High Energy Physics (Theory)
Measuring correlations using local and nonlocal quenches
Alexey G. Mikhaylenko and Andrew G. Semenov
We present a theoretical proposal for measuring correlation functions in a
quantum field system based on the use of
quantum quenches. Using the examples of massive and massless scalar field theories, we show that a short-term perturbation of the system leads to a dependence of the further
evolution of the average field on the initial state's correlation functions. Considering two types of perturbations, namely local and nonlocal quenches, we show which correlation functions can be measured in each of these cases. The proposed procedure can be applied to study non-Gaussian correlations.
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Computer Vision
DTKDP: A Dual Teacher Knowledge Distillation and Pruning Framework for Lightweight Oriented SAR Ship Detection
Yuming Li, Fan Zhang and Alin M. Achim
Two-stage oriented detectors achieve high localization accuracy in synthetic aperture radar (SAR) ship detection, but their large backbones, feature pyramids, proposal modules, and heavy region of interest (RoI) heads hinder deployment. Existing lightweight SAR ship detectors typically use one-stage frameworks that lack proposal-level refinement for precise rotated localization. This paper presents a dual-teacher knowledge
distillation and
pruning (DTKDP) framework for lightweight oriented SAR ship detection. DTKDP introduces learnable gates into convolutional, normalization, and linear layers to prune convolutional channels and RoI-head
neurons. Rotated proposal
alignment (RPA) distills teacher and student predictions in a shared teacher-generated rotated proposal space, while a dual-teacher scheme combines classification and regression guidance from a homogeneous main teacher with complementary classification cues from a heterogeneous auxiliary teacher. Experiments on the SAR Ship Detection
Dataset (SSDD) and Rotated Ship Detection
Dataset in SAR Images (RSDD-SAR) show that DTKDP reduces the parameters of Oriented Region-based Convolutional Neural Network (Oriented R-CNN) and RoI
Transformer equipped with ResNet-50 backbones by 87.5-91.8% and their floating-point operations (FLOPs) by 75.6-79.9%. In terms of average precision (AP) and mean average precision (mAP), the resulting Oriented R-CNN-slim and RoI
Transformer-slim retain accuracy close to their full-scale counterparts. Relative changes across $\mathrm{AP}_{50}$, $\mathrm{AP}_{75}$, $\mathrm{mAP}_{50:75}$, and $\mathrm{mAP}_{50:95}$ range from a 2.38% decrease to a 0.65% improvement. Compared with RTMDet-tiny, they improve all four metrics on both
datasets by 0.52-27.55% and consistently surpass representative
distillation methods, demonstrating a favorable accuracy-efficiency trade-off.
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Numerical Analysis
GradAgent: A Knowledge-Guided Multi-Agent System for Structure-Preserving Gradient-Flow Computation with an Application to Multicomponent Vesicle Dynamics
Zhenlin Guo, Jiale Meng, Shuqi Tang et al. (+4 more)
High-order differential operators and nonlinear coupling make it challenging to construct conservative and energy-stable schemes for coupled gradient-flow systems. We present GradAgent, a knowledge-guided multi-
agent system that coordinates three
agents across model analysis,
algorithm design and proofs, and numerical implementation and validation. Independent audits strengthen reliability by uncovering mathematical errors and proof gaps, guiding revisions, and maintaining consistency across stages. In Reconstruction Mode, GradAgent reconstructs 20 published studies and organizes audited knowledge in an extensible knowledge graph (KG), GradAgent-KG, linking model structures, discretization strategies and proofs, implementations, and numerical evidence. In Design Mode, the
agents assess the applicability of retrieved knowledge and develop new schemes informed by relevant discretization strategies. Applied to the fully coupled multicomponent vesicle phase-field-fluid model, GradAgent yields three first-order and three second-order schemes across three algorithmic families, including four linear, decoupled schemes. Under stated assumptions, all six schemes conserve membrane component mass and vesicle volume and dissipate their respective temporally discrete energies unconditionally. Comparisons with and without GradAgent-KG show that it promotes diversity in structure-preserving scheme design for this target model. Numerical tests confirm second-order spatial accuracy, the expected temporal orders, conservation, and temporally discrete energy dissipation, while three-dimensional shear-flow simulations agree qualitatively with experiments. These results demonstrate GradAgent's ability to combine reusable knowledge, coordinated
reasoning, and independent
auditing to develop and validate structure-preserving
algorithms for complex coupled systems.
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All authors Zhenlin Guo, Jiale Meng, Shuqi Tang, Haiyan Su, Maosheng Jiang, Kaiwen Shi, Meng Zhao
Earth and Planetary Astrophysics
Upside down: GJ3090 b the first retrograde exoplanet around an M dwarf detected with NIRPS
Y. Carteret, V. Bourrier, L. Parc et al. (+57 more)
The angle between
stellar spin axis and planetary orbital plane can provide key insights into the formation and dynamical
evolution of planetary systems. In particular, this measurement in multi-
planet systems can be used to further discriminate between different competing migration scenarios. We present six transit observations of the sub-Neptune GJ3090 b obtained with NIRPS and HARPS. GJ3090 b is the inner
planet of a confirmed multi-
planet system orbiting an M
dwarf (M2). Using high spectral resolution and high temporal cadence spectroscopic observations, we analyzed the Rossiter-McLaughlin (RM) effect induced by GJ3090 b to determine its orbital obliquity. Through the RM revolutions technique, we find that the
planet is on a retrograde orbit with a derived 3D obliquity of $\psi = 136^{+24}_{-18}\,\mathrm{deg}$. We find no evidence of massive outer planetary or wide
stellar binary companions, which disfavors scenarios involving gravitational perturbations from a massive body and instead points toward a
primordial misalignment of the protoplanetary disk. Our results establish GJ3090 b as the first
planet on a retrograde orbit discovered around an M
dwarf and the first highly misaligned confirmed multi-
planet system without a known massive companion. We further propose late secondary disk
accretion around GJ3090, in which the disk is not expected to be aligned with the
stellar spin axis, followed by disk-driven migration as the most likely mechanisms to explain the observed architecture. This work also illustrates the capability of the RM revolutions technique when applied to near-infrared data to probe orbital architectures of the smallest
planets around M
dwarfs that have remained mostly inaccessible.
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All authors Y. Carteret, V. Bourrier, L. Parc, A. Winter, R. Allart, I. de Castro Leão, F. Bouchy, V. M. Passegger, C. Cadieux, P. Lamontagne, É. Artigau, F. Baron, S. C. C. Barros, B. Benneke, X. Bonfils, M. Bryan, B. L. Canto Martins, R. Cloutier, E. Cristo, J. Gagné, N. J. Cook, N. B. Cowan, J. R. De Medeiros, X. Delfosse, R. Doyon, X. Dumusque, D. Ehrenreich, J. I. González Hernández, D. Lafrenière, C. Lovis, L. Malo, C. Melo, L. Mignon, C. Mordasini, F. Pepe, R. Rebolo, J. Rowe, N. C. Santos, D. Ségransan, A. Suárez Mascareño, S. Udry, D. Valencia, G. Wade, K. Al Moulla, R. G. de Souza Barros de Amorim, L. Dang, E. Deibert, D. O. Fontinele, T. Forveille, Y. G. C. Frensch, R. de Lima Gomes, D. Mounzer, S. Pelletier, R. Rosener, B. N. Skinner, A. Srivastava, A. K. Stefanov, V. Vaulato, J. P. Wardenier, D. Weisserman
Complex Variables
Bergman Kernel Asymptotics for Semipositive Line Bundles near Curvature-Degenerate Points
Yueh-Lin Chiang
We study the asymptotic behavior of Bergman kernels for high tensor powers of semipositive line bundles over Hermitian
manifolds. At points where the
curvature degenerates, the classical asymptotic expansion may fail. In this paper, we establish a full local asymptotic expansion and rapid off-diagonal decay near degenerate points at which the metric admits a local decoupled model. More generally, we make the following two spectral hypotheses: a localized mild spectral gap for the Kodaira
Laplacian and a spectral gap for the rescaled local model. Under these assumptions, we prove a localization property and the rapid off-diagonal decay for the Bergman kernel. Furthermore, if the metric has a local quasi-homogeneous structure, we obtain a full local asymptotic expansion in the $C^\infty$-
topology. As an application, we study pull-backs of positive line bundles under branched coverings. Near a smooth ramification hypersurface, the resulting asymptotic expansion reflects the branching order. Finally, for certain non-quasi-homogeneous models, we still obtain localization and leading-order asymptotics.
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Robotics
DualWAM: Dual-System World Action Models for Asynchronous Global Planning and Local Refinement
Yixin Zheng, Jiangran Lyu, Yuntian Deng et al. (+6 more)
World Action Models (WAMs) jointly generate
robot actions and predict future world states, transferring priors from
video pretraining to
robot control. However, future visual prediction is computationally expensive, so existing WAMs often rely on long action chunks to amortize inference cost across control steps, at the cost of closed-loop responsiveness. We present \method, a dual-system WAM that preserves broader-horizon world-action generation while enabling high-frequency closed-loop action updates by decoupling global planning and local refinement. \systwo periodically performs high-noise bidirectional denoising over a broader world-action chunk to establish a global plan, while wrist-only \sysone extracts a temporally aligned short window from the intermediate denoising state and completes low-noise refinement using the latest wrist observations, which provide action-aligned cues about local geometry, motion, and contact during interaction. The two systems operate asynchronously along a shared denoising trajectory: each global plan is reused across multiple local updates, while \sysone repeatedly incorporates fresh interaction feedback. Across zero-shot manipulation tasks on Franka and Galbot, \method improves success over the strongest evaluated baseline by 4.5 percentage points on average, while achieving a 16.6$\times$ critical-path speedup. Further studies show that role-matched
egocentric and UMI data improve success by 14 percentage points, and that the decoupled design naturally supports edge–cloud deployment with substantially lower communication overhead than the baseline.
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All authors Yixin Zheng, Jiangran Lyu, Yuntian Deng, Kai Liu, Yizhou Zhou, Yizhou Wang, Xiaoguang Zhao, He Wang, Zhizheng Zhang
Probability
Heat flow and repeated differentiation of polynomials with i.i.d. roots
Jonas Jalowy
How do the zeros of a
polynomial evolve under the holomorphic heat flow or repeated differentiation? In this work, we develop a unified probabilistic proof to three
conjectures on the
evolution of
polynomial zeros under holomorphic heat flow and repeated differentiation. We study these two evolutions for random
polynomials with i.i.d.~roots $z_1,\dots,z_n\sim\mu_0$, where $\mu_0$ on $\mathbb C$ satisfies suitable $2+\delta$ logarithmic moment conditions. For the heat flow of small time $t>0$ and Lipschitz continuous Stieltjes transform of $\mu_0$, we identify the limit distribution $\mu_t$ as explicit push-forward of $\mu_0$ under a transport map. For rotationally invariant $\mu_0$, we characterize the limit after $\lfloor tn\rfloor$ differentiations through its radial
quantiles. For instance, the heat flow evolves the circular law into the
elliptic and semicircle law, while differentiation moves surviving mass towards the origin. We also show that $o(n)$ derivatives preserve the initial distribution $\mu_0$. In fact, all convergences hold almost surely. The common proof strategy crucially relies on recursion identities from leaving out a root, and concentration inequalities, which lead to self-consistent equations for Stieltjes transforms. This brings a method familiar from random
matrix theory to
polynomial evolutions. Moreover, it allows for generalizations beyond rotational symmetric distributions and quantitative stability estimates for $o(n)$ differentiations and vanishing heat-flow times.
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Systems and Control
Control Synthesis against LTL Specifications with Long-Run Visit Proportion Objectives
Zhiyuan Huang, Zhao Tong, Jiakai Li et al. (+2 more)
This paper investigates the path-planning problem for systems required to satisfy a linear temporal logic (LTL) specification while achieving a desired long-run visit proportion. For a path represented in prefix-suffix structure, the long-run visit proportion quantifies the asymptotic occurrence proportion of an atomic proposition (AP) sequence of interest in the suffix trace. Such a quantitative requirement generally cannot be expressed by standard LTL specifications. Furthermore, we develop a planning approach that synthesizes an LTL-satisfying path whose long-run visit proportion remains within a prescribed tolerance of a desired value while satisfying an overall cost constraint. By adjusting the desired proportion, the synthesized path can allocate more or less long-run
attention to the atomic proposition sequence of interest, thereby improving the flexibility and efficiency of the task execution. Finally, experiments on a quadruped
robot demonstrate the practical significance of the proposed long-run visit proportion and the effectiveness of the proposed planning approach.
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All authors Zhiyuan Huang, Zhao Tong, Jiakai Li, Chenrui Xiang, Bingzhuo Zhong
Robotics
SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction
Jeffrey Eiyike, Masoud Ataei, Elvis Gyaase et al. (+1 more)
Reaching a 6-DoF grasp pose in clutter requires a collision-free trajectory, conventionally obtained by reconstructing the scene in 3D and planning inside that reconstruction, at the cost of its accuracy and compute. Potential fields learned directly from images remove that dependency but inherit the classical weakness of artificial potential fields: where attractive and repulsive gradients cancel, the descent grazes the obstacle instead of going around it, and can stall short of the goal. We present an SE(3) neural potential field learned from posed RGB images and supervised with a
navigation function, the geodesic distance to the grasp through free space recovered from those same images during training, which removes both failures. On two tabletop scenes, from obstacle-blocked starts executed on a UR10, the field converges within 3 cm of the grasp from every start and every path it executes is collision-free against the ground-truth geometry, against 25% and 0% under image supervision alone; mean clearance rises from under a centimeter to 8.6-8.8 cm and arm-link contacts fall from 20.6-50.4% to 2.7-5.5% of executed configurations. Executed grasp success is 90.0% and 40.0% on the two scenes, the residual failures being refusals of the Cartesian executor rather than of the field. Planning takes about 2 s against 67-133 s for RRT* on a reconstruction of the same images, though under a common offline harness the two are comparable: the deployed margin is the cost of collision-checking a dense reconstruction, not planner complexity.
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All authors Jeffrey Eiyike, Masoud Ataei, Elvis Gyaase, Vikas Dhiman
Human-Computer Interaction
ATCion: Exploring the Design of Icon-based Visual Aids for Enhancing In-cockpit Air Traffic Control Communication
Yue Lyu, Xizi Wang, Hanlu Ma et al. (+2 more)
Effective communication between pilots and air traffic control (ATC) is essential for aviation safety, but verbal exchanges over
radios are prone to miscommunication, especially under high workload conditions. While cockpit-embedded visual aids offer the potential to enhance ATC communication, little is known about how to design and integrate such aids. We present an exploratory, user-centered investigation into the design and integration of icon-based visual aids, named ATCion, to support in-cockpit ATC communication, through four phases involving 22 pilots and 1 ATC controller. This study contributes a validated set of design principles and visual icon components for ATC messages. In a comparative study of ATCion, text-based visual aids, and no visual aids, we found that our design improved readback accuracy and reduced
memory workload, without negatively impacting flight operations; most participants preferred ATCion over text-based aids, citing their clarity, low cognitive cost, and fast interpretability. Further, we point to implications and opportunities for integrating icon-based aids into future
multimodal ATC communication systems to improve both safety and efficiency.
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All authors Yue Lyu, Xizi Wang, Hanlu Ma, Yalong Yang, Jian Zhao
Machine Learning
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
Yifan Hu, Xilin Dai, Zhiyuan Qu et al. (+4 more)
Agentic time series
forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models,
reasoning strategies, and intervention rules inherently time-varying. Consequently, a time series
agent must adapt the forecasts it produces and the orchestration policy that determines which components to trust and how to coordinate them. The deployment process naturally provides supervision for this adaptation as forecast horizons elapse and realized targets reveal the effectiveness of earlier decisions. Committing all numerical expert forecasts and candidate
agent paths before target observation allows each realized outcome to evaluate the entire alternative set, providing delayed feedback without additional annotation. However, existing time series
agents primarily incorporate prior experience through forecast refinement, reflection, or
retrieval, without systematically converting realized outcomes into persistent updates to the joint orchestration policy governing later origins. To exploit this delayed feedback systematically, we introduce TimEvolve, a frozen-backbone time series
agent that converts each realized outcome into persistent joint updates of expert trust,
agent path selection, and intervention strength. A temporally ordered predict, reveal, and update protocol applies this feedback to subsequent forecasts. Experiments across eight Time-MMD domains show that TimEvolve achieves the best average MSE and MAE ranks among fifteen methods and the lowest errors on both metrics in seven domains. These results demonstrate the value of learning
forecasting policies from the futures encountered during deployment.
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All authors Yifan Hu, Xilin Dai, Zhiyuan Qu, Yiding Liu, Zewei Dong, Jiang-ming Yang, Qiang Xu
High Energy Physics (Theory)
Quantum vacuum inversion and multi-kink unbinding in the non-degenerate double sine-Gordon model
Jonathan Lozano-Mayo
Quantum fluctuations can unbind a composite
soliton while the correction to its mass stays small. We show this beyond perturbation theory for the multi-kinks of the non-degenerate double sine-Gordon model. At one loop the
vacuum at the centre of the multi-kink becomes the true
vacuum at a coupling of order the classical lifting of the
vacua. Tensor-network calculations of the
lattice theory show that this inversion survives beyond one loop and has a continuum limit, and that the Gaussian effective potential captures it. On finite chains the multi-kink unbinds when the tension of the string of central
vacuum between its two halves falls through zero
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Geometric Topology
Trefoil Probabilities and Polyhedra
Joseph Geisz, Chris Peterson and Clayton Shonkwiler
We determine the exact probability that the closed hexagonal polygon obtained by cyclically joining six independent points uniformly distributed on the unit sphere is knotted. The only possible nontrivial
knot is a trefoil. Almost surely, the convex hull of the six points has one of two simplicial combinatorial types: a combinatorially regular octahedron and a combinatorially non-regular octahedron. We show that the straight-line complete graph on the six vertices of the hull of the combinatorially regular octahedral type admits exactly one unoriented trefoil
Hamiltonian cycle. On the other hand, no cycle connecting vertices of the irregular octahedron can produce a trefoil. We then use stereographic projection to transform the probability of the regular hull type to a Sylvester-type problem for the planar beta-
prime probability measure $d\mu(x,y)=\frac{dx dy}{\pi\ (1+x^2+y^2)^2}$. Applying Stokes'
theorem and the Blaschke-Petkantschin formula, we compute the expected squared $\mu$-content of a random triangle. This yields a regular octahedral probability of $\frac{15}{4\pi^2}$ and a trefoil probability of $\frac{1}{16\pi^2}$.
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Human-Computer Interaction
Small-world Networks of Agents Brainstorm AI Risks to Support Ideation
Ke Zhou, Edyta Bogucka and Daniele Quercia
The ideation phase of participatory AI risk assessment often starts with a blank slate or a limited list of predefined risks, making it difficult to surface indirect or systemic harms. To address this limitation, we propose a three-stage ideation support tool. The tool complements participatory AI, rather than replacing it, and helps focus later engagement with affected communities. First, it dynamically discovers stakeholders depending on the given AI use and recursively expanding outward, allowing overlooked or indirect stakeholders to emerge. Second, it simulates these stakeholders with LLMs, connecting them into a network of a given
topology, and having them ideate about risks. Third, it prioritizes risks using network centrality measures. In an initial evaluation, we found that betweenness centrality run through
agents connected in a small-world network works best as it elevates risks raised by stakeholders who bridge disconnected groups, surfacing novel, systemic harms that traditional methods often miss. On an AI chatbot companion use case, this approach increased the novelty of the identified risks by approximately 1.1 points over single LLM brainstorming, and by 0.5 points over agentic LLM brainstorming, measured on a normalized five-point Likert scale, without reducing the plausibility or severity of the identified risks. To test whether our framework helps a human-led ideation session using the Futures Wheel approach, we divided 11 teams of non-western young chatbot users into two types: control (team) and treatment (team) in a participatory AI risk assessment. The control teams started from a list of risks generated by the 45 AI practitioners in the initial evaluation; the treatment teams started from a list generated by our framework. The treatment teams identified more risks overall, and more systemic, human-computer interaction, and environmental risks.
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Quantum Physics
Overcoming the Hong-Ou-Mandel interference limitation for the second photon from a two-photon cascade
Raphael Joos, Michal Vyvlecka, Tim Strobel et al. (+8 more)
Quantum emitters such as
semiconductor quantum dots can provide entangled
photon pairs emitting from a system of cascaded states. However, the resulting temporal correlation between the two
photons from the three-level ladder system has been shown to set a limit on the achievable two-
photon interference. In this work, we show that such limitation, for
photons emitted by the second transition, is only due to reduced temporal overlap at the interference beam splitter. We investigate this theoretically and experimentally by determining time-resolved four-
photon coincidences between two successively emitted
photon pairs. Post-emission synchronization shows the recovery of maximum interference visibility; in prospective
quantum networks this could be achieved via deterministic
quantum memories.
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All authors Raphael Joos, Michal Vyvlecka, Tim Strobel, Benjamin Breiholz, Furkan Aglarci, Ponraj Vijayan, Hans-Georg Babin, Arne Ludwig, Michael Jetter, Simone L. Portalupi, Peter Michler
Strongly Correlated Electrons
Quantum-interference-driven orbital density wave and high-temperature superconductivity in trilayer nickelates
Youichi Yamakawa and Hiroshi Kontani
Intertwined charge-density-wave (CDW) and
spin-density-wave (SDW) orders are a hallmark of high-temperature superconducting multilayer nickelates. In trilayer La4Ni3O_{10}, charge correlations develop at temperatures above the onset of long-range
spin order, and the characteristic ordering wavevectors satisfy $Q_{cdw} \approx 2Q_{sdw}$. Here, using a density-wave equation with vertex corrections, we show that
quantum interference between short-range SDW fluctuations at $q \approx Q_{sdw}$ on the outer NiO2 layers generates an inter-outer-layer bond order at $Q_{cdw} \approx 2 Q_{sdw}$. This bond order induces a pronounced inner-layer-centered orbital order, with antiphase modulations of the Ni $d_{3z^2-r^2}$ and $d_{x^2-y^2}$ occupations, producing strong orbital polarization but only weak total charge modulation. This intertwined bond-and-orbital order accounts for the layer-selective electronic reconstruction inferred from NMR/NQR and is consistent with Raman spectroscopy and scanning tunnelling microscopy measurements. The same orbital and
spin fluctuations also cooperate to stabilize $s_{\pm}$-wave superconductivity through $M_z$ mirror-parity selection rules. Our results provide a unified microscopic framework for intertwined density-wave order and high-Tc superconductivity in multilayer nickelates.
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Materials Science
Scandium diboride: a semi-metallic, lattice, thermally matched substrate for vertical AlGaN power electronics
MVS Chandrashekhar, Daniel Joel Harrison, Ahamed Raihan et al. (+22 more)
We report the properties of hexagonal (space group P6/mmm) scandium diboride ($\mathrm{ScB}_2$) single
crystals grown by a
laser diode floating zone method at growth rates of ~1mm/hr under B-rich conditions with (002) rocking curve widths $\Delta\omega$=38” approaching the quality of commercial SiC/GaN substrates.
Lattice expansion measurements reveal matching to $\mathrm{Al_{0.55}Ga_{0.45}N}$ with a coefficient of thermal expansion ~5ppm/K at typical AlGaN growth temperatures, enabling thick AlGaN layers for ultra-wide bandgap (UWBG) power electronics >1kV. We measure semi-metallic room temperature resistivity ~15$\mu\Omega$ cm, climbing to ~93$\mu\Omega$ cm at 773K with a $T^2$ dependence effectively eliminating substrate parasitic resistance, the limiting factor in exploiting the full potential of UWBG. The Debye temperature $\theta_{D,ScB_2}$ from heat capacity and
lattice expansion is ~850K well matched to $\theta_{D,ScB_2}$, but lower than the 1100K measured for Sc-rich growth conditions. We discuss Debye matching as a key substrate codesign criterion providing significant overlap in
phonon modes for heat removal and thermal matching during AlGaN growth. The competitive thermal conductivity at room temperature 53W/mK is half that from full first principles calculations, a discrepancy we attribute to the presence of Sc-vacancies generated by B-rich growth. while the resistivity is ~2x the theoretical value, indicating that both electrons and
phonons play equal role in thermal transport. The smooth ~2.5nm rms roughness surface enables advanced heat removal modalities through engineered
phonon bridges and
phonon polaritons in $\mathrm{ScB}_2$/AlGaN interfacial
heterostructures, potentially allowing ~10-100x increase in power handling over state-of-the-art GaN/SiC.
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All authors MVS Chandrashekhar, Daniel Joel Harrison, Ahamed Raihan, Astrid D. Kengne, R. Shipra, Han Xie, Tasnia Jabin, Monte Hendrix, Ethan Scott, Roshan S. Annam, Sharad Mahatara, Evan N. Crites, Allana G. Iwanicki, Luke J. Meiler, Maxime Siegler, Renae N. Gannon, Steven R Spurgeon, Ashutosh Giri, Rajeswari Kolagani, Joshua A. Burrow, Stephan Lany, Patrick Hopkins, Tyrel M. McQueen, Michael Spencer, Satya Khushwaha
Artificial Intelligence
Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection
Sofiane Elguendouze, Erwan Hain, Elena Cabrio et al. (+1 more)
Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying them into components such as claims and premises. While research on this subtask remains relatively limited compared to other AM tasks, most existing approaches formulate it as a simplified sequence labeling problem, component classification, or a pipeline of component
segmentation followed by classification. In this paper, we propose ITFACD, a novel approach based on instruction-tuned Large
Language Models (LLMs) using compact instruction-based
prompts, and reframe ACD as a
language generation task, enabling arguments to be identified directly from plain text without relying on pre-segmented components. Experiments on standard
benchmarks show that our approach achieves higher performance compared to state-of-the-art systems. To the best of our knowledge, this is one of the first attempts to fully model ACD as a generative task, highlighting the potential of instruction tuning for complex AM problems. Our code and the
datasets used are openly available in the following GitHub repository.
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All authors Sofiane Elguendouze, Erwan Hain, Elena Cabrio, Serena Villata
Strongly Correlated Electrons
Anisotropic Surface State Band Splitting and Low Energy Flat Bands in 3d Correlated Topological Kondo Insulator Candidate FeSb$_2$
Ziling Cao, Jie Pang, Yu Xu et al. (+22 more)
FeSb$_2$ is a correlated narrow-gap
semiconductor that has often been discussed as a $3d$-electron Kondo
insulator candidate and exhibits a low-temperature resistance plateau with possible surface-dominated conduction. We carried out a systematic high-resolution
laser-based angle-resolved photoemission spectroscopy (ARPES) study of FeSb$_2$ to investigate its electronic structure. The surface states around the zone center show clear anisotropic splitting. When the temperature is lowered into the resistance plateau regime ($<6\,\mathrm{K}$), the surface states remain robust, but their photoemission peaks become much sharper and gain spectral weight. Two distinct flat-band-like features are observed at low energy. One is located at $\sim$127 meV below the
Fermi level, which exists only along a specific high-
symmetry direction, while the other is located at $\sim$70 meV below the
Fermi level and is present along all the measured momentum cuts around the zone center. These results provide new information to understand the renormalization effects, the resistance plateau, and the topological nature of FeSb$_2$.
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All authors Ziling Cao, Jie Pang, Yu Xu, Taimin Miao, Bo Liang, Wenpei Zhu, Neng Cai, Mingkai Xu, Jumin Shi, Yingjie Shu, Yiwen Chen, Jiachen Wang, Shenjin Zhang, Fengfeng Zhang, Feng Yang, Zhimin Wang, Qinjun Peng, Zhihai Zhu, Xintong Li, Hanqing Mao, Guodong Liu, Zuyan Xu, Youguo Shi, Lin Zhao, X. J. Zhou
Instrumentation and Methods for Astrophysics
The Sun can flip the shielding effect of added spacecraft mass
Yasser Maghrbi and Nelson Christensen
Spacecraft structure is usually treated as shielding against energetic particles. We find that the Sun can reverse the effect of adding that structure. Free-falling test masses are the inertial references of gravitational-wave observatories and equivalence-principle experiments; charge accumulated on them couples to stray electric fields and produces acceleration noise that limits sensitivity. In a LISA-like system, increasing the surrounding aluminium from 16 to 24 g cm -2 reduces the stopping-
proton population by (2.3 $\pm$ 0.5)% at
solar minimum but increases it by (17.0 $\pm$ 1.0)% at
solar maximum, the crossover lying in the deep-
solar-minimum regime. Complete 2-24 g cm -2 mass curves show a broad maximum at
solar minimum but a monotonic rise at maximum. The reversal arises because added material both attenuates incident
protons and feeds a
nuclear-processed population into the stopping-energy window. A first-order projection onto published charging rates gives changes of about -2% and +11% in the net charging rate, affecting charge accumulation, discharge requirements and the associated noise budget. The sign of a structural trade is therefore not fixed by the structure alone.
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Computer Vision
Revisiting Multi-View Stereo: A Sequence-to-Sequence Formulation
Aoxiang Fan, Corentin Dumery, Nicolas Talabot et al. (+1 more)
Computing accurate geometry from multi-view images is a fundamental problem in computer
vision. Recent feed-forward (FF) models jointly estimate 3D geometry and camera parameters, but they typically suffer from geometry distortion caused by reconstruction ambiguity, even when ground-truth camera parameters are supplied. In this paper, we study the multi-view stereo (MVS) problem with known camera parameters and propose a novel approach that bridges conventional MVS and FF methods. Rather than casting MVS as a sequence-to-one mapping that predicts depth only for a single reference view, we reformulate it as a sequence-to-sequence task, akin to FF models, that jointly predicts geometry for all input views. We introduce a global
transformer-based architecture with two components that explicitly exploit camera-induced priors: ray-map
embeddings that inject camera parameters into image patch
tokens, making the
transformer camera-aware, and a unified global cost volume that replaces conventional per-view cost volumes to jointly capture 3D structure across all views. Extensive experiments on multiple public
benchmarks show our approach achieves state-of-the-art performance, surpassing both MVS and FF reconstruction baselines.
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All authors Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Pascal Fua
Optimization and Control
Quantitative finite-population approximation of heterogeneous linear–quadratic mean-field control with common noise
Aqib Ahmed
We study the finite-population approximation of a heterogeneous linear–quadratic mean-field control problem with common noise and random coefficients. Starting from the centralized social planner problem for $N$ non-exchangeable
agents, we derive its exact finite-dimensional
stochastic Riccati system and identify the scaling of its diagonal, off-diagonal, affine, and scalar components. We then compare this system with the
Hilbert-space-valued Riccati system of the continuum model. Our main estimates give quantitative convergence of the complete backward system, including its common-noise martingale integrands. The error separates coefficient and kernel consistency, common-noise approximation, interface effects, exact diagonal corrections, and the mass of the limiting interaction kernel on the discrete diagonal band. We propagate these estimates to the feedback gains and, by a cellwise coupling, to the optimal states, controls, and initial social values. Finally, a cellwise projection of the limiting representative-
agent feedback yields a decentralized finite-population strategy whose optimality gap vanishes. Under the stated regularity assumptions, the error bounds are expressed through explicit approximation and concentration
moduli. Quantitative bounds on these
moduli yield algebraic rates, including an $N^{-1/2}$ rate for the backward system in finite-type regimes; the rates for optimal trajectories additionally reflect the available conditional moment bounds.
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Probability
Optimal support and condensation in random allocations
Andrea Ottolini
How many distinct symbols should a password use? If its length is fixed at $n$ and an observer learns only which symbols appear, the number of compatible passwords is maximized asymptotically when $k/n\to1/(2\log2)$. We ask what changes when, in addition to the length, aggregate information about the repetition pattern is revealed. We model this by fixing a second additive profile $V_k=\sum_i v(J_i)$ at scale $V_k/n\approx\rho$. For $v(j)=\log(j!)$, the profile records the reduction in the logarithm of the number of compatible words caused by repetitions; we show that once the normalized profile $\rho$ exceeds $0.507834\ldots$, the limiting optimal fraction is pinned at $1/2$. For a typical multiplicity profile at the optimal support above this threshold, the excess in $V_k$ is carried by a vanishing fraction of used symbols. We interpret this as a form of non-equivalence of ensembles and extend the mechanism to other profiles and non-uniform allocation models.
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Hardware Architecture
SPECTRA: Adaptive Execution of Speculative Decoding on a Runtime-Reconfigurable Tiled Architecture
Gabriele Tombesi, William Baisi, Je Yang et al. (+5 more)
LLM inference on edge devices is constrained by computational and
memory resources, making efficient autoregressive decoding challenging. Speculative decoding alleviates this bottleneck by generating
tokens with a smaller draft model and verifying multiple
tokens in parallel with a batched target model pass. However, verification introduces a runtime-dependent intermediate regime between
memory-bound general
matrix-vector (GEMV) operations in decoding and compute-bound general
matrix-
matrix (GEMM) operations in prefill, as its arithmetic intensity varies with speculation length and acceptance rate. We present SPECTRA, a runtime-reconfigurable tiled architecture that sustains high utilization across the full speculative decoding pipeline. Within each tile, the compute engine switches between systolic execution for GEMMs and vector-lane execution for GEMVs. Across tiles, SPECTRA dynamically adapts computation parallelism by selecting tile count, kernel partitioning, and communication pattern. Both tile-level and system-level reconfiguration operate on a per-kernel basis, enabling efficient execution across these diverse regimes. Evaluated on a 20-tile FPGA prototype across the Pythia, SmolLM2, and GPT-2 families, SPECTRA achieves up to $2.09\times$ speedup from tile-level reconfiguration and a further $1.25\times$ gain from system-level adaptability over fixed designs.
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All authors Gabriele Tombesi, William Baisi, Je Yang, Elisavet Lydia Alvanaki, Kevin Lee, Michael Lippe, Biruk Seyoum, Luca P. Carloni
Robotics
Range-Aided SLAM Initialization Exploiting Accurate Heading Information
Isabel Lougheed and James Richard Forbes
This paper presents a novel initialization method for range-aided simultaneous localization and mapping (RA-SLAM). The general SLAM problem has a well-known separable structure where landmark and
robot positions can be solved for in a linear fashion given known
robot headings. This paper considers the case where highly accurate heading information is available, which is typical of autonomous
underwater vehicle (AUV)
navigation, to solve for the range transponder and
robot positions. The proposed approach consists of two steps. First, using a generalized trust region subproblem (GTRS), the positions of the transponders relative to the AUV are solved for given the nonlinear range measurements. Second, the relative transponder positions and the known heading of the AUV are used to estimate the transponder and AUV positions by solving a linear least-squares problem. These transponder and AUV position estimates, combined with the highly accurate heading information, provide a reliable initialization method for the general nonlinear RA-SLAM problem. The effectiveness of the proposed approach is tested on a real-world AUV
dataset where long baseline (LBL) range measurements are provided in concert with highly accurate heading information provided by an inertial
navigation system (INS).
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Symbolic Computation
Complete Reductions and Idempotent Representations for $R\Pi\Sigma^*$-towers
Yiman Gao, Jakob Obrovsky and Carsten Schneider
$R\Pi\Sigma^*$-extensions form a rich class of difference rings that provide a unified algebraic framework for modeling indefinite nested sums, transcendental products, and nested products over roots of unity structures that frequently appear in combinatorics, number theory, and particle physics. For a large subclass of these extensions whose ring of constants is a field, we introduce a complete reduction approach to resolve the telescoping problem without solving any difference equations. More precisely, we explicitly construct a complement to the subspace of differences over the constant field and develop an
algorithm that decomposes any element of the extension into the sum of a difference and a component lying in this complement. Consequently, summability holds if and only if this complementary component is zero. This structural approach yields significant speed-ups for parameterized telescoping and, notably, creative telescoping for deriving linear recurrences of definite sums. Finally, we compute an explicit idempotent representation that extends existing telescoping
algorithms and our complete reduction framework to the general class of $R\Pi\Sigma^*$-extensions, opening up previously untreatable classes of sums and products.
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Statistical Mechanics
Navier-Stokes hydrodynamics near the critical point with out-of-equilibrium modes
Md Hasanujjaman, Golam Sarwar, Mahfuzur Rahaman et al. (+2 more)
We investigate how slow out-of-
equilibrium modes (OEM), introduced to extend the regime of validity of hydrodynamics near the critical point, fundamentally reshape the power spectrum of dynamical density fluctuations. We have used the equation of motion of slow modes for the situation when the extensive nature of thermodynamics is not altered due to the introduction of OEM. We find that the extensivity condition puts an extra constraint on the coupling of OEM with the four divergence of velocity. In the absence of OEM, the dynamic structure factor exhibits three Lorentzian peaks, one at zero frequency ($\omega$), called Rayleigh peak and the other two called Brillouin peaks located symmetrically about $\omega=0$. When the system is away from the critical point the inclusion of the OEM preserves the
symmetry of the distributions but all the peaks acquire reduced widths and the Brillouin peaks adopt unequal heights. Irrespective of the value of OEM (zero or nonzero) the Brillouin peaks vanish near the critical point. The width of the Rayleigh peak substantially reduced in presence of OEM. Such reductions of the widths in presence of the OEM indicates the reduction in the decay rate of the fluctuation which leads to slowing down of decay of the fluctuations, a distinctive feature of the critical point.
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All authors Md Hasanujjaman, Golam Sarwar, Mahfuzur Rahaman, Abhijit Bhattacharyya, Jane Alam