phase diagram. However, for finite magnetization, additional ballistic light cone propagation gets superimposed on the previous universality classes, arising from magnon propagation.The transverse correlator displays very fast, exponential decay of correlations without wavefront propagation in the easy-plane case. At the isotropic point, it follows KPZ scaling due to $SU(2)$ symmetry, while in the easy-axis regime, it is characterized by ballistic spreading of correlations. Under full Lindbladian dynamics, the universality classes are preserved at early times, while the correlations acquire an overall exponential damping in the long-time limit. In terms of methods, we have used vectorized TEBD for numerical simulations and exact analytical results obtained via a Pfaffian representation and the third-quantization framework for the noninteracting XX case.
Published: 2026-05-06 17:28:54
Authors: P. D. Taylor-Burdett, C. A. Burhan, S. Mason, F. R. Lebrun-Gallagher, S. Weidt, W. K. Hensinger
Categories: quant-ph
Abstract:
Across many areas of science, there is a need to generate magnetic fields that are both ultra-stable and switchable on and off. While permanent and superconducting magnets offer exceptionally low-noise fields, they are not readily switchable. Conversely, electromagnets are switchable but are susceptible to current noise. We present a hybrid technique to switch the field of any arbitrary magnet through use of a non-linear ferromagnetic circuit, named the Saturable Electronic Reluctance Switch (SERS). The circuit achieves bi-stable switching of the field by applying a current above a given threshold, akin to a transistor for magnetic fields. Crucially, the applied current has minimal influence on the magnetic field output and demagnetisation of the magnet is avoided, drastically reducing power dissipation. SERS is also robust to fabrication errors, suppressing noise in the control current by several orders of magnitude in a non-ideal device. To illustrate its application, a SERS-driven device is proposed for generating ultra-stable magnetic field gradients in a scalable trapped-ion quantum computer. We find this device offers an order of magnitude reduction in power dissipation compared to state-of-the-art current carrying wires, while reducing magnetic field noise originating from current fluctuations by up to five orders of magnitude.
Published: 2026-05-06 17:25:54
Authors: Mikael Brudfors
Categories: eess.IV
Abstract:
This paper presents and validates CTseg, a freely available software for brain CT segmentation, spatial normalisation, and volumetrics. CTseg builds on the Multi-Brain generative modelling framework, providing a CT-specific pipeline that produces tissue maps, deformation fields, and brain volume estimates in the same format as SPM's unified segmentation, thereby extending SPM's established analysis chain from MRI to CT. CTseg is designed for routine hospital CT scans without requiring preprocessing or resampling in deployment. Although CTseg has been adopted in clinical research spanning, among other things, stroke, dementia, and brain morphometry, a systematic validation against an independent reference standard has been lacking. Using paired MR/CT head scans, we evaluate CTseg across four dimensions: segmentation accuracy against an MRI-derived silver standard; spatial normalisation consistency through group-average sharpness and voxelwise coefficient of variation; brain volume agreement via intraclass correlation and Bland-Altman analysis; and downstream sex classification performance from normalised tissue maps. As a baseline, we apply SPM's MRI-based unified segmentation directly to the CT images. CTseg significantly outperformed this baseline for segmentation and normalisation, showed stronger TBV agreement, and achieved comparable TIV agreement. CTseg is freely available at https://github.com/WCHN/CTseg, and all experiment code is included in the repository for full reproducibility.
Published: 2026-05-06 16:55:58
Authors: Erhan Bayraktar
Categories: econ.GN, math.OC
Abstract:
Automation raises productivity and reduces paid human labor, but it also reallocates income and ownership claims. This paper studies that tradeoff in a static benchmark and in a stationary heterogeneous-agent general equilibrium. Firms choose automation from a profit function. Households differ by skill and wealth, save in a capital/equity claim, and face incomplete insurance. Wages and returns are determined by market clearing from a Cobb--Douglas final-good firm, while the wealth distribution is pinned down by a Hamilton--Jacobi--Bellman (HJB) equation and a Kolmogorov forward equation (KFE). The paper is deliberately two-sided. With strong productivity growth, high-skill complementarity, low obsolescence, and broad ownership, automation raises output, capital, and consumption. With strong exposure of low-wealth, high-marginal-propensity-to-consume (high-MPC) households and concentrated ownership, privately chosen automation can be excessive even though it raises high-skilled labor income. The central object is the derivative of household consumption demand and collective wage bill with respect to automation. Fiscal policy is modeled as a government problem rather than as an abstract planner: a tax changes the firm's automation first-order condition, raises revenue only on the remaining automation base, and must specify rebates and administrative losses.
Published: 2026-05-06 16:44:27
Authors: Parameshwar R. Pasnoori
Categories: math-ph, cond-mat.str-el, hep-th
Abstract:
In this work we consider the time-dependent $SU(2)$ Gross-Neveu model, which is a quantum field theory of fermions which interact with each other through spin exchange interaction with time-dependent coupling strength $g(t)$. Using the recently formulated generalized Bethe ansatz framework, we show that the system is integrable provided the time-dependent coupling strength is such that its trajectories in time are exactly same as that of the renormalization group (RG) flow equations corresponding to the static model, where time `$t$' of the time-dependent model is identified with the logarithm of the cutoff `$\ln Λ$' of the static model. In the scaling regime $Λ\rightarrow\infty$, the above relation between time and the logarithm of the cutoff provides a characteristic time scale $t_0$. We analyze the exact time-dependent wavefunction in the case of coupling strength decreasing with time and show that in the adiabatic regime, which corresponds to $t\sim t_0$ for drive rate $α_0=1$, the system exhibits a time-dependent dynamical dimensional transmutation where a time dependent mass gap is generated, which at time $t=t_0+Δt$ is given by $m(Δt)=m_0 e^{-πα_0Δt}$, where $m_0=Λe^{-πα_0 t_0}$. Comparing this with the mass gap of the static model, we identify the adiabatic regime of the time-dependent model with the scaling regime of the static model. In the case of very large time scales $t\gg t_0$ for drive rate $α_0$ or for very fast drive rates $α$ such that $αt \gg α_0t_0$, for any $t
Published: 2026-05-06 16:39:44
Authors: Jennifer T. Bui, Dominic Groß
Categories: eess.SY
Abstract:
This paper proposes dynamic stability and performance conditions for grid-connected inverter-based resources (IBRs). To this end, we extend the notion of steady-state droop coefficients to dynamic droop coefficients to capture the small-signal dynamics of IBRs and synchronous generators (SGs). Notably, the dynamic droop coefficients can be obtained from input-output data collected at the unit's (e.g., IBR or SG) point of interconnection without requiring prior knowledge of IBR internals or controls structure. To obtain frequency stability conditions, this IBR model is combined with a lightweight dynamic transmission network model that accounts for uncertainty of line dynamics. The resulting stability conditions are highly scalable and, given a few key network parameters, can be verified at the unit level. To make the conditions practical and offer intuitive and illustrative interpretations, we map the frequency stability conditions to bounds on the Bode plot of the dynamic droop coefficient for two broad types of IBR responses. Moreover, our specifications on the dynamic droop coefficient (i) translate basic frequency control ancillary services into verifiable requirements, and (ii) provide insights into the much-debated question of how to certify an IBR as grid-forming (GFM). The results are illustrated using dynamic droop coefficients obtained using detailed simulations of GFM and GFL IBRs as well as SGs.
Published: 2026-05-06 16:39:22
Authors: Gerald Ogbonna, David Bindel, Lindsay C. Anderson
Categories: eess.SY, math.DS
Abstract:
The reliable operation of large-scale electric power networks is increasingly challenging, particularly with the integration of stochastic renewable generation. In this work, we address the problem of minimizing network transients by optimally modifying the underlying network. We formulate the problem in terms of graph Laplacian matrices and show that, under certain assumptions, the problem is convex. We derive a linear matrix inequality whose feasibility guarantees the existence and uniqueness of phase cohesive steady-state angles; this condition can be directly incorporated as a convex constraint in the optimization framework and we provide several geometric interpretations of the optimization problem. The proposed method is validated on the IEEE 30-bus test system, where results demonstrate that our approach effectively identifies critical links on the network. Dynamic simulations show a significant reduction in network transients and overall improvements across several performance metrics. We explore the sparsity-optimality trade-off using a reweighted $\ell_1$ heuristic.
Published: 2026-05-06 16:31:50
Authors: Nian Hong Zhou
Categories: math.NT, math.CA, math.CO
Abstract:
We establish new transformation formulas involving theta functions for certain bilateral basic hypergeometric series. From these, we construct companion $q$-series for a class of $q$-series such that the asymptotic expansion of their quotient admits a simple closed form. This allows us to prove some conjectures of McIntosh on asymptotic transformations of $q$-series.
Published: 2026-05-06 16:31:20
Authors: Jia Wei He, R. Ayesha Ali, Gerarda Darlington
Categories: stat.ML, cs.LG, stat.CO, stat.ME
Abstract:
Regularization is often used in high-dimensional regression settings to generate a sparse model, which can save tremendous computing resources and identify predictors that are most strongly associated with the response. When the predictors can be represented by a Gaussian graphical model, the structure of the predictor graph can be exploited during regularization. Our proposed model exploits this underlying predictor graph structure by decomposing the estimated coefficient vector into a sum of latent variables that correspond to the sum of each node contribution to the coefficient vector. Regularization is then performed on the latent variables rather than on the coefficient vector directly. We use a penalty function that permits a clear user-defined trade-off between the L1 and L2 penalties and propose a novel proximal projection during optimization. Further, our implementation computes the projection operator for the intersection of selected groups, which conserves more computing resources compared to predictor duplication methods, especially for high-dimensional data. Through simulation, we evaluate the performance of our approach under different graph structures and node counts, and present results on real-world data. Results suggest that our method exhibits stable performance relative to other singly or doubly sparse graphical regression models.
Published: 2026-05-06 16:30:48
Authors: Haozhuang Chi, Daosheng Qiu, Hao Su, Haochen Liu, Zirui Li, Haoruo Zhang, Chen Lv
Categories: cs.RO, cs.AI, cs.CV
Abstract:
Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driving world models forecast the external environment, in-cabin intelligence remains strictly recognition-oriented and lacks multi-step rollout capabilities for driver dynamics. We introduce Driver-WM, a driver-centric latent world model that rolls out in-cabin dynamics causally conditioned on out-cabin traffic context. This formulation unifies physical kinematics forecasting with auxiliary behavioral and emotional semantic recognition. Operating in a compact latent space constructed from frozen vision-language features, Driver-WM adopts a dual-stream architecture to separately encode external traffic and internal driver states. These streams are directionally coupled via a gated causal injection mechanism, which uses a learned vector gate to modulate external contextual perturbations while strictly enforcing temporal causality. Evaluations on a multi-task assistive driving benchmark demonstrate that Driver-WM yields robust long-horizon geometric forecasting for reactive high-motion maneuvers and improves semantic alignment for both driver and traffic states. Finally, the explicit external-to-internal conditioning allows for controlled test-time interventions to systematically analyze mechanism responses.
Published: 2026-05-06 16:19:58
Authors: Abdessamie Chhieb, Mostafa Mansour, Mohamed Ouchrif
Categories: hep-th
Abstract:
Our work explore the time evolution of entanglement, local quantum uncertainty, and correlated coherence, within a system modeled by two double quantum dots. The dynamics is represented using a time-fractional Schrödinger equation, which includes memory effects in a non-Markovian regime. We vary the fractional parameter $τ$, the tunneling amplitudes $δ_A$ and $δ_B$, as well as the inter-dot interaction strength $\mathcal{V}$, to investigate how these key parameters govern the generation, stabilization, and decay of quantum resources within the system. The obtained results reveal that, for both initial states, fractional dynamics with a low $τ$ rapidly generates entanglement expecting maximal values $\mathcal{LN}\approx 1$ and non-classical correlations quantified by local quantum uncertainty. Conversely, higher values of $τ$ lead to slower entanglement but memory effects allow quantum resources to remain significant for a longer time, with the negativity remaining above ($\approx 0.6$). We also find that higher interaction frequencies $\mathcal{V}$ accelerate correlations and stabilize coherence, while a strong tunneling asymmetry degrades entanglement and coherence despite the initial benefits of increasing quantum resources.
Published: 2026-05-06 16:19:29
Authors: Xiaofan Xia, Qin Li, Wenlong Mou
Categories: cs.LG, math.AP, math.OC, physics.plasm-ph
Abstract:
We consider the stabilization of Vlasov--Poisson plasma dynamics, a central control problem in nuclear fusion. Our focus is the gap between what an ideal controller would use and what experiments can actually observe: while optimal policy may rely on the full phase-space state, practical feedback is typically limited to sparse macroscopic diagnostics. We therefore study imitation learning methods that distill a fully observed expert policy into controllers operating only on macroscopic measurements. We show the stability guarantees of the learned policy, where the error floor depends on the minimal behavior cloning loss achievable under the observation constraints. We further characterize this minimal loss in terms of a notion of entropy that quantifies the complexity of the initial distribution. Our results demonstrates the theoretical feasibility of learning stabilizing feedback policies for kinetic plasma dynamics from macroscopic observations, and exhibits the adaptivity of the learning approach to low-complexity structures. Through extensive numerical experiments, we validate our theory and show that the learned policies can stabilize the system using only macroscopic observations, within a significantly longer time horizon than non-adaptive baseline controllers.
Published: 2026-05-06 16:11:09
Authors: Arian Maleki, Subhabrata Sen, Sivaraman Balakrishna, Verena Zuber, Chao Gao, Rishabh Dudeja, Christos Thrampoulidis, Anru Zhang, Weijie Su, Jason M. Klusowski, Po-Ling Loh, Ali Shojaie
Categories: math.ST, stat.CO, stat.ME, stat.ML
Abstract:
Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced the cost and effort for data collection and storage across a broad range of domains, including biology, medicine, astronomy, and the social and environmental sciences. Modern datasets are increasingly complex, often exhibiting rich dependency, heterogeneity, and other features that challenge traditional statistical methods. In response, high-dimensional statistics has evolved to address more sophisticated estimation and inference problems. This evolution has, in turn, fostered deep connections with and contributions to a wide range of research areas, including optimization, concentration of measure, random matrix theory, information theory, and theoretical computer science. Given the rapid pace of recent developments in high-dimensional statistics, our goal is to synthesize representative advances, highlight common themes and open problems, and point to important works that offer entry points into the field.
Published: 2026-05-06 15:52:10
Authors: Yamato Igarashi
Categories: econ.EM
Abstract:
This paper develops a difference-in-differences (DiD) estimation method that selects the optimal length of pre-trends by minimizing the mean squared error (MSE). Conventional DiD regression models, such as the two-way fixed effects model or the event study model, may suffer from accuracy and validity concerns. If the sample size is small, the estimator may have a larger variance. Also, pre-tests often lack power to detect violations of the parallel trends assumption as Roth (2022) highlights. By focusing on the bias and variance tradeoff, the proposed method derives the MSE-optimal estimator from the optimal length of pre-trends. Simulation results and an empirical application demonstrate the practical applicability of the proposed method.
Published: 2026-05-06 15:41:24
Authors: Philip Wootaek Shin, Ajay Narayanan Sridhar, Sivani Devarapalli, Rui Zhang, Jack Sampson, Vijaykrishnan Narayanan
Categories: cs.CV, cs.CL
Abstract:
Vision-language models (VLMs) achieve strong multimodal performance but remain prone to relation hallucination, which requires accurate reasoning over inter-object interactions. We study the impact of visual perturbations, specifically rotation and noise, and show that even mild distortions significantly degrade relational reasoning across models and datasets. We further evaluate prompt-based augmentation and preprocessing strategies (orientation correction and denoising), finding that while they offer partial improvements, they do not fully resolve hallucinations. Our results reveal a gap between perceptual robustness and relational understanding, highlighting the need for more robust, geometry-aware VLMs.
Published: 2026-05-06 15:26:11
Authors: Honghu Pan, Xiaoling Luo, Yongyong Chen, Zhenyu He, Pengyang Wang
Categories: cs.CV
Abstract:
Recent deep learning approaches seek to automate CAD creation by representing a model as a sequence of discrete commands and parameters, and then generating them using autoregressive models or continuous diffusion operating in Euclidean embedding space. However, continuous diffusion perturbs representations in a continuous Euclidean domain that does not reflect the inherently discrete and heterogeneous nature of CAD tokens, often producing perturbed representations that map to semantically invalid symbols. To overcome this limitation, we propose a cascaded discrete diffusion framework for CAD generation, which consists of a command diffusion for generating CAD commands and a parameter diffusion conditioned on CAD commands. Unlike isotropic Gaussian perturbation, the forward process of our approach operates directly over categorical token distributions using delicate transition matrices. For commands, we adopt an absorbing-state transition matrix that progressively corrupts tokens to a designated symbol; for parameters, we introduce specific transition matrices tailored to heterogeneous attributes: a Gaussian kernel for coordinate continuity, a scale-invariant kernel for dimensional values, and a prior-preserving kernel for boolean attributes. The reverse process is achieved by two denoising networks: a Transformer-based encoder for command recovery, and a parameter network with extra local self-attention for command-level interaction and cross-attention for conditional injection. Experiments on the DeepCAD dataset show that the proposed approach surpasses existing autoregressive and continuous diffusion models on unconditional generation metrics, while qualitative results validate effective controllability in conditional generation tasks. Source codes will be released.
Published: 2026-05-06 15:22:52
Authors: Bartlomiej Sobieski, Matthew Tivnan, Dawid Płudowski, Michał Jan Włodarczyk, Pengfei Jin, Przemyslaw Biecek, Quanzheng Li
Categories: cs.CV, cs.AI
Abstract:
Diffusion models are prone to generating structural hallucinations - samples that match the statistical properties of the training data yet defy underlying structural rules, resulting in anomalies like hands with more than five fingers. Recent research studied this failure mode from several viewpoints, offering partial explanations to their occurrence, such as mode interpolation. In this work, we propose a complementary perspective that treats hallucinations as instabilities on the model-induced manifold. We begin by showing that a hallucination filter based on such instabilities matches or exceeds the performance of the recently proposed temporal one. By tracing the source of these instabilities, we identify local intrinsic dimension (LID) as their primary driver and propose Intrinsic Quenching (IQ), a direct corrective mechanism that deflates it to alleviate hallucinations. IQ consistently outperforms standard hallucination reduction baselines across a wide array of benchmarks and offers a highly promising solution for enforcing anatomical consistency in downstream medical imaging tasks.
Published: 2026-05-06 15:16:05
Authors: Xiaoliang Fan, Jiarui Chen, Zhuodong Liu, Ziqi Yang, Peixuan Xu, Ruimin Shen, Junhui Liu, Jianzhong Qi, Cheng Wang
Categories: cs.AI, cs.RO
Abstract:
Embodied AI (EAI) systems are rapidly transitioning from simulations into real-world domestic and other sensitive environments. However, recent EAI solutions have largely demonstrated advancements within isolated stages such as instruction, perception, planning and interaction, without considering their coupled privacy implications in high-frequency deployments where privacy leakage is often irreversible. This position paper argues that optimizing these components independently creates a systemic privacy crisis when deployed in sensitive settings, thereby advancing the position that privacy in EAI is a life cycle-level architectural constraint rather than a stage-local feature. To address these challenges, we propose Secure Privacy Integration in Next-generation Embodied AI (SPINE), a unified privacy-aware framework that treats privacy as a dynamic control signal governing cross-stage coupling throughout the entire EAI life cycle. SPINE decomposes the EAI pipeline into various stages and establishes a multi-criterion privacy classification matrix to orchestrate contextual sensitivity across stage boundaries. We conduct preliminary simulation and real-world case studies to conceptually validate how privacy constraints propagate downstream to reshape system behavior, illustrating the insufficiency of fragmented privacy patches and motivating future research directions into secure yet functional embodied AI systems. We detail the SPINE framework and case studies at https://github.com/rminshen03/EAI_Privacy_Position.
Published: 2026-05-06 15:13:27
Authors: Omar Bachain, Elhabib Jaloum, Mohamed Amazioug, Nazek Alessa, Wedad R. Alharbi, Rachid Ahl Laamara, Abdel-Haleem Abdel-Aty
Categories: quant-ph
Abstract:
Neutrino oscillations confirm the presence of mode entanglement, as each flavor eigenstate is composed of a coherent superposition of distinct mass eigenstates. In this work, we investigate the dynamics of quantum resources in neutrino oscillation systems by analyzing quantum steering, logarithmic negativity, and quantum coherence within a two-flavor framework. Treating neutrino oscillations as an effective two-level quantum system, we study the influence of environmental decoherence on these nonclassical features by modeling the system as an open quantum system. Three representative noise channels are considered, namely amplitude damping (AD), phase flip (PF), and phase damping (PD), allowing us to capture both dissipative and dephasing mechanisms. We examine the evolution of quantum resources in both Markovian and non-Markovian regimes, highlighting the role of memory effects in the system-environment interaction. The results reveal a clear hierarchy in the robustness of quantum resources under decoherence. Steering is the most sensitive correlation in the hierarchy under decoherence effects. while logarithmic negativity exhibits intermediate robustness. Quantum coherence displays the highest resilience, persisting over a wider range of parameters. In the PF and PD channels, logarithmic negativity and coherence are shown to exhibit identical dynamical behavior, reflecting their common dependence on phase-related noise. In contrast, the non-Markovian regime leads to delayed decoherence and partial revivals of entanglement and coherence due to information backflow, whereas quantum steering remains strongly suppressed. These findings provide a comparison of different quantum resources in neutrino oscillation systems and offer new insights into the interplay between decoherence mechanisms and quantum correlations.
Published: 2026-05-06 15:05:58
Authors: M. Zotov, A. Trusov, P. Klimov, K. Asatryan, A. Belov, G. Gabaryan, V. Kudryavtsev, A. Murashov
Categories: astro-ph.IM, astro-ph.HE
Abstract:
We report on the successful detection of extensive air showers (EAS) generated by ultra-high-energy cosmic rays using a small-aperture fluorescence telescope (FT) deployed at the Mount Aragats high-altitude research station. The instrument is equipped with a 25 cm diameter Fresnel lens and operates with a 2.625 $μ$s time resolution. To our knowledge, this represents the first-ever observation of EAS achieved with an FT of such a compact aperture. To isolate shower events from the observational data, we implemented two independent event selection pipelines: a conventional cut-based analysis and a deep learning approach utilizing neural networks. Both algorithms successfully identified over 15 high-confidence EAS tracks from data acquired during clear, moonless nights. We present selected event topologies and detail the background rejection methodology employed to discriminate true shower tracks from spurious focal-plane signals mimicking EAS signatures. These results provide an important proof-of-concept for the advancement of fluorescence detection techniques, demonstrating their viability for forthcoming ground-based and space-borne missions. Future efforts will focus on primary energy reconstruction utilizing a previously developed neural-network framework.
Published: 2026-05-06 14:59:41
Authors: A Tutorial for Evaluating Cure Model Appropriateness Geethanjalee Mudunkotuwa, Durbadal Ghosh, Subodh Selukar
Categories: stat.ME
Abstract:
In survival analysis, traditional models assume all individuals will eventually experience the event of interest. However, advances in therapeutics have led to multiple clinical contexts with potentially curative therapies, and in these contexts, certain individuals may never experience the event. Statisticians have developed cure models as a methodology to address this challenge. Nonetheless, despite significant statistical advances in cure models, we have seen more limited uptake in biomedical applications, and we hypothesize that this is caused by limited guidance in the appropriate application of cure models. Cure models require specific identifiability conditions for valid parameter estimation, and previous reports have demonstrated significant issues with the inappropriate application of cure models. Existing tutorials for cure models focus on model implementation and either assume or provide only limited guidance on whether cure modeling is appropriate for the given dataset. This tutorial addresses this gap by describing a systematic procedure that integrates clinical judgment, visual inspection of Kaplan-Meier curves, and quantitative evaluation. We provide a worked example using data from a randomized clinical trial in acute myeloid leukemia, and we also summarize findings from a series of other datasets of hematopoietic cell transplantation to suggest broad practical guidance for choosing to apply cure models. By systematically evaluating cure model appropriateness before fitting these models, researchers can achieve more reliable survival analysis and improved clinical decision-making.
Published: 2026-05-06 14:53:23
Authors: Vasilis Perifanis, Foteini Nikolaidou, Nikolaos Pavlidis, Panagiotis Thomakos, Andreas Sendros
Categories: cs.LG, cs.AI
Abstract:
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real-time charging optimization. In this work, we study the problem of early prediction of charging demand, where the total energy of a session is estimated using only information available at plug-in time and during the first minutes of charging. This enables actionable decisions while the session is still in progress, which is of direct importance for EV network operators. We construct a session-level dataset from the Adaptive Charging Network (ACN), combining session metadata with early-window charging measurements, and derive tabular features capturing user intent, temporal patterns, and initial charging behavior. We focus on a single operational depot, Caltech, and model intra-depot heterogeneity through station-level client partitions while evaluating multiple model families in a federated learning (FL) setting. Our results show that federated models can approach centralized predictive performance while keeping data in-depot, enabling privacy-enhanced training across distributed charging infrastructures. Overall, we demonstrate that reliable demand estimates can be obtained early in the session with minimal data, and that FL provides a practical pathway toward scalable and privacy-aware analytics for EV charging networks. Code is available at https://github.com/Indigma-Innovations/federated-learning-ev-charging-demand.
Published: 2026-05-06 14:47:20
Authors: Adam Blažek, Kevin G. Hare, Edita Pelantová
Categories: math.NT
Abstract:
Let $M = \left(\begin{matrix} 1 & 1 \\ 0 & 1 \end{matrix}\right)$ be a $2 \times 2$ Jordan block with eigenvalue $1$, and let $\mathcal{D} = \{\left(\begin{smallmatrix}0 \\ 1 \end{smallmatrix}\right), \left(\begin{smallmatrix} 0 \\ -1 \end{smallmatrix} \right)\}$. In this paper, we answer a question of Caldwell, Hare, and Vávra about the minimal length representation of $\left( \begin{smallmatrix} a \\ b \end{smallmatrix} \right) = \sum_{i=0}^{k-1} M^i d_i$ with $d_i \in \mathcal{D}$. Further, we extend the work of Caldwell, Hare, and Vávra to consider the case of $n \times n$ Jordan blocks with eigenvalue $-1$.
Published: 2026-05-06 14:46:35
Authors: Paulo C. C. Freire, Yinfeng Dai, Mario Cadelano, Cristina Pallanca, Zurong Zhou, Zhichen Pan, Luca Rosignoli, Davide Massari, Mattia Libralato and, Craig Heinke
Categories: astro-ph.HE
Abstract:
PSR J1905+0154A is a binary millisecond pulsar located in the globular cluster (GC) NGC 6749. It was discovered in 2004 in a search for pulsars in GCs carried out with the Arecibo 305-m radio telescope. The pulsar has a spin period of 3.2 ms, an orbital period of 0.81 days, and is in a low-eccentricity orbit with a low-mass WD companion. Combining early Arecibo and latter Five Hundred meter Aperture Spherical Telescope (FAST) data, we were able to derive a phase-coherent timing solution for this pulsar, which now spans 20 years. This includes a precise measurement of the astrometric, spin and orbital parameters of the system. The small range of predicted accelerations expected from the gravitational field of this GC allows an estimate of the intrinsic spin-down: the inferred magnetic field at the surface (2.2 - 2.4 * 10^8 G) and characteristic age (2.8 - 3.5 Gyr) are typical of what one finds among MSPs in the Galactic field. The position of this pulsar coincides with the position of one of the very few candidate white dwarfs (WDs) in the whole HST dataset on this GC. The position of the companion in the colour-magnitude diagram is consistent with a Helium WD with a mass of 0.17 - 0.19 M_sun, a cooling age of 0.4 - 0.7 Gyr, and a surface temperature of 11,600 - 14,800 K. A comparison with the characteristic age of the pulsar indicates that at the start of the WD cooling the latter had a spin period of ~2.7 ms. The velocity of the system relative to the GC, which is 4.5-sigma significant and an order of magnitude larger than the escape velocity, raises the possibility that, despite its location close to the centre of the GC, the pulsar might not be associated with it. Finally, our effort to confirm a second pulsar candidate in this GC did not yield a positive confirmation, nor the discovery of any additional pulsar in this GC.
Published: 2026-05-06 14:31:38
Authors: Yuusuke Sugiyama, Taro Yamanoi
Categories: math.AP
Abstract:
In this paper, we consider the upper and lower bounds of the lifespan of classical solutions of the Cauchy problem for the one-dimensional quasilinear wave equation $u_{tt}-c(u_x)^2u_{xx}=0$ where the derivative of $c(θ)$ tends to $0$ near the origin. In particular, our result shows that the lifespan of the solution extends algebraically depending on the smallness of the initial data. Furthermore, we also show that when $c(θ)$ is flat at the origin, the lifespan extends exponentially depending on the smallness of the initial data. Our proof is based on the method of Lax's characteristics and Riemann invariants.
Published: 2026-05-06 14:24:54
Authors: Philip Naumann, Jacob Kauffmann, Klaus-Robert Müller, Grégoire Montavon
Categories: cs.LG, cs.AI
Abstract:
Optimal transport (OT) is a central framework for modeling distribution shifts. Because OT compares distributions directly in input space, a well-designed ground metric between observations is essential to ensure that the optimizer does not violate the true geometry of change. We propose Displacement-Reshaped Optimal Transport (ReshapeOT), a method that reshapes the ground metric by integrating observed sample displacements as an additional source of knowledge. Technically, ReshapeOT replaces the Euclidean metric with a Mahalanobis distance estimated from displacement second moments. This effectively carves expressways through the input space, inviting transport solutions that better align with observed displacements. Our method is computationally lightweight, integrates seamlessly into any OT solver that operates on a cost matrix, and can be kernelized for further flexibility. Experiments on synthetic and real-world data show that ReshapeOT achieves substantial gains in transport reliability. We further demonstrate our method's usefulness in two practical use cases.
Published: 2026-05-06 14:24:43
Authors: Yu-Xuan Zhang, Jing-Ling Chen
Categories: quant-ph, hep-th, math-ph
Abstract:
We propose a simple ansatz to construct exact wave solutions of the sourceless SU(2) Yang-Mills equations in (3+1) dimensions. The ansatz employs a $y$-dependent rotated Pauli basis and assumes a phase $θ=kz-ωt$ dependence for the gauge potentials. Owing to this ansatz, the nonlinear field equations reduce to nine algebraic constraints, whose complete solution yields three families of exact waves. Family I describes linear (Abelian) electromagnetic waves embedded in the non-Abelian theory; all commutator terms vanish and the dispersion relation is $ω=kc$. Family II represents genuinely nonlinear self-interacting waves that also propagate at the speed of light but exhibit a constant field offset, nonvanishing commutators, and do not obey superposition. The constant offset is gauge-invariant and gives rise to a non-zero time-averaged color-electric field. The energy density has nodes whose position ($θ=0$ or $θ=π$) is controlled by a discrete topological parameter $ξη=\pm1$, providing an observable signature. Family III is a pure gauge solution with vanishing field strengths, valid for arbitrary $k$ and $ω$ without any dispersion relation. All solutions are closed-form and provide new insights into how non-Abelian self-interactions fundamentally alter wave propagation.
Published: 2026-05-06 14:15:10
Authors: Antonia Hager, Torleiv H. Bryne, Mia Jukić
Categories: quant-ph, physics.app-ph
Abstract:
Airborne magnetometry requires rigorous calibration to isolate geomagnetic signals from sensor errors and platform magnetic fields. This magnetic compensation is needed for applications like geophysical exploration and magnetic anomaly navigation. The standard approach utilizes a quantum scalar Optically Pumped Magnetometer (OPM) and a less sensitive fluxgate vector sensor for attitude information. This configuration typically results in a scalar approximation of the platform field.
Advancements in high-sensitivity Diamond Nitrogen-Vacancy (NV) vector magnetometers now enable a re-evaluation of the standard hardware configuration and full vector calibration models. We show through rigorous theoretical analysis that scalar calibration models are robust to misalignment. Vector calibration models, however, are intrinsically first-order sensitive to attitude errors, irrespective of the accuracy of the magnetic field measurements. These errors arise from inaccurate representation of the background field direction in the body frame, and can amplify small orientation errors into noticeable calibration residuals.
Using realistic sensor models and flight trajectories, we evaluate different sensor configurations for magnetic calibration and assess the use of onboard Inertial Navigation Systems (INS) as an independent attitude reference to enable stable compensation. Our results suggest that quantum vector magnetometers like NV sensors are not sufficient to solve the attitude bottleneck for airborne vector magnetic calibration. However, as a single sensor capable of providing both absolute field and directional measurements, they may offer benefits regarding sensor placement, synchronization, and scalar calibration accuracy.
Published: 2026-05-06 14:13:19
Authors: Jordan Lambert, Lonardo Rabelo
Categories: math.AT
Abstract:
This paper computes the integral homology of real flag manifolds associated with split real forms of classical and exceptional semisimple Lie algebras. Using the cellular homology provided by the Bruhat decomposition, we introduce a unified framework to systematically determine the coefficients of the boundary operator, explicitly resolving the issue of calculating their signs. This is achieved by computing the degree of change of coordinate maps between different reduced decompositions of Weyl group elements, analyzing commutation and braid relations through Lie bracket computations and exponential identities. By adopting the normal form of Weyl group elements as a canonical choice for reduced decompositions, we establish an explicit algorithmic implementation for these homology computations. As a direct application, we derive the Poincaré polynomials for the classical types $B_n, C_n$, and $D_n$ for $n \leqslant 7$, and for the exceptional types $F_4, E_6$, and $E_7$. With the aid of these polynomials, we address the question of the orientability of split real flag manifolds of exceptional Lie algebras.
Published: 2026-05-06 14:04:24
Authors: Vivien Parmentier, Kevin B. Stevenson, Luis Welbanks, Jake Taylor, Everett Schlawin, Louis-Philippe Coulombe, Yao Tang, Mike Line, Hinna Shivkumar, Xianyu Tan, Jacob L. Bean, Jean-Michel Désert, Jonathan J. Fortney, Peter Gao, Mark Hammond, Eliza M. -R. Kempton, Thaddeus D. Komacek, Megan Weiner Mansfield
Categories: astro-ph.EP
Abstract:
Hot Jupiters have temperature gradients of several hundreds of degrees between their permanent day and nightsides. In equilibrium, the primary carbon reservoir is expected to transition from CO on the dayside to CH4 on the nightside. Theory predicts that the atmospheric circulation, characterised by km/s winds, can advect chemical species from the dayside to the nightside faster than the time needed for the CO-to-CH4 chemical reaction to reach equilibrium. However direct evidence of this process has, so far, remained elusive, partly because it is often degenerate with other processes, such as vertical mixing or non-stellar elemental abundances. Here, we present observational evidence for such day-to-night transport of chemical species by observing both the dayside and the nightside of the hot Jupiter NGTS-10A b with the JWST/NIRSpec instrument. We constrain the presence of H2O and CO with similar abundances on both the dayside and nightside. Our observations are compatible with a solar-composition atmosphere at chemical equilibrium on the dayside, but indicative of disequilibrium chemistry for the nightside as it is significantly depleted in CH4 compared to equilibrium chemistry predictions. We further show that the lack of CH4 on the planet's nightside cannot be attributed to non-solar elemental abundances or to vertical mixing mechanisms and must therefore be due to horizontal chemical quenching. Our study shows the fundamental role atmospheric transport plays in shaping the distribution of chemical species on exoplanet atmospheres.
Published: 2026-05-06 13:55:13
Authors: Qiong Qin, Jie Wu, Congjun Wu
Categories: cond-mat.supr-con
Abstract:
We propose a response tensor $\mathbf{\hat χ}$ to characterize the non-reciprocal critical current response of the superconducting (Josephson) diode effect. It describes the coupling between the dipole component of the angular distribution of the critical current and the applied magnetic field -- an analogue to the Hall response in the normal state. In quasi-2D systems with Rashba spin-orbit coupling and point group symmetries $C_{3v}$, $C_{4v}$ or $C_{6v}$, this tensor takes a fully antisymmetric form. When nematicity is present, a symmetric contribution emerges, providing an indicator of the nematic order in the superconducting state. In contrast, for systems exhibiting Dresselhaus spin-orbit coupling with the $D_{2d}$ symmetry, the tensor becomes diagonal traceless, and nematicity brings in a trace part. Our analysis not only accounts for the superconducting diode effect under external applied or intrinsic effective magnetic fields, but also predicts the symmetry conditions for realizing the diode effect when the magnetic field is aligned with the current. Beyond this, the proposed tensor provides a promising tool for detecting nematicity and potential nematic transitions deep within the superconducting phase. It may also encode additional information about the underlying electronic structure and symmetry-breaking orders, warranting further experimental investigation.
Published: 2026-05-06 13:52:32
Authors: Bingkun Zhao, Chenwei Zhang, Hao Peng
Categories: cs.CL
Abstract:
Promotional language has been increasingly used to aid the communication of innovative ideas in science. Yet, less is known about its role in the context of technological innovation. Here, we use a validated and domain-diagnosed lexicon of 135 promotional words to study the association between promotional language and patent evaluation outcomes among 2.7 million USPTO patent applications. Our large-scale study reveals three unexpected findings. First, in contrast to scientific evaluation, we find that a higher frequency of promotional words is negatively associated with the probability of an application being (i) granted a patent, (ii) transferred ownership, and (iii) successfully appealed. This promotional penalty holds even after accounting for a range of confounding factors and is largely robust across different technological areas. Among matched samples, the difference in the success rate between the lowest and highest promotional density quintile is 5.5, 5.9, and 5.3 percentage points for patentability, transferability, and rejection reversal. Second, contrary to institutional skepticism, we show that promotional language is not a mask of weak technology, but objectively reflects the degree of combinatorial novelty and future citation impact. Third, digging into the mechanisms, we find that the tolerance to promotional framing is strongly moderated by human factors, with men and experienced examiners showing a higher acceptance of promotional narratives than women and novice examiners. By revealing an emerging paradox in the patent system, our study offers theoretical and practical implications for improving patent evaluation through more objective scrutiny of linguistic patterns in patent filings.
Published: 2026-05-06 13:50:40
Authors: Jiangwen Dong, Bo Li, Wanyu Lin
Categories: cs.MA, cs.AI
Abstract:
LLM-empowered multi-agent systems offer new potential to accelerate scientific discovery by generating novel research ideas. However, existing methods typically coordinate agents through temporary texts, such as drafts or chat logs; it is difficult to pinpoint the weaknesses in the generated ideas and how the agents refine them. To this end, we introduce \textbf{Evolving Idea Graphs} (EIG), a graph-based multi-agent scientific ideation framework that can generate high-performance research ideas across various benchmark-native metrics, such as novelty, feasibility, and clarity. Instead of coordinating solely through texts, EIG represents a partially formed proposal as an evolving idea graph, where nodes capture scientific claims and edges encode relations (e.g., support and conflict), enabling unresolved weaknesses to remain identifiable throughout the idea evolving process. Specifically, a learned two-head controller operates over the evolving graph to guide the ideation: one head selects graph edits for agents to execute, while the other decides when the graph is ready for commit as final proposal synthesis. On AI Idea Bench 2025 and LiveIdeaBench, EIG outperforms all compared systems on both automatic benchmark scores and blind expert ratings. Ablations further show that explicit graph state provides the main performance gains, and learned edit-and-commit control adds consistent improvements.
Published: 2026-05-06 13:49:12
Authors: Leonardo Marchesin, Alessandra Menafoglio, Piercesare Secchi
Categories: stat.ME, stat.AP
Abstract:
Sea surface temperature (SST) is a fundamental determinant of global climate dynamics and economic activity. Reliable projections of future SST patterns depend critically on a rigorous characterization of the underlying spatial random field. In this study, we introduce a novel convolution-based covariance framework tailored to geostatistical domains constrained by physical barriers and influenced by vector-driven flows. By discretizing the continuous marine domain into a directed linear network that preserves the orientation of ocean currents, we construct a moving-average stochastic process whose dynamic is encoded via a Markovian transition-probability matrix on the network's vertices. The induced covariance structure emerges as a weighted combination of a spatial kernel and flow-dependent weights, giving rise to a complex estimation problem. To stabilize inference, we propose a penalized estimator that regularizes covariance parameters while enforcing consistency with known hydrodynamic properties. We then embed this covariance model into a Monte Carlo simulation framework to refine RCP-based SST projections and to identify thermal 'hot spots' of heightened ecological risk. Our approach delivers a statistically principled framework that prevents physical inconsistencies -- such as correlations across land barriers -- providing a robust basis for quantifying uncertainty in future SST forecasts and for guiding targeted environmental assessments.
Published: 2026-05-06 13:46:17
Authors: Weibin Gu, Chen Yang, Lu Shi
Categories: cs.LG, cs.RO
Abstract:
Learning tractable linear representations of nonlinear dynamical systems via Koopman operator theory is often hindered by dictionary selection, temporal memory encoding, and numerical ill-conditioning. Inspired by Reservoir Computing (RC) paradigm, this paper introduces the RC-Koopman framework, which interprets reservoir as a stateful, finite-dimensional Koopman dictionary whose temporal depth is explicitly controlled by its spectral radius. We show that the Echo State Property (ESP) guarantees well-posedness and favorable numerical conditioning of the lifted Koopman approximation. A correlation-based spectral radius selection algorithm aligns reservoir memory with dominant system timescales. Analysis reveals how the finite memory of the reservoir determines which Koopman eigenfunctions remain observable from the lifted features. Evaluation on synthetic benchmarks demonstrates that RC-Koopman achieves a favorable balance between reconstruction accuracy of the underlying nonlinear dynamics and dynamical stability, compared to Extended Dynamic Mode Decomposition (EDMD) and Hankel-based lifting approaches. Code available at: https://github.com/NEAR-the-future/RC-Koopman.git
Published: 2026-05-06 13:38:16
Authors: Xinyan Han, Yan Lu, Xiaoyu Lin, Yuanyuan Jiang, Yuanrui Wang, Xuanyue Li, Wenchao Zou, Xingxuan Zhang
Categories: cs.LG
Abstract:
Tabular data synthesis aims to generate high-quality data while preserving privacy. However, we find that existing tabular generative models exhibit a clear tradeoff in the small-data regime: improving data quality typically comes at the cost of increased memorization of training samples, thereby weakening privacy protection. This tradeoff arises because small training sets make it difficult for dataset-specific generative models to distinguish generalizable structure from sample-specific patterns. To address this, we propose DiffICL, which formulates tabular data generation as an in-context learning problem. Instead of fitting each dataset from scratch,DiffICL leverages pretrained structural priors learned from a large collection of datasets, enabling it to infer data distributions from limited context rather than memorizing individual samples. We evaluate DiffICL on 14 real-world datasets. Results show that DiffICL improves both data quality and privacy, and generate synthetic data that provides effective data augmentation. Our findings suggest that the quality-privacy tradeoff can be improved through better training paradigms.