Algebraic Topology
Bounded cohomology, Codimension two submanifolds and Pontryagin-Thom constructions
Thorben Kastenholz
In this note we develop a novel approach for proving the non-vanishing of bounded
cohomology. This utilizes a splitting argument whose simplest form is as follows: Let M denote an n-
manifold of non-zero simplicial volume and N a codimension two submanifold of M, then one can conclude that the n-th bounded
cohomology of the fundamental group of M \ N is non-zero. We then translate the existence of a complement with a given fundamental group into an easily accessible homology computation, which might be of independent interest.
Read the full abstract ▾
Robotics
RoboCafé in the Open: Interaction Continuity in Long-Term Public Human-Robot Interaction
Kaitlynn Taylor Pineda, Kush Kumar Kushwaha, Jie Wang et al. (+5 more)
As
robots remain in public spaces over extended periods, they must maintain interaction continuity by preserving and correctly applying context as people, encounters, and circumstances change. To study interaction continuity in long-term public human-
robot interactions, we developed RoboCafé, an autonomous conversational coffee
robot designed to support repeated interactions through task-aware dialogue, real-time
multimodal perception, and
memory of prior encounters. We deployed RoboCafé for 12 days in a university building, where it received 148 orders. The deployment involved repeat customers, passersby, changing groups, and back-to-back orders that repeatedly crossed the boundaries assumed by the system's order-centered interaction model. We found that successful interaction continuity requires a
robot to determine who is currently present, which prior context belongs to whom, where interactions begin and end, and whether its representation of an interaction matches what is occurring in the physical world. From these observations, we derive four system design requirements for maintaining interaction continuity in longitudinal public human-
robot interactions: contextual interaction state, persistent person grounding, explicit interaction life-cycle management, and interaction observability.
Read the full abstract ▾
All authors Kaitlynn Taylor Pineda, Kush Kumar Kushwaha, Jie Wang, Jiaming Du, Anvii Mishra, Emilie Basu Suri, Angela Guo, Chien-Ming Huang
Earth and Planetary Astrophysics
Kelvin-Helmholtz vortices in the Martian +E hemisphere downstream of crustal magnetic anomalies
Junxi Niu, Tong Dang, Jiuhou Lei et al. (+6 more)
At unmagnetized planetary bodies, the Kelvin-Helmholtz (KH) instability at the
solar-wind boundary is a key driver of atmospheric escape, fundamentally shaping long-term
climate evolution. This shear-driven
plasma mixing is theoretically predicted to be asymmetric, strongly favoring the hemisphere where the
solar-wind electric field points toward the
planet (-E hemisphere). Here we report the observation of a coherent train of fully developed KH
vortices in the traditionally unfavorable +E hemisphere of Mars, using in-situ MAVEN measurements. Contemporaneous upstream constraints from Tianwen-1 confirm a steady interplanetary
magnetic field, indicating sustained shear-driven growth rather than a transient boundary response. We find that these
vortices developed within an expanded, weak-field boundary layer downstream of strong southern crustal
magnetic anomalies. Our analysis suggests that regional boundary structuring can override the large-scale hemispheric preference to facilitate localized KH growth. These findings demonstrate that shear-driven escape pathways can operate in traditionally unfavorable regions, altering our understanding of atmospheric volatile loss at weakly magnetized
planets.
Read the full abstract ▾
All authors Junxi Niu, Tong Dang, Jiuhou Lei, Binzheng Zhang, Wenya Li, Binbin Tang, Junjie Chen, Sudong Xiao, Tielong Zhang
Machine Learning
Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction
Ragamayi Puli and Shunya Nagashima
PPG-to-vital-sign reconstruction turns a wrist-worn photoplethysmogram into clinical waveforms such as the ECG. Long-horizon multivariate time-series
forecasting underpins planning in energy, weather, and traffic. Both generate a target sequence from a condition sequence, and current models hard-code where each target position reads it, as a same-position copy or seasonal recurrence, so neither transfers between tasks. We propose ROOSTER, one conditioning module that handles vital-sign reconstruction and time-series
forecasting alike by learning this correspondence. Its core is a periodic-comb bias over the target-condition offset whose center, period, and sharpness are learned per head, so one module settles on the identity
alignment or a seasonal lag and reports which it found. On vital-sign reconstruction from PPG, ROOSTER outperformed the published baselines on four heart-rate and respiratory-rate
benchmarks. On multivariate time-series
forecasting, it achieved the best horizon-averaged MSE on four
benchmarks and outperformed the
forecasting model it extends on 20 of 24
dataset-horizon settings under matched three-seed training. An ablation study indicated that the relative bias, not content matching, carried the
alignment.
Read the full abstract ▾
High Energy Physics (Phenomenology)
The semileptonic decays of $\Omega_{b}^{*}$, $\Sigma_{b}^{*}$ and $\Xi_{b}^{\prime*}$ baryons
Peng Yang, Guo-Liang Yu, Zhi-Gang Wang et al. (+2 more)
In this article, we employ the QCD sum rules basing on three-point correlation function to systematically analyze the transition form factors for the semileptonic decays of the
spin \(\frac{3}{2}^{+}\) bottom
baryons (\(\Omega_{b}^{*}\), \(\Sigma_{b}^{*}\), and \(\Xi_{b}^{\prime *}\)) to their corresponding
spin \(\frac{1}{2}^{+}\) charmed partners. On the phenomenological side, we eliminate the contaminations of the
baryons with negative parity and those with lower
spin states. The operator product expansion is carried out up to dimension-six condensates, including the perturbative part,
quark condensate \(\langle \bar{q} q\rangle\),
gluon condensate \(\langle g_{s}^{2}GG\rangle\), mixed condensate \(\langle \bar{q} g_{s}\sigma Gq\rangle\), and four-
quark condensates \(\langle \bar{q} q\rangle^{2}\) and \(g_{s}^{2}\langle \bar{q} q\rangle^{2}\). The form factors are evaluated in the space-like region and then are fitted into the time-like physical region via a $z$-series expansion approach. Finally, the semileptonic decay widths for $\Omega_{b}^{*}$, $\Sigma_{b}^{*}$ and $\Xi_{b}^{\prime*}$
baryons are calculated with our predicted form factors, and various polarization observables are also predicted. The predicted results in this work are compared with those predicted by other collaborations, and are expected to be useful for studying the properties of these singly heavy
baryons.
Read the full abstract ▾
All authors Peng Yang, Guo-Liang Yu, Zhi-Gang Wang, Jie Lu, Bin Wu
High Energy Physics (Phenomenology)
Dynamical gluon effects in proton transversity and off-forward parton distributions from a light-front Hamiltonian approach
Pengxiang Zhang, Jiangshan Lan, Siqi Xu et al. (+3 more)
Building on
proton light-front wave functions in the $|qqq\rangle$ and $|qqqg\rangle$ Fock sectors, obtained by diagonalizing the
proton light-front
Hamiltonian within the Basis Light-Front Quantization framework, we present a unified study of the
proton's leading-twist transversity parton distribution functions, tensor charges, generalized tensor form factors, and
chiral-odd skewed generalized parton distributions in the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi region. Our results compare reasonably with available phenomenological extractions and
lattice-QCD simulations and follow similar qualitative trends.
Read the full abstract ▾
All authors Pengxiang Zhang, Jiangshan Lan, Siqi Xu, Chandan Mondal, Xingbo Zhao, James P. Vary
Computer Vision
DeltaS: Reading the Gated Linear Attention State for KV Cache Eviction in Streaming Video
Taeyoun Kwon, Seungjin Kim, Hyeonyu Kim et al. (+1 more)
Recent
video-
language models increasingly adopt hybrid architectures that interleave linear and full
attention layers for efficient long-context processing. While the recurrent state of linear
attention remains fixed in size, the KV cache of full
attention continues to grow with the
video stream, making eviction necessary under a bounded
memory budget. The key challenge in streaming is that eviction must occur before the question arrives, so what to retain has to be decided without the question. Existing eviction methods derive
token scores from the KV cache itself, using position,
attention, or key-value representations, and
attention-based scores further require proxy queries or extra computation. Hybrid backbones offer another source of signal. In gated-delta linear
attention, the recurrent state is updated by the residual between each input and what can already be retrieved from the state, so its change over a chunk of frames reflects how much new information the chunk brings. We propose DeltaS, a query-agnostic, training-free method that retains
video chunks inducing larger normalized state change, or state drift. In a controlled comparison with the budget and retention policy held fixed, state drift outperforms position-,
attention-, and key-value-based signals. With a signal costing only 1.9% of the forward pass, DeltaS surpasses the strongest query-agnostic bounded-
memory baseline by 2.1 points on average across six long-
video benchmarks and by 5.6 points on the longest
benchmark. These results suggest that the two
memories of hybrid architectures can work cooperatively. Code is available at https://github.com/MaumAI-Company/DeltaS.
Read the full abstract ▾
All authors Taeyoun Kwon, Seungjin Kim, Hyeonyu Kim, Moon Hwan Kim
Robotics
Collocated Shape Regulation for Soft Robots
Pietro Pustina, Ebrahim Shahabi, Daniel Feliu-Talegon et al. (+2 more)
Controlling the shape of a continuum soft
robot typically requires an accurate dynamic model and actuation of all degrees of freedom. We show that regulating only the actuated coordinates, through collocated shape control, achieves provably stable convergence of those coordinates and, under an explicit compatibility condition, of the entire
robot shape. While collocated control is a cornerstone of high-performance motion control in rigid robotics, extending this formulation to continuum soft
robots has remained challenging due to the complexity of their dynamics. We present the first general framework for collocated control of continuum soft
robots and derive a unified family of controllers, including PD, PID, PsatID, and their counterparts with compensation and cancellation components. The framework unifies existing approaches while introducing new controller designs. In particular, we develop three classes of PD and PID like regulators with local, semi-global, and global stability guarantees, and provide rigorous convergence analyses for each. Extensive experimental validation demonstrates the effectiveness of the proposed methods across different model discretizations and controller parameters. The resulting framework provides practical design guidelines for selecting and implementing controllers with known stability guarantees, without requiring a complete dynamic model of the
robot
Read the full abstract ▾
All authors Pietro Pustina, Ebrahim Shahabi, Daniel Feliu-Talegon, Alessandro De Luca, Cosimo Della Santina
Computer Vision
CereVLA: Cerebellum-Inspired Consequence-Aware Residual Governance for Efficient Vision-Language-Action Execution
Shuai Zeng, Yuxuan Liang, Hangmiao Hu et al. (+4 more)
Action-chunked
vision-
language-action (VLA) policies improve inference efficiency, but limited feedback within committed action chunks can lead to accumulated execution errors. Residual adaptation can correct such deviations without retraining the VLA; however, existing corrections are typically optimized for reference-action consistency without explicitly considering their downstream consequences. To address this limitation, we present Cerebellum-Inspired Consequence-Aware Residual Governance (CereVLA), a unified framework that integrates lightweight residual refinement and predictive consequence evaluation into frozen VLA execution. Corrective actions are first generated by flow-based residual refinement, and their short- and interval-horizon consequences are then evaluated by a recurrent state-space model and a history-aware classifier. Residual corrections predicted to be unfavorable are selectively suppressed by a lightweight governor. Comparisons with state-of-the-art methods on LIBERO-10 and LIBERO-GOAL demonstrate the effectiveness of CereVLA. On SO-101, CereVLA increases task success from 57.5% to 90.0% and reduces mean control steps by 19.6% among successful trials, relative to the frozen SmolVLA baseline.
Read the full abstract ▾
All authors Shuai Zeng, Yuxuan Liang, Hangmiao Hu, Fobao Zhou, Zixiang Wang, Wenxi Hong, Hang Zhao
Robotics
Kairos: Grounded Forecasting of Presence and Directional Flow in 4D Scene Graphs
Iacopo Catalano, Julio A. Placed, Javier Civera et al. (+1 more)
Long-term autonomy in human-populated environments requires anticipating whether and how people will move at times a
robot has not yet observed. Existing representations of pedestrian motion face a tradeoff: they either forecast future activity, reducing each location to a scalar rate, or model the full directional distribution, holding it fixed in time. We present Kairos, a predictive directional-flow
memory that extends a hierarchical 3D scene graph (3DSG) to a 4D scene graph (4DSG). Every observed voxel of the reconstructed geometry stores a directional mixture and a presence rate, and spectral predictors forecast, for any future query time, both the probability that people are present and the full directional distribution of their motion. Pairwise flow dependence between adjacent voxels supports conditional queries, and per-voxel predictive variances yield calibrated credible intervals that tighten as observations accumulate. We evaluate Kairos on three real pedestrian environments: a
robot-collected campus
dataset, a shopping mall, and a station concourse recorded continuously for eleven months. Its learned state remains consistent under loop-closure corrections, and its forecasts are competitive with dedicated occupancy and flow models trained on the full detection stream, although Kairos learns from only the small fraction available to a patrolling
robot. Finally, we validate the representation on a downstream encounter-probability planning task, where plans computed over the Kairos forecasts encounter more people than plans computed over any time-invariant map at an equal success rate. We provide the code at https://github.com/IacopomC/kairos.
Read the full abstract ▾
All authors Iacopo Catalano, Julio A. Placed, Javier Civera, Jorge Peña Queralta
Robotics
A Modular Dual-Arm Robotic Cell for Disassembly and Repair of Industrial Control Electronics
Maximilian Ruhe, Fabian Harlacher, Christian Friedrich et al. (+1 more)
Industrial control electronics such as programmable logic controllers, servo drives and operator panels are routinely repaired in plant maintenance, but were never designed for automated disassembly. This paper presents a modular dual-arm robotic
cell for repair-oriented disassembly, using two collaborative manipulators, interchangeable tools, red-green-blue-depth (RGB-D) and wristlevel perception, force/torque sensing and a
Robot Operating System (ROS) 2- based control with Behavior Tree (BT) execution, teleoperation, digital-twin support and bounded learning-based contact skills. The process is decomposed into sequence planning, symbolic execution with fallbacks, force-limited tool skills, visual condition assessment and demonstration-based adaptation. A CADderived device graph encodes the disassembly order, access constraints, tools, feasible removal directions and verification states and converts them into operation objects for the BT and motion layers. Grounded in three representative devices, the
cell covers screw removal, damaged-fastener fallback, snap-fit opening, connector release, cooperative printed
circuit board (PCB) extraction and condition-based repair decisions. The main contribution is an architecture linking sequence knowledge, perception, verification and force-aware skills through one ROS 2 interface across simulation, teleoperation and real hardware.
Read the full abstract ▾
All authors Maximilian Ruhe, Fabian Harlacher, Christian Friedrich, Martin Kipfmueller
Statistical Methodology
Pooling Sequential Evidence Across Hypotheses: Rate-Optimal Multiple Testing at a Fixed Horizon
Prasanjit Dubey and Xiaoming Huo
We study sequential testing of a fixed family of hypotheses when observations are costly and a sampling horizon is specified in advance. The challenge is to pool evidence for earlier decisions when the number and identities of false hypotheses are unknown, while controlling the probability of any false rejection at level $\alpha$. Existing merges attain the pooled growth rate at a single number of false hypotheses: averaging when one is false, multiplying when all are. Under an independent-stream model with common simple null and alternative distributions, we test each intersection with a prior-weighted mixture of products of marginal likelihood ratios. Closed testing combines these elementary-symmetric-
polynomial mixtures to identify individual false hypotheses. Design-specific boundary calibration gives finite-horizon family-wise error control, with exact finite-state guarantees or a confidence qualification for Monte Carlo calibration. The prior-matched mixture uniquely maximizes expected log evidence at each horizon. Mixtures assigning positive weight to every nonempty subset of streams attain log-growth rate $lD$ when $l$ streams follow the alternative. Here $D$ is the mean log likelihood ratio per alternative observation, and a round supplies one observation per stream. This rate attains the first-order intersection-delay lower bound as $\alpha\downarrow0$ at fixed dimension, configuration, weights, and a long enough horizon. Power for an individual hypothesis cannot exceed the best single-stream power at a given deadline, but closure removes the multiplicity penalty when all are false. Gaussian, basket-trial,
language-model and advertising studies illustrate both. The primary basket boundaries are 40-52% below $1/\alpha$. Across 41 simulated configurations, the median reduction in capped mean patient outcomes relative to prespecified interim-look Bonferroni tests is 31%.
Read the full abstract ▾
Materials Science
Investigation of Hopping Conduction and Its Impact on the Subthreshold and Transition Region Transport in Oxide Semiconductor Transistors by Magneto-Transport Measurements
Chen Wang, Liankai Zheng, Kai Jiang et al. (+4 more)
In this work, magneto-transport measurements, including
Hall effects and magnetoresistance (MR) at various temperatures, are employed to directly probe the electron transport properties in crystalline indium
oxide (In2O3) and amorphous indium-zinc
oxide (IZO) transistors. For the first time, we develop a subgap density of states (DOS) extraction method based on the MR measurements at low temperature, considering both the interference and orbital shrinkage effects. The sign and magnitude of MR are used as direct evidence to distinguish the dominating transport mechanisms between hopping conduction and free-electron conduction. It is found that crystalline In2O3 exhibits a subgap DOS more than two orders of magnitude lower than that of amorphous IZO, together with a smaller localization radius of hopping sites. As a result, crystalline In2O3 not only enhances the carrier mobility but also significantly reduces the supply voltage (VDD) because of the smaller transition region enabled by the much-suppressed subgap DOS. This study reveals that structural disorder critically influences the device operation in the subthreshold and transition regions of
oxide semiconductor transistors.
Read the full abstract ▾
All authors Chen Wang, Liankai Zheng, Kai Jiang, Jinxiu Zhao, Zhenyu Zhang, Xuefei Li, Mengwei Si
Robotics
Safety-Filtered Distributed Koopman-MPC
Shengjun Zhang, Wenhao Li, Zhenxin Lin et al. (+1 more)
Distributed model predictive control (DMPC) often constructs both predictions and collision constraints from neighbor trajectories, so packet loss can remove both. We separate these roles: received trajectories drive Koopman-MPC, while local sensing and shelf geometry define a hard-constrained quadratic program (QP) that projects the applied input. Its radial demand is the least constant acceleration that keeps a supporting-plane clearance nonnegative throughout one zero-order-hold interval. Complementary pair rows recover the coupled demand without exchanging safety decisions. We give an intersample separation
theorem under bounded snapshot and directional plant errors, an exact max-min test for simultaneous local feasibility, and a sensing-radius condition for switching interaction graphs. Anticipatory high-order rows may be relaxed for performance, but the finite-hold rows contain no safety slack. Matched eight-
robot warehouse simulations use a frozen Koopman model, nonlinear drift, bounded inputs and speed, shelf constraints, a 120 ms control period, and packet dropout. The full controller is collision-free in 20/20 matched trials and reaches 160/160
robot goals; predictive Koopman-MPC without the final projection is collision-free in 1/20 trials. All 38,400 full-method hard-row sets pass the online feasibility test, and every local QP solves. Five-stream fleet sweeps are collision-free and hard-row feasible through 16
robots; the 20-
robot boundary fails only after the online margin turns negative, while the reconstructed per-
agent critical path remains below the sampling period. Bounded-sensing and differential-drive tests provide additional deployment stress.
Read the full abstract ▾
All authors Shengjun Zhang, Wenhao Li, Zhenxin Lin, Zhenglong Sun
Computer Vision
Hybrid Gaussians for Robust Open-Vocabulary 3D Segmentation with Multi-View Object Association and Boundary Refinement
Xueqi Qiu, Yueming Sun, Tianyu Zhang et al. (+2 more)
Open-vocabulary 3D
segmentation localizes objects from free-form text queries, but remains challenging in real image sequences: incomplete or noisy 2D supervision destabilizes multi-view identity assignment, while full-scene semantic learning weakens object-level discriminability. We introduce Hybrid Gaussians, a unified 3D representation jointly modeling object association and
language-aligned semantics. Its Multi-View Object Association mechanism combines Observation Fusion and Semantic Contrastive Learning to improve identity consistency and semantic discrimination. Boundary Reconstruction Optimization further refines local boundary structure to improve contour quality. Experiments on LERF and 3D-OVS demonstrate strong quantitative and qualitative performance. Our method achieves 59.1% mIoU on LERF, yielding a 13.4% relative gain over the baseline. Project page: https://nora202.github.io/hybridgaussians.
Read the full abstract ▾
All authors Xueqi Qiu, Yueming Sun, Tianyu Zhang, Yuxuan Xia, Yang Long
Computer Vision
Invisible in Space, Visible in Time: Motion Vision CAPTCHA against GUI Agents
Zeyu Zhang, Dingyi Rong, Zijian Chen et al. (+3 more)
Most existing visual CAPTCHAs remain spatially solvable: the required information is exposed by static appearance, local structure, and interface state. This assumption is weakened by advances in
multimodal large
language models (MLLMs) and Graphical User Interface (GUI)
agents, which exhibit strong visual perception,
reasoning, and browser interaction capabilities. We propose Motion
Vision CAPTCHA (MVCAP), a hierarchical motion-based CAPTCHA framework in which target semantics are instantiated as motion-defined foreground structures and become recoverable only through temporal segregation from a dynamically evolving background. Built on this shared principle, MVCAP is instantiated in three perceptually progressive levels: coherent motion, structural motion, and biological motion. To evaluate this framework, we introduce MVCAP-Bench, a browser-based
benchmark with 600 live CAPTCHA instances, together with a matched foreground-only control
benchmark, MVCAP-Bench-FG. We evaluate humans, Browser Use
agents, native computer use
agents, and a supplementary offline VQA setting derived from the same instances. Results reveal a substantial human–
agent gap: on the full MVCAP-Bench, human accuracy reaches 99.6%, whereas the best GUI
agent achieves only 16.8%, close to the six-way chance level. The foreground-only control further shows that the key difficulty comes from dynamic background camouflage rather than answer format or browser interaction alone. These findings identify a measurable human–
agent perception gap and position MVCAP-Bench as a
benchmark for studying motion-defined perception in current
agents.
Read the full abstract ▾
All authors Zeyu Zhang, Dingyi Rong, Zijian Chen, Zicheng Zhang, Xiongkuo Min, Guangtao Zhai
Astrophysics of Galaxies
Ubiquitous nuclear disks and bars embedded in massive galaxies in the cosmic morning
Jianyuan Luo, Fangzhou Jiang, Jinning Liang et al. (+8 more)
The morphology of the central regions of high-
redshift galaxies remains relatively unexplored, while recent case studies suggest that
giant disks can emerge without first developing a prominent central spheroid. Here, we investigate the inner structures of a mass-complete sample of 45 massive
galaxies at $z=3$ in the TNG50 simulation. Through double-Sérsic profile decomposition, isodensity ellipse fitting, intrinsic three-dimensional shape measurements, and
stellar kinematics, we find that these central structures are ubiquitously flattened and rotation-supported with low Sérsic indices ($n \lesssim 1$). This indicates that the central regions are predominantly
nuclear disks and bars rather than bulges. Tracking their
evolution, we show that these
nuclear disks and bars emerge during gas-rich compaction and do not transform into spheroidal and dispersion-dominated systems until $z \lesssim 1$, before which extended
stellar disks have already developed. Our results suggest that massive disks can assemble around rotation-supported centers and bulge-deficient massive
galaxies may be a common outcome of
galaxy evolution.
Read the full abstract ▾
All authors Jianyuan Luo, Fangzhou Jiang, Jinning Liang, Boris S. Kalita, Pinsong Zhao, Jinyi Shangguan, Bingcheng Jin, Zeyu Gao, Weichen Wang, Luis C. Ho, Yingjie Peng
Neural and Evolutionary Computing
Spiking Neural Network Predicting Sequence of the External Worlds States in Model-Based Reinforcement Learning
Mikhail Kiselev
This paper presents a spiking neural network (SNN) designed to predict the sequence of the external world states starting from the current world state. This SNN does not create the world dynamics model - instead it incorporates the SNN trained to predict the next world state and provides all mechanisms necessary to make the chain of predicted world states. These mechanisms are entirely spiking - they are implemented as spiking
neuron ensembles. The present article describes this neuronal structure and tests its operation on a classic RL
benchmark - ATARI ping-pong.
Read the full abstract ▾
Functional Analysis
Iterates of Ritt operators close to the identity
Catalin Badea
We study bounded operators whose prescribed powers remain uniformly close to the identity, and resolvent conditions sampled along sequences of powers. The displacement bound $\sup_{n\ge1}\|I-T^n\|\le q<2$ forces a finite peripheral spectrum consisting of odd-order roots of unity and makes an explicitly determined odd power of $T$ a Ritt operator. The sharp unconditional threshold for $T$ itself is $\sqrt3$. If $\Phi_*(q)$ denotes the optimal universal Ritt resolvent bound below this threshold, then $\Phi_*(q)=2/(\sqrt3-q)+O(1)$ as $q\uparrow\sqrt3$. At every higher peripheral threshold the optimal finite-peripheral resolvent bound has reciprocal order of growth. We also obtain constructive bounds for general weighted Wiener symbols. For each $1<q<2$, an angular escape invariant characterises exactly the sampling sequences for which a displacement bound by $q$, together with the peripheral spectral condition, forces the Ritt property. We compute this invariant for asymptotically
geometric sequences. At the endpoint $q=1$, suitable phase conditions imply the full displacement bound without assuming power boundedness; normal contractions admit an exact scalar criterion. Finally, we establish quantitative
Hilbert-space and $L^p$ resolvent estimates, a characterisation of operators with odd Ritt powers on uniformly convex spaces, and strict norm gaps for operator-valued disc-
algebra functions, with explicit
matrix-
polynomial bounds from Fejér–Riesz factorisation.
Read the full abstract ▾
Computer Vision
Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection
Linghao Zhang, Siyu Xiang, Junwei Kuang et al. (+1 more)
Vision-
language models (VLMs) are often reported to outperform task-specific
vision backbones for unmanned aerial vehicle (
UAV) power-line defect assessment. We test that claim on ElecVQA-Bench, a 56,972-item
benchmark derived from the public InsPLAD
dataset, across six evaluation choices: partition, evaluated item set, label space, replication, input resolution, and side information. On a matched partition, a Swin
Transformer and the strongest adapted VLM differ by only 0.03 points at binary screening. At seven-way defect typing, increasing the
vision backbones from 224 px to the measured pixel budget of the VLM preprocessor narrows the gap against InternVL3.5-8B from +20.53 to -0.57 points for ResNet-50 and from +23.67 to +4.70 points for Swin-T. A pixel-budget audit shifts Qwen3-VL-8B macro recall by 10.78 points, yet a source-pixel-matched InternVL control still leaves Qwen ahead by 7.43 to 13.61 points while using 56% fewer visual
tokens, so neither source pixels nor
token budget explains the difference between the two VLMs. A two-seed global replication changes Qwen binary accuracy and seven-way macro recall by 0.86 and 1.02 points. After split-specific retraining, Qwen does not lead at crop or image level, and a 14-tower, three-seed replication reverses the sign across seeds, giving mean common-six macro recall of 0.9085 for Qwen against 0.9509 for ResNet-50. No split regime yields a family-level advantage that survives multiple-comparison correction. The study supports a
benchmark-audit contribution rather than a general claim of VLM superiority.
Read the full abstract ▾
All authors Linghao Zhang, Siyu Xiang, Junwei Kuang, Peiyu Yi
Quantum Physics
Precision and resource scaling of real-time flux distortion compensation for superconducting quantum control
Qi Zhou, Zi-Hao Mei, Peng Duan et al. (+7 more)
Real-time waveform generation supports dynamic
quantum circuits without pre-storing complete waveforms for every execution path. However, long-lived distortions in flux-control lines degrade gate fidelity, requiring compensation to account for the actual pulse history. A frequency-domain inversion and time-domain fitting method is proposed for resource-efficient real-time flux distortion compensation. The method fits the reconstructed compensation impulse response with a compact hybrid infinite impulse response (IIR) and finite impulse response (FIR) filter. Look-ahead parallelization enables this filter to process synthesized waveforms at 1.2GSa/s on a field-programmable gate array (FPGA). Two-
qubit cross-
entropy benchmarking shows that real-time IIR filtering achieves a median controlled-Z Pauli fidelity close to the software-reference value of 99.57%. Numerical analysis and FPGA synthesis indicate approximately logarithmic growth in hardware resource use with compensation timescale. Extending compensation from microsecond to hundred-microsecond timescales increases look-up table (LUT) and digital signal processing (DSP) resource use by only about 14% and 4%, respectively, while maintaining a relative arithmetic error below $10^{-4}$. This work provides a scalable hardware foundation for high-fidelity flux control in dynamic superconducting
quantum circuits.
Read the full abstract ▾
All authors Qi Zhou, Zi-Hao Mei, Peng Duan, Peng Wang, Liang-Liang Guo, Hao-Ran Tao, Wei-Cheng Kong, Hui Yang, Guo-Ping Guo, Zhao-Yun Chen
Computer Vision
LeWAM: Latent evolving World Action Model
Xueji Fang, Boqiang Duan, Hua Wu et al. (+2 more)
World Action Models (WAMs) jointly model action generation and environment dynamics and are mostly built on pretrained
Video Diffusion Models (VDMs). In VDM-based WAMs, observations are first encoded by a VAE, and the resulting compressed latents are then processed by large
video diffusion backbones to extract effective features for action generation. However, this paradigm ties WAM performance and training cost to large-scale
video generation pretraining, limiting WAM efficiency and scalability. In this paper, we theoretically and empirically investigate how visual representations affect action generation in WAMs. Our results show that predictive
embeddings from Joint-
Embedding Predictive Architecture (JEPA) encoders better support action generation than compressed VAE latents, with I-JEPA performing best in our encoder comparison. Based on these findings, we propose LeWAM, which conditions action generation on JEPA
embeddings and models environment
evolution by predicting future
embeddings in the same space, without relying on a
video diffusion backbone. We further find that imitation learning matches demonstrated actions but does not distinguish better actions from worse ones, even though small action deviations can greatly affect task success. To address this limitation without additional environment interaction or the human oversight required for resets and safety, we introduce Demonstration-Guided DPO (DemoDPO), an offline preference refinement stage that derives preference supervision directly from demonstrations. With only 0.4B trainable parameters, LeWAM achieves an average success rate of 92.28% on RoboTwin 2.0, comparable to that of state-of-the-art VLAs and WAMs, and maintains practical effectiveness on real-world manipulation tasks.
Read the full abstract ▾
All authors Xueji Fang, Boqiang Duan, Hua Wu, Jingdong Wang, Guo-Jun Qi
General Relativity and Quantum Cosmology
Conserved charges and the first law of black holes for field dependent symmetry generators in the covariant phase space formalism
Hai-Feng Ding
In this paper, we generalize the covariant phase space formalism conjugate to field dependent vectors to include internal gauge transformations when gauge fields are present. In our formalism, the
symmetry generators are combination of diffeomorphisms plus internal gauge transformations and depend on the field configuration. When the field dependence of the
symmetry generators is considered in the covariant phase space formalism, the first law of black hole thermodynamics is a direct result for generally invariant gravitational theories. To check the validity of our formalism, we investigate the conserved charges and the first laws of thermodynamics for a
torus-like black hole in Einstein-Maxwell theory and a charged Einstein-Euler-Heisenberg AdS black hole in Einstein-Euler-Heisenberg nonlinear electrodynamics.
Read the full abstract ▾
Hardware Architecture
Implementation and Evaluation of BitNet Inference on a CGLA by Signed-Int4 Instructions
Takuto Ando and Yasuhiko Nakashima
Large
language model (LLM) inference transfers model weights and activations for every generated
token, making
memory traffic and its energy cost part of the decode path. BitNet b1.58 represents its low-bit weights by ternary values and uses integer activations. However, this arithmetic does not match conventional int8 or floating-point general
matrix multiplication, and existing BitNet accelerators implement it in specialized datapaths. We instead map this operation to a CPU-Grounded Linear Array (CGLA), a programmable ASIC with explicit direct
memory access, local
memories, and reusable compiler-visible integer lanes. The mapping adds OP_SMA4 as a reusable signed-int4 multiply-accumulate instruction rather than a BitNet-only datapath. Each ternary weight occupies one signed 4-bit lane. Each int8 activation is split into two signed-int4 fragments and reconstructed by shift-and-add. Frequency scaling of the 145 MHz FPGA measurement to an 840 MHz 28 nm CGLA achieved 0.390 ns per signed-int4 product. We showed that CGLA-offloaded BitNet C++ execution measures 2.52
tokens/s.
Read the full abstract ▾
Cryptography and Security
Issuer-Sovereign Agentic Payments
Dishant Sharma, Rajneesh Kaushal and Ashu Kanaujia
AI
agents are beginning to make real payments. Current approaches let an
agent pay by relying on a credential provider that, in the approaches deployed today, typically sits outside the cardholder's bank. The spending rules are then enforced by the card network or that provider, and not by the bank itself. This leaves the issuing bank, which carries the financial risk, with little direct control at the moment a payment happens. This paper describes Issuer-Sovereign Agentic Payments, a method that keeps that control with the issuer. The cardholder approves a spending rule once, and the bank's own authentication component records it. Later, when the
agent pays a specific merchant, the bank checks the merchant against the approved rule and generates the card authentication value only if the merchant is allowed. The payment then travels the normal card rails and is validated by the issuer, with no extra dependency introduced at execution.
Read the full abstract ▾
High Energy Physics (Phenomenology)
Polarization and correlation effects in $\gamma\gamma \to W^-W^+$ at Next-to-Leading-Order Electroweak Accuracy
Jun Jiang, Peng-Cheng Lu, Zongguo Si et al. (+2 more)
We study the next-to-leading order (NLO) electroweak (EW) corrections to the polarization and correlation of $W$
boson pairs produced through
photon-
photon fusion in
proton-
proton ultra-peripheral collisions at the LHC, with both $W$
bosons decaying leptonically. The azimuthal correlation distributions of the charged
leptons, which receive no contribution from anomalous quartic gauge couplings (AQGCs) at linear order, are perturbatively stable at the 0.40% level, whereas individual correlation coefficient $C_{2,2}$ receives relative corrections of up to $-33.84\%$. Some of correlation coefficients that vanish at leading order (LO) in the Standard Model but are sensitive to AQGCs acquire non-zero one-loop values of order $10^{-4}-10^{-3}$. We quantify the region of the ($a_0/\Lambda^2,a_c/\Lambda^2$) plane in which these loop-induced baselines compete with the AQGC contributions and find that, while irrelevant at the current experimental sensitivity, they must be included once the correlation coefficients are measured at the per-mille level.
Read the full abstract ▾
All authors Jun Jiang, Peng-Cheng Lu, Zongguo Si, Han Zhang, Xin-Yi Zhang
Robotics
BEE: Intervention-Adaptive Real-World Reinforcement Learning with Vision-Language-Action Models
Weihui Zhao, Xiaohan Yan, Zunian Wan et al. (+14 more)
Vision-
language-action (VLA) models handle long-horizon manipulation, yet success hinges on a few precision-critical phases where millimeter-scale errors undo all prior progress. Online reinforcement learning (RL) can optimize exactly these actions, but free exploration is far too costly on real
robots, which makes human corrections indispensable. However, existing online RL methods for VLAs either cannot incorporate such corrections or fold them into undifferentiated supervision. Yet human corrections are not uniformly noisy but reliable along some action dimensions and variable along others. Building on this, we introduce BEE, an intervention-adaptive framework for real-world RL on a frozen VLA that lets the policy go BEyond Expert imitation. We formulate human corrections not as actions to reproduce but as evidence about a constraint: a Correction Model predicts how a human would correct a given VLA proposal and how consistent the correction is along each action dimension. This predicted consistency sets the per-dimension tightness of a constraint on policy optimization. Where corrections are consistent the policy stays close to the human, and where they vary, the constraint relaxes. We evaluate BEE on three real-world manipulation tasks and one LIBERO-Pro simulation task at a matched online-data budget. BEE attains the highest success rate on every task, 91.2% on average against 57.5% for RLT and 42.1% for DSRL, and the lowest human intervention rate on all real-world tasks.
Read the full abstract ▾
All authors Weihui Zhao, Xiaohan Yan, Zunian Wan, Xuan Du, Zhaozhan Chi, Jianbo Mao, Ruipu Wu, Rushuai Yang, Houlin Li, Shukai Yang, Jing Wu, Yuxiang Yan, Yongcheng Liu, Chuankang Li, Guanghui Ren, Wei Shan, Maoqing Yao
Robotics
X2Real: an eXtensive simulation benchmark for real-world generalist policies
Lian Ruan, Jade Yang, Sherphylan Gao et al. (+23 more)
Generalist
robot manipulation policies have developed rapidly, yet their reliable evaluation remains challenging due to fundamental flaws in existing simulation
benchmarks: prominent sim-to-real gaps, narrow task coverage, and unfair evaluation caused by ambiguous training-test pipelines. Prior works only partially resolve these issues and lack simultaneous faithfulness, diversity, and
fairness, while static
benchmark designs fail to sustain long-term policy development. We present X2Real, an evolvable simulation
benchmark for faithfully evaluating the real-world performance of robotic manipulation policies based on Nvidia Isaac Lab-Arena. Following three core principles (faithfulness, diversity, and
fairness), X2Real calibrates simulation visual and physical properties to align with real hardware, achieving a 0.84 linear correlation between simulated and real-
robot evaluation results. It features a comprehensive taxonomy with 10 capability dimensions and 44 hierarchical long-horizon tasks, covering basic manipulation skills and advanced capacities such as visual grounding,
language understanding, and bimanual control. We further adopt multi-axis domain randomization and strictly disjoint training-evaluation pipelines to mitigate
benchmark exploitation and ensure credible evaluation. Powered by a custom physical domain-specific
language, the Mana simulation ecosystem supports modular task design and iterative performance analysis, alongside a nearly 300-hour annotated simulation trajectory
dataset. X2Real offers a faithful, diverse, and fair evolving evaluation infrastructure, effectively bridging the sim-to-real evaluation gap and supporting the advancement of generalist robotic manipulation policies.
Read the full abstract ▾
All authors Lian Ruan, Jade Yang, Sherphylan Gao, Felix Gao, Kyson Liang, Galen Liu, Ligo Wu, Lane Jin, Guu Gu, Bevan Xie, Cloud Yan, Zongzi Yuan, Kino Luo, Emma Chen, Shuwen Chen, Yang Ping, Miles Guo, Rain Sun, Kayden Zhang, Alex Du, Ruihai Wu, Liang Hao, Zhaoshuo Li, Roy Gan, Hao Wang, Qian Wang
High Energy Physics (Phenomenology)
ALP photo-production in the parton and hadron cascade model PACIAE
Dai-Mei Zhou, Zhi-Lei She, Jian Cao et al. (+6 more)
In this work, the parton and
hadron cascade model PACIAE is employed to simulate the events of Au+Au collisions at $\sqrt{s_{NN}}=$200 GeV within the 60-80 % centrality interval. Based on the abundant
photons in final hadronic state, the statistical-mechanics-based coalescence model DCPC is applied to phenomenologically recombine post-freeze-out
photon pairs into
axion-like particles (ALPs) for the first time. The coalescence probability is constrained by the branching-ratio $B_r(a \rightarrow \gamma\gamma)=0.57$ under the threshold of twice of $K$
meson mass. The ALP observables including yields, transverse-momentum distributions and rapidity spectra are calculated for several
benchmark ALP-mass bins. These theoretical predictions are worthy to be investigated by the experiments.
Read the full abstract ▾
All authors Dai-Mei Zhou, Zhi-Lei She, Jian Cao, An-Ke Lei, Wen-Chao Zhang, Hua Zheng, Li-Lin Zhu, Yu-Liang Yan, Ben-Hao Sa
Quantum Physics
A 75-mL intrinsically stable atom-filtered laser enabling deployable quantum devices
Zijie Liu, Zheng Xiao, Zhiyang Wang et al. (+10 more)
Numerous
quantum devices require
lasers strictly locked to atomic transitions. Atom-filtered
lasers (AFLs) are considered a leading candidate for
quantum device
laser sources due to their ability to self-align to atomic transitions. However, the sub-GHz sharp transmission spectra of conventional atomic filters impose constraints on both
laser miniaturization and output stability. Herein, we demonstrate a micro Faraday anomalous dispersion optical filter ({\mu}FADOF) operating within the extreme hyperfine Paschen-Back regime, generating a 7.5 GHz flat-top transmission window. By integrating this filter, the miniaturization bottleneck of the AFLs is overcome, achieving a 30-fold volume reduction to a compact package volume of 75 mL. Simultaneously, investigations into the optical self-feedback characteristics of the {\mu}FADOF reveal a stable operating regime, where the optical self-feedback of the atomic filter acts as a stabilizing mechanism, enabling the
laser to achieve a power instability of 9 x 10^-6 at 1 s and 3.2 x 10^-5 at 8400 s. The optical frequency standard based on this 75-mL AFL achieves a further improvement in long-term frequency stability to 3 x 10^-13 at 10000 s and maintains turn-key operation under environmental shocks, highlighting the critical role of this
laser in the development of deployable
quantum devices.
Read the full abstract ▾
All authors Zijie Liu, Zheng Xiao, Zhiyang Wang, Xiaolei Guan, Xiaomin Qin, Suyang Wei, Hongtian Song, Baoshuai Wang, Jia Zhang, Xiaopeng Xie, Tiantian Shi, Anhong Dang, Jingbiao Chen