Functional Analysis
An explicit counterexample to the Hinrichs–Vybiral conjecture
Jan Vybiral
Conjecture~2 of Hinrichs and Vybíral asserts that every continuous, nonnegative, positive definite function $f$ on $\R^d$, normalized by $f(0)=1$, satisfies $[f(x_j-x_k)]_{j,k=1}^n\succeq \one\one^T/n$. We give an explicit trigonometric
polynomial on $\R^7$ and eight points for which this inequality fails. All hypotheses are verified directly: positive definiteness follows from nonnegative
Fourier coefficients, and pointwise nonnegativity follows from interpolation on a cube. For an explicit vector of signs, the quadratic form is $22/5$, whereas the proposed lower bound is $9/2$. The resulting gap is exactly $-1/10$. The example is compatible with the established bound for functions of the form $|g|^2$ with $g$ positive definite.\\ The solution was found in a single
prompt try by a colleague using his private licence of ChatGPT 6. Our aim is to make the solution public, as well as to collect some ideas that emerged during the analysis of the solution.
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High Energy Astrophysical Phenomena
Beyond Disk Truncation: X-ray Reverberation Signatures of an Outflowing Corona
Ke Qin, Bei You, Adam Ingram et al. (+1 more)
The corona in black hole X-ray binaries (BHXRBs) is likely dynamic during outbursts. A mildly relativistic outflowing corona has been proposed to explain hard spectra, weak disk reflection, and the higher than expected polarization degree observed in the hard state of X-ray binaries. In this work, we investigate the spectral and timing effects of coronal
outflow using Monte Carlo radiative-transfer simulations. The model consists of a geometrically thin, optically thick truncated disk and an ellipsoidal corona with a prescribed bulk
outflow velocity. We calculate the Comptonized continuum, the disk reflection component, and the lag-frequency spectra for different
outflow velocities and disk truncation radii. We find that increasing the
outflow velocity reduces the reflection fraction through relativistic beaming, because fewer Comptonized
photons irradiate the disk. For $\beta \lesssim 0.5$, the high-frequency soft lag is only weakly affected by the
outflow velocity, whereas increasing the disk truncation radius shifts the zero-crossing frequency($\nu_0$) to substantially lower frequencies. Thus, timing properties can help distinguish the coronal-
outflow scenario from the disk-truncation scenario. We further apply the model to MAXI J1820+070 and find that its unusual $R-\Gamma$ anti-correlation during the plateau phase is qualitatively consistent with a contracting corona accompanied by increasing bulk velocity. Disk recession alone would predict the opposite
evolution of $\nu_0$ under the assumptions of our model. Future polarization calculations will provide an additional test of extended outflowing-corona models.
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All authors Ke Qin, Bei You, Adam Ingram, Barbara De Marco
Superconductivity
Universal Dzyaloshinski-Moriya interaction dictates pairing in unconventional superconductor families
Baishun Yang, Yida Chu, Xuelei Sui et al. (+3 more)
The collinear-
antiferromagnetic spin-fluctuation paradigm has long guided unconventional superconductivity research, yet fails to reconcile the noncollinear
spin phenomena observed across cuprates, iron-based
superconductors, and nickelates. Using extensive first-principles calculations and unbiased large-scale DMRG simulations, we show that Dzyaloshinski-Moriya interaction (DMI)-arising from local inversion-
symmetry breaking-is a common ingredient across these families. This DMI unifies hallmark observations in parent compounds-incommensurate orders,
spin-wave gaps, and noncollinear textures. Under hole doping, strong DMI drives
spin vortices to merge with pi-shifted hole stripes, forming hybrid
vortex-hole stripe phases. These phases stabilize charge order while supporting, not suppressing, superconductivity. By contrast, under electron doping, these
vortices pin holes and suppress long-range superconductivity. Our results establish DMI as a unifying link between noncollinear magnetism and superconductivity, identifying hole-strip-
vortex coupling as a microscopic pairing engine. Given that DMI is common across major
superconductor families, these findings challenge the prevailing pairing mechanism and offer an experimentally testable roadmap for materials optimization.
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All authors Baishun Yang, Yida Chu, Xuelei Sui, Haiqing Lin, Shijie Hu, Bing Huang
Information Theory
Connections Between Quadratic Transform for Fractional Programming and Schur Complement
Kaiming Shen, Kareem M. Attiah, Yannan Chen et al. (+1 more)
This paper shows that there are intimate connections between the quadratic transform technique for solving fractional programming (FP) problems and the Schur-complement technique in
matrix analysis. We demonstrate that the quadratic transform technique is related to two aspects of the Schur complement: (i) the linear
matrix inequality (LMI) condition for positive semidefiniteness and (ii) the
matrix determinant formula. Specifically, we establish that the quadratic transform and the Schur-complement LMI condition imply each other. This connection allows us to provide new interpretations of the auxiliary variable in the quadratic transform, and it allows us to rederive the Schur-complement determinant formula. Furthermore, this connection leads to generalizations of the quadratic transform in FP and the Schur-complement LMI that can accommodate generalized
matrix inverse. As an application in information theory, we apply the generalized FP framework to the least-favorable-noise minimax formulation of the Gaussian vector broadcast channel sum capacity problem. When the least-favorable noise covariance is singular,
matrix-inverse-based Karush-Kuhn-Tucker (KKT) analysis would require a careful analysis of the input and output spaces of the channel. We show using generalized FP that an auxiliary-variable representation of the singular
matrix fraction directly yields the reciprocal multiple-access channel and recovers the uplink-downlink duality relation for sum capacity.
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All authors Kaiming Shen, Kareem M. Attiah, Yannan Chen, Wei Yu
Robotics
Visual Proactivity: Enhancing Human-Robot Collaboration Through Intent Communication
Valerio Bo, Edison Bejarano, Anaís Garrell et al. (+1 more)
As
robots transition from performing repetitive tasks to collaborating with humans, understanding human intent becomes crucial to effective interaction. Anticipation enables
robots to predict human actions, while proactivity allows them to take initiative and guide human behavior toward optimal outcomes. Although research has largely focused on how
robots infer and respond to human intentions, less
attention has been paid to how
robots communicate their own intent. This paper introduces visual proactivity, a novel, simple yet effective approach that enables
robots to communicate their intentions through visual feedback, influencing human behavior and enhancing transparency and fluency. We develop and evaluate proactive robotic behaviors in a human-to-
robot handover scenario, where a user study validates human perception of reactive, anticipatory, and proactive behaviors. The results demonstrate that effective visual proactivity fosters better
alignment and coordination, paving the way for more intuitive human-
robot collaboration.
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All authors Valerio Bo, Edison Bejarano, Anaís Garrell, Alberto Sanfeliu
Cryptography and Security
Verifiable Computation with Trusted Execution Environments and On-Chain Digital Rights Tokens
Bingle Stegmann Kruger and Co-Pierre Georg
We present an architecture that enables data owners to combine private data into data pools using Trusted Execution Environments (TEEs) and manage these pools by issuing narrowly scoped computational rights, encoded as Digital Rights
Tokens (DRTs), to third-party data analysts. Each DRT binds specific open-source code to a pool and is issued and redeemed on a distributed ledger. Data analysts can obtain the right to execute open-source code on the combined sealed data inside a TEE and receive the result from this code execution, but not the underlying data. We argue for a control-centric view of
privacy in which creators retain ex ante control over how their data is processed. A reference implementation runs WebAssembly (WASM)/Python jobs over sealed
datasets and records redemptions on Solana, illustrating the feasibility and limitations of the platform.
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Optics
Symmetry Properties of Bound States in the Continuum in Hexagonal Lattice Photonic Crystal Slabs
Rhys Jones and Sang Soon Oh
Bound states in the continuum (BICs) within photonic
crystal (PhC) cavities realise the full confinement of light despite lying within the continuum of radiation modes. While their diverse practical applications has attracted considerable research interest, developing the fundamental understanding of BICs is important for the design of future devices. This paper uses a group theoretical approach to determine the basis functions of the irreducible representations (irreps) of a PhC with the
symmetry of the $C_{6v}$ space group, from which the spatial distribution of the electromagnetic fields within the cavity can be determined. Furthermore, the reflection parity of the electric field and a $k \cdot p$ perturbation theory approach are used to determine the far-field polarisation of each irrep. The $k \cdot p$ perturbation explains why the $E_2$ irrep has a topological charge of $-2$, as well as showing that a topological charge does not necessarily indicate the presence of a BIC. Additionally, it is found that the quality factor of the modes do not necessarily scale as $Q \propto 1/|k|^{2|q|}$, but are instead dependent on the coupling to the radiative mode in the $k \cdot p$ perturbation, which is determined from the
symmetry of each irrep.
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Robotics
ZeroTouch: Tactile-Supervised Visual Contact Estimation for Contact-Rich Manipulation
Dmitriy Kosenkov, Daniia Zinniatullina, Miguel Altamirano Cabrera et al. (+3 more)
Reliable robotic grasping benefits from estimating the evolving physical interaction and selecting a grasp-dependent compression target. Tactile sensors provide direct interaction measurements but require dedicated hardware at deployment. We introduce ZeroTouch, a tactile-supervised framework that predicts dense contact deformation, the instantaneous six-axis wrench, and a grasp-dependent desired compression target from wrist RGB observations, gripper state, and local
gravity direction. Tactile measurements are used only as privileged supervision during training and are not required at deployment. On the full validation set, the complete architecture reduces normal-force MAE from 2.017 N for a state-only baseline to 0.531 N. In physical evaluation with 20 trials per condition, ZeroTouch achieves 95% success on an unseen object, 80% in a seen-object/unseen-grasp condition, and 90% under a content/load shift. Under the same evaluation protocol, OpenVLA achieves 25%, 40%, and 55%, while SmolVLA achieves 10%, 25%, and 35%, respectively.
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All authors Dmitriy Kosenkov, Daniia Zinniatullina, Miguel Altamirano Cabrera, Iana Zhura, Mikhail Derevianchenko, Dzmitry Tsetserukou
Superconductivity
Collective excitation-mediated transport in nanoscale Josephson junctions that exhibit quantum confinement
Zhengyuan Liu, Sebastian Scherb, Werner M.J. van Weerdenburg et al. (+3 more)
Quantum confinement can strongly modify transport through Josephson junctions. Here, we study local tunneling transport through nanoscale Josephson junction stacks in the Coulomb blockade regime, where the metallic layers exhibit strong vertical
quantum confinement. We find that quasiparticle transport is strongly enhanced by a collective excitation mode intrinsic to the junction and localized in the isolated metallic overlayer. We quantify both the collective-mode energy and the Coulomb gap and show that both exhibit strong layer-dependent modulation, consistent with the modulation of the underlying
quantum well states. We further investigate how the collective excitation responds to various perturbations, including mechanical motion and an applied
magnetic field. Our results suggest that this collective mode is sensitive to quasiparticles near the
Fermi level and may therefore provide an indirect probe of the superconducting state.
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All authors Zhengyuan Liu, Sebastian Scherb, Werner M.J. van Weerdenburg, Daniel Wegner, Nadine Hauptmann, Alexander A. Khajetoorians
Statistical Mechanics
Self-avoiding trails in two and three dimensions
Xiaodi Su, Zongzheng Zhou and Qianqian Wu
The self-avoiding trail is an important variant of the self-avoiding walk. In this work, we employ an irreversible Markov chain Monte Carlo
algorithm, together with the reversible Berretti-Sokal
algorithm for comparison, to simulate self-avoiding trails on the square and simple cubic
lattices with periodic boundary conditions. Based on finite-size analyses of the unwrapped end-to-end distance and the Binder ratio, we accurately estimate the critical points on the simple cubic and square
lattices to be 0.206\,376\,9(2) and 0.367\,561\,1(1), respectively, improving the precision of previous best estimates by factors of 500 and 70. At the estimated critical point of the simple cubic
lattice, we numerically demonstrate that both the critical scaling behaviors of various quantities and the length distributions of self-avoiding trails and walks are consistent with each other. Our accurate numerical results are attributed to the efficiency of the irreversible
algorithm, whose advantage over the reversible
algorithm is even more pronounced for the self-avoiding trail model than for the self-avoiding walk model.
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Signal Processing
Secure RIS-Aided Multicasting: Globally Optimal Beam Management and Discrete-Phase RIS Configuration
Luis F. Abanto-Leon and Setareh Maghsudi
Reconfigurable intelligent surfaces (RISs) are poised to revolutionize wireless multicasting by enabling extended coverage and reliable operation in obstructed environments. These benefits, however, can be undermined by security vulnerabilities arising from practical deployment factors. This work addresses three such critical factors, (i) the discrete nature of RIS phase shifts, (ii) the presence of colluding eavesdroppers, and (iii) the inefficiency of static illumination beams, each threatening security if not properly accounted for in system design. To mitigate these issues, we formulate a joint resource allocation problem that minimizes the wiretap signal-to-noise ratio (SNR) across all eavesdroppers by co-optimizing the RIS configuration and the base station (BS) beam management. This yields a complex, nonconvex mixed-integer nonlinear program (MINLP), which we equivalently reformulate into a tractable mixed-integer quadratically constrained program (MIQCP) solvable to global optimality. Numerical results confirm that the proposed scheme significantly bolsters security, suppressing the wiretap SNR by up to 58% compared to existing baselines.
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Computation and Language
CIBuzzBench: A Benchmark for Cross-Lingual Understanding of Chinese Internet Buzzwords
Yifan Wang, Junyu Lu, Qifan Wang et al. (+6 more)
Chinese social media has generated a vast and continually evolving lexicon of internet buzzwords whose meanings are often non-literal and deeply rooted in local cultural and pragmatic contexts. Existing research has primarily focused on interpreting these buzzwords within Chinese, leaving largely unexplored whether LLMs can transfer such culturally grounded knowledge across
languages and accurately convey the intended meanings in English. This cross-lingual capability is also critical for safety, as harmful expressions may obscure their offensive content through culture-specific homophony, euphemism, irony, or coded
language. In this paper, we investigate the ability of advanced LLMs to understand Chinese internet buzzwords across
languages. To this end, we introduce CIBuzzBench, the first
benchmark for cross-lingual Chinese-to-English understanding of Chinese internet buzzwords. CIBuzzBench comprises 3,001 Chinese internet buzzwords annotated with English meaning explanations, English equivalents, category labels, and harmfulness labels. Based on these annotations, we design three evaluation tasks: Meaning Explanation, Cross-lingual Equivalent Matching, and Culturally Grounded Harmfulness Detection. We evaluate representative state-of-the-art proprietary and Chinese LLMs under both English- and Chinese-prompting settings. Our results show that LLMs continue to struggle with the cross-lingual understanding of Chinese internet buzzwords, particularly in fine-grained non-literal interpretation, robust equivalent matching under option perturbations, and calibrated harmfulness detection. These findings highlight the persistent challenges posed by culturally grounded
language phenomena for
multilingual LLMs and safety-oriented evaluation. The
dataset and code are available at https://github.com/SuperYFan/CIBuzzBench.
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All authors Yifan Wang, Junyu Lu, Qifan Wang, Shun Zhang, Chaozhuo Li, Jiahao Liu, Zhijun Cao, Lingbin Bu, Fanliang Bu
Differential Geometry
Rigidity and weak notions of spectral scalar curvature
Xiaoxiang Chai
We give a weak formulation of spectral scalar
curvature bounded from below via establishing a dihedral
rigidity result. Given some special convex polyhedron, if another metric are of non-negative spectral scalar
curvature in the interior, with weighted mean-convex faces, and with its dihedral angles less than or equal to their flat polyhedral model everywhere along the edges, then the metric must be flat. This is motivated by Gromov's definition of a weak notion of non-negative scalar
curvature.
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Symplectic Geometry
On intrinsic homological mirror symmetry for toric degenerations
Shaoyun Bai and Sebastian Haney
This paper studies the Floer-theoretic aspects of homological mirror
symmetry inspired by proposals of Perutz and Siebert and the Gross–Siebert intrinsic mirror
symmetry program. Given a maximally unipotent degeneration of smooth projective Calabi–Yau
manifolds over the punctured disk, we construct a ring using the fixed point Floer
cohomology groups of the iterates of the monodromy of the degeneration equipped with the pair of pants product. Under the assumption that this ring is commutative, we can consider a candidate mirror family defined by the relative Proj construction. Further assuming that a smooth fiber $X_t$ contains a so-called tropical Lagrangian section, we construct a fully faithful
embedding from the derived category of perfect complexes on our candidate mirror family into the Fukaya category of $X_t$. We verify both of these assumptions for certain Batyrev–Borisov toric degenerations, as well as some toric degenerations of Calabi–Yau threefolds coming from the Gross–Siebert reconstruction
algorithm. These two
geometric hypotheses are both phrased to support the general study of mirror
symmetry for maximally unipotent degenerations of Calabi–Yau
manifolds, largely reducing the
symplectic inputs for proving homological mirror
symmetry to the problem of constructing tropical Lagrangian sections.
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Numerical Analysis
Finite Volume Element Method on Curved-Edge Meshes
Xiaoxiao Chen, Zexi Hu, Zhiming Gao et al. (+3 more)
This paper proposes and analyzes a high-order finite volume element method on curved-edge quadrilateral meshes for
elliptic equations. Unlike existing theories, which are primarily based on straight-edge meshes, this study is the first to establish an analysis of the stability and optimal convergence of the finite volume method on curved-edge meshes. By constructing a dual mesh based on Gaussian points, we overcome the accuracy degradation issues caused by
geometric deformation and Jacobian non-uniformity of curved-edge meshes. We prove the coercivity of the discrete bilinear form under weak mesh regularity conditions, thereby obtaining an optimal error estimate in the energy norm. Furthermore, using orthogonality and the Aubin-Nitsche technique, we derive an optimal $L^2$ error estimate. Numerical experiments cover problems with constant and anisotropic coefficients, different dual partition strategies, complex curved boundary domains, and interfaces with large deformations. Numerical results indicate that this method consistently achieves the optimal convergence order in both the $H^1$ and $L^2$ norms on a variety of curved-edge meshes. Compared to straight-edge meshes, curved-edge meshes offer significant advantages in approximating complex curved boundaries and demonstrate better resistance to distortion in cases involving sudden changes in coefficients and large deformations at interfaces. This paper provides a unified theoretical framework for the finite volume element method on curved-edge meshes and verifies the efficiency and robustness of the proposed method.
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All authors Xiaoxiao Chen, Zexi Hu, Zhiming Gao, Junliang Lv, Xiang Wang, Hongtao Yang
Robotics
A Novel Path-Tracking Algorithm for Automated Tractor-Trailer Forward and Backward Maneuvers
Alexandre Lombard, Florent Perronnet, Théo Rigal et al. (+2 more)
Fully autonomous tractor-trailer systems are increasingly deployed in logistics, agriculture, and industrial environments, where precise and robust path-tracking capabilities are essential. However, the articulation between the tractor and the trailer introduces additional nonlinearities and significantly complicates lateral and longitudinal control, particularly during reversing maneuvers. This paper introduces a novel path-tracking
algorithm specifically designed for articulated vehicles with a single trailer. The proposed method combines a lateral control law applied at the trailer level with a short-horizon predictive adjustment of the tractor steering angle, ensuring stable convergence toward the desired path in both forward and backward motion. The approach is geometry-based and requires no per-vehicle calibration or training. Simulation studies in a high-fidelity physics simulator show steady-state or final lateral errors of $0.02$–$0.05$~m on straight-line, circular, and reverse docking trajectories, which is of the same order of magnitude as, or lower than, the errors reported in the literature. Real-world experiments on a small-scale tractor–trailer
robot, with the whole controller running on a low-cost microcontroller, further confirm the feasibility of the approach, with lateral errors below 13~mm during repeated reverse docking maneuvers despite a pose detection latency of about 200~ms.
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All authors Alexandre Lombard, Florent Perronnet, Théo Rigal, Nicolas Gaud, Abdeljalil Abbas-Turki
Cryptography and Security
A Framework to Quantify the Probability of Future Cyber Loss Events
Siem Peters and Martin Eian
Cybersecurity risk quantification remains challenging due to limited operational data and difficulties in quantifying Loss Event Frequency (LEF). This paper introduces the Loss Event Frequency Security Analyser (LEFSA), a probabilistic framework that reformulates LEF estimation as machine-level Cyber Loss Event (CLE) prediction combined with hierarchical infrastructure-level aggregation. LEFSA estimates calibrated machine-level CLE probabilities from operational cybersecurity telemetry and aggregates them across infrastructure layers while accounting for machine-level dependencies. This provides a foundation for scalable, explainable, and operationally applicable cyber risk estimation at the level of machines, services, business processes, and the entire organization. The framework was evaluated using proprietary Managed Detection & Response telemetry from 23 organizations using Microsoft Defender for Endpoint. XGBoost achieved the strongest predictive performance, with a mean area under the receiver operating characteristic curve of 0.90 and consistently low calibration error across evaluation periods. The results demonstrate that operational cybersecurity telemetry contains substantial predictive information for future CLE occurrence, supporting probabilistic machine-level modeling and hierarchical aggregation as a promising foundation for quantitative, data-driven cyber risk management.
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Robotics
AgenticSwarm: Semantic Perception and Adaptive Task Allocation for Heterogeneous Multi-UAV Missions
Muhammad Ahsan Mustafa, Yasheerah Yaqoot, Faryal Batool et al. (+3 more)
Multi
UAV missions in complex environments require the system to understand both the surrounding scene and the intent of a human operator while maintaining feasible task allocation as mission conditions change. This paper presents AgenticSwarm, an agentic framework for semantic perception and adaptive task allocation in heterogeneous multi
UAV missions. An
agent interprets aerial imagery and natural
language instructions to construct a grounded mission representation that links perceived objects and regions with task requirements, capability constraints, and mission dependencies. This information augments a constrained task allocation process in which obstacle aware path feasibility, energy consumption, and protected return home requirements are incorporated before assignment. During execution, changes such as
UAV failure, battery degradation, or task modification trigger residual mission reconstruction from the current system state, while completed work and reconnaissance progress are retained. AgenticSwarm is evaluated across five diverse Gazebo environments and an indoor real test environment, demonstrating its ability to connect semantic
reasoning with constrained allocation and adaptive multi
UAV mission execution. Compared with a Grounding DINO+SAM~2.1 perception baseline, the SAM3-based pipeline improves class-aware recall by 25.2 percentage points (pp) and semantic label accuracy by 29.5 pp. Ablating residual mission replanning increases mean repeated work from 0% to 61.7% and post-event recovery time by 58.6%, highlighting the contribution of adaptive replanning to mission execution.
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All authors Muhammad Ahsan Mustafa, Yasheerah Yaqoot, Faryal Batool, Roohan Ahmed Khan, Valerii Serpiva, Dzmitry Tsetserukou
Numerical Analysis
Integral chemical reaction neural networks
Abraham Reyes-Velázquez and Stefan Güttel
Discovering the structure and kinetics of chemical reaction networks (CRNs) from time-series concentration data is a fundamental challenge in chemical kinetics, with existing approaches relying on prior mechanistic assumptions or suffering from high computational cost and noise sensitivity. In this work, we present integral chemical reaction neural networks (iCRNNs), a framework that combines the interpretable, physics-constrained, architecture of chemical reaction neural networks (CRNNs) with an integral collocation formulation of the governing dynamics. Rather than solving ODEs forward in time at each training step, we approximate the integral of the learned rate functions directly using numerical quadrature. This yields an entirely algebraic forward pass consisting only of
matrix operations, eliminating the repeated adaptive ODE solves of standard CRNN and producing smoother, more predictable training. We provide a recovery error analysis characterising the structural sources of ill-conditioning (conservation laws, reaction reversibility, and shared reactant sets) that limit network identifiability. On two
benchmark CRNs, iCRNN trains in roughly half the wall-clock time of the baseline CRNN method on a four-species mechanism and between three and four times faster on a five-species mechanism, while completing every training run reliably and attaining comparable or lower loss.
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Earth and Planetary Astrophysics
Some challenges for the long-term survival of Naiad, Neptune's innermost moon
Harrison Agrusa, Matija Ćuk, David Nesvorný et al. (+1 more)
The Naiad-Thalassa 73:69 mean-motion resonance implies these moons have co-existed for $\gtrsim$1 Gyr, raising the question of how they survived to the present day. We examine three challenges to Naiad's long-term survival:
tidal disruption, heliocentric bombardment, and runaway collisional erosion by planetocentric debris. We constrain Naiad's internal strength requirements from its nominal density and shape, compute present-day impact rates on Neptune's inner moons from heliocentric bombardment, and use $N$-body simulations to model the fate of ejecta produced by non-disruptive impacts. First, Naiad's nominal density of ${\sim}0.8\text{ g cm}^{-3}$ and elongated shape suggest it cannot be held together by self-
gravity alone, implying a cohesive strength of $\gtrsim10$ kPa to avoid
tidal disruption, although this constraint is relaxed if Naiad has a higher density of ${\gtrsim}1.3\text{ g cm}^{-3}$. Second, we show that Naiad may have been disrupted in the last 1 Gyr by heliocentric bombardment, although this depends sensitively on the size-frequency distribution of Kuiper Belt objects at small sizes and on Naiad's catastrophic disruption threshold, both of which are poorly constrained. Third, and most notably, we find that even small, non-disruptive impacts can trigger runaway collisional erosion by planetocentric debris on extremely short timescales. Avoiding this “sesquinary catastrophe” requires that Naiad has a collisional strength of at least several MPa, which is difficult to reconcile with being a reaccumulated “rubble pile”, leftover from the capture of Triton and the subsequent cataclysm of Neptune's
primordial satellite system. Together, these results suggest that Naiad may be a physically unusual object among small ring-moons – possibly a largely coherent, monolithic fragment – and that our understanding of Neptune's inner satellite system leaves much to be explained.
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All authors Harrison Agrusa, Matija Ćuk, David Nesvorný, David Minton
Cryptography and Security
TERMon: Detecting Persistent Behavioral Threats in Edge AI via Hardware-Native Ternary Runtime Monitor
Arish Sateesan and Edlira Dushku
Edge AI accelerators are increasingly deployed in safety-critical environments, where model outputs may control physical actuators, make access-control decisions, or trigger alarms. In these settings, runtime failures often remain undetected because model corruption, distribution shift, and
adversarial inputs can still produce well-formed, confident predictions. This paper presents TERMon, a lightweight hardware runtime monitor that detects such anomalies by observing inference behavior rather than re-executing or formally verifying the model. TERMon represents class-conditional trusted behavior as hardware-efficient ternary patterns that are matched in parallel against a thermometer-encoded fingerprint. The ternary encoding reproduces the corresponding unquantized range decision exactly. TERMon detects harmful weight corruptions in proportion to their behavioral impact, while out-of-distribution and
adversarial inputs are largely not separable using the monitored features at a strict false-positive operating point. We implemented TERMon on a PYNQ-Z2 FPGA, and the pipelined design requires no on-chip block RAM or DSPs and has a two-cycle decision latency.
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Computer Vision
ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation
Boni Hu, Xiong Wei, Haoming Huang et al. (+20 more)
Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and
vision-
language-action driving policies, but production deployment exposes three unresolved requirements: faithfully reproducing a mixed fisheye-pinhole rig at native resolutions; reconciling causal, per-timestep interaction with long-horizon stability and low latency; and preserving scene identity when a location is revisited. We present ZYT-World, a single architecture that natively generates four fisheye views with field of view > 180{\deg} and three pinhole views. Projection-specific Plucker adapters encode camera geometry, ego-motion adaptive layer normalization provides global motion control, and a lightweight pixel-aligned layout conditions traffic participants and signals through instance-level boxes, headings and colors. Heterogeneous training combines full-rig
geometric coverage with high-resolution detail. Teacher forcing, causal consistency
distillation, self-rollout distribution matching
distillation, and RigCritic transform a 40-step bidirectional teacher into a one-step, per-latent streaming generator, with RigCritic evaluating the seven-view rig jointly. A 19M-parameter variational autoencoder decoder (TinyVAE), W8A8 quantization, and our inference engine reduce decoding, backbone, and incremental-execution costs, respectively. Finally, cross-trajectory pairs derived from real captures train a plug-in implicit-
memory module that preserves place-specific evidence. On the internal multi-view test set, the one-step model retains more than 90% of the teacher's PSNR and SSIM, while FID, FVD, and LPIPS stay within 11% of the teacher. Under the generator-only timing in Figure 2, it is 107.7 times faster than the 40-step bidirectional teacher. TinyVAE decodes 59.8 times faster than Wan. 30s rollouts and cross-trajectory revisits show the intended long-horizon and
memory behavior.
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All authors Boni Hu, Xiong Wei, Haoming Huang, Yong Huang, Chenbo Wang, Yi Yang, Jiancheng Wang, Ruicheng Zhu, Zhimin Yang, Guanglai Liu, Qiaowan Jin, Dongzhuo Wang, Haiwei Kuang, Jiajun Fan, Yue Wu, Jiaxin Wei, Hao Sun, Feihong Yan, Yuyao Zhou, Wei Bi, Kaixuan Wang, Zichao Guo, Xiaozhi Chen
High Energy Astrophysical Phenomena
X-ray polarization of the Compton-thin Seyfert galaxy NGC 5506 with IXPE
Daniele Tagliacozzo, Alessandro Leonardo Lai, Andrea Gnarini et al. (+14 more)
We report on the observation of the Compton-thin Seyfert
galaxy NGC~5506 performed with the Imaging X-ray Polarimetry Explorer (IXPE). The observation, with a net observing time of 975~ks, was partly simultaneous with NuSTAR (52~ks) and XMM-Newton (37~ks). Using a simple baseline model with IXPE data only, we find an upper limit on the 2–8~keV polarization fraction at the $99\%$ confidence level: $\Pi<3.1\%$. We then performed a spectropolarimetric analysis combining data from the three X-ray observatories. While pure spectral modeling confirms the presence of both relativistic and cold distant reflection, in addition to the primary continuum power law, their contributions lead to somewhat different constraints on the coronal $\Pi$, depending on the specific assumptions. We also performed Monte Carlo simulations with the radiative transfer code MONK, exploring various coronal geometries. Comparing these results with the IXPE data, and assuming a radially extended corona, as suggested by previous observations of similar sources, the corresponding polarization upper limits require inclination angles below 40{\deg}.
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All authors Daniele Tagliacozzo, Alessandro Leonardo Lai, Andrea Gnarini, Giorgio Matt, Stefano Bianchi, Francesco Ursini, Andrea Marinucci, Alessia Tortosa, Sudip Chakraborty, Federico Ferretti, Vittoria Elvezia Gianolli, Elias Kammoun, Frédéric Marin, Herman Marshall, Riccardo Middei, Romana Mikusincova, Dawoon Kim
Statistics Theory
Parameter estimation for graphon-interacting particle systems from discrete observations
Chiara Amorino and Matteo Sfragara
In this paper, we address the joint parameter estimation of drift and
diffusion coefficients for heterogeneously interacting particle systems. Unlike the homogeneous setting, the interactions are governed by a graphon-weighted mean-field framework, which introduces significant analytical complexity: while homogeneous systems yield i.i.d. limits, our setting results in particles that are independent but non-identically distributed in the limit. Based on discrete observations of the system over a fixed time interval $[0, T]$, we propose a contrast function based on a pseudo-likelihood approach. We prove the consistency of the resulting
estimators as the discretization step $\Delta_n \to 0$ and the number of particles $N \to \infty$. Furthermore, we establish asymptotic normality under the additional constraint $N \Delta_n \to 0$, demonstrating that the underlying graphon structure can be rigorously handled despite the lack of identical distribution in the limit.
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Computer Vision
SignGPT: Toward LLM-Mediated Sign Language Interaction through Gloss-Free Translation and Generation
Ronghui Li, Jun Dong, Zhongyuan Hu et al. (+8 more)
Large
language models (LLMs) provide limited support for sign
language interaction. Unifying sign
language translation (SLT) and generation (SLG) to enable sign
language as both input and output can reduce switching between separate models during sign-text interaction. We present SignGPT, a unified, pose-based framework for gloss-free SLT and SLG. SignGPT integrates part-aware hierarchical representations of body, hand, and facial motion into a shared
language model and employs asymmetric multi-
token prediction and progressive training for bidirectional modeling. We evaluate SignGPT on How2Sign (ASL) and Phoenix-2014T (DGS) through
benchmark comparisons, qualitative analyses, and component ablations. An exploratory study with 12 Deaf ASL signers assesses an LLM-mediated sign-to-sign response pipeline, highlighting the potential of unified modeling to support sign
language conversation (SLC).
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All authors Ronghui Li, Jun Dong, Zhongyuan Hu, Zunnan Xu, Jun Zhou, Liyuan Chen, Shuoling Liu, Jiangpeng Yan, Jie Guo, Xiu Li, Linchao Bao
High Energy Astrophysical Phenomena
COBALT-BLUE: a fast and accurate model for gravitational self-lensing binary systems
Danny J. Dixon, Adam Ingram, Matthew J. Middleton et al. (+5 more)
We present COBALT-BLUE, a fast public model for the optical light curve of a luminous
star in a non-accreting binary with a compact object. The model includes gravitational self-
lensing, Doppler beaming, ellipsoidal variations, eccentric orbital geometry, and wavelength-dependent limb darkening. In sufficiently edge-on systems, it predicts sharp periodic self-
lensing flares together with phase-locked orbital modulation outside the
flare. We improve upon earlier models by including non-linear limb darkening laws based on
stellar atmosphere simulations to calculate the self-
lensing flare profile. This enables light curves to be self-consistently calculated in multiple wavebands from one set of physical parameters, thus offering the prospect of constraining binary parameters such as compact object mass from photometry alone if multiple self-
lensing flares are observed. We also include the option to utilise simpler limb darkening prescriptions to save computational expense. As a proof of principle, we fit our model to ZTF data of 14 candidate self-
lensing sources. The model describes the
flare morphology well for most candidates, but we find that the best fitting parameters are not physically plausible. We conclude that the observed
flares are most likely
stellar flares and not self-
lensing events. In future, we plan to use the model to systematically search optical survey data for signatures of binary motion.
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All authors Danny J. Dixon, Adam Ingram, Matthew J. Middleton, Adam McMaster, Grzegorz Wiktorowicz, Hugh Dickinson, Allison Crossland, Eric C. Bellm
Robotics
NeuRIO: A Streaming Neural Estimator for Zero-Shot Sim-to-Real Multi-Robot Relative Inertial Odometry
Zhehan Li, Jiadong Lu, Shengwei Ren et al. (+2 more)
We present NeuRIO, a streaming neural
estimator for anchor-free 6-DoF relative inertial odometry using only identified inter-
robot bearings, ranges, and IMU measurements. NeuRIO canonicalizes measurements into
gravity-aligned coordinates, represents
robots as nodes and mutual observations as factors, and uses
attention for spatial
reasoning and GRUs for temporal modeling. As a graph network, NeuRIO applies shared node-wise and factor-wise operators throughout the network, enabling it to handle different team sizes and time-varying observation graphs. NeuRIO is trained on a simulator that couples various motion patterns, device-level sensor characteristics, and diverse, realistic modeled, and temporally persistent sensor corruptions. In this way, NeuRIO achieves zero-shot sim-to-real transfer. Across $24$ real-world sequences, NeuRIO achieves $14.1\,\mathrm{cm}$ position RMSE and $3.9^\circ$ rotation RMSE. More importantly, NeuRIO demonstrates strong computational scalability, maintaining an update cost below $20\,\mathrm{ms}$ with up to $400$
robots in simulation, while optimization-based methods exceed $20\,\mathrm{ms}$ at only $24$
robots. Moreover, even trained on limited team sizes, NeuRIO transfers directly to unseen larger teams without architectural or parameter changes.
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All authors Zhehan Li, Jiadong Lu, Shengwei Ren, Chao Xu, Yanjun Cao
Earth and Planetary Astrophysics
Amit Reza, Ludmila Carone and Christiane Helling
With the rapid advancement of
telescopes like JWST and Ariel, there is an urgent need for efficient 3D
climate models to interpret observations of
exoplanet atmospheres. Traditional 3D general circulation models (GCMs) are computationally intensive, prompting the development of machine learning (ML) emulators to accelerate simulations. Recent work, such as that by Plaschzug et al. 2026 \cite{plaschzug2026accelerating}, uses a dense neural network (DNN) to predict local gas temperatures and winds from input parameters, including local gas pressure, spatial coordinates (longitude and latitude), and global temperature. However, this model relies on predicting individual temperature values (points) at specific grid points, which can be limited by the resolution and constraints of the training grid. In this work, we investigate a couple of alternative frameworks based on latent-space representations of local gas temperature ($\text{T}_{\text{gas}}$) to obtain a faithful, low-dimensional representation of these profiles. This represents the first step toward developing a latent space regression model, offering a structurally cohesive alternative to the existing point-wise prediction method \cite{plaschzug2026accelerating}. By capturing the optimal
embedding space of atmospheric data, our proposed framework can produce simulated profiles while maintaining computational efficiency, making it suitable for large-scale
exoplanet ensemble studies.
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Astrophysics of Galaxies
Morphology classification for galaxies in the Kilo Degree Survey using a label-efficient self-supervised learning framework
Xu Huang, Rui Li, Liang Gao et al. (+14 more)
Galaxy morphology classification is fundamental to understanding
galaxy formation and
evolution. The advent of large-scale sky surveys has produced an unprecedented volume of
galaxy images, making traditional manual classification impractical. Although supervised deep learning can achieve high accuracy, it requires large labeled
datasets that are time-consuming to construct. In contrast, unsupervised methods often show limited classification performance. To address this limitation, we propose a label-efficient self-supervised learning framework for
galaxy morphology classification. Our method first learns robust morphological representations from 305,583 unlabeled KiDS
galaxy images through contrastive learning, and then trains a classifier using only 5,000 human-labeled images. The classifier separates
galaxies into five categories: elliptical, spiral, lenticular-disk, irregular, and "other." Using a ResNet-50 model with a crop size of 64x64 pixels, our approach achieves an overall test accuracy of up to 91.0% (90.5% +/- 0.2% on average) on the human-classified catalog. The corresponding F1 scores for elliptical, spiral, irregular, lenticular-disk, and "other"
galaxies are 0.96, 0.86, 0.86, 0.95, and 0.92, respectively. We apply this pipeline to the Kilo-Degree Survey Data Release 5 and produce a publicly available morphology catalog of 310,583
galaxies. This is the first morphology catalog for KiDS
galaxies and provides a valuable resource for future studies of
galaxy evolution. Our results show that self-supervised learning can substantially reduce the need for manual labels while maintaining high classification accuracy, making it a promising and scalable approach for automated
galaxy morphology classification in the era of large-scale surveys.
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All authors Xu Huang, Rui Li, Liang Gao, Liqing Chen, Hui Li, Huijun Mu, Hao Su, Fucheng Zhong, Zhenping Yi, Xiaoyue Cao, Ran Li, Haicheng Feng, Nicola N. Napolitano, Yue Dong, Yida Deng, Sihan Li, Kang Jiao
Machine Learning
SpecQuant: Speculative Decoding with Multi-Parent Quantization for Adaptive LLM Inference
Harish KB, Jagadeeswaran M, Pradheep P et al. (+2 more)
Running large
language models (LLMs) locally continues to be limited by restrictions of compute and
memory on consumer hardware. The popular acceleration technologies, such as quantization, speculative decoding, and adaptive inferencing, offer substantial speed boosts but usually necessitate retraining, per architecture tuning, or draft models. SpecQuant is a trainingfree framework, that combines speculative decoding with multiparent quantization to perform adaptive, efficient inference of LLMs. SpecQuant derives multiple quantized variants (INT4, FP8, FP16) from a shared base model, and dynamically routes queries based on predicted complexity; lightweight variants are used for simple or factual tasks, and full-precision models are used for complex
reasoning tasks or long-context inputs. The shared-weight design of SpecQuant ensures sufficient
token acceptance for speculative decoding without compatibility issues using separate draft parent models. We evaluate SpecQuant on Qwen2.5 based models on the MMLU, AlpacaEval, and GSM8K
datasets, or
benchmarks, demonstrating 35-43% speedups without degrading accuracy greater than 2%, substantial within the LLM community. SpecQuant enables practical on-device LLM deployment across diverse hardware without special infrastructure or expertise.
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All authors Harish KB, Jagadeeswaran M, Pradheep P, Yuvanesh S, Sivakumar T