arXiv Papers

Published: 2026-04-06 17:59:58

Authors: Siyuan Liu, Chaoqun Zheng, Xin Zhou, Tianrui Feng, Dingkang Liang, Xiang Bai

Categories: cs.CV

Abstract:
Scene-level point cloud understanding remains challenging due to diverse geometries, imbalanced category distributions, and highly varied spatial layouts. Existing methods improve object-level performance but rely on static network parameters during inference, limiting their adaptability to dynamic scene data. We propose PointTPA, a Test-time Parameter Adaptation framework that generates input-aware network parameters for scene-level point clouds. PointTPA adopts a Serialization-based Neighborhood Grouping (SNG) to form locally coherent patches and a Dynamic Parameter Projector (DPP) to produce patch-wise adaptive weights, enabling the backbone to adjust its behavior according to scene-specific variations while maintaining a low parameter overhead. Integrated into the PTv3 structure, PointTPA demonstrates strong parameter efficiency by introducing two lightweight modules of less than 2% of the backbone's parameters. Despite this minimal parameter overhead, PointTPA achieves 78.4% mIoU on ScanNet validation, surpassing existing parameter-efficient fine-tuning (PEFT) methods across multiple benchmarks, highlighting the efficacy of our test-time dynamic network parameter adaptation mechanism in enhancing 3D scene understanding. The code is available at https://github.com/H-EmbodVis/PointTPA.

            {
  "tldr": "PointTPA introduces a dynamic network parameter adaptation framework for 3D scene understanding, achieving 78.4% mIoU on ScanNet validation while maintaining low parameter overhead. It outperforms existing methods by adapting to scene-specific variations during inference.",
  "whats_new": [
    "• Proposes a test-time parameter adaptation framework for scene-level point clouds.",
    "• Introduces Serialization-based Neighborhood Grouping (SNG) and Dynamic Parameter Projector (DPP) modules.",
    "• Achieves state-of-the-art performance with over 98% reduction in trainable parameters compared to full fine-tuning.",
    "• Outperforms existing parameter-efficient fine-tuning methods across multiple benchmarks."
  ],
  "method": "PointTPA utilizes SNG to create locally coherent patches from point clouds and DPP to generate adaptive weights for these patches. This allows the model to dynamically adjust its parameters based on the input during inference, enhancing its adaptability to diverse scene characteristics.",
  "results": [
    "• Achieves 78.4% mIoU on ScanNet validation, surpassing existing PEFT methods.",
    "• Operates approximately 4× faster in training and inference compared to PointGST.",
    "• Improves mAcc by 1.4% and mIoU by 0.4% on S3DIS compared to full fine-tuning.",
    "• Outperforms PointGST by 0.9% on ScanNet++ dataset."
  ],
  "limitations": [
    "• Performance may still be improved further; potential for optimization exists.",
    "• The framework's effectiveness on extremely complex scenes remains to be fully validated.",
    "• Dynamic parameter adaptation could introduce instability if not carefully managed."
  ],
  "why_useful": "PointTPA significantly enhances 3D scene understanding by allowing models to adapt dynamically to varying scene characteristics, which is crucial for applications in autonomous driving and robotics. Its low parameter overhead makes it suitable for deployment in resource-constrained environments.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/H-EmbodVis/PointTPA."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:59:35

Authors: Sixun Dong, Juhua Hu, Steven Li, Wei Wen, Qi Qian

Categories: cs.CV

Abstract:
Most vision-language models (VLMs) apply a large language model (LLM) as the decoder, where the response tokens are generated sequentially through autoregression. Therefore, the number of output tokens can be the bottleneck of the end-to-end latency. However, different models may require vastly different numbers of output tokens to achieve comparable performance. In this work, we conduct a comprehensive analysis of the latency across different components of VLMs on simulated data. The experiment shows that a large model with fewer output tokens can be more efficient than a small model with a long output sequence. The empirical study on diverse real-world benchmarks confirms the observation that a large model can achieve better or comparable performance as a small model with significantly fewer output tokens. To leverage the efficiency of large models, we propose a multi-agent inference framework that keeps large models with short responses but transfers the key reasoning tokens from the small model when necessary. The comparison on benchmark tasks demonstrates that by reusing the reasoning tokens from small models, it can help approach the performance of a large model with its own reasoning, which confirms the effectiveness of our proposal.

            {
  "tldr": "This paper analyzes the efficiency of vision-language models (VLMs) by comparing large and small models in terms of output tokens and proposes a multi-agent inference framework to enhance performance while reducing latency.",
  "whats_new": [
    "• Introduces a multi-agent inference framework that combines large and small models for improved efficiency.",
    "• Demonstrates that larger models can outperform smaller ones with fewer output tokens.",
    "• Proposes reasoning transfer from small models to large models to enhance performance without significant latency increase.",
    "• Provides empirical evidence across multiple benchmarks showing the effectiveness of the proposed methods."
  ],
  "method": "The study conducts a comprehensive analysis of VLMs' latency based on output tokens. It proposes a multi-agent inference framework that utilizes reasoning tokens from smaller models to enhance the performance of larger models while maintaining efficiency.",
  "results": [
    "• An 8B model with 128 output tokens is more efficient than a 2B model with 256 output tokens.",
    "• The multi-agent framework improves performance on benchmark tasks by reusing reasoning tokens from small models.",
    "• Achieves up to 8.05× speedup in inference time compared to full reasoning transfer.",
    "• Large models consistently outperform small models in terms of performance with fewer output tokens."
  ],
  "limitations": [
    "• The framework has not yet considered GPU memory constraints for inference.",
    "• The effectiveness of the proposed methods may vary across different tasks and datasets.",
    "• Further exploration is needed to optimize the framework for memory savings."
  ],
  "why_useful": "This paper provides insights into optimizing inference efficiency in VLMs, which is crucial for real-time applications. The proposed multi-agent framework can significantly reduce latency while maintaining or improving performance, making it relevant for developers and researchers in AI and machine learning.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:59:18

Authors: Erik Nilsson, Silvano Pitassi

Categories: math.FA, math.NA

Abstract:
In this paper, we construct a novel global bounded cochain extension operator for differential forms on Lipschitz domains. Building upon the classical universal extension of Hiptmair, Li, and Zou, our construction restores global commutativity with the exterior derivative in the natural $HΛ^k(Ω)$ setting. The construction applies to domains and ambient extension sets of arbitrary topology, with strict commutation holding on the orthogonal complement of harmonic forms, as dictated by the underlying topological obstruction. This provides a missing analytical tool for the rigorous foundation of Cut Finite Element Methods (CutFEM). We also obtain continuous uniform Poincaré inequalities and lower bounds for the first Neumann eigenvalue on non-convex domains.

            {
  "tldr": "This paper introduces a global bounded cochain extension operator for differential forms on Lipschitz domains, enhancing existing methods by restoring commutativity with the exterior derivative. It also establishes uniform Poincaré inequalities and provides lower bounds for the first Neumann eigenvalue on non-convex domains.",
  "whats_new": [
    "• Introduces a novel global bounded cochain extension operator for differential forms.",
    "• Restores global commutativity with the exterior derivative in the HΛk(Ω) setting.",
    "• Establishes continuous uniform Poincaré inequalities without topological restrictions.",
    "• Provides lower bounds for the first Neumann eigenvalue on non-convex domains."
  ],
  "method": "The paper constructs a graded family of extension operators designed to restore commutativity at a global level. The construction applies to bounded Lipschitz domains of arbitrary topology, isolating topological obstructions by working on the orthogonal complement of harmonic forms.",
  "results": [
    "• The extension operator satisfies E_kω|Ω = ω for all ω in HΛk(Ω).",
    "• Stability constant C_ext depends on the Lipschitz character of the boundary and the Dirichlet Poincaré constant.",
    "• Uniform Poincaré inequalities established for differential forms of arbitrary degree on Lipschitz domains.",
    "• Lower bound for the first nonzero Neumann eigenvalue on non-convex domains: λ(k)_1(Ω) ≥ C diam(Conv(Ω+))^4."
  ],
  "limitations": [
    "• The analysis is restricted to basic Sobolev spaces, potentially limiting regularity.",
    "• The construction does not address the extension of harmonic forms directly.",
    "• Open questions remain regarding higher regularity and optimal bounds."
  ],
  "why_useful": "This paper provides essential tools for the rigorous foundation of Cut Finite Element Methods (CutFEM) and enhances the mathematical framework for numerical analysis of differential equations. The results have implications for various applications in computational mathematics and engineering.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:55:05

Authors: Tommie Kerssies, Gabriele Berton, Ju He, Qihang Yu, Wufei Ma, Daan de Geus, Gijs Dubbelman, Liang-Chieh Chen

Categories: cs.CV

Abstract:
Anticipating diverse future states is a central challenge in video world modeling. Discriminative world models produce a deterministic prediction that implicitly averages over possible futures, while existing generative world models remain computationally expensive. Recent work demonstrates that predicting the future in the feature space of a vision foundation model (VFM), rather than a latent space optimized for pixel reconstruction, requires significantly fewer world model parameters. However, most such approaches remain discriminative. In this work, we introduce DeltaTok, a tokenizer that encodes the VFM feature difference between consecutive frames into a single continuous "delta" token, and DeltaWorld, a generative world model operating on these tokens to efficiently generate diverse plausible futures. Delta tokens reduce video from a three-dimensional spatio-temporal representation to a one-dimensional temporal sequence, for example yielding a 1,024x token reduction with 512x512 frames. This compact representation enables tractable multi-hypothesis training, where many futures are generated in parallel and only the best is supervised. At inference, this leads to diverse predictions in a single forward pass. Experiments on dense forecasting tasks demonstrate that DeltaWorld forecasts futures that more closely align with real-world outcomes, while having over 35x fewer parameters and using 2,000x fewer FLOPs than existing generative world models. Code and weights: https://deltatok.github.io.

            {
  "tldr": "This paper presents DeltaTok and DeltaWorld, an efficient generative world modeling approach that uses delta tokens to predict diverse future states from video data, achieving significant reductions in parameters and computational cost.",
  "whats_new": [
    "• Introduces DeltaTok, a tokenizer that encodes frame differences into a single delta token.",
    "• Proposes DeltaWorld, a generative model that operates on delta tokens for efficient future predictions.",
    "• Achieves over 35× fewer parameters and 2,000× fewer FLOPs compared to existing generative models.",
    "• Demonstrates improved prediction accuracy on dense forecasting tasks."
  ],
  "method": "DeltaTok compresses the difference between consecutive frames into a single delta token, reducing the representation from a 3D spatio-temporal format to a 1D sequence. DeltaWorld uses these delta tokens to generate multiple plausible futures in a single forward pass, leveraging a Best-of-Many training objective to enhance diversity in predictions.",
  "results": [
    "• DeltaWorld forecasts futures that align more closely with real-world outcomes.",
    "• Achieves over 35× fewer parameters compared to Cosmos models.",
    "• Uses 2,000× fewer FLOPs than existing generative world models.",
    "• Outperforms Cosmos on all metrics while maintaining competitive mean scores."
  ],
  "limitations": [
    "• The Best-of-Many objective lacks an explicit distributional objective, limiting predictive coverage.",
    "• Error accumulation may occur during multi-step autoregressive rollouts, potentially leading to feature drift.",
    "• The model's performance may degrade with increased complexity in scene dynamics."
  ],
  "why_useful": "This work significantly enhances the efficiency of generative world modeling, making it applicable for real-time predictions in autonomous systems. The compact representation of video data through delta tokens allows for scalable and effective future state forecasting.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & weights available at deltatok.github.io."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:53:40

Authors: Louis-S. Bouchard

Categories: cond-mat.other, math.NA, physics.chem-ph

Abstract:
The distant dipolar field (DDF) is a long-range, nonlocal contribution to liquid-state spin dynamics that arises from intermolecular dipolar couplings and can generate multiple-quantum coherences and novel MRI contrast. Its sign-changing kernel makes Bloch-DDF dynamics strongly geometry dependent, and FFT-based dipolar convolutions naturally assume periodic or padded Cartesian domains rather than bounded samples with reflective diffusion boundaries. We study the Bloch equations with the DDF on bounded domains under homogeneous Neumann diffusion conditions. We derive a finite-element weak formulation that supports spatially varying diffusion and relaxation parameters and uses a short-distance regularization of the secular DDF kernel with length a>0. For fixed a we prove boundedness of the DDF operator, establish an L2 energy balance in which precession is neutral while diffusion and transverse relaxation are dissipative, and obtain local well-posedness with continuous dependence on the data, with global existence under energy-neutral transport. For the Galerkin semi-discretization we show a discrete energy identity mirroring the continuum estimate. For computation, we evaluate the DDF in real space with a matrix-free near/far scheme and advance in time using a second-order IMEX splitting method that treats diffusion and relaxation implicitly and precession explicitly. The explicit stage applies a Rodrigues rotation at DDF quadrature points followed by an L2 projection, enabling stable multi-cycle lab-frame simulations. We validate against three closed-form benchmarks and quantify curved-boundary effects by comparing mapped finite elements with a voxel-mask finite-difference baseline on spherical Neumann eigenmode decay. These results provide an analyzable and reproducible route for Bloch-DDF dynamics on bounded domains with complex geometry.

            {
  "tldr": "This paper presents a finite-element weak formulation of the Bloch equations with a distant dipolar field, addressing challenges in simulating spin dynamics in complex geometries.",
  "whats_new": [
    "• Introduces a weak formulation for Bloch equations on bounded domains.",
    "• Establishes energy stability and well-posedness under varying diffusion parameters.",
    "• Validates against closed-form benchmarks, demonstrating accuracy in complex geometries.",
    "• Proposes a matrix-free method for efficient computation of the distant dipolar field."
  ],
  "method": "The study derives a finite-element weak formulation for the Bloch equations with a distant dipolar field, incorporating spatially varying diffusion and relaxation parameters. A matrix-free near/far evaluation scheme is employed for computational efficiency, alongside a second-order IMEX time-stepping method.",
  "results": [
    "• Proved boundedness of the regularized DDF operator.",
    "• Established local well-posedness with continuous dependence on initial data.",
    "• Achieved a maximum relative error of 6.89×10^-4 in benchmark tests.",
    "• Demonstrated geometry-driven advantages of finite elements over finite differences."
  ],
  "limitations": [
    "• Results depend on the chosen regularization length.",
    "• Fully rigorous error estimates for far-field operators are not included.",
    "• The implementation is not publicly available."
  ],
  "why_useful": "This work enhances the simulation of spin dynamics in complex geometries, which is crucial for applications in materials characterization and MRI. The findings provide a robust framework for future research in nonlocal spin dynamics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:50:17

Authors: Nicolas Barré

Categories: physics.optics, physics.comp-ph

Abstract:
This article presents an optimization method to find the most spatially concentrated basis of a multimode fiber, obtained by minimizing the sum of the spatial spreads of the individual modes over all unitary transformations of a given orthonormal mode set. The resulting modes are the optical analogue of maximally localized Wannier functions in solid-state physics. We apply the method to the Laguerre-Gaussian basis of a graded-index fiber for mode counts ranging from 6 to 55. In all cases, the modes spontaneously organize into concentric rings without any geometric constraint being imposed. The spot sizes and ellipticities evolve from one ring to the next in ways that geometric packing approaches cannot predict. For large mode counts, the optimizer finds solutions where neither the number of spots per ring nor the spots within a given ring follow a regular pattern, indicating that the fully symmetric arrangement is no longer a minimum of the spread functional. A constrained variant of the method enables the optimizer to target any prescribed bundle geometry while quantifying its localization cost, opening a route to physically grounded photonic lantern design.

            {
  "tldr": "This paper introduces a method to optimize the spatial arrangement of modes in multimode fibers, revealing that they naturally organize into concentric rings. The findings enhance the design of photonic lanterns without geometric constraints.",
  "whats_new": [
    "• Introduces Maximally Localized Fiber Modes (MLFM) through an optimization method.",
    "• Reveals spontaneous organization of modes into concentric rings without geometric constraints.",
    "• Demonstrates that the arrangement evolves systematically with mode count.",
    "• Provides a constrained optimization variant for targeting specific bundle geometries."
  ],
  "method": "The approach minimizes a spread functional over unitary transformations of an orthonormal mode set. It uses matrix optimization to derive the most spatially concentrated basis, applying it to the Laguerre-Gaussian basis of graded-index fibers.",
  "results": [
    "• Modes self-organize into concentric rings for mode counts from 6 to 55.",
    "• Beyond a critical mode count, ring structures deviate from regular patterns.",
    "• The constrained optimization recovers near-optimal symmetric solutions with total spread deviations of only 0.8% to 1.7% from the unconstrained optimum.",
    "• The method reveals that outer spots are larger and more elliptical than inner ones."
  ],
  "limitations": [
    "• The optimization may not yield local minima for symmetric configurations.",
    "• The method's applicability to other modal bases or fiber geometries remains to be fully explored.",
    "• Sensitivity to angular offsets in target geometries indicates quasi-symmetry rather than true symmetry."
  ],
  "why_useful": "This research provides a quantitative tool for optimizing photonic lantern designs by linking modal structures to optimal geometries. It opens pathways for improved mode conversion in applications like space-division multiplexing and astrophotonics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Data and code underlying the results presented in this paper are available from the author upon reasonable request."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:29:16

Authors: Chatchai Noytaptim, Xiao Zhong

Categories: math.DS, math.AG, math.NT

Abstract:
Motivated by the study of unlikely intersection in the moduli space of rational maps, we initiate our investigation on algebraic dynamics for families of regular polynomial skew products in this article. Our goals are threefold. (1) We classify special loci -- which contain a Zariski dense set of postcritically finite points -- in the moduli space of quadratic regular polynomial skew products. More precisely, special loci include families of homogeneous polynomial endomorphisms, families of split endomorphisms, and polynomial endomorphisms of the form $(x^2,y^2+bx)$ up to conjugacy. As a consequence, we verify a special case of a conjecture proposed by Zhong. (2) Let $F_t$ be a family of regular polynomial skew products defined over a number field $K$ and let $P_t, Q_t\in K[t]\times K[t]$ be two initial marked points. We introduce a good height $h_{P_t}(t)$ which is built from the theory of adelic line bundles for quasi projective varieties. We show that the set of parameters $t_0\in \overline{K}$ for which $P_{t_0}$ and $Q_{t_0}$ are simultaneously $F_{t_0}$-preperiodic is infinite if and only if $h_{P_t}=h_{Q_t}$. (3) As an application of $h_{P_t}$, we show that, under some degree conditions of $P_t$, if there is an infinite set of parameters $t_0$ for which the marked point $P_{t_0}$ is preperiodic under $F_{t_0}$, then the Zariski closure of the forward orbit of $P_t$ lives in a proper subvariety of $\mathbb{P}^2$. As a by-product, we conditionally verify a special case of a conjecture of DeMarco--Mavraki which is a relative version of the Dynamical Manin--Mumford Conjecture.

            {
  "tldr": "This paper investigates unlikely intersections in the moduli space of polynomial skew products, classifying special loci and establishing connections to conjectures in algebraic dynamics.",
  "whats_new": [
    "• Classifies special loci in the moduli space of quadratic regular polynomial skew products.",
    "• Introduces a new height function for analyzing preperiodicity in families of skew products.",
    "• Provides conditional verifications of conjectures related to dynamical systems and preperiodic points."
  ],
  "method": "The authors classify special loci in the moduli space and introduce a height function based on adelic line bundles. They analyze the conditions under which marked points are preperiodic and establish connections to conjectures in algebraic dynamics.",
  "results": [
    "• Special loci containing Zariski dense sets of postcritically finite points are identified.",
    "• An infinite set of parameters exists for which two marked points are simultaneously preperiodic if their heights are equal.",
    "• The Zariski closure of the forward orbit of a marked point is shown to lie in a proper subvariety under certain conditions."
  ],
  "limitations": [
    "• The results are conditional on certain degree restrictions.",
    "• The implications for higher-dimensional dynamics remain largely unexplored."
  ],
  "why_useful": "This paper contributes to the understanding of dynamical systems in algebraic geometry, particularly in the context of polynomial skew products. Its findings have implications for conjectures in the field and can inform future research on preperiodic points and their distributions.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:29:03

Authors: P. Kumar, P. Das, B. K. Kuanr, S. Patnaik

Categories: cond-mat.mtrl-sci, cond-mat.other, cond-mat.str-el

Abstract:
Ferrimagnetic spinel materials of formula AB2X4, where A and B are transition metals and X is oxygen or sulphur, hold promise for the realization of multiferroic characteristics. In this work, we report synthesis of spinel CoMn2O4 and explore its magnetic, dielectric, and ferroelectric aspects and their correlations. Polycrystalline CoMn2O4 was synthesized by using the conventional solid-state method. The X-ray diffraction (XRD) and Raman spectroscopy confirmed the phase purity of the synthesized compound. The crystal structure was identified with tetragonal symmetry (I41/amd space group). DC magnetization measurements indicate two magnetic transitions: one at temperature T1 ~ 186 K, followed by another Yafet-Kittel (YK) ferrimagnetic transition at T2 ~ 86 K. A frequency independent anomaly in the temperature dependent dielectric permittivity is observed near the low magnetic ordering temperature (T2). This reflects the possibility of the correlation between lattice dynamics and spin ordering in spinel CoMn2O4. A substantial exchange bias was also observed below T2 ~ 86 K. The change in dielectric permittivity in the presence of applied magnetic field follows the square of the magnetization dependence, which is consistent with Ginzburg-Landau theory. However, the detailed pyroelectric current measurements reveal the absence of intrinsic ferroelectric order.

            {
  "tldr": "This study investigates the multiferroic properties of CoMn2O4, revealing two magnetic transitions and significant magnetodielectric coupling, but no intrinsic ferroelectric order.",
  "whats_new": [
    "• Demonstrates strong magnetodielectric coupling in CoMn2O4 below 86 K.",
    "• Identifies two distinct magnetic transitions at approximately 186 K and 86 K.",
    "• Establishes the absence of intrinsic ferroelectric order despite observed pyroelectric currents.",
    "• Supports findings with Ginzburg-Landau theory regarding dielectric and magnetic interactions."
  ],
  "method": "CoMn2O4 was synthesized using a solid-state method. The study employed X-ray diffraction, Raman spectroscopy, and magnetic measurements to analyze the material's properties. Dielectric and pyroelectric current measurements were conducted to explore the coupling between magnetic and dielectric properties.",
  "results": [
    "• Two magnetic transitions observed: T1 ~ 186 K and T2 ~ 86 K.",
    "• Significant exchange bias effect noted below T2, with HZEB values of 0.875 kOe at 5 K.",
    "• Dielectric permittivity shows a pronounced change near T2, indicating magnetodielectric coupling.",
    "• No intrinsic ferroelectric order detected; pyroelectric currents attributed to thermally stimulated depolarization."
  ],
  "limitations": [
    "• The presence of a minor cubic phase complicates the interpretation of magnetic transitions.",
    "• The study does not explore potential applications of the observed magnetodielectric coupling.",
    "• Further investigation is needed to clarify the mechanisms behind the observed pyroelectric currents."
  ],
  "why_useful": "This research enhances understanding of multiferroic materials, particularly CoMn2O4, and its potential applications in electronic devices. The findings on magnetodielectric coupling could inform future studies and applications in non-volatile memory and sensors.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Data available from the corresponding author upon reasonable request."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:28:29

Authors: Leonardo Ermann, Alexei D. Chepelianskii, Dima L. Shepelyansky

Categories: cond-mat.stat-mech, cond-mat.quant-gas, nlin.CD, quant-ph

Abstract:
The Boltzmann-Loschmidt dispute of 1876 questioned the possibility of a statistical irreversible description by time reversible classical equations of motion of atoms. Here we show analytically and numerically that the quantum chaos diffusion of cold atoms, or ions, in a harmonic trap and pulsed optical lattice can be inverted back in time with up to 100\% efficiency. This is in sharp contrast to classical evolution where exponentially small errors break time reversibility. We argue that the existing experimental skills allow highlighting the Boltzmann-Loschmidt dispute from a quantum perspective.

            {
  "tldr": "This paper revisits the Boltzmann-Loschmidt dispute, demonstrating that quantum chaos diffusion in cold atoms can be reversed with nearly 100% efficiency, unlike classical systems where errors disrupt time reversibility.",
  "whats_new": [
    "• Provides a quantum perspective on the Boltzmann-Loschmidt dispute.",
    "• Shows time reversal of quantum chaos diffusion in cold atoms with high efficiency.",
    "• Contrasts quantum and classical time reversibility, highlighting quantum stability against errors."
  ],
  "method": "The authors analytically and numerically explore the time reversal of quantum chaos in cold atoms within a harmonic trap and pulsed optical lattice. They utilize a Hamiltonian model to describe the system's dynamics and implement time reversal through specific manipulations of the system parameters.",
  "results": [
    "• Time reversal achieved with up to 100% efficiency for cold atoms in a harmonic trap.",
    "• Quantum fidelity remains stable against noise, contrasting with classical systems where fidelity decays exponentially.",
    "• Demonstrated that classical dynamics show significant error growth, breaking time reversibility."
  ],
  "limitations": [
    "• Time reversal is effective primarily for atoms with specific fractional quasimomentum.",
    "• Interactions between atoms can degrade the time reversal signal.",
    "• The experimental realization of time reversal for all atomic states remains challenging."
  ],
  "why_useful": "This study enhances understanding of time reversibility in quantum systems, providing insights into quantum chaos and potential applications in quantum computing and information processing. It also reignites discussions on foundational issues in statistical mechanics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "All obtained data are contained in this article."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:24:18

Authors: Ahan Shabanov, Peter Hedman, Ethan Weber, Zhengqin Li, Denis Rozumny, Gael Le Lan, Naina Dhingra, Lei Luo, Andrea Vedaldi, Christian Richardt, Andrea Tagliasacchi, Bo Zhu, Numair Khan

Categories: cs.CV

Abstract:
We present Free-Range Gaussians, a multi-view reconstruction method that predicts non-pixel, non-voxel-aligned 3D Gaussians from as few as four images. This is done through flow matching over Gaussian parameters. Our generative formulation of reconstruction allows the model to be supervised with non-grid-aligned 3D data, and enables it to synthesize plausible content in unobserved regions. Thus, it improves on prior methods that produce highly redundant grid-aligned Gaussians, and suffer from holes or blurry conditional means in unobserved regions. To handle the number of Gaussians needed for high-quality results, we introduce a hierarchical patching scheme to group spatially related Gaussians into joint transformer tokens, halving the sequence length while preserving structure. We further propose a timestep-weighted rendering loss during training, and photometric gradient guidance and classifier-free guidance at inference to improve fidelity. Experiments on Objaverse and Google Scanned Objects show consistent improvements over pixel and voxel-aligned methods while using significantly fewer Gaussians, with large gains when input views leave parts of the object unobserved.

            {
  "tldr": "The paper introduces Free-Range Gaussians, a method for reconstructing 3D objects from sparse images using non-grid-aligned 3D Gaussians. It improves reconstruction quality in unobserved regions while using significantly fewer Gaussians than traditional methods.",
  "whats_new": [
    "• Proposes a generative approach using flow matching for non-grid-aligned 3D Gaussian reconstruction.",
    "• Introduces a hierarchical patching scheme to efficiently manage Gaussian tokens.",
    "• Implements a timestep-weighted rendering loss and guidance mechanisms to enhance fidelity.",
    "• Achieves state-of-the-art results with up to 10× fewer Gaussians compared to grid-aligned methods."
  ],
  "method": "The method uses flow matching to predict 3D Gaussian parameters from sparse images, allowing for non-grid-aligned placements. A hierarchical structure organizes Gaussians for efficient training, while guidance mechanisms improve detail and fidelity during inference.",
  "results": [
    "• Achieves PSNR of 29.92 on Objaverse with only 8K Gaussians, outperforming pixel-aligned methods.",
    "• Shows significant improvements in FID scores, indicating better quality in unobserved regions.",
    "• Reduces Gaussian count by approximately 10× compared to traditional grid-aligned methods.",
    "• Demonstrates consistent performance across both full and partial observation settings."
  ],
  "limitations": [
    "• The current Gaussian budget limits fine detail in reconstructions.",
    "• Iterative denoising process is slower than single-pass methods.",
    "• Further exploration needed for scalability with larger Gaussian counts.",
    "• Open questions remain regarding the generalization to more complex scenes."
  ],
  "why_useful": "This paper addresses the challenge of 3D reconstruction from sparse views, providing a more efficient and effective method that can be applied in various fields such as computer graphics, virtual reality, and robotics. Its ability to handle unobserved regions makes it particularly relevant for real-world applications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-06 17:07:30

Authors: M. Lamba, P. Saha, K. Yadav, N. Kamboj, S. Patnaik

Categories: cond-mat.str-el, cond-mat.mtrl-sci, cond-mat.other

Abstract:
We present a thorough magnetization and magneto-transport study of the diluted Fe-intercalated CrTe2 family member, Fe1/5CrTe2, a van der Waals ferromagnet. Fe1/5CrTe2 shows an elevated Curie transition temperature of 182 K in comparison to the Fe1/3CrTe2 composition, indicating the sensitive role of Fe concentration in modulating magnetic exchange interactions within the CrTe2 framework. The saturated magnetization exhibits a quadratic dependence with temperature, indicating the presence of long-wavelength spin fluctuations. Analysis of the temperature dependent resistivity reveals a dominant T3/2 contribution over the typical T2 behavior, signaling substantial coupling between conduction electrons and localized spins. The magnetoresistance shows a linear and non-saturating negative field dependency throughout a wide temperature range below TC, which is compatible with the increasing suppression of spin-disorder dispersion related to ferromagnetic spin fluctuations. A thorough analysis of the anomalous Hall effect (AHE) shows that extrinsic skew-scattering contribution, which is associated to Fe-related disorder, dominates the anomalous Hall response. The systematic separation of intrinsic and extrinsic components reveals that, over a wide temperature range, the intrinsic anomalous Hall conductivity scales linearly with the saturation magnetization, despite the substantial extrinsic dominant background. The linear behavior of intrinsic anomalous Hall conductivity with magnetization is in line with a long wavelength spin-fluctuation framework, where thermal spin disorder lowers net magnetization without significantly altering the underlying electronic structure. These findings reveal Fe1/5CrTe2 as a newly investigated van der Waals ferromagnet where spin fluctuations and disorder coexist with a well-defined intrinsic Berry-curvature contribution to the Hall response.

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Published: 2026-04-06 17:02:06

Authors: Runhao Mao, Hanshi Wang, Yixiang Yang, Qianli Ma, Jingmeng Zhou, Zhipeng Zhang

Categories: cs.CV

Abstract:
The integration of Vision-Language Models (VLMs) into autonomous driving promises to solve long-tail scenarios, but this paradigm faces the critical and unaddressed challenge of catastrophic forgetting. The very fine-tuning process used to adapt these models to driving-specific data simultaneously erodes their invaluable pre-trained world knowledge, creating a self-defeating paradox that undermines the core reason for their use. This paper provides the first systematic investigation into this phenomenon. We introduce a new large-scale dataset of 180K scenes, which enables the first-ever benchmark specifically designed to quantify catastrophic forgetting in autonomous driving. Our analysis reveals that existing methods suffer from significant knowledge degradation. To address this, we propose the Drive Expert Adapter (DEA), a novel framework that circumvents this trade-off by shifting adaptation from the weight space to the prompt space. DEA dynamically routes inference through different knowledge experts based on scene-specific cues, enhancing driving-task performance without corrupting the model's foundational parameters. Extensive experiments demonstrate that our approach not only achieves state-of-the-art results on driving tasks but also effectively mitigates catastrophic forgetting, preserving the essential generalization capabilities that make VLMs a transformative force for autonomous systems. Data and model are released at FidelityDrivingBench.

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Published: 2026-04-06 16:58:20

Authors: Amit Kiran Rege

Categories: cs.LG

Abstract:
We study how generator access constrains autoregressive post-training. The central question is whether the learner is confined to fresh root-start rollouts or can return to previously built prefixes and query the next-token rule there. In the root-start regime, output sampling, generated-token log probabilities, top-$k$ reports, and full next-token distributions along sampled trajectories all reduce to one canonical experiment, limited by the on-policy probability of reaching informative prefixes. Weak prefix control breaks this barrier, and once control is available, richer observations such as conditional sampling or logits can outperform top-$1$ access. Changing only the generator interface creates an exponential gap for KL-regularized outcome-reward post-training.

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Published: 2026-04-06 16:53:08

Authors: Cinar Ari, Robert Hildebrand

Categories: math.OC

Abstract:
Integer Quadratic Programming (IQP), $\min\{x^T Q x + c^T x : Ax \le b,\, x\in\Z^n\}$, is a fundamental problem in combinatorial optimization. While the convex and concave special cases admit polynomial-time algorithms for fixed~$n$, the general indefinite case is considerably harder: it was only recently shown to lie in NP, and the FPT algorithm, due to Lokshtanov, establishes fixed-parameter tractability parameterized by $n$ and the largest coefficient~$L$ without giving an explicit running time. We give the first single-exponential algorithm for IQP, solving it in time $ \bigl(n\,L^n_A\,Δ(A)\,L_Q\bigr)^{O(n)}\cdot\mathrm{poly}(\varphi), $ which is $(nL)^{O(n^2)}\cdot\mathrm{poly}(\varphi)$ in general using the same parameterization. We achieve improvements for structured cases like total unimodularity and further state explicit complexity results for a number of FPT algorithms and optimization problems. The single-exponential bound is achieved via curvature batching: we classify kernel directions by the sign of their quadratic curvature and observe that when no negative-curvature direction exists, all gradient constraints can be imposed simultaneously in a single batch. This replaces the chain of determinant squarings inherent in sequential branching with a single polynomial inflation, after which the remaining problem is an ILP. As a secondary contribution, we give an explicit bound for concave integer minimization over a polytope $\{Ax \le b\} \cap \Z^n$ whose parametric complexity depends only on the constraint matrix~$A$ and is independent of the right-hand side~$b$.

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Published: 2026-04-06 16:50:43

Authors: Amir Rafe, Anika Baitullah, Subasish Das

Categories: cs.CY

Abstract:
Public attitudes toward artificial intelligence (AI) and driving safety are typically studied in isolation using variable-centered methods that assume population homogeneity, yet risk perception theory predicts that these evaluations covary within individuals as expressions of underlying worldviews. This study identifies latent profiles of AI risk perception among U.S. adults and tests whether these profiles are differentially associated with community driving safety concerns. Latent class analysis was applied to nine AI risk-perception indicators from a nationally representative survey (Pew Research Center American Trends Panel Wave 152, n = 5,255); Bolck-Croon-Hagenaars corrected distal outcome analysis tested class differences on nine driving-safety outcomes, and survey-weighted multinomial logistic regression identified demographic and ideological predictors of class membership. Four classes emerged: Moderate Skeptics (17.5%), Concerned Pragmatists (42.8%), AI Ambivalent (10.6%), and Extreme Alarm (29.1%), with all nine driving-safety outcomes significantly differentiated across classes. Higher AI concern mapped monotonically onto greater perceived driving-hazard severity; the exception, comparative evaluation of AI versus human driving, was driven by trust rather than concern level. The cross-domain covariation provides person-level evidence for the worldview-based risk structuring posited by Cultural Theory of Risk and yields a four-class segmentation framework for AV communication that links AI risk orientation to transportation safety attitudes.

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Published: 2026-04-06 16:44:02

Authors: Yude Zou, Junji Gong, Xing Gao, Zixuan Li, Tianxing Chen, Guanjie Zheng

Categories: cs.CV, cs.AI

Abstract:
Human-object-scene interactions (HOSI) generation has broad applications in embodied AI, simulation, and animation. Unlike human-object interaction (HOI) and human-scene interaction (HSI), HOSI generation requires reasoning over dynamic object-scene changes, yet suffers from limited annotated data. To address these issues, we propose a coarse-to-fine instruction-conditioned interaction generation framework that is explicitly aligned with the iterative denoising process of a consistency model. In particular, we adopt a dynamic perception strategy that leverages trajectories from the preceding refinement to update scene context and condition subsequent refinement at each denoising step of consistency model, yielding consistent interactions. To further reduce physical artifacts, we introduce a bump-aware guidance that mitigates collisions and penetrations during sampling without requiring fine-grained scene geometry, enabling real-time generation. To overcome data scarcity, we design a hybrid training startegy that synthesizes pseudo-HOSI samples by injecting voxelized scene occupancy into HOI datasets and jointly trains with high-fidelity HSI data, allowing interaction learning while preserving realistic scene awareness. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both HOSI and HOI generation, and strong generalization to unseen scenes. Project page: https://yudezou.github.io/InfBaGel-page/

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Published: 2026-04-06 16:35:57

Authors: Jiajun Zhai, Hao Shi, Shangwei Guo, Kailun Yang, Kaiwei Wang

Categories: cs.CV, cs.MM, cs.RO, eess.IV

Abstract:
Robotic Vision-Language-Action (VLA) models generalize well for open-ended manipulation, but their perception is fragile under sensing-stage degradations such as extreme low light, motion blur, and black clipping. We present E-VLA, an event-augmented VLA framework that improves manipulation robustness when conventional frame-based vision becomes unreliable. Instead of reconstructing images from events, E-VLA directly leverages motion and structural cues in event streams to preserve semantic perception and perception-action consistency under adverse conditions. We build an open-source teleoperation platform with a DAVIS346 event camera and collect a real-world synchronized RGB-event-action manipulation dataset across diverse tasks and illumination settings. We also propose lightweight, pretrained-compatible event integration strategies and study event windowing and fusion for stable deployment. Experiments show that even a simple parameter-free fusion, i.e., overlaying accumulated event maps onto RGB images, could substantially improve robustness in dark and blur-heavy scenes: on Pick-Place at 20 lux, success increases from 0% (image-only) to 60% with overlay fusion and to 90% with our event adapter; under severe motion blur (1000 ms exposure), Pick-Place improves from 0% to 20-25%, and Sorting from 5% to 32.5%. Overall, E-VLA provides systematic evidence that event-driven perception can be effectively integrated into VLA models, pointing toward robust embodied intelligence beyond conventional frame-based imaging. Code and dataset will be available at https://github.com/JJayzee/E-VLA.

arXiv Page | PDF

Published: 2026-04-06 16:26:18

Authors: Seungjae Son

Categories: math.PR, math.ST

Abstract:
We study the spectral gaps of parallel and simulated tempering chains targeting multimodal Gibbs measures. In particular, we consider chains constructed from Metropolis random walks that preserve the Gibbs distributions at a sequence of harmonically spaced temperatures. We prove that their spectral gaps admit polynomial lower bounds of order $11$ and $12$ in terms of the low target temperature. The analysis applies to a broad class of potentials, beyond mixture models, without requiring explicit structural information on the energy landscape. The main idea is to decompose the state space and construct a Lyapunov function based on a suitably perturbed potential, which allows us to establish lower bounds on the local spectral gaps.

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Published: 2026-04-06 16:24:28

Authors: Jing-Zhi Cao, Huan-Yu Wei, Jiao-Xue Yang, Jian Sun, Chu-Wen Xiao

Categories: hep-ph

Abstract:
In the present work, we systematically investigate the meson-baryon molecular properties of the hidden charm pentaquark states $P_c$ and $P_{cs}$ within a coupled channel framework that combines heavy quark spin symmetry and the local hidden gauge formalism. By solving the Bethe-Salpeter equation with the momentum cutoff method, we obtain the pole trajectories, wave functions, and root-mean-square radii. For the hidden charm system, the full coupled channel interactions respecting the heavy quark spin symmetry are essential to generate the $P_c$ states, as they significantly affect the poles' widths. The dominant bound channels are $\bar{D} Σ_c$ and $\bar{D}^* Σ_c$, which couple strongly to lower decay channels. In contrast, for the hidden charm strange system, the full heavy quark spin symmetry treatment is not necessary, where the splitting PB and VB sectors yield similar results. The main bound channels $\bar{D} Ξ_c$ and $\bar{D}^* Ξ_c$ couple strongly to $\bar{D}_s Λ_c$ and $\bar{D}_s^* Λ_c$, respectively, but only weakly to the lower decay channels, differing from the hidden charm case. The trajectories of the pole widths for the loosely bound channels $\bar{D} Ξ'_c$, $\bar{D}^* Ξ'_c$, and $\bar{D}^* Ξ_c^*$ exhibit distinct behaviors. Notably, all the primary bound channels have similar binding energies in the single channel interactions due to equally attractive potentials. Furthermore, we also calculate the wave functions and root-mean-square radii of the corresponding poles. The wave functions are localized within $0\sim 6$ fm and vanish fast beyond $4$ fm. The root-mean-square radii, evaluated by two consistent methods, typically lie between $0.5$ and $2$ fm, comparable to the characteristic scale of molecular states.

arXiv Page | PDF

Published: 2026-04-06 16:24:07

Authors: Xu Mingze

Categories: cs.AI, cs.CL

Abstract:
AI agents, autonomous digital actors, need agent-native protocols; existing methods include GUI automation and MCP-based skills, with defects of high token consumption, fragmented interaction, inadequate security, due to lacking a unified top-level framework and key components, each independent module flawed. To address these issues, we present ANX, an open, extensible, verifiable agent-native protocol and top-level framework integrating CLI, Skill, MCP, resolving pain points via protocol innovation, architectural optimization and tool supplementation. Its four core innovations: 1) Agent-native design (ANX Config, Markup, CLI) with high information density, flexibility and strong adaptability to reduce tokens and eliminate inconsistencies; 2) Human-agent interaction combining Skill's flexibility for dual rendering as agent-executable instructions and human-readable UI; 3) MCP-supported on-demand lightweight apps without pre-registration; 4) ANX Markup-enabled machine-executable SOPs eliminating ambiguity for reliable long-horizon tasks and multi-agent collaboration. As the first in a series, we focus on ANX's design, present its 3EX decoupled architecture with ANXHub and preliminary feasibility analysis and experimental validation. ANX ensures native security: LLM-bypassed UI-to-Core communication keeps sensitive data out of agent context; human-only confirmation prevents automated misuse. Form-filling experiments with Qwen3.5-plus/GPT-4o show ANX reduces tokens by 47.3% (Qwen3.5-plus) and 55.6% (GPT-4o) vs MCP-based skills, 57.1% (Qwen3.5-plus) and 66.3% (GPT-4o) vs GUI automation, and shortens execution time by 58.1% and 57.7% vs MCP-based skills.

arXiv Page | PDF

Published: 2026-04-06 16:20:54

Authors: Khai Nguyen, Duc M. Doan, Hung Q. Nguyen

Categories: quant-ph

Abstract:
Contextuality and nonlocality are distinct manifestations at the foundation of quantum mechanics, yet their coexistence within a single quantum state remains subtle. In a hybrid CHSH--KCBS scenario involving the entanglment of a qubit and a qutrit, the qutrit supports the KCBS contextuality test, and the CHSH nonlocality arises from correlations between the qubit and qutrit. Here, we derive the analytical closed-form expressions for both inequalities and also simulate this physics on a quantum circuit. We show that contextuality is governed solely by a population parameter $p_2$, associated with the occupation of the qutrit subsystem in the $|2\rangle$ level, which plays a distinguished role in the KCBS structure. In contrast, nonlocality depends irreducibly on coherence, involving both amplitudes and phases encoded in parameters $(X_i, Y_i)$. This separation of physical resources reveals parameter regimes that optimize KCBS violation while suppress CHSH violation, and vice versa. As a result, the optimal regions do not overlap, and coexistence is restricted to a narrow intermediate regime in parameter space.

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Published: 2026-04-06 16:20:47

Authors: Cheng Xu, Changhong Jin, Yingjie Niu, Nan Yan, Yuke Mei, Shuhao Guan, Liming Chen, M-Tahar Kechadi

Categories: cs.CL, cs.AI

Abstract:
The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to complex reasoning. However, evaluation frameworks have not kept pace. Current benchmarks are static, making them vulnerable to benchmark data contamination (BDC) and ineffective at assessing reasoning under temporal uncertainty. To address this, we introduce LiveFact a continuously updated benchmark that simulates the real-world "fog of war" in misinformation detection. LiveFact uses dynamic, temporal evidence sets to evaluate models on their ability to reason with evolving, incomplete information rather than on memorized knowledge. We propose a dual-mode evaluation: Classification Mode for final verification and Inference Mode for evidence-based reasoning, along with a component to monitor BDC explicitly. Tests with 22 LLMs show that open-source Mixture-of-Experts models, such as Qwen3-235B-A22B, now match or outperform proprietary state-of-the-art systems. More importantly, our analysis finds a significant "reasoning gap." Capable models exhibit epistemic humility by recognizing unverifiable claims in early data slices-an aspect traditional static benchmarks overlook. LiveFact sets a sustainable standard for evaluating robust, temporally aware AI verification.

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Published: 2026-04-06 16:19:59

Authors: Yue Hu

Categories: astro-ph.HE, astro-ph.GA

Abstract:
Galactic cosmic rays (CRs) are a fundamental non-thermal component of the interstellar medium (ISM). Understanding the transport of super-high-energy particles is essential for interpreting observations of Galactic PeVatrons. Classical diffusion models assuming a homogeneous and isothermal medium oversimplify the multiphase ISM. We utilize high-resolution 3D MHD simulations to self-consistently generate a multiphase ISM, comprising the warm (WNM), unstable (UNM), and cold neutral medium (CNM), and investigate 1.5-15 PeV particle transport using a test-particle approach. We find that thermal phase transitions induce steep magnetic field strength gradients at phase boundaries, creating localized magnetic fluctuations that act as efficient sites for adiabatic mirror reflections and non-adiabatic pitch-angle scattering, strongly enhancing cross-field transport at these interfaces. However, because phase boundaries occupy only a small volume fraction and particles spend most of their trajectory in the weakly scattering WNM and UNM, the global pitch-angle scattering coefficient in the multiphase ISM is smaller than in an equivalent isothermal medium. This locally strong scattering nevertheless drives both parallel and perpendicular spatial diffusion coefficients to $\sim10^{30} {\rm cm^2 s^{-1}$ at 1.5~PeV, with the perpendicular component exceeding its isothermal counterpart ($\sim 10^{28}{\rm cm^2 s^{-1}$) by two orders of magnitude. Using a phase--phase diffusion matrix decomposition, we show that global CR transport is governed by the volume-filling, trans-Alfvénic WNM and UNM, where particles stream along stochastically wandering field lines. Cross-phase displacement correlations are universally positive, indicating cooperative transport between thermal phases. In contrast, the super-Alfvénic CNM acts as an efficient confinement that substantially suppresses local diffusion.

arXiv Page | PDF

Published: 2026-04-06 16:16:00

Authors: Songyuan Yang, Huibin Tan, Kailun Yang, Wenjing Yang, Shaowu Yang

Categories: cs.RO, cs.CV, cs.HC

Abstract:
We introduce AnyUser, a unified robotic instruction system for intuitive domestic task instruction via free-form sketches on camera images, optionally with language. AnyUser interprets multimodal inputs (sketch, vision, language) as spatial-semantic primitives to generate executable robot actions requiring no prior maps or models. Novel components include multimodal fusion for understanding and a hierarchical policy for robust action generation. Efficacy is shown via extensive evaluations: (1) Quantitative benchmarks on the large-scale dataset showing high accuracy in interpreting diverse sketch-based commands across various simulated domestic scenes. (2) Real-world validation on two distinct robotic platforms, a statically mounted 7-DoF assistive arm (KUKA LBR iiwa) and a dual-arm mobile manipulator (Realman RMC-AIDAL), performing representative tasks like targeted wiping and area cleaning, confirming the system's ability to ground instructions and execute them reliably in physical environments. (3) A comprehensive user study involving diverse demographics (elderly, simulated non-verbal, low technical literacy) demonstrating significant improvements in usability and task specification efficiency, achieving high task completion rates (85.7%-96.4%) and user satisfaction. AnyUser bridges the gap between advanced robotic capabilities and the need for accessible non-expert interaction, laying the foundation for practical assistive robots adaptable to real-world human environments.

arXiv Page | PDF

Published: 2026-04-06 16:10:57

Authors: Long Feng, Xiaoyi Wang, Le Zhou

Categories: stat.ME

Abstract:
We study high-dimensional regression in principal components space when the predictors are observed with additive measurement error and the response errors may be heavy-tailed. The starting point is the $\ell_1$-penalized principal-components estimator of Song and Zou (2026), which enjoys a blessing-of-dimensionality phenomenon under predictor contamination but senstive for heavy-tailed data or outliers. We replace the squared loss by a Wilcoxon-type rank loss and then apply a one-step adaptive reweighting scheme to reduce the shrinkage bias of the initial $\ell_1$ fit. The resulting procedure combines robustness to heavy-tailed response errors with the contamination geometry induced by the empirical principal-components basis. Our main theorem gives a prediction bound for the fixed-$λ$ second-stage fitted mean. Simulations show that the rank-based procedure is competitive under Gaussian noise and substantially more stable under heavy-tailed errors, especially when predictor contamination is present.

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Published: 2026-04-06 16:10:14

Authors: Zane Ma, Predrag Despotovic, Pranab Mishra, Kevin Rossel, Athanasios Avgetidis, Zane Ma

Categories: cs.CR

Abstract:
The namespace for filenames and DNS names has overlapped since the introduction of DNS in 1985: \texttt{.com} was the original binary format used for DOS and CP/M systems. Recently the introduction of gTLDs such as \texttt{.zip} and \texttt{.mov}, coupled with the growing prevalence of web resources, has ignited new concerns about potential issues related to DNS and filename confusion. Thus far, the discourse on DNS/filename confusion has been piecemeal and hypothetical, making it unclear what, if any, security concerns credibly exist. To address this gap, we provide the first enumeration of how DNS/filename confusion can be abused. We then perform the first empirical case studies of DNS/filename confusion in the wild, which highlights suspected confusion across a wide range of software. Finally, based on our preliminary findings, we provide suggestions and guidance for future research on this topic.

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Published: 2026-04-06 16:08:42

Authors: Edward Shuryak, Ismail Zahed

Categories: hep-ph, hep-lat, hep-th, nucl-th

Abstract:
We present a constituent two-gluon description of the lowest-lying glueball states in pure Yang--Mills theory, calibrated against quenched lattice results. The framework incorporates an instanton-induced dynamical gluon mass, Casimir-scaled adjoint confinement, the short-distance adjoint Coulomb interaction, and instanton-induced central and tensor forces. The scalar $0^{++}$ glueball is found to be exceptionally compact, with a radius of order the instanton size, $ρ\sim \frac 13\,\mathrm{fm}$, consistent with lattice indications. By contrast, the tensor $2^{++}$ state remains spatially extended due to the centrifugal barrier. We also discuss the role of $S$-$D$ mixing. A semiclassical analysis further supports Regge behavior for excited states, in agreement with lattice results.

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Published: 2026-04-06 16:06:07

Authors: Houzhe Wang, Xiaojie Zhu, Chi Chen

Categories: cs.LG, cs.CR

Abstract:
With the increasing importance of data privacy and security, federated unlearning has emerged as a novel research field dedicated to ensuring that federated learning models no longer retain or leak relevant information once specific data has been deleted. In this paper, to the best of our knowledge, we propose the first complete pipeline for federated unlearning, which includes a federated unlearning approach and an evaluation framework. Our proposed federated unlearning approach ensures high efficiency and model accuracy without the need to store historical data.It effectively leverages the knowledge distillation model alongside various optimization mechanisms. Moreover, we propose a framework named Skyeye to visualize the forgetting capacity of federated unlearning models. It utilizes the federated unlearning model as the classifier integrated into a Generative Adversarial Network (GAN). Afterward, both the classifier and discriminator guide the generator in generating samples. Throughout this process, the generator learns from the classifier's knowledge. The generator then visualizes this knowledge through sample generation. Finally, the model's forgetting capability is evaluated based on the relevance between the deleted data and the generated samples. Comprehensive experiments are conducted to illustrate the effectiveness of the proposed federated unlearning approach and the corresponding evaluation framework.

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Published: 2026-04-06 15:59:17

Authors: Amit Levy, Itzik Klein

Categories: eess.SP

Abstract:
The unscented Kalman filter (UKF) is a commonly used algorithm capable of estimating the states of nonlinear dynamic systems. It carefully chooses a set of sample points, called sigma points that capture the nonlinear system states posterior mean and covariance. The filter is based on the scaled unscented transform, where the scaling parameters impact the spreading of the sigma points, determining the estimated model capturing. In its current form, the UKF employs a single set of scaling parameters shared by all sigma points. Because states in multi-dimensional models often exhibit substantially different behaviors, this imposes a critical limitation: the standard UKF parameters cannot be tuned to extend the spread for one dimension while reducing it for another. To bridge this gap, we propose the multi-scaled UKF to enable spreading differently per state, while maintaining the key properties of the sigma points and UKF. A rigorous mathematical foundation is provided, introducing a novel theoretical approach to multi-scaling. The benefits of this approach are demonstrated through two distinct nonlinear dynamic systems. Consequently, our multi-scaled UKF captures the nonlinear behavior of multi-dimensional states more effectively, leading to improved estimation accuracy.

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Published: 2026-04-06 15:55:52

Authors: Rituparna R, Harsha Varthini, Aswani Kumar Cherukuri

Categories: quant-ph, cs.AI

Abstract:
This paper presents a quantum search approach to combinatorial constraint satisfaction problems, demonstrated through the generation of magic squares. We reformulate magic square construction as a quantum search problem in which a reversible, constraint-sensitive oracle marks valid configurations for amplitude amplification via Grover's algorithm. Classical pre-processing using the Siamese construction and partial constraint checks generates a compact candidate domain before quantum encoding. Rather than integrating classical and quantum solvers in an iterative loop, this work uses the classical component for structured initialisation and the quantum component for search, and benchmarks the quantum approach against classical brute-force enumeration and backtracking. Our Qiskit implementation demonstrates the design of multi-register modular arithmetic circuits, oracle logic, and diffusion operators. Experiments are conducted on small grid instances, as larger grids are intractable on classical statevector simulators due to exponential memory growth. The results validate the correctness of the proposed quantum search pipeline and confirm the theoretical quadratic query advantage over classical search.

arXiv Page | PDF

Published: 2026-04-06 15:49:01

Authors: Junior da Silva Bessa, Jehan Oh

Categories: math.AP

Abstract:
In this paper we establish optimal $C^{1,α}$ regularity up to the boundary for viscosity solutions of fully nonlinear elliptic equations with double phase degeneracy law and oblique boundary conditions. The approach developed here relies on first deriving uniform boundary Hölder estimates for perturbed models with oblique boundary data in ``almost $C^{1}$-flat'' domains. Building upon these estimates, the desired regularity is obtained through a compactness and stability framework for viscosity solutions. As a byproduct of our analysis, we determine the optimal Hölder exponent for solutions when the governing operator is quasiconvex or quasiconcave. In addition, we establish an improved regularity result along vanishing points of the source term.

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Published: 2026-04-06 15:46:41

Authors: Amir Rafe, Subasish Das

Categories: cs.CY

Abstract:
Road traffic crashes claim approximately 1.19 million lives annually worldwide, and human error accounts for the vast majority, yet the autonomous vehicle acceptance literature models adoption almost exclusively through technology-centered pull factors such as perceived usefulness and trust. This study examines a moderated mediation model in which perceived community driving-safety concern (PCSC) predicts evaluations of AI versus human driving capability, mediated by Generalized AI Orientation and moderated by personal driving frequency. Weighted structural equation modeling is applied to a nationally representative U.S. probability sample from Pew Research Center's American Trends Panel Wave 152, using Weighted Least Squares Mean and Variance Adjusted (WLSMV)-estimated confirmatory factor analysis on ordinal indicators, bias-corrected bootstrap inference, and seven robustness checks including Imai sensitivity analysis, E-value confounding thresholds, and propensity score matching. Results reveal a dual-pathway mechanism constituting an inconsistent mediation: PCSC exerts a small positive direct effect on AI driving evaluation, consistent with a domain-specific push interpretation, while simultaneously suppressing Generalized AI Orientation, which is itself a strong positive predictor of AI driving evaluation. Conditional indirect effects are negative and statistically significant at low, mean, and high levels of driving frequency. These findings establish a risk-spillover mechanism whereby community driving-safety concern promotes domain-specific AI endorsement yet suppresses domain-general AI enthusiasm, yielding a near-zero net total effect.

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Published: 2026-04-06 15:40:49

Authors: Xing-Fu Zhang, Ruo-Yu Liu, Dmitriy Khangulyan, Cui-Yuan Dai, Xiang-Yu Wang

Categories: astro-ph.HE

Abstract:
Cygnus X-3 has recently been established as a variable ultra-high-energy(UHE) gamma-ray source with photons detected up to 3.7~PeV. The temporal correlation between its PeV activity and GeV flares, together with the possible orbital modulation, suggests that the emission is produced within or close to the binary system. In this work, we test whether the contemporaneous GeV emission zone can also host the acceleration of the parent protons responsible for the multi-PeV photons. We jointly model the contemporaneous \textit{Fermi}-LAT spectrum and orbital light curve with a one-zone leptonic scenario dominated by anisotropic external inverse-Compton scattering. The fit places the GeV emission region at $H\sim2.8\times10^{11}\,$cm and constrains the magnetic field--size product to $BH\lesssim10^{13.3}\,$G\,cm at the $3σ$ level. This implies a maximum proton energy of only $\sim0.3$~PeV from the Hillas criterion, far below that required by the observed PeV emission. We therefore conclude that the GeV zone cannot be the main PeV acceleration site. Instead, the PeV emission should originate from a more compact inner region, and the jet magnetic field must dissipate rapidly between the PeV and GeV emitting zones.

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Published: 2026-04-06 15:30:53

Authors: Dmitriy Chunaev, Polina Evdokimova

Categories: math.AG

Abstract:
We say that a locally nilpotent derivations $δ$ is maximal if there are no inequivalent locally nilpotent derivations that commute with $δ$. The paper gives a description of isotropy groups of maximal homogeneous locally nilpotent derivations on affine toric varieties and on certain trinomial hypersurfaces. Moreover, the criteria for homogeneous locally nilpotent derivations to be maximal were obtained for these classes of varieties.

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Published: 2026-04-06 15:29:37

Authors: Alex Krasnok

Categories: quant-ph, physics.app-ph, physics.optics

Abstract:
The phrase ``buy a quantum computer'' hides several different procurement problems. An institution may be seeking cloud access for teaching, reserved capacity for research, a local instrument for hardware training, an optimization appliance, or a strategic installation that reshapes facilities, staffing, and budgets. Because these choices differ in purpose, operating burden, and useful lifetime, the decision should be framed as acquisition of \emph{quantum capability} rather than selection of a presumed hardware winner. This manuscript develops a practical procurement framework that distinguishes five capability layers, separates peer-reviewed results from commercial offerings, pricing anchors, and public roadmaps, and compares the main commercial platform families -- superconducting circuits, trapped ions, neutral atoms, quantum annealing, and photonics -- through the lens of institutional fit, access model, and refresh pressure. The main conclusion is that most institutions should begin with the smallest layer of capability that produces repeatable near-term value, builds internal expertise, and preserves strategic flexibility. Large on-premises systems are justified only when mission requirements, site readiness, staffing, governance, and upgrade paths are already clear.

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Published: 2026-04-06 15:27:05

Authors: Zijun Wang, Haoqin Tu, Letian Zhang, Hardy Chen, Juncheng Wu, Xiangyan Liu, Zhenlong Yuan, Tianyu Pang, Michael Qizhe Shieh, Fengze Liu, Zeyu Zheng, Huaxiu Yao, Yuyin Zhou, Cihang Xie

Categories: cs.CR, cs.AI, cs.CL

Abstract:
OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services such as Gmail, Stripe, and the filesystem. While these broad privileges enable high levels of automation and powerful personalization, they also expose a substantial attack surface that existing sandboxed evaluations fail to capture. To address this gap, we present the first real-world safety evaluation of OpenClaw and introduce the CIK taxonomy, which unifies an agent's persistent state into three dimensions, i.e., Capability, Identity, and Knowledge, for safety analysis. Our evaluations cover 12 attack scenarios on a live OpenClaw instance across four backbone models (Claude Sonnet 4.5, Opus 4.6, Gemini 3.1 Pro, and GPT-5.4). The results show that poisoning any single CIK dimension increases the average attack success rate from 24.6% to 64-74%, with even the most robust model exhibiting more than a threefold increase over its baseline vulnerability. We further assess three CIK-aligned defense strategies alongside a file-protection mechanism; however, the strongest defense still yields a 63.8% success rate under Capability-targeted attacks, while file protection blocks 97% of malicious injections but also prevents legitimate updates. Taken together, these findings show that the vulnerabilities are inherent to the agent architecture, necessitating more systematic safeguards to secure personal AI agents. Our project page is https://ucsc-vlaa.github.io/CIK-Bench.

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Published: 2026-04-06 15:11:57

Authors: Lei Zhang, Junjiao Tian, Zhipeng Fan, Kunpeng Li, Jialiang Wang, Weifeng Chen, Markos Georgopoulos, Felix Juefei-Xu, Yuxiang Bao, Julian McAuley, Manling Li, Zecheng He

Categories: cs.CV

Abstract:
Humans paint images incrementally: they plan a global layout, sketch a coarse draft, inspect, and refine details, and most importantly, each step is grounded in the evolving visual states. However, can unified multimodal models trained on text-image interleaved datasets also imagine the chain of intermediate states? In this paper, we introduce process-driven image generation, a multi-step paradigm that decomposes synthesis into an interleaved reasoning trajectory of thoughts and actions. Rather than generating images in a single step, our approach unfolds across multiple iterations, each consisting of 4 stages: textual planning, visual drafting, textual reflection, and visual refinement. The textual reasoning explicitly conditions how the visual state should evolve, while the generated visual intermediate in turn constrains and grounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: how can models evaluate each partially-complete image? We address this through dense, step-wise supervision that maintains two complementary constraints: for the visual intermediate states, we enforce the spatial and semantic consistency; for the textual intermediate states, we preserve the prior visual knowledge while enabling the model to identify and correct prompt-violating elements. This makes the generation process explicit, interpretable, and directly supervisable. To validate proposed method, we conduct experiments under various text-to-image generation benchmarks.

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Published: 2026-04-06 13:48:44

Authors: Ngoc Phuc Le, Mohamed-Slim Alouini

Categories: eess.SP

Abstract:
In this paper, we investigate high-altitude platform station (HAPS) wireless networks for simultaneous non-orthogonal unicast and multicast transmissions. Specifically, stacked intelligent metasurface (SIM)-based wave-domain beamforming is proposed to enable efficient HAPS-to-ground communications. Also, the system performance is investigated from an energy-efficiency (EE) perspective, which is a crucial for HAPS operations. For performance analysis, we derive approximate closed-form expressions for the outage probability over Rician fading channels. For EE optimization, we jointly optimize the transmit power and the SIM phase-shifts for the maximal EE. Two methods are proposed to solve this non-convex optimization problem. The first method develops an efficient alternating optimization (AO) framework based on golden-section search and projected gradient ascent (PGA) for transmit power and phase-shift optimization, respectively. The second method uses unsupervised deep neural network (DNN) that does not require labeling. Performance comparison between the two methods, as well as with other benchmarks schemes are examined. Additionally, the impacts of the number of SIM elements per layers, the number of SIM layers, the maximum transmit power on the EE performance are evaluated. Simulation results are provided to demonstrate the performance of the proposed systems.

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Published: 2026-04-06 13:43:07

Authors: Ruan Diego da Silva Paiva, José Francisco de Oliveira

Categories: math.AP, math.FA

Abstract:
We establish a Trudinger-Moser type inequality with a Tintarev-type constraint in fractional-dimensional spaces and prove the existence of maximizers in the critical regime. Our results provide a refinement of those in (Calc. Var. 52 (2015), 125-163) in the setting of fractional-dimensional spaces, as well as of those in (Ann. Global Anal. Geom. 54 (2018), 237-256) for classical Sobolev spaces.

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Published: 2026-04-06 13:32:37

Authors: Daniel Andrew Coulson, Martin T. Wells

Categories: math.ST, cs.LG, stat.ML

Abstract:
Bayesian neural networks (BNNs) offer a natural probabilistic formulation for inference in deep learning models. Despite their popularity, their optimality has received limited attention through the lens of statistical decision theory. In this paper, we study decision rules induced by deep, fully connected feedforward ReLU BNNs in the normal location model under quadratic loss. We show that, for fixed prior scales, the induced Bayes decision rule is not minimax. We then propose a hyperprior on the effective output variance of the BNN prior that yields a superharmonic square-root marginal density, establishing that the resulting decision rule is simultaneously admissible and minimax. We further extend these results from the quadratic loss setting to the predictive density estimation problem with Kullback--Leibler loss. Finally, we validate our theoretical findings numerically through simulation.

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Published: 2026-04-06 13:30:54

Authors: Tom Garcia-Sanchez

Categories: math.PR

Abstract:
We study the topological structure of random geometric forests $G$ in the Euclidean plane under mild assumptions: non-crossing edges, stationarity, and finite edge intensity. The framework covers a broad range of constructions, including models based on stationary point processes as well as lattices, and encompasses many already well-studied examples among drainage networks, geodesic forests arising from first- and last-passage percolation, and minimal or uniform spanning trees. First, denoting by $N_k$ the number of $k$-ended connected components in $G$ for each $k\geq0$, we show that almost surely, all trees of $G$ have at most two topological ends, $N_0\in\{0,\infty\}$, $N_1\leq2$, and $N_1=2\implies N_2<\infty$. We then construct explicit examples realizing all possibilities compatible with these constraints, yielding a complete classification of the admissible topological structures for $G$. As a second result, we prove that under the additional assumptions that $G$ is non-empty, oriented, out-degree one, with all its directed paths going to infinity along a fixed deterministic direction, the situation reduces to a dichotomy: $G$ consists almost surely of either a unique one-ended tree, or infinitely many two-ended trees. Our proofs combine classical Burton-Keane type arguments with substantial new conceptual ideas using planar topology, resulting in a robust, unified approach.

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Published: 2026-04-06 13:30:25

Authors: Arka Majhi, Aparajita Mondal, Satish B. Agnihotri

Categories: cs.HC, cs.CY

Abstract:
Community Health Workers (CHWs) play a critical role in delivering primary healthcare services in low-resource settings, yet sustaining their training and performance remains a persistent challenge. Prior research has explored digital and game-based approaches for CHW training. However, limited work has synthesized longitudinal design insights into generalizable guidelines for interactive health interventions. Building on a four-year design-based research program involving multiple game-based refresher training systems, including quiz-based mobile apps, physical and augmented reality games, card-based games, and location-based games, we examine which design guidelines support sustained engagement, learning transfer, and contextual appropriateness in CHW training. We conducted a mixed-methods analysis across deployments with Accredited Social Health Activists and Anganwadi Workers in India, including interviews, field observations, and usage logs. Through thematic synthesis, we derive eight design guidelines addressing contextual realism, adaptive learning, hybrid interaction, social motivation, explainability, professional identity, and ethical considerations. Our findings contribute actionable design knowledge for researchers and practitioners developing interactive health interventions in low-resource healthcare contexts.

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Published: 2026-04-06 13:21:17

Authors: Selim Ahmet Iz, Francesco Nex, Norman Kerle, Henry Meissner, Ralf Berger

Categories: cs.CV, cs.LG, cs.RO

Abstract:
Real-time depth reconstruction from ultra-high-resolution UAV imagery is essential for time-critical geospatial tasks such as disaster response, yet remains challenging due to wide-baseline parallax, large image sizes, low-texture or specular surfaces, occlusions, and strict computational constraints. Recent zero-shot diffusion models offer fast per-image dense predictions without task-specific retraining, and require fewer labelled datasets than transformer-based predictors while avoiding the rigid capture geometry requirement of classical multi-view stereo. However, their probabilistic inference prevents reliable metric accuracy and temporal consistency across sequential frames and overlapping tiles. We present ZeD-MAP, a cluster-level framework that converts a test-time diffusion depth model into a metrically consistent, SLAM-like mapping pipeline by integrating incremental cluster-based bundle adjustment (BA). Streamed UAV frames are grouped into overlapping clusters; periodic BA produces metrically consistent poses and sparse 3D tie-points, which are reprojected into selected frames and used as metric guidance for diffusion-based depth estimation. Validation on ground-marker flights captured at approximately 50 m altitude (GSD is approximately 0.85 cm/px, corresponding to 2,650 square meters ground coverage per frame) with the DLR Modular Aerial Camera System (MACS) shows that our method achieves sub-meter accuracy, with approximately 0.87 m error in the horizontal (XY) plane and 0.12 m in the vertical (Z) direction, while maintaining per-image runtimes between 1.47 and 4.91 seconds. Results are subject to minor noise from manual point-cloud annotation. These findings show that BA-based metric guidance provides consistency comparable to classical photogrammetric methods while significantly accelerating processing, enabling real-time 3D map generation.

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Published: 2026-04-06 13:13:49

Authors: Yihan Sun, Yuqi Cheng, Junjie Zu, Yuxiang Tan, Guoyang Xie, Yucheng Wang, Yunkang Cao, Weiming Shen

Categories: cs.CV

Abstract:
Industrial 3D anomaly detection performance is fundamentally constrained by the scarcity and long-tailed distribution of abnormal samples. To address this challenge, we propose Synthesis4AD, an end-to-end paradigm that leverages large-scale, high-fidelity synthetic anomalies to learn more discriminative representations for 3D anomaly detection. At the core of Synthesis4AD is 3D-DefectStudio, a software platform built upon the controllable synthesis engine MPAS, which injects geometrically realistic defects guided by higher-dimensional support primitives while simultaneously generating accurate point-wise anomaly masks. Furthermore, Synthesis4AD incorporates a multimodal large language model (MLLM) to interpret product design information and automatically translate it into executable anomaly synthesis instructions, enabling scalable and knowledge-driven anomalous data generation. To improve the robustness and generalization of the downstream detector on unstructured point clouds, Synthesis4AD further introduces a training pipeline based on spatial-distribution normalization and geometry-faithful data augmentations, which alleviates the sensitivity of Point Transformer architectures to absolute coordinates and improves feature learning under realistic data variations. Extensive experiments demonstrate state-of-the-art performance on Real3D-AD, MulSen-AD, and a real-world industrial parts dataset. The proposed synthesis method MPAS and the interactive system 3D-DefectStudio will be publicly released at https://github.com/hustCYQ/Synthesis4AD.

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Published: 2026-04-06 13:05:13

Authors: Songge Zhang, Wen Wu, Liang Li, Ye Wang, Xuemin, Shen

Categories: cs.DC

Abstract:
Low Earth orbit (LEO) satellites play an essential role in intelligent Earth observation by leveraging artificial intelligence models. However, limited onboard memory and excessive inference delay prevent the practical deployment of large language models (LLMs) on a single satellite. In this paper, we propose a communication-efficient collaborative LLM inference scheme for LEO satellite networks. Specifically, the entire LLM is split into multiple sub-models, with each deployed on a satellite, thereby enabling collaborative LLM inference via exchanging intermediate activations between satellites. The proposed scheme also leverages the pipeline parallelism mechanism that overlaps sub-model inference with intermediate activation transmission, thereby reducing LLM inference delay. An adaptive activation compression scheme is designed to mitigate cumulative errors from multi-stage model splitting while preserving inference accuracy. Furthermore, we formulate the LLM inference delay minimization problem by jointly optimizing model splitting and compression ratios under onboard memory and inference accuracy constraints. The problem is transformed into a shortest-path search problem over a directed acyclic graph that edge weights explicitly quantify the inference delay induced by model splitting and compression strategies, which is solved via a modified A Star-based search algorithm. Extensive simulation results indicate that the proposed solution can reduce inference delay by up to 42% and communication overhead by up to 71% compared to state-of-the-art benchmarks, while maintaining the inference accuracy loss of less than 1%.

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Published: 2026-04-06 13:00:38

Authors: Yizhou Liu, Qi Sun, Yulin Chen, Siyue Zhang, Chen Zhao

Categories: cs.AI

Abstract:
Agents equipped with search tools have emerged as effective solutions for knowledge-intensive tasks. While Large Language Models (LLMs) exhibit strong reasoning capabilities, their high computational cost limits practical deployment for search agents. Consequently, recent work has focused on distilling agentic behaviors from LLMs into Small Language Models (SLMs). Through comprehensive evaluation on complex multi-hop reasoning tasks, we find that despite possessing less parametric knowledge, SLMs invoke search tools less frequently and are more prone to hallucinations. To address this issue, we propose \policy, a lightweight fine-tuning approach that explicitly trains SLMs to reliably retrieve and generate answers grounded in retrieved evidence. Compared to agent distillation from LLMs, our approach improves performance by 17.3 scores on Bamboogle and 15.3 scores on HotpotQA, achieving LLM-level results across benchmarks. Our further analysis reveals that adaptive search strategies in SLMs often degrade performance, highlighting the necessity of consistent search behavior for reliable reasoning.

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Published: 2026-04-06 12:50:20

Authors: Chonghu Guan, Zuo Quan Xu

Categories: math.OC, math.AP, q-fin.MF, q-fin.PM

Abstract:
We consider an optimal dividend payout problem for an insurance company whose surplus follows the classical Cramér-Lundberg model. The dividend rate is subject to a ratcheting constraint (i.e., it must be nondecreasing over time), and the company may inject capital at a proportional cost to avoid ruin. This problem gives rise to a stochastic control problem with a self-path-dependent control constraint, costly capital injections, and jump-diffusion dynamics. The associated Hamilton-Jacobi-Bellman (HJB) equation is a partial integro-differential variational inequality featuring both a nonlocal integral term and a gradient constraint. We develop a systematic probabilistic and PDE-based approach to solve this HJB equation. By discretizing the space of admissible dividend rates, we construct a sequence of approximating regime-switching systems of ordinary integro-differential equations. Through careful a priori estimates and a limiting argument, we prove the existence and uniqueness of a \emph{strong solution} in a suitable space. This regularity result is fundamental: it allows us to characterize the optimal dividend policy via a switching free boundary and to construct an explicit optimal feedback control strategy. To the best of our knowledge, this is the first complete solution -- comprising both the value function and an implementable optimal strategy -- for a dividend ratcheting problem with capital injection under the Cramér-Lundberg model. Our work advances the mathematical theory of optimal stochastic control beyond the standard viscosity solution framework, providing a rigorous foundation for dividend policy design in economics.

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Published: 2026-04-06 12:39:22

Authors: Hongju Pae

Categories: cs.AI

Abstract:
What kind of internal organization would allow an artificial agent not only to adapt its behavior, but to sustain a history-sensitive perspective on its world? I present a minimal architecture in which a slow perspective latent $g$ feeds back into perception and is itself updated through perceptual processing. This allows identical observations to be encoded differently depending on the agent's accumulated stance. The model is evaluated in a minimal gridworld with a fixed spatial scaffold and sensory perturbations. Across analyses, three results emerge: first, perturbation history leaves measurable residue in adaptive plasticity after nominal conditions are restored. Second, the perspective latent reorganizes perceptual encoding, such that identical observations are represented differently depending on prior experience. Third, only adaptive self-modulation yields the characteristic growth-then-stabilization dynamic, unlike rigid or always-open update regimes. Gross behavior remains stable throughout, suggesting that the dominant reorganization is perceptual rather than behavioral. Together, these findings identify a minimal mechanism for history-dependent perspectival organization in artificial agents.

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Published: 2026-04-06 12:37:03

Authors: Gen Zu, Ning Mao, Claudia Felser, Yang Zhang

Categories: cond-mat.dis-nn, cond-mat.mtrl-sci, cs.LG

Abstract:
Characterizing crystalline energy landscapes is essential to predicting thermodynamic stability, electronic structure, and functional behavior. While machine learning (ML) enables rapid property predictions, the "black-box" nature of most models limits their utility for generating new scientific insights. Here, we introduce Kolmogorov-Arnold Networks (KANs) as an interpretable framework to bridge this gap. Unlike conventional neural networks with fixed activation functions, KANs employ learnable functions that reveal underlying physical relationships. We developed the Element-Weighted KAN, a composition-only model that achieves state-of-the-art accuracy in predicting formation energy, band gap, and work function across large-scale datasets. Crucially, without any explicit physical constraints, KANs uncover interpretable chemical trends aligned with the periodic table and quantum mechanical principles through embedding analysis, correlation studies, and principal component analysis. These results demonstrate that KANs provide a powerful framework with high predictive performance and scientific interpretability, establishing a new paradigm for transparent, chemistry-based materials informatics.

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Published: 2026-04-06 12:30:28

Authors: Matthew Shu Liang, Yi-Jie Wang, Geng-Dong Zhou, Zhi-Da Song, Xi Dai

Categories: cond-mat.str-el, cond-mat.mes-hall, cond-mat.supr-con

Abstract:
We study strongly correlated superconductivity in magic-angle twisted bilayer graphene (MATBG) using variational Gutzwiller wavefunction where the Gutzwiller projector $\hat{P}_{R}$ is allowed to break charge U(1) symmetry to accommodate superconducting (SC) order. The ground state energy is evaluated via the Gutzwiller Approximation applied to an 8-band model consisting of correlated f-orbitals and uncorrelated c-orbitals, with interactions including onsite Coulomb repulsion $U$, phonon-mediated anti-Hund's coupling $\hat{H}_{J_A}$, and intra-orbital Hund's coupling $\hat{H}_{J_H}$. At filling $ν=2.5$, we map out the phase diagram as a function of $U$ and $J_A$, finding a dome-shaped Fermi liquid (FL) phase that separates a weakly correlated BCS-like SC (BCS-SC) at small $U$ from a strongly correlated SC (SC-SC) at large $U$. A nematic SC state, stabilized over a large region of the phase diagram including the realistic parameter regime of MATBG, acquires a nodal gap structure with V-shaped density of states at large $U$ via interaction-driven SC gap reconstruction. In the SC-SC regime, the off-diagonal (charge-U(1)-breaking) components of $\hat{P}_{R}$ strongly suppress $f$-orbital charge fluctuations while maintaining finite pairing order and a sizeable quasiparticle weight $Z$, distinguishing it from a conventional Mott insulator. We further identify a novel small Fermi liquid (sFL) state with effective Fermi surface volume $=ν+2$. Interestingly, in the intermediate- ($U \lesssim 40$ meV) and large-$U$ ($U \gtrsim 40$ meV) regimes, the conventional FL and the sFL are the lowest-energy normal phases, respectively, potentially serve as the parent states of the SC-SC phase. These results illuminate the interplay between strong correlations and unconventional pairing in MATBG, and establish a versatile Gutzwiller framework applicable to other strongly correlated superconductors.

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