Systems and Control
Model-Free Current Control of Permanent Magnet Synchronous Motors via ESO-Based Disturbance Feedforward and Data-Driven H-infinity Residual Feedback
YongBo Li, Shuang Liang and HongWei Ma
This paper proposes a model-free current control method for permanent magnet synchronous motors (PMSMs) based on disturbance feedforward and residual feedback. An ultra-local current model incorporates motor dynamics, parameter uncertainties, cross-coupling effects, and other nonideal factors into generalized lumped disturbances. An extended state observer (ESO) estimates these lumped disturbances and compensates for them through feedforward action, transforming the original PMSM current-control problem into regulation of a simplified post-compensation residual system. A state-feedback H-infinity controller for the residual system is then learned directly from operating data using off-policy integral reinforcement learning. Owing to the simplified residual dynamics, the value function and control policies are parameterized in quadratic and linear forms, reducing the learning problem to low-dimensional parameter estimation without neural-network approximation. The proposed method requires neither prior knowledge nor online identification of PMSM electrical parameters: input-gain mismatch is incorporated into the ESO-estimated lumped dynamics, while the residual-feedback policy is obtained from operating data. Comparative simulations against deadbeat predictive current control, model-based H-infinity control, and model-free predictive current control show fast current tracking, low current distortion, and strong robustness to large parameter variations. With the learned H-infinity policy fixed and without retraining or retuning, nearly unchanged control performance is maintained when stator resistance, stator inductance, and permanent-magnet flux linkage are simultaneously varied to 20% and 200% of their nominal values.
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Astrophysics of Galaxies
Mass Distribution of M31 from its Rotation Curve up to 167 kpc
Hao Wu, Yang Huang, Huawei Zhang et al. (+2 more)
The mass distribution of the Andromeda
galaxy (M31) is fundamental for understanding its dark matter content, assembly history, and dynamical role within the Local Group. However, estimates of M31's total mass remain quite uncertain, partly because of limited dynamical constraints at large galactocentric radii. Moreover, M31's recent merger history has produced abundant
tidal substructures and may have left its outer
halo out of dynamical
equilibrium, complicating
equilibrium-based mass estimates. We use over 8,500 tracers with line-of-sight velocities to constrain the rotation curve (RC) of M31 over R=2–167 kpc. In the disk, we derive the RC from a clean tracer sample using the Jeans equation and find good agreement with previous H I measurements. In the
halo, we use Gaussian mixture modeling to account for
tidal substructures and
Galactic foreground contamination and constrain the kinematics of the residual dynamically hot
halo. Assuming this
halo population can be treated as an
equilibrium tracer, we infer the outer RC and constrain M31's mass distribution for different velocity anisotropies beta and dark matter
halo profiles. The NFW and Dekel–Zhao (DZ) models yield broadly consistent total masses ranging from 1.12(+0.11/-0.10) to 1.63(+0.21/-0.17)x10^12 Msun for beta=0.3–0.7. In contrast, the Einasto profile favors substantially lower masses of 0.41(+0.09/-0.06) to 0.86(+0.21/-0.16)x10^12 Msun, highlighting significant dependence on the adopted
halo profile, although Einasto solutions are more in tension with independent mass estimates based on M31 satellite
galaxies than the NFW and DZ models at fixed beta. Overall, our analysis provides a comprehensive measurement of M31's mass distribution under the assumption of dynamical
equilibrium, while also quantifying the systematic uncertainties associated with
halo velocity anisotropy and the choice of dark matter density profile.
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All authors Hao Wu, Yang Huang, Huawei Zhang, Qikang Feng, Cheng Liu
Machine Learning
Minimal Recurrent Behavioral Memory for Imitation under Partial Observability
Xianyao Li, Fang Xu, Rui Min et al. (+2 more)
What is the least recurrent
memory needed to reproduce a specified expert under partial observability? The instantaneous requirement is the conditional
entropy of the expert's behavioral quotient, but recurrence must also preserve distinctions that future observations will not restore before use. We characterize this minimal recurrent behavioral
memory by a compatibility relation: under transitivity its classes attain the exact minimum, while the general case is an
entropy minimization over closed compatible state assignments, with exact certificates on finite instances. A sole-carrier measurement protocol separates behavioral sufficiency, excess code rate, and information carried by observations or other
memory paths; experimental bit requirements refer to the induced symbolic behavioral model under the stated occupancy. Across manipulation tasks, learned code rates remain near zero- and two-bit requirements as hidden modes grow to $512$, and anticipatory
memory follows a $2\to1\to0$ requirement despite zero instantaneous demand during waiting. Learning this representation remains difficult: event-agnostic future-behavior supervision yields $36/40$ sufficient seeds with one frozen configuration and improves the longest-horizon pixel setting from $0/8$ to $6/8$ sufficient held-out seeds (closed-loop success from $0.08$ to $0.57$). On unmodified community
benchmarks, the protocol certifies delay-independent requirements, which sufficient codes match at mid-delay. The supervision aids commitment but can induce predictive surplus; annealing it lets imitation and rate training reduce that surplus, separating the information-theoretic target from the ability to learn it.
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All authors Xianyao Li, Fang Xu, Rui Min, Ruitong Tian, Jing Du
Robotics
MedVLA: A Hierarchical Vision-Language-Action Framework for Closed-Loop Precision Medical Robot Manipulation
Junjie Xie, Angen Ye, Chuxuan He et al. (+2 more)
Precision medical robotics demands adaptive decision-making under strict safety, interpretability, and execution constraints. Although recent
Vision-
Language-Action (VLA) models show strong
multimodal reasoning ability, their continuous action generation paradigm is not well suited for precision medical tasks, where reliable closed-loop operation may also depend on non-action system function calls. To address this gap, we propose MedVLA, a hierarchical framework that couples high-level
multimodal reasoning with low-level function-constrained execution. We further introduce a scalable multi-
agent pipeline to generate skill-oriented chain-of-thought(CoT) data for structured training. Built on different
multimodal large-model backbones, MedVLA consistently improves performance after fine-tuning, demonstrating the effectiveness of the proposed framework across model variants. Under identical initial conditions, we perform 100 closed-loop flexible electrode implantation trials. The results show that MedVLA achieves a 95.0% task success rate, substantially outperforming representative VLA baselines, including OpenVLA (8%) and $\pi_0$ (15%), in accuracy, stability, and safety. These results indicate that structured
reasoning with constrained function-level execution is a practical route toward deployable precision medical robotics.
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All authors Junjie Xie, Angen Ye, Chuxuan He, Yujia Song, Dapeng Zhang
Computation and Language
Syndrome, Synergy, and Safety: Structured Reasoning and Knowledge-Driven Alignment for TCM Prescription Generation
Zheng Chen, ZhiCheng Du, Haoxuan Li et al. (+1 more)
Applying large
language models to Traditional Chinese Medicine (TCM) prescription generation reveals three clinically critical gaps: models produce end-to-end mappings without auditable
reasoning following the li-fa-fang-yao paradigm (SR Gap), treat each encounter in isolation without follow-up adjustment via sui zheng jia jian (LA Gap), and fail to enforce absolute contraindication rules such as Shi Ba Fan (SC Gap). We propose a progressive four-stage framework (SFT $\to$ PG-CoT $\to$ Dynamic $\to$ K-RL) that addresses each gap: PG-CoT constrains CoT
distillation under the li-fa-fang-yao paradigm to produce auditable diagnostic chains, Dynamic SFT models patient trajectories with explicit transition
reasoning, and K-RL encodes deterministic pharmacological rules as rule-based DPO preference signals. Across 12 fine-tuned models and 6 zero-shot baselines, our framework substantially improves prescription quality over zero-shot baselines—with a 7B model (Mistral-7B) surpassing zero-shot GPT-5 on all three TCM evaluation metrics.
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All authors Zheng Chen, ZhiCheng Du, Haoxuan Li, Peiwu Qin
Robotics
PLAT: Sparse Timed Keyframe Motion Tracking for Humanoid Control via Privileged Latent Transition Learning
Zepeng Wang, Jiangxing Wang, Chao Ma et al. (+2 more)
Humanoid motion tracking policies rely on dense frame-by-frame references, limiting their use as high-level motion controllers for planning and interactive motion generation. We study Sparse Timed Keyframe Motion Tracking, where a policy receives only sparse future keyframes and their desired arrival times, and must execute stable whole-body motions that reach successive goals. We propose PLAT, a three-stage sparse timed keyframe motion tracking policy learning framework with Privileged LAtent Transition learning. PLAT bridges dense motion tracking and sparse goal-conditioned control by exploiting dense goal sequences as privileged supervision during training while requiring only sparse timed keyframe commands at deployment. A pretrained dense tracking expert first provides robust motion priors. A privileged latent prior is then learned through DAgger-style imitation, followed by latent residual reinforcement learning that refines latent transitions instead of directly optimizing actions. Extensive simulation experiments demonstrate that PLAT maintains accurate and stable sparse timed keyframe tracking across varying planning horizons, with particularly strong performance under long-horizon commands. Successful deployment on a Unitree G1
humanoid robot further demonstrates the effectiveness and practicality of PLAT for sparse
humanoid motion control.
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All authors Zepeng Wang, Jiangxing Wang, Chao Ma, Xiaochuan Shi, Zongqing Lu
Quantum Physics
Photon-Statistics Control of Strong-Field Rydberg-State Excitation
Tao Jiang, Jinlei Liu, Haopu Dou et al. (+8 more)
Photon statistics provides a new control dimension for strong-field bound-state formation. Using a
quantum-optical phase-space formulation coupled to time-dependent Schrödinger-equation calculations, we show that field fluctuations enhance multiphoton Rydberg excitation by broadening AC-Stark-shifted resonances, but suppress tunneling-mediated recapture even as ionization increases. A
quantum-statistical recapture model attributes the latter to ensemble-induced dispersion of relative path phases.
Photon statistics also reshapes Rydberg-
manifold parity, linking field statistics to recapture coherence and final-state
symmetry.
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All authors Tao Jiang, Jinlei Liu, Haopu Dou, Lingyi Zhao, Peng Xiang, Liangjun Peng, Renyu Wang, Jing Zhao, Xiaowei Wang, Yue Lang, Zengxiu Zhao
Optimization and Control
Efficient Parameter-Free First-Order Methods for Nonsmooth Composite Minimax Optimization
Shaozhe Ke, Sanyou Mei and Jiheng Zhang
In this paper we propose first-order methods for a class of nonsmooth composite strongly convex–strongly concave and nonconvex–concave minimax optimization. We first develop an inexact proximal method and an accumulative regularizated method for strongly convex–strongly concave problems. The latter achieves the optimal dependence on the
curvature parameters and places the smaller
curvature inside the accuracy logarithm. Using this method as a subsolver, we propose a proximal point method for nonconvex–concave problems. Under suitable assumptions, it finds an $\epsilon$-stationary point with an operation complexity of $O(\epsilon^{-5/2})$, which improves the best-known $O(\epsilon^{-5/2}\log(1/\epsilon))$ bounds by removing the logarithmic factor. We further develop parameter-free variants for both problem classes, and achieve the same complexity without knowledge of any problem constants. All proposed methods are equipped with verifiable termination criteria.
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Superconductivity
Analysis of Josephson junctions switching time distributions for the detection of single microwave photons
A. S. Piedjou Komnang, C. Guarcello, C. Barone et al. (+5 more)
We investigate an optimal scheme for the detection of single microwave
photons by a Josephson junction through the analysis of its switching times distribution. The proposed analysis is of support for the decision about the existence of the
photon field, which is important in the case of rare events. We assume that the cavity and the transmission line are ideal (each
photon absorbed to the cavity gives a current pulse as the output of the transmission line) and the
photon source is periodic. The employed methodology consists in comparing the switching probabilities of a Josephson junction exposed to a train of current pulses, simulating a weak
photon field, with that of the same device in absence of pulses. In both cases, thermal noise can induce thermal activated switchings. The investigation of the unbalance in the number of switching events in the two cases, gives an estimate of the efficiency of the detection. Furthermore, in the assumption of escapes described by Kramers model, it is possible to provide a relationship between the properties of the
photons field, the
quantum efficiency of the detection process, and the Josephson junctions switching features at finite temperatures.
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All authors A. S. Piedjou Komnang, C. Guarcello, C. Barone, C. Gatti, S. Pagano, Vincenzo Pierro, A. Rettaroli, G. Filatrella
Robotics
Fisheye-VLA: Decoupling Coverage and Acuity for Manipulation with a Single Fisheye Camera
Ziang Ren, Zike Yan, Raymond Zhang et al. (+2 more)
Manipulation requires both broad scene awareness and detailed local feedback, yet conventional camera rigs provide them through separate front and wrist cameras. We present Fisheye-VLA, a visual interface that brings these capabilities together using a single passive fisheye. A global view preserves the workspace, while local perspective crops direct detail toward the interaction. The key design question is where this local visual budget should go. We answer it through a controlled re-rendering study, comparing alternative crop directions on the same recorded observations. The study finds that end-effector-centered views capture most of the estimated benefit of a much larger candidate pool, motivating a compact allocation around both hands. Our interface uses calibrated end-effector projection and motion lead to track the crops, while a shared ray encoding preserves their spatial meaning as they move. Integrated with a pretrained VLA, it achieves 84% and 82% success in the two expanded tabletop regions, where some target placements extend beyond the front-camera coverage, and supports shelf and conveyor manipulation. Ablations show that local crops and their viewing directions become more important in the larger workspace regions. The results demonstrate that a single fisheye can support these manipulation tasks without physical wrist cameras.
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All authors Ziang Ren, Zike Yan, Raymond Zhang, Xuguo He, Zhongyu Li
Nuclear Theory
Electronic-Structure Control of Nuclear Excitation by Electron Capture in Finite-Density $^{229}$Th
Yang-Yang Xu, Jin-Tao Qi, Qiong Xiao et al. (+4 more)
Nuclear excitation by electron capture (NEEC) provides a unique pathway for coupling electronic and
nuclear dynamics, but its description in dense matter commonly relies on electronic structures of isolated ions. Here we show how dense environments reshape the available NEEC capture channels. Using a finite-temperature average-atom model, we assess channel availability by jointly considering electronic localization, resonance energy matching, and vacancy availability. Near solid density, the shallow $6p$ states remain sufficiently localized to support resonant electron capture that drives the 8.356-eV isomeric transition in $^{229}$Th, whereas higher valence-like states merge into the continuum and no longer constitute localized capture channels. Calculations at different temperatures and densities reveal distinct windows of channel availability arising from the interplay among pressure-induced delocalization, shifts in resonance energy, and vacancy formation. Coupling the reconstructed channels to particle-in-
cell simulations of
laser-driven $^{229}$Th further shows that electronic structure at finite density can substantially alter the predicted cumulative NEEC yield. These results demonstrate how the electronic environment governs resonant capture pathways, highlighting its essential role in
nuclear excitation driven by electrons in dense matter.
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All authors Yang-Yang Xu, Jin-Tao Qi, Qiong Xiao, Jun-Hao Cheng, Xin-Yan Li, Tai-Wu Huang, Tong-Pu Yu
Analysis of PDEs
Global regularity for the unidirectional Euler-alignment system with supercritical dissipation
Changhui Tan and Liutang Xue
We prove global well-posedness of the periodic unidirectional Euler-
alignment system in every dimension for supercritical dissipation of order $0<\alpha<1$ and arbitrary non-
vacuum initial data in the Sobolev class of the local theory. The key idea is to propagate simultaneously two
moduli of continuity at critical scales for the density $\rho$ and the potential gradient $\Gamma=\nabla\Lambda^{-\alpha}u$, both of which have scaling-invariant amplitudes. We also establish exponential
alignment of the velocity and exponential convergence of the density to a traveling profile.
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Optimization and Control
On the optimal control of entry-exit phenomena in planar fast-slow dynamical systems
Jacopo Borsotti, Christian Kuehn and Mattia Sensi
We study a
geometric singular perturbation theory (GSPT) approach to optimal control problems involving entry-exit phenomena in planar fast-slow dynamical systems. In contrast to previous work, we assume that the control acts only on the slow dynamics and we consider minimum-time optimal control problems (i.e., we aim at minimizing the time to reach a certain target). We apply this method to a simplified fast-slow toy model, characterized by a repetition of entry-exit processes, enabling a transparent analytical exploration of the control dynamics and allowing us to derive the key results explicitly. First, we analyze each non-autonomous entry-exit phenomenon separately, and we derive the optimal way to control them. Second, by artificially generating a stable limit cycle, we determine under which conditions it is possible to reach the target. Finally, under such conditions, we combine all entry-exit processes together and we demonstrate the existence of an optimal control. Our approach mainly exploits techniques of GSPT and dynamic programming. Indeed, we show that our problem can be formulated in a way resembling the Bellman equation, even though not in its standard setting. Numerical simulations, built on such Bellman-like formulation, illustrate our analytical results. In particular, the optimal control is derived with an
algorithm that converges in a finite number of steps. Lastly, we extend this novel approach to more complex planar fast-slow dynamical systems.
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Robotics
Dual Covariance Gaussian Splatting SLAM: Decoupling Rendering and Registration for Robust Real-Time Tracking
Edward Beng Wai Tan and Siew-Kei Lam
ICP-based 3D Gaussian Splatting (3DGS) SLAM tracks in real time by registering incoming frames against map Gaussians, using each primitive's covariance for both rendering and registration. These two uses place conflicting demands on one covariance. The mapper shapes it to minimize photometric error, often flattening it against surfaces, while robust registration typically benefits from measurement uncertainty. We propose a dual-covariance parameterization. Each Gaussian keeps a single mean but holds two covariances: a rendering covariance optimized by the mapper, and a tracking covariance derived from an RGB-D sensor noise model. We further use the tracking covariances as Gaussian anchors for image corners, providing constraints in directions where depth geometry is weak. We evaluate on TUM RGB-D, ScanNet, Replica, and two outdoor sequences recorded with a RealSense D435i on wheeled and handheld platforms. We achieve robust tracking performance across multiple scenes and reduced odometry drift, while tracking at $\sim$ 60 FPS.
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Machine Learning
Modular Norm RandOpt: Population-Efficient Ensembling through Architecture-Aware Perturbations
Kirato Yoshihara and Hiroaki Hamade
RandOpt samples weight-perturbed
language models and ensembles top-ranked candidates through plurality voting, but its global perturbation scale ignores heterogeneous module geometry. We propose Modular Norm RandOpt, an architecture-aware sampling method using module-wise natural norms and calibrated scales while preserving selection and voting. It outperforms RandOpt using $3\times$ fewer candidates on Countdown and at least $12\times$ fewer on GSM8K, with corresponding wall-clock savings. Evaluations across seven tasks and three Qwen scales ($0.5$B–$3$B) show higher mean accuracy than RandOpt on Countdown, GSM8K, and MATH-500 at every scale. The gains extend to Llama 3.2 $3$B and Gemma 3 $4$B on Countdown and GSM8K. On Qwen2.5-1.5B, our ensembles also achieve higher mean accuracy than iterative baselines on both tasks at comparable main-run evaluation budgets. On GSM8K, a tail-density diagnostic implies only a $1.2$–$1.8\times$ candidate reduction, while most ensemble improvement is associated with more favorable correct-expert support. These results highlight perturbation geometry as a key design choice for population-efficient, gradient-free search around pretrained models.
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Optics
Intra-bandgap Bound-State Magnonics in a Photonic Crystal Waveguide
Chong-Xin Run, Xue-Feng Pan, Bang-Yao Wu et al. (+5 more)
Cavity and waveguide magnonics have recently emerged as frontiers for investigating light-matter interactions. In these systems, coupling
magnons into meticulously engineered photonic
lattices with rich band structures provides a promising platform for tailoring
magnon-
photon interactions. However, how these hybrid interactions behave in the bandgaps of photonic
lattices—specifically, the formation of
magnon-
photon bound states (MPBSs)—remains largely unaddressed. Crucially, these bound states arise through a mechanism that is fundamentally distinct from bound states in the continuum (BICs). Here, we experimentally demonstrate intra-bandgap bound-state magnonics by coupling yttrium iron garnet (YIG) spheres to a microwave photonic
crystal waveguide. At room temperature, we observe MPBSs featuring highly tunable spatial localization, power-dependent interaction modulation under microwave pumping, and long-range
magnon-
magnon interactions mediated by mode overlap. These unique features bridge fundamental insights into intra-bandgap light-matter interactions with practical applications, offering an avenue for scaling up coherent networks and investigating many-body physics through engineered interactions.
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All authors Chong-Xin Run, Xue-Feng Pan, Bang-Yao Wu, Kai-Min Hsieh, Wei-Min Zhou, Anton Frisk Kockum, Peng-Bo Li, Io-Chun Hoi
Computer Vision
SAMI3D-DW: Interactive Segmentation of Any 3D Medical Images
Ping Gong, Shiyuan Su, Fandong Zhang et al. (+4 more)
Interactive
segmentation of 3D medical images supports quantitative analysis of anatomical structures and disease while allowing users to specify and refine their targets. Despite substantial progress by nnInteractive and VISTA3D, reliable
segmentation across diverse clinical targets remains challenging, particularly for complex anatomical structures and the heterogeneous, long-tailed spectrum of pathology. We present SAMI3D-DW V1 (hereafter SAMI3D-DW), an interactive 3D
segmentation model trained on Deepwise's large-scale proprietary medical image
datasets. We evaluate the model under simulated user interactions on a CT/MR
benchmark comprising 4,326 cases from 219 source
datasets, spanning 107 anatomical and pathological categories, organized by a medical taxonomy and evaluated with a category-balanced DSC score. SAMI3D-DW achieves the highest category-macro Dice among evaluated methods in both interaction modes. With one point, it scores 0.5756 versus 0.5316 for nnInteractive, the strongest baseline, rising to 0.7771 versus 0.7495 with five points. With bounding-box initialization, the scores are 0.7129 versus 0.6530. After five corrective clicks, SAMI3D-DW reaches 0.8004 versus 0.7868. For radiologists and clinicians, SAMI3D-DW enables
segmentation of complex anatomical structures, including intracranial vessel trees on CT and MR angiography, with a few clicks. In a preliminary in-house comparison involving neurofibromatosis type 1 (NF1), SAMI3D-DW-assisted tumor annotation took minutes per case and approximately one-fifteenth of the time required for manual annotation, highlighting its potential to support volumetric treatment-response assessment.
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All authors Ping Gong, Shiyuan Su, Fandong Zhang, Xinchen Han, Haowei Sun, Yiming Li, Yizhou Yu
Combinatorics
Zero-Run Spectra of the $(3,2)$ Raney numbers Modulo Primes
Sen-Peng Eu, Zai-Ting Huang and Louis Kao
We study the zero-run structure of the $(3,2)$-Raney numbers modulo a
prime $p$. For every
prime $p$, we determine the left-to-right maxima of the zero-run lengths, the complete zero-run spectrum, and the exact number of nonzero entries in $0\le n<p^m$. Interestingly, these results fall into three cases: $p=2$, $p=3$, and $p\geq5$, and the behaviors in these three cases are very different. For $p=2$, we characterize exactly the odd terms and determine the positions of the left-to-right maxima and the results involve Fibbinary integers, Fibonacci numbers, and Jacobsthal numbers. For $p=3$, we characterize exactly the nonzero terms and determine their residues. For $p\ge 5$, the zero runs are governed by a multiscale system of residue intervals modulo powers of $p$, from which both the record values and the complete zero-run spectrum are obtained.
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Computer Vision
Fysiverse-3D-Vision Technical Report: Generating Executable 3D Worlds from Images through Unified Spatial Reasoning
Dingkang Yang, Yizhou Liu, Wendong Cheng et al. (+5 more)
Generative models have advanced image-conditioned 3D content creation, yet generating controllable and executable 3D scenes from a single image remains challenging. Existing 3D generative approaches can synthesize visually plausible objects and scenes, but their spatial layout estimation is coupled with specific asset generators. They struggle to jointly model object semantics, metric geometry, and scene-level spatial relationships, which are essential for interactive editing, physical simulation, and
embodied applications. We propose Fysiverse-3D-
Vision, a unified
vision-
language-geometry framework for generative 3D scene reconstruction and executable asset construction from a single image. We establish a shared representation where spatial
reasoning and
geometric reconstruction mutually enhance each other, allowing object layouts to be inferred beyond the constraints of individual asset generators. Our model integrates textual supervision, semantic visual cues, and
geometric representations within a unified
Transformer to capture scene context, metric geometry, and object-level interactions. An object-conditioned layout module performs cross-
attention between target object representations and global
geometric features to predict object translation, rotation, and scale. Training progressively learns geometry-
language alignment, introduces layout
reasoning while preserving reconstruction capability, and refines physical consistency through collision-aware optimization. By separating spatial layout
reasoning from asset synthesis, Fysiverse-3D-
Vision provides an adaptable interface for interactive scene editing, object-level manipulations, and executable 3D content generation. Experiments demonstrate that our framework achieves superior
geometric consistency, layout estimation, rendering quality, and physical property understanding compared with existing approaches.
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All authors Dingkang Yang, Yizhou Liu, Wendong Cheng, Zizhi Chen, Shunli Wang, Yang Liu, Hongsheng Li, Lihua Zhang
Algebraic Geometry
Equality and iso-duality of Generalized Geometric Goppa codes
Jyoti Dasgupta and Nupur Patanker
Geometric Goppa codes are constructed by evaluating elements of an algebraic function field at the places of degree one. Extending this, generalized
geometric Goppa codes were defined in [XNL99] and [Hey02] by allowing evaluation at places of arbitrary degree. In this paper, we establish necessary and sufficient conditions for equality of generalized
geometric Goppa codes, following the approach of [MP93]. These conditions serve as a key tool in our characterization of iso-dual and self-dual generalized
geometric Goppa codes. Given a separable extension M/F of algebraic function fields and an iso-dual generalized
geometric Goppa code over F, we extend the construction of [CPQT24] to obtain iso- dual generalized
geometric Goppa codes over M. Furthermore, we study self-duality of generalized AG codes ([XNL99], [DM03]).
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Number Theory
On the positivity of truncated pentagonal number series and some conjectures of Merca
Manjil P. Saikia and Abhishek Sarma
Let $\nu_2(m)$ denote the $2$-adic valuation of a positive integer $m$ and set $N_m=m\bigl(1+\nu_2(m)/2\bigr)$. We prove four
conjectures of Merca on the nonnegativity of truncated pentagonal number series weighted by the infinite products $\prod_{m\ge 1}(1-q^{2N_m})$ and $\prod_{r\ge 1}\bigl(q^{2^r r};q^{2^{r+1}r}\bigr)_\infty$. Our method is to regard the exponent map $m\mapsto m(\nu_2(m)+2)$ as a dynamical system on the positive integers: the factors of the associated quotient link into chains along its forward orbits, and the three orbits seeded at $1$, $4$ and $5$ are pairwise disjoint and
telescope to exactly $1/\bigl((1-q)(1-q^4)(1-q^5)\bigr)$. The exponent triple $(1,4,5)$ is admissible in the sense of earlier work by Liu, which yields the desired factorization into two series with nonnegative coefficients. This orbit telescoping technique appears to be a mechanism complementary to the Pólya–Szeg\H o criterion that underlies most existing positivity results of this kind in the literature.
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Artificial Intelligence
OmniFysics-Nano-V2 Technical Report: Understanding the Physical World Across Modalities
Yizhou Liu, Jinghang Han, Kaixiang Qiu et al. (+8 more)
Omni-modal models have expanded
multimodal interaction across
vision, audio,
speech, and
language. However, their training is predominantly organized around semantic descriptions and general-purpose objectives, leaving physical attributes, interaction states, and causal mechanisms only partially specified. This gap is not simply a matter of modality coverage: adding more modalities does not by itself provide the supervision needed to connect observations with the physical structure of the world. We present OmniFysics-Nano-V2, a compact omni-modal model for physical-world perception and understanding. The model supports image,
video, audio,
speech, and text inputs within a shared
reasoning framework, together with text and
speech generation. To address the lack of explicit physical supervision, we construct a dual-branch physics-aware data pipeline that grounds salient objects in structured physical attributes and aligns visual changes with acoustic events, intermediate responses, and interaction outcomes. To address homogeneous training objectives, we curate reinforcement-learning
prompts by reward diversity and adopt a two-stage Group Relative Policy Optimization curriculum that progresses from general task correctness to fine-grained physical perceptual
reasoning. Experiments across
multimodal, audio-visual, and physical
reasoning benchmarks show that the proposed data and training strategy improves physical-world understanding while preserving broad omni-modal competence. The proposed model achieves leading result on 17 of 21
benchmarks against SOTA omni-modal models. By equipping AI systems with both omni-modal and physical-world perception capabilities, OmniFysics-Nano-V2 is poised to become a cornerstone of next-generation Physical AI.
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All authors Yizhou Liu, Jinghang Han, Kaixiang Qiu, Qi He, Minghao Han, Yue Jiang, Xujia Chen, Wei Zou, Shunli Wang, Lihua Zhang, Dingkang Yang
Signal Processing
Scalable Extended-Target Handover in Distributed Integrated Sensing and Communication
Liping Bai, Yu Ge, Ali Al Khansa et al. (+3 more)
Distributed integrated sensing and communication (DISAC) networks require multi-target tracking methods whose per-node computation and inter-base-station communication remain bounded as both the network and target populations grow. Existing multisensor fusion methods provide a strong theoretical foundation, but scalability is seldom treated as the primary design objective. We develop a grouped-measurement belief-propagation multi-target tracking (MTT) method and combine it with a proposed event-triggered target-handover protocol. When a tracked target is predicted to become observable at another base station, the owner transfers a predicted track message while retaining its local copy. Under bounded local workload and neighbor degree, we show that all variants of the proposed handover methods have network-size-independent per-node loads. Simulations demonstrate that handover reduces boundary track loss relative to uncoordinated processing and approaches coordinated-processing accuracy with lower communication overhead.
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All authors Liping Bai, Yu Ge, Ali Al Khansa, Hyowon Kim, Erik Leitinger, Henk Wymeersch
Dynamical Systems
Maximal Multiplicity Method for Optimal Damping Problem
Maxim Arnold, Oleg Gendelman and Vadim Zharnitsky
We study the problem of designing optimal damping for a system of $n$ coupled linear oscillators. Using the Weyl–Horn
theorem, we construct an explicit damping
matrix that realizes the optimal asymptotic decay rate of the system. We prove that this decay rate is sharp and is determined by the
geometric mean of the natural frequencies. The resulting damping strategy is shown to outperform the classical Rayleigh (proportional) damping approach. We conclude by characterizing the parameter regimes in which the optimal damping
matrix necessarily ceases to be positive definite and discuss the implications of this phenomenon.
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Machine Learning
Beyond Class Marginals: Bounding Rehearsal Gaps without Freezing Class Co-occurrence
Congren Dai, Nat Roongjirarat and Fei Ye
Class-balanced replay controls class frequency but does not determine the interval between successive replay appearances of a class. We study this interval, the rehearsal gap, separately from the class marginal and class co-occurrence, and introduce randomised-pass replay (RPR), which visits each resident class once per shuffled pass. For a fixed set of C resident classes and replay batch size b less than or equal to C, RPR preserves the balanced time-averaged class marginal and bounds every gap by 2*ceil(C/b)-1; a churn-conditional bound applies while the resident set changes. The scheduler uses no future class information and adds no replay examples or forward passes. In a linear-head ER-ACE diagnostic, joint absence from the incoming and replay batches produces a one-sided classifier-bias gradient. Longer absence episodes are associated with larger negative bias displacement, and removing the incoming-loss mask attenuates the scheduling effect. In the primary ER-ACE experiments, RPR improves final average accuracy by 0.72-1.67 percentage points relative to independent class-balanced
retrieval under reservoir storage, with positive effects also observed under balanced storage. Pretrained ViTs show positive effects on the tested LT10 streams with small replay batches, while matched larger-batch controls show no material effect. Fixed-cycle and reused-pass controls change more than one temporal statistic, so the experiments do not isolate rehearsal-gap length from all other forms of temporal dependence. The accuracy effects depend on the learner and operating regime.
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Cryptography and Security
GuidedRay: Diversity-Guided Direction Discovery for Targeted Hard-Label Black-Box Attacks
Fei Yuan, Yantian Shen, Qingyuan Yu et al. (+5 more)
Deep neural networks are vulnerable to
adversarial attacks. Among black-box attacks, targeted decision-based attacks are particularly difficult: the attacker observes only the target model's top-1 label and aims to make it predict a prespecified target class under a bounded perturbation. Before perturbation refinement, the attacker must discover a direction that reaches the prescribed target region. This initialization step can incur substantial query cost. We propose GuidedRay, a targeted decision-based attack based on diversity-guided direction discovery. GuidedRay builds on two observations: target-class reference samples provide useful target-conditioned direction priors, and diverse candidates increase the probability of discovering a targeted
adversarial direction. GuidedRay generates varied candidates from one or multiple target-class references and uses a one-query Fast Test to screen their induced sign directions. Once a feasible direction is found, GuidedRay applies Ray Search to reduce its decision-boundary radius. Experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate that GuidedRay consistently outperforms five state-of-the-art decision-based attacks at four evaluated query budgets from 500 to 5,000, with particularly pronounced gains in direction discovery during initialization. Against models protected by
adversarial training or TRADES, it likewise achieves the highest attack success rate at all four query budgets.
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All authors Fei Yuan, Yantian Shen, Qingyuan Yu, Yi Chen, Binghui Wang, Hongbo Yu, Anyu Wang, Xiaoyun Wang
Probability
A law of thin processes with neighbour-count thinning
Kateryna Hlyniana
We consider the superposition $S_n$ of $n$ i.i.d. simple point processes on $\mathbb{R}^d$ and apply a dependent thinning $T_n$, where the retention probability of a point depends on its local neighbour count within a radius $r_n$. While superpositions under independent thinning converge to Poisson processes, we show that under a critical
geometric scaling $n v_d r_n^d \to \tau \in (0,\infty)$, the local interactions are transformed in the limit into an inhomogeneous Poisson point process with modified intensity. We prove that the thinned sequence $T_n(S_n)$ converges to a Poisson process with a non-linearly modified intensity $\tilde\lambda(x) = \lambda(x)\alpha(\tau\lambda(x))$.
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Materials Science
Realization of quantum spin Hall insulator superlattice with emergent multigap-like helical edge states
Hui Guo, Xianghe Han, Fang Qin et al. (+15 more)
The functional
quantum spin Hall insulators (QSHI), protected by time-reversal
symmetry against single-particle backscattering, hold great promise for dissipationless
quantum electronics. Realization of QSHI with gapped helical edge states, which would enable deterministic on/off switching of the edge-channel conductance, is a key requirement for programmable topological
circuits. Here, we report realization of superlattice-modulated QSHI HfTe5 hosting emergent multigap-like helical edge states. Using scanning tunneling microscopy and spectroscopy, we identify a reconstruction-induced periodic superlattice modulation in epitaxial monolayer HfTe5 and directly observe multiple gap-like features in the edge channel, accompanied by a series of sharp peaks in the density of states. Combined with theoretical modelling, we attribute the observed edge gap to the finite-width coupling between the two edges significantly enhanced by the superlattice modulation, whereas the sharp peaks are the manifestations of mini-gaps opening at the reduced Brillouin zone boundaries by the periodic modulation of
spin-orbit coupling. Notably, these sharp peaks exhibit clear Zeeman splitting under
magnetic fields, consistent with the helical nature of the topological edge states. Our results establish a viable route to engineering gapped helical edge states in QSHI and provide a promising platform for topological devices with desired on/off switchability.
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All authors Hui Guo, Xianghe Han, Fang Qin, Xiaoshuai Fu, Hao Peng, Hongqin Xiao, Hengxin Tan, Chen Liu, Zihao Huang, Jiayi Wang, Qian Fang, Rui Chen, Haitao Yang, Wang Yao, Li Huang, Hai-Zhou Lu, Hui Chen, Hong-Jun Gao
Computer Vision
Annual Earth-observation embeddings encode wildfire disturbance and support simplified burned area mapping
Jovana Knezevic, Clement Atzberger, Zhengpeng Feng et al. (+3 more)
Medium-resolution (10-30 m) burned area mapping is vital for monitoring wildfires and their impacts, but remains difficult to scale. Existing methods require either curated fire-specific imagery or dense time-series analysis. Here, we tested whether annual Earth-observation
embeddings retain wildfire disturbance signals sufficiently to map burned areas without either requirement. Using Tessera and AlphaEarth
embeddings, we tested individual burn-scar delineation, mapping of all same-year fires within an area, regional wall-to-wall mapping, cross-continental transfer, and intra-annual fire timing. Tessera strongly encoded wildfire disturbance, allowing even linear models to separate burned from unburned pixels; the signal was weaker in AlphaEarth. Models trained on a single Tessera
embedding matched or exceeded equivalent models using paired pre- and post-fire HLS imagery, and outperformed post-fire imagery alone. The same approach mapped all same-year fires within
benchmark scenes (F1 = 0.90). Applied across California, with no California fire data used for downstream training, it recovered 97% of reference burned area and detected substantially more small and medium-sized fires than GABAM or MCD64A1. Separately, a model trained on 2018-2021 US fires transferred without retraining to 88 European fires from 2024-2025 (F1 = 0.88). For well-detected fires, ignition timing was recovered with a mean absolute error of 13 days. Performance declined for fires ignited near the end of the calendar year, and wall-to-wall deployment produced systematic false positives in some unseen landscapes. Annual
embeddings nevertheless achieve high
segmentation accuracy while moving the burden of dense time series processing upstream, providing a promising path towards simpler regional burned area mapping.
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All authors Jovana Knezevic, Clement Atzberger, Zhengpeng Feng, Adam F. A. Pellegrini, Srinivasan Keshav, David Coomes
Signal Processing
Toward Service-Balanced ISAC: From Coupled RAN to De-Coupled RAN
Gangyong Zhu, Linghui Miao, Boyang Ji et al. (+8 more)
Sixth-generation (6G) applications require
radio access networks (RANs) to support reliable communication and seamless sensing across their operating regions. In coupled RAN deployments, shared downlink transmitting and uplink receiving sites constrain the network's ability to accommodate asymmetric links and different sensing geometries. De-Coupled RAN (DC-RAN) separates these functions, allowing independently deployed and coordinated base stations to extend uplink and downlink communication and sensing coverage. How this flexibility translates into balanced communication and sensing services, however, remains insufficiently explored. This article revisits the
evolution from coupled to DC-RAN from the perspective of service-balanced integrated sensing and communication (
ISAC). It examines how architectural choices affect the availability of both services, with communication-sensing coverage
symmetry capturing their spatial
alignment under application-specific quality requirements. Practical challenges include preserving communication consistency, maintaining sensing continuity, and coordinating distributed resources. Two case studies illustrate how DC-RAN can support coverage
symmetry alongside consistent communication, and how complementary observations can sustain continuous and accurate sensing. These examples inform a discussion of future research toward service-balanced
ISAC.
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All authors Gangyong Zhu, Linghui Miao, Boyang Ji, Jiahui Liang, Shijian Gao, Weicheng Zhang, Xuesong Cai, Jianan Zhang, Wei Wang, Xiang Cheng, Quan Yu