Robotics
VIRGA: Virtual-Agent-Intermediated Riemannian Geometry for Active-Sensing Air-Ground Coordination
Fenghe Guo, Runjie Shen, Chenyang Sun et al. (+1 more)
Air-ground autonomy becomes harder when the unmanned aerial vehicle (
UAV) must remain observable by a gimbal light detection and ranging (
LiDAR) mounted on the unmanned ground vehicle (UGV). The platforms must avoid dynamic obstacles while coordinating heterogeneous motion, limited sensing, and changing task initiative within one closed loop. This paper presents VIRGA, a neural
geometric coordination framework that turns dual-
LiDAR observations into bounded source-specific Riemannian fields and couples them through a virtual
agent with reciprocal elastic feedback. Platform-aware execution maps convert the shared coordination reference into feasible
UAV, UGV, and gimbal commands while enforcing active-observation safeguards. Evaluation against three complementary baselines reveals distinct limitations. An adapted Ray-RMP controller provides the fastest Riemannian response but produces insufficient clearance in the coupled air-ground task. A dense analytical Riemannian field improves
geometric avoidance, yet its high evaluation cost prevents stable field-of-view maintenance. An adapted ColAG controller achieves the lowest latency but still incurs safety and observability violations. VIRGA completes all paired warehouse conditions safely, while a long-range cave stress test without retraining demonstrates sustained coordination in irregular and confined geometry. Ablations confirm contributions from online
geometric evaluation, virtual-
agent mediation, and reciprocal feedback.
Read the full abstract ▾
All authors Fenghe Guo, Runjie Shen, Chenyang Sun, Junrui Zhang
Chemical Physics
Orbital-Free Surrogate Functionals Yield Transferable Interatomic Potentials and Electron Densities
Simon Wagner, Marc K. Ickler, Manuel V. Klockow et al. (+2 more)
Orbital-free density functional theory seeks to compute the energy of an electronic system directly from its electron density, avoiding one-electron wave functions and thereby offering a route to scalable electronic structure calculations. Machine-learned orbital-free density functionals have recently achieved promising results on small organic molecules, predicting energies with sub-millihartree accuracy. However, their convergence in density optimization remains sensitive to hyperparameter tuning and architectural choices. Here, we extend the recently introduced (weak) surrogate functional framework - designed to predict ground-state electron densities only - to also yield their energy, resulting in "strong" surrogate functionals. We find that these learned functionals enable stable convergence across all tested neural network backbones, reducing electron density errors relative to the Kohn-Sham reference by an order of magnitude compared to previous OF-DFT methods. More importantly, the predicted energies are competitive with state-of-the-art machine-learned interatomic potentials (MLIPs) trained only on energies, while exhibiting superior generalization to larger, unseen systems.
Read the full abstract ▾
All authors Simon Wagner, Marc K. Ickler, Manuel V. Klockow, Fred A. Hamprecht, Roman Remme
Earth and Planetary Astrophysics
Time-resolved JWST Retrieval Analysis of the Coldest Brown Dwarf: Temperature and Chemical Variations in WISE 0855-07
Harshil Kothari, Caroline V. Morley, Brittany E. Miles et al. (+12 more)
WISE J0855-07 is the coldest known brown
dwarf (estimated Teff~250 K), offering a rare opportunity to probe atmospheric physics in the temperature and mass regime that overlaps directly with
exoplanets. At these temperatures, modest perturbations to the thermal structure and chemical abundances can produce measurable changes in the emergent mid-infrared spectrum, making time-resolved spectroscopy a powerful way to test whether the atmosphere is longitudinally and temporally homogeneous. Using James Webb Space
Telescope NIRSpec/G395M time-series spectra spanning ~3 to 5.2 {\mu}m over 11 hr, we analyze every third spectrum from the
dataset with a series of time-resolved atmospheric
retrievals using the Brewster
retrieval framework. Using this approach, we model epoch-to-epoch changes in the atmospheric state that drive the observed variability, including the thermal structure and key molecular abundances. From the time-series
retrievals, we find that the primary source of the observed spectral variability is driven by the variations in the thermal structure, which range from ~1 to <100 K from epoch to epoch, particularly between the pressures of ~0.4 and 10 bar. In addition, changes in the abundances of CO, and possibly PH3 and CO2, further modulate the spectrum within specific molecular features. These measurements provide a
retrieval-based view of atmospheric variability in an ultracold,
planet-like object, directly constraining how thermal and chemical structure evolve on observable timescales.
Read the full abstract ▾
All authors Harshil Kothari, Caroline V. Morley, Brittany E. Miles, Melanie J. Rowland, Natasha Batalha, Michael C. Cushing, Andrew J. Skemer, James Mang, Brianna Lacy, Johanna M. Vos, Channon Visscher, Adam C. Schneider, Genaro Suarez, Mikayla J. Wilson, Allison M. McCarthy
Optimization and Control
Near-Optimal Acceleration for Smooth $\ell_p$ / $\ell_q$ Nondual Convex First-Order Oracle Optimization
David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán et al. (+1 more)
We study the optimization of convex objectives with $(L,\kappa-1)$-Hölder-continuous gradients in $\ell_q$ over $R B_p^d$, $1<\kappa\le 2$. (MG26) provides selectors with a movement bound for the problem of chasing high-dimensional convex nested sets for every $p<q$ and generally reduces Lipschitz convex optimization to bounds on the movement of selectors. We couple that movement with Hölder descent yielding a
polynomial-runtime first-order method whose feasible output, in the high-dimensional regime $T\le d$ and for $p<\min\{q,2\}$, has error $$ \widetilde O_{\kappa,p,q}\!\left( \frac{LR^\kappa}{T^{\kappa(1+1/p-(1/q-1/2)_+)-1}} \right), $$ after $T$ queries to a first-order oracle, solving the COLT 2015 open problem of (Guz15), up to logarithmic factors. At $(p,q)=(1,2)$, the rate is $\widetilde{O}(LR^\kappa/T^{2\kappa-1})$, including $\widetilde{O}(LR^2/T^{3})$ cubic decay in the smooth case.
Read the full abstract ▾
All authors David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán, Mathieu Molina
Computer Vision
Benchmarking the Explanatory Quality of Open-Weight Vision-Language Models in Face Recognition
Laurent Colbois and Sébastien Marcel
Vision-
Language Models (VLMs) have recently been proposed as promising tools for face recognition, as they can produce natural
language explanations alongside similarity scores. This capability is considered appealing for face comparisons in forensic contexts, which require decisions to be transparent and auditable. However, existing evaluations of VLMs for that use case focus mostly on recognition accuracy, while the validity of generated explanations remains unquantified. In this work, we introduce a benchmarking framework for VLM-based face recognition that treats explanation quality as a core evaluation axis. We propose two criteria that explanations should satisfy: relevance, i.e., reliance on identity-stable facial features; and faithfulness, i.e.,
alignment with the visible image content without hallucinated features. We jointly develop a methodology enabling the quantification of relevance and faithfulness of evaluated models, based on constraining model outputs to a structured explanation format that supports automated querying and
auditing. Using this framework, we
benchmark several families of open-weight VLMs, jointly evaluating face verification accuracy and explanation quality. Our results highlight remaining shortcomings of produced explanations, and emphasize the need for such explanation quality metrics to get a complete picture of model performance. The proposed
benchmark and open-source evaluation harness provide a foundation for proper benchmarking and future fine-tuning of explainable face recognition systems.
Read the full abstract ▾
Optimization and Control
Application of Optimal Inexact Second-Order Acceleration to Distributed Stochastic Optimization under Statistical Similarity
Yury A. Sokolov, Maxim K. Mashtaler, Alexander V. Gasnikov et al. (+3 more)
We consider distributed
stochastic convex optimization with a fixed budget of $N$ independent samples split among $m$ workers. Sample average approximation reduces the problem to a regularized finite-sum problem whose local Hessians are statistically similar. This allows the Hessian of the local objective at the server to be used as an inexact Hessian of the global objective, while the workers communicate only gradients. We apply the optimal accelerated inexact Newton extragradient method of (Chen et al., 2026) and propose its distributed restarted variant for the strongly convex empirical problem. The method reaches the statistical accuracy of order $N^{-1/2}$ in $\widetilde O\left(\max\{N^{1/7},m^{1/4}\}\right)$ communication rounds. Hence, with $m=N^{4/7}$ workers, it requires $\widetilde O\left(N^{1/7}\right)$ rounds, improving the dependence on the total sample size from $\widetilde O\left(N^{1/6}\right)$ for the previous accelerated cubic Newton construction of (Agafonov et al., 2021). Each iteration uses two gradient aggregation rounds and does not require Hessian communication.
Read the full abstract ▾
All authors Yury A. Sokolov, Maxim K. Mashtaler, Alexander V. Gasnikov, Martin Takáč, Dmitry I. Kamzolov, Artem D. Agafonov
Differential Geometry
A Gage-type estimate for the $p$-fundamental tone and applications to submanifolds
Matheus Nunes Soares and Fábio reis dos Santos
A generalization of the classical Gage's upper bound for the fundamental tone of the
Laplacian on complete non-compact Riemannian
manifolds to the $p$-
Laplacian context is obtained. As an application, upper estimates for the $p$-fundamental tone of complete non-compact submanifolds of space forms with constant nonpositive sectional
curvature, and for a class of product spaces, are presented.
Read the full abstract ▾
Machine Learning
Geometric Mean Pooling for Equal-Weight Multiplicative Coarse-Graining
Ang-Kun Wu, Fangdi Wen and Jingtao Zhang
As an alternative to the additive and extremal biases of average and max pooling, we introduce
Geometric Mean Pooling (GMP), a signed pooling operator that combines the product of feature signs with the
geometric mean of feature magnitudes. Motivated by local-to-global composition in
quantum many-body physics, GMP retains both joint sign information and a characteristic multiplicative scale without introducing learnable pooling parameters. We show that non-overlapping hierarchical GMP preserves the corresponding global multiplicative statistic and evaluate it on synthetic sequence tasks, iterative coarse-graining, image classification, and molecular lipophilicity regression. On the synthetic tasks, GMP recovers product-based signals more accurately than average and max pooling and maintains predictive performance under the tested levels of multiplicative input noise. On image and molecular data, however, its effectiveness depends on the representation, target parameterization, and placement of local and global pooling. These results position GMP as a complementary, regime-dependent inductive bias for tasks in which equal-weight multiplicative composition is plausible, rather than as a universal replacement for standard pooling operators.
Read the full abstract ▾
Quantum Physics
Uhlmann Geometry of Fixed-Rank Density Matrices: A Fiber-Bundle Approach
Xu-Yang Hou and Hao Guo
For full-rank density
matrices the state space is contractible, the Uhlmann bundle is topologically trivial, and the holonomy admits no quantized invariants; although the Uhlmann phase is genuinely
geometric, this topological poverty has kept it from serving as a robust physical diagnostic of mixed-state matter. Mixed states of fixed rank below the
Hilbert-space dimension, however, are ubiquitous: reduced states of constrained subsystems, states confined to invariant sectors, and states supported on decoherence-free subspaces can have a support smaller than the
Hilbert space, a support that can vary with parameters. Their geometry is far richer: the support carries genuine
curvature, non-Abelian holonomy, and Chern
topology. We formulate Uhlmann's theory directly on the
manifold of rank-$k$ density
matrices: minimal purifications make the fixed-rank stratum the base of a principal $U(k)$-bundle whose Uhlmann connection is uniquely determined by a Sylvester equation, and in an eigenframe this connection takes a closed form reducing to the Berry, Wilczek–Zee, and faithful Uhlmann connections at $k=1$, at equal weights, and at $k=N$. The fixed-rank bundle inherits the
topology of the Grassmannian, and non-factorizable higher Chern
topology requires failure of the global eigenline splitting, which in the present spectral setting requires degeneracy, minimally realized by a rank-2 Yang monopole whose quantized second Chern number links the geometry to the four-dimensional
quantum Hall response. Solvable models, from a genuinely non-Abelian holonomy to a dissipatively driven orbit whose Uhlmann holonomy is steered by the reservoir through an
elliptic-integral spectral dressing, illustrate the content: the supporting subspace carries the
topology, the spectral weights shape the transport, and Uhlmann holonomy provides a direct
geometric probe of the resulting mixed-state structures.
Read the full abstract ▾
Combinatorics
Boolean building set arrangements
Leonie Mühlherr and Sven Wiesner
In this article, we generalize the notion of connected subgraph arrangements, established by Cuntz and Kühne by allowing edges of cardinality greater than two, with a focus on local properties. We show that the class of free connected hypersubgraph arrangements is larger than the class of free connected subgraph arrangements, but that there exists a necessary condition for freeness that refers back to the graphic case. Furthermore, we obtain a unique description of these arrangements by associating them with Boolean building sets. We study the nested complex of the building set in relation to the
lattice of intersections, and characterize all simplicial arrangements within this new class.
Read the full abstract ▾
Cryptography and Security
SFPF: Spatio-Frequency Polarization Fingerprint for Anomalous Wireless Device Detection
Xiaoxuan Huang, Jinlong Xu, Daoyuan Shen et al. (+2 more)
Periodic inspection of deployed wireless devices is necessary because unauthorized hardware replacement may preserve communication functions, credentials, and logical identity, making anomalous devices difficult to detect. Such inspections are conducted under controlled measurement conditions to verify that each device remains consistent with its enrolled hardware state. Conventional
radio-frequency fingerprint (RFF) may provide insufficient separation when replacement hardware closely resembles legitimate hardware, while a polarization fingerprint (PF) constructed at one observation direction may miss spatially nonuniform polarization changes. This paper proposes the spatio-frequency polarization fingerprint (SFPF), which jointly represents complex polarization responses over multiple frequencies and observation directions; conventional PF is its fixed-direction slice. We derive SFPF formation from hardware-dependent modal excitation, directional far-field radiation, and polarization projection. A first-order sensitivity analysis shows that the response to the same hardware change varies with both frequency and direction, motivating joint spatio-frequency acquisition. Electromagnetic simulations confirm the nonuniform spatio-frequency sensitivity and show that, under the same observation budget, SFPF improves normalized distance, Fisher score, and the inter-/intra-class ratio over PF by 17.7%, 45.8%, and 11.3%, respectively. Experiments show that SFPF consistently outperforms RFF and PF over 0–20~dB. At 15–20~dB, SFPF achieves anomalous-device F1 scores of 87.3–90.4% and AUROC values of 85.4–95.5%.
Read the full abstract ▾
All authors Xiaoxuan Huang, Jinlong Xu, Daoyuan Shen, Meng Zhang, Dong Wei
Computer Vision
Chronosphere: Space-Time Tessellation of Local Climate Experts
Daniel Cher, Eric Xing, Kexing Li et al. (+3 more)
We introduce Chronosphere, a spatio-temporal neural field that learns representations of
climate. A central challenge in geographic representation learning is modeling environmental processes whose spatial and temporal complexity varies widely. Yet existing location encoders typically fix a single level of detail everywhere. Global bases such as spherical harmonics spread capacity uniformly across space and time. Localized bases resolve only predefined regions. Learned tessellations adapt, but are inefficient at representing higher frequencies. Chronosphere unifies these approaches, pairing an adaptive tessellation of learnable sites on the spacetime
torus $S^2\times S^1$ with a shared bank of local basis functions. Both where capacity is placed and how much detail each region carries adapt to the data, across space and time. Trained to reconstruct climatology, Chronosphere matches or leads state-of-the-art location encoders across spatial and temporal tasks, with the largest gains under spatial and temporal transfer.
Read the full abstract ▾
All authors Daniel Cher, Eric Xing, Kexing Li, Brian Wei, Isaac Corley, Nathan Jacobs
Numerical Analysis
A filtered time stepping scheme for curve shortening flow for open and closed curves
Klaus Deckelnick and Robert Nürnberg
We propose a filtered time stepping finite element scheme for curve shortening flow of open and closed curves in arbitrary codimension that is second-order accurate in time. Open curves are assumed to evolve inside a given domain $\Omega \subset \mathbb R^n$, $n\geq2$, and meet the external boundary $\partial\Omega$ orthogonally. We prove optimal error bounds for the $L^2$– and $H^1$–norms. In practice only a single linear system needs to be solved at each time step. Numerical experiments confirm the accuracy and practicality of the introduced method, including an asymptotic equidistribution property.
Read the full abstract ▾
Machine Learning
Neural Cellular Automata Learn General Features in their Hidden Channels
Etienne Guichard and Stefano Nichele
Modern deep learning models achieve impressive generalization through over-parameterization, but this paradigm often struggles with overfitting and memorization in few-shot regimes. Neural Cellular Automata (NCAs) offer a highly parameter-efficient alternative, yet research has focused primarily on their output, leaving the role of their internal hidden channels largely unexplored. In this paper, we investigate the internal dynamics of NCA hidden channels and introduce a novel transfer-learning mechanism that injects a pretrained teacher's hidden states into a student model to guide early optimization. Evaluated on few-shot and scale-variant MNIST
benchmarks, NCAs outperform comparable recurrent and feed-forward architectures, demonstrating superior generalization with a minimal parameter budget (~9,800 parameters). Mechanistic analysis reveals that the hidden channels decouple feature extraction from uniform classification consensus by absorbing morphological complexity and converging to mutually orthogonal states. Furthermore, we demonstrate that these hidden channels capture general, scale-invariant topological primitives rather than class-specific templates. This allows a student model to achieve strong few-shot performance on unseen classes using features transferred from a teacher trained only on a subset of digits (0-5). Our results highlight the potential of utilizing hidden-state dynamics as a robust, decentralized computational substrate for parameter-efficient transfer learning
Read the full abstract ▾
Human-Computer Interaction
Comparing Haptic Feedback Across Hand Tracking and Controllers in VR Object Interaction Tasks
Natalia Ocampo, J. Felipe Gonzalez, Robert J. Teather et al. (+1 more)
Hand tracking offers natural VR interaction but lacks controllers' inherent tactile feedback, while haptic feedback across input methods remains understudied. We conducted a participant study comparing vibration, impulse, and no feedback in hand- and controller-based VR interaction across grasp-, pinch-, and tap-like gestures assessing performance, workload, and preference. A custom glove and controller-mounted device provided both feedback types, aiming for consistency across input modalities. Controllers were faster for grasp and pinch, whereas hand tracking was more accurate for pinch and preferred overall. Haptics had limited performance effects, although impulse reduced grasp accuracy with controllers. Participants preferred vibration and impulse over no haptic feedback, favouring vibration overall. Our findings reveal a more nuanced relationship between haptic feedback, input device, and interaction context than suggested by previous work.
Read the full abstract ▾
All authors Natalia Ocampo, J. Felipe Gonzalez, Robert J. Teather, Kiyoshi Kiyokawa
Numerical Analysis
Tangential stability and fully discrete convergence of the classical BGN scheme for curve shortening flow
Qiqi Rao
We prove fully discrete convergence of the classical Barrett–Garcke–Nürnberg (BGN) scheme for curve-shortening flow of smooth embedded closed planar curves. The main obstruction is that the mass form controls only normal motion, whereas the tangential motion is determined implicitly by the
curvature equation and governs the parametrization. The usual length-decay estimate therefore does not control perturbations of the full position update. We separate temporal and spatial errors through the time-semidiscrete BGN solution. A scalar normal resolvent and exact
curvature and length identities yield uniform regularity and first-order time convergence. For the spatial analysis, an adapted normal–tangential norm gives a near-contractive estimate for the linearized update. Gauss–Lobatto cancellations and an exact covariance identity for the assembled nodal normals produce an $H^1$ one-step defect of order $h^{k+1}$, while an exact difference identity controls the nonlinear remainder. For every fixed $k\ge1$, including the original piecewise linear method, we obtain the matched $W^{1,\infty}$ error bound $C(\tau+h^k)$ on periodic quasi-uniform meshes with $h\le c\tau^2$. All discrete steps are uniquely solvable, and the numerical curves remain regular and embedded. To the best of our knowledge, this is the first fully discrete convergence result for the classical BGN curve-shortening scheme without additional stabilization.
Read the full abstract ▾
High Energy Physics (Theory)
Diagnosing Inconsistencies in 4d N=1 Gauge Theories with Explainable AI
Seong-Jin Lee and Rak-Kyeong Seong
Brane tilings are bipartite graphs on a 2-
torus that encode the Lagrangians of 4d N=1 supersymmetric gauge theories arising on D3-branes probing toric Calabi-Yau 3-folds. Among these bipartite graphs, only those that satisfy
geometric consistency conditions correspond to well-behaved
quantum field theories. We train a convolutional neural network (CNN) to distinguish geometrically consistent from inconsistent brane tilings directly from their Kasteleyn
matrices. We study a family of 4d N=1 theories obtained by adding diagonal edges to the hexagonal faces of the brane tiling for the abelian orbifold C^3/Z_3 x Z_3, and find that the CNN identifies
geometric inconsistency with high accuracy. For inconsistent brane tilings that can be rendered consistent by Higgsing a single bifundamental
chiral field, we show that gradient-based saliency analysis can be used to localize the responsible
chiral fields with accuracy well above a matched random baseline. These results demonstrate that explainable AI (XAI) can be used to identify local defects responsible for inconsistencies in supersymmetric gauge theories.
Read the full abstract ▾
Computer Vision
Morphology-Aware Ambiguity Learning for Wafer Defect Decision Support
Seungjun Chu and Seokhyun Chung
Wafer map defect recognition is commonly formulated as a fixed-taxonomy classification problem that assigns each wafer to a single defect class. However, some wafers exhibit morphologies near class boundaries, for which forcing a single prediction may be less informative than providing plausible diagnostic alternatives. This paper proposes a morphology-aware ambiguity learning framework that supports three diagnostic actions: automatic single-class diagnosis, assisted diagnosis with two plausible defect classes, and full review. Using the radial, angular, and
geometric characteristics of training wafer maps, the framework constructs a class-level ambiguity
matrix representing defect-class pairs with similar morphology and plausible diagnostic alternatives. It guides the model to learn plausible alternative classes rather than treating all incorrect classes equally. During inference, the
matrix determines whether an uncertain prediction can be represented by a meaningful two-class diagnostic set or should be escalated for full review. Experiments on WM-811K show that the proposed framework outperforms conventional approaches in defect recognition and diagnostic decision support, providing meaningful two-class alternatives while reserving full review for cases with unresolved ambiguity. Illustrative cost analyses further show the potential cost advantage of the proposed routing strategy. The diagnostic behavior of the framework remains consistent across different backbone architectures.
Read the full abstract ▾
Superconductivity
Tunneling Spectroscopy Study of Low Baked Bulk Niobium for Superconducting RF Cavity Applications
Ivana Curci, Gregoire Jullien, Luc Maurice et al. (+3 more)
Point-contact tunneling (PCT) spectroscopy was used to probe the local superconducting properties of cavity-grade niobium following surface treatments representative of superconducting
radio-frequency (SRF) cavities. Electropolished (EP) samples were compared with samples baked at 120~$^\circ$C for 48 h and subjected to a two-step bake at 75~$^\circ$C for 2 h followed by 120~$^\circ$C for 48 h. Although the superconducting gap $\Delta$ is predominantly distributed around the bulk Nb value (~1.5 meV), regions exhibiting strongly suppressed gaps (minigaps) are observed in 15–29% of junctions on EP samples. Their occurrence decreases to 6% after the 120~$^\circ$C bake and to only 1% after the two-step treatment. Spectra exhibiting minigaps are well described by the Usadel proximity-effect model, consistent with superconducting Nb coupled to normal regions that we attribute to hydride formation. These results provide direct microscopic evidence that low-temperature baking suppresses regions of degraded surface superconductivity in cavity-grade Nb. The pronounced reduction achieved by the two-step bake further supports a link between hydride-related proximity effects and the surface superconducting properties governing SRF cavity performance.
Read the full abstract ▾
All authors Ivana Curci, Gregoire Jullien, Luc Maurice, Fabien Eozenou, Patrick Sahuquet, Thomas Proslier
Instrumentation and Detectors
Proof-of-Concept and User Perspective on Photon-to-Digital Converter Applications in Particle Physics
M. Á. García-Peris, B. Palmeiro, G. Lessard et al. (+7 more)
Photon-to-Digital Converters (PDCs) are photosensors with single
photon resolution and large dynamic range that digitise the output of individual single-
photon avalanche diodes directly on-chip, offering potential advantages with respect to analogue Silicon Photo-Multipliers (SiPMs). In this work, we assess the PDC technology from the perspective of particle physics, using early, low-coverage PDC prototypes for proof-of-concept studies in calorimetry and tracking. Under simple, but extrapolable, detector conditions, we qualitatively compare PDCs with SiPM-based systems to illustrate their potential in terms of detector performance and readout. As the first application of this technology to particle physics instrumentation, we place particular emphasis on the user perspective, reporting on the practical experience of operating PDCs, from setup and calibration to data handling, and highlighting the relative ease of integration compared to conventional SiPM readout. We discuss the potential of PDCs to address key challenges in large-scale instrumentation, and their prospective integration into next-generation high-energy physics experiments.
Read the full abstract ▾
All authors M. Á. García-Peris, B. Palmeiro, G. Lessard, R. Guenette, S. A. Charlebois, E. Gramellini, J.-F. Pratte, T. Rossignol, N. Roy, F. Vachon
Artificial Intelligence
AutoRecLab: Describe the Experiment, Get the Code!
Moritz Baumgart, Philipp Meister, Justus Krell et al. (+3 more)
Empirical evaluation is central to recommender-systems (RecSys) research, but turning experimental designs into executable code remains a manual and error-prone task. We present AutoRecLab, a Python-based autonomous RecSys lab that automates RecSys experiments from natural-
language prompts. Given a research idea, AutoRecLab derives explicit experiment requirements, builds and validates a prototype, and iteratively expands it into the requested full experiment. The workflow combines
retrieval-augmented generation (RAG) for documentation lookup, static type verification, and execution-steered tree search. In our demonstration, AutoRecLab autonomously implements an explicit-to-implicit feedback conversion study. In a baseline comparison across six
algorithms and three
datasets, 8 of 9 runs succeed at an average cost of approx- imately $1 per run with GPT-5.4-mini.
Read the full abstract ▾
All authors Moritz Baumgart, Philipp Meister, Justus Krell, Michael Schmidt, Bela Gipp, Joeran Beel
Numerical Analysis
Isogeometric analysis for the Helmholtz transmission eigenvalue problem
Nizheng Liao, Guanghui Hu and Xucheng Meng
The transmission
eigenvalue problem plays an increasingly important role in inverse scattering theory. Although significant progress has been made in developing efficient numerical methods for the problem over the last two decades, its numerical treatment for curved domains in $\mathbb{R}^d$ ($d=2,3$) remains challenging. In this paper, we introduce and analyze a geometrically flexible and $H^2$-conforming isogeometric method for solving a fourth-order, quadratic and non-self-adjoint
eigenvalue problem arising from the Helmholtz transmission
eigenvalue problem. Using the spectral approximation theory for compact non-self-adjoint operators, we derive optimal error estimates for the discrete
eigenvalues and eigenfunctions. Numerical results for the two- and three-dimensional
benchmark problems, including those defined in curved domains, are presented to verify our theoretical results, and to demonstrate the advantages of the method over existing numerical methods in terms of both accuracy and
geometric flexibility.
Read the full abstract ▾
Cosmology and Nongalactic Astrophysics
A two-component profile of baryonic feedback: hydrostatic and diffuse gas
Kaitlyn Shavelle, Jared Siegel, Leah Bigwood et al. (+2 more)
Baryonic feedback processes redistribute gas within and beyond dark matter
halos, however, the resulting gas distribution remains highly uncertain, limiting predictions of the matter distribution for precision cosmology. We introduce a physically motivated approach to modelling the gas distribution that decomposes it into an approximately hydrostatic inner component and a diffuse outer component. This approach takes advantage of the growing observational landscape: X-ray measurements primarily probe the dense inner
halo, while the kinetic Sunyaev-Zel'dovich (kSZ) effect is sensitive to diffuse ionized gas extending to larger radii. We implement this two-component profile within the baryonification framework, though we note that it is applicable to any model of the large-scale gas distribution. We jointly fit X-ray gas-fraction measurements from the HSC-XXL and eROSITA samples with stacked kSZ effect measurements of BOSS CMASS
galaxies from the Atacama Cosmology
Telescope, demonstrating that the proposed profile retains sufficient flexibility. This proof-of-concept shows that a decomposition between hydrostatic and diffuse gas provides a simple, physically interpretable framework for connecting complementary observations of the gas distribution, and a foundation for future models of baryonic feedback.
Read the full abstract ▾
All authors Kaitlyn Shavelle, Jared Siegel, Leah Bigwood, Alexandra Amon, Romain Teyssier
Rings and Algebras
Cohomology, Extensions and Deformations of restricted Lie triple systems
Jon Beristain and Abdenacer Makhlouf
The main purpose of this paper is to provide a
cohomology theory of restricted Lie triple systems in low degrees and their algebraic interpretations. We define restricted cochains up to degree 5, together with the corresponding coboundary operators. Moreover, we provide interpretations of the low-degree restricted
cohomology groups in terms of restricted derivations, extensions of restricted modules, formal deformations, and extensions of restricted Lie triple systems.
Read the full abstract ▾
Computation and Language
TrialAtlas: Multi-Agent Research Organization for Clinical Trial Design and Optimization
Jiacheng Lin, Zifeng Wang, Zheng Chen et al. (+5 more)
Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely on clinical development planning (CDP) and probability of technical and regulatory success assessment to anticipate development risks, yet these decisions remain labor-intensive and subjective, requiring experts across clinical science, statistics, regulatory affairs, and competitive intelligence to jointly acquire, synthesize, and reason over heterogeneous evidence. Here, we introduce TrialAtlas, a
memory-augmented multi-
agent research organization for CDP that mirrors this collaborative process by coordinating specialized
agents for literature synthesis, competitive trial intelligence, regulatory precedent analysis, and integrated
reasoning over trial design and development risk. TrialAtlas further learns from historical clinical trials and regulatory outcomes, including prior New Drug Applications (NDAs), to ground its decisions in accumulated development experience. To evaluate these capabilities in an authentic regulatory setting, we introduce TrialAtlasBench, constructed from 291 FDA Complete Response Letters and spanning three practical tasks: detecting trial design deficiencies, recommending actionable design improvements, and predicting technical and regulatory success. TrialAtlas achieves an F1 score of 50.0% for deficiency detection, outperforming the strongest baseline by 6.1 points, and reaches 85.3% balanced accuracy and 84.7% F1 for prediction of technical and regulatory success, improving over the best baselines by 6.7 points in balanced accuracy and 12.0 points in Cohen's kappa. In expert evaluation, 86.4% of TrialAtlas-generated concerns were judged valid, compared with 83.1% for OpenAI DeepResearch and 59.3% for Gemini DeepResearch.
Read the full abstract ▾
All authors Jiacheng Lin, Zifeng Wang, Zheng Chen, Erick Scott, Ziwei Yang, Fanyang Yu, Sheng Zhong, Jimeng Sun
Cryptography and Security
Watermarkable Multi-Draft Speculative Sampling via Poisson Processes
Yanxiao Liu, Sicheng Wan, Zhan Gao et al. (+1 more)
Large
language models (LLMs) have achieved state-of-the-art performance across a wide range of tasks, motivating two important aspects of deployment: inference efficiency and output provenance, which can be tackled by speculative sampling and watermarking, respectively. However, recent works have shown that combining these two goals is highly nontrivial and can be potentially impossible. In this work, we develop a novel multi-draft speculative sampling
algorithm based on Poisson processes that improves the frontier of this fundamental trade-off. The proposed
algorithm has strong sampling efficiency on its own and, more interestingly, is naturally watermarkable: we can embed an unbiased watermark without degrading speculative acceptance. Moreover, our
algorithm is based on an exact list-coupling-without-communication scheme, which yields a drafter invariance property that benefits both sampling and watermarking. It is the first multi-draft, drafter-invariant speculative sampling scheme that maintains both watermark strength and sampling efficiency, and we experimentally verify its strong performance in both aspects.
Read the full abstract ▾
All authors Yanxiao Liu, Sicheng Wan, Zhan Gao, Deniz Gündüz
Computation and Language
Do Personality-Tuned LLMs Make Better Social Agents?
Tim Krabbe and Xiaodan Shi
LLMs are increasingly used in social simulations for socially interactive
agents and
robots, offering more flexibility than rule-based systems. However, even though they mimic human behaviour very well, there is a persistent alienness to them. This work investigates whether personality-aware fine-tuning can reduce this gap by improving the consistency and controllability of personality-conditioned dialogue generation compared with instruction prompting alone. We fine-tune two small open-weight LLMs, Qwen2.5-7B-Instruct and Ministral-8B-Instruct, using a corpus that combines personality-labelled social media posts and dialogues to create a personality-based dialogue engine for social simulation. The resulting models are evaluated across multiple social interaction scenarios using three independent LLM judges, which assess personality fidelity and provide evidence-based behavioral interpretations. We additionally quantify inter-rater agreement and lexical characteristics of the generated dialogue. Results indicate that fine-tuned models are not better at role-playing different personalities than their respective baseline models. However, low inter-rater agreement limits the confidence with which these results can be interpreted. Concerning the quality of generated texts, fine-tuned models are mostly comparable to the baselines, with fine-tuning improving the linguistic diversity of the Qwen models. While the results appear generally usable and the baseline models offer the best overall performance, future studies should place greater emphasis on the quality and domain
alignment of training data for accurate personality role-playing.
Read the full abstract ▾
Systems and Control
Distributed Multiconsensus Control of BESSs Based on Centrality of Eigenvectors
Yalin Zhang, Zhongxin Liu and Zengqiang Chen
Secondary control and the State-of-Charge (SoC) balance control are important control objectives for battery energy storage systems (BESSs). In this brief, a communication weight allocation method based on the centrality of eigenvectors is designed for a connected and directed graph, which results in the adjacency
matrix having a given eigenvector. Subsequently, a distributed secondary voltage controller and an SoC balancing controller are designed for droop-controlled BESSs to achieve voltage leader-following multiconsensus and SoC balancing, respectively. It is worth mentioning that under the designed voltage secondary control scheme, only a single leader is needed to achieve voltage multiconsensus control. In addition, the capacity information/droop coefficient does not need to be transmitted in the communication network to achieve power sharing according to capacity and SoC balance. For SoC balance control, the control gain is also well analyzed to ensure stability. The relevant simulations verify the effectiveness of the designed scheme.
Read the full abstract ▾
Solar and Stellar Astrophysics
GIARPS simultaneous optical and infrared high-spectral-resolution observations of Cepheids I. Limb darkening from cross-correlated radial velocities
N. Nardetto, R. S. Rathour, M. C. Bailleul et al. (+17 more)
The Baade-Wesselink method, while theoretically applicable for calibrating the distance scale via Cepheids, is not currently employed in the
cosmic distance ladder. There are various versions of this method. In all these approaches, the projection factor used to convert the radial into the pulsation velocity remains the limiting quantity. By comparing for the first time simultaneous cross-correlated optical and H-band radial velocities of five Cepheids, we aim to better understand the
geometric part of the projection factor that is directly linked to the limb darkening of the
stars. The five Cepheids were observed with the HARPS-N and GIANO-B instruments mounted at the Telescopio Nazionale Galileo in the GIARPS configuration. A two-parameter linear fit was applied to the simultaneous radial velocity measurements of each
star in order to extract the amplitude ratio of both curves (fVH) as well as the difference in mean velocities. We find a mean value of fVH of 1.056 with a root mean square over the five
stars of 0.016, which means that the V-band radial velocity amplitudes are around 5% larger than the ones obtained in the H band. In addition, we find that the H-band radial velocity curves have an average (i.e., a gamma-velocity) of about 1 km/s blueshifted compared to the visible ones, except for one
star, X Cyg, which has a blueshift of 0.4 km/s. At first order, the fVH values are directly linked to the ratio of limb darkening coefficients in both bands (or equivalently the ratio of geometrical projection factors). The fVH values are found to be consistent with expectations from static
stellar atmosphere models. This work shows that the differential limb darkening of Cepheids in visible and H bands, and consequently the geometrical part of the projection factor, follows what is expected from
stellar atmosphere models.
Read the full abstract ▾
All authors N. Nardetto, R. S. Rathour, M. C. Bailleul, V. Hocdé, P. Kervella, G. Bras, W. Gieren, J. Storm, G. Pietrzynski, B. Zgirski, P. Wielgorski, A. Merand, A. Gallenne, E. Poretti, M. Rainer, L. Breuval, M. Duparc, B. Apostolova, W. Kiviaho, K. Sivkova
Astrophysics of Galaxies
Fanyi Meng, Chao-Wei Tsai, Jingwen Wu et al. (+13 more)
Supernovae (SNe) are among the leading candidates for powering
galactic-scale
turbulence. SNe drive expanding shells of neutral atomic hydrogen (HI) known as superbubbles. Due to the lack of a sensitive, dynamically complete,
galaxy-wide census, superbubbles have not been used to quantify the
galactic-scale turbulent energy budget. Here we present a combined Five-hundred-meter Aperture Spherical
radio Telescope (FAST) and Jansky Very Large Array HI survey of the Andromeda
galaxy (M31), the nearest
giant spiral, with superior sensitivity and dynamical coverage. We identify 118 superbubbles across the entire disk of M31 with dynamical ages up to 40 Myr, consistent with the expected duration of SN activity in a
star cluster and extending the age coverage well beyond previous surveys. Inferred from these superbubbles, the kinetic energy injection rates ($10^{49}$–$10^{51.5}$ erg kpc$^{-3}$ Myr$^{-1}$) from SNe closely match the
turbulence dissipation rates derived independently from the same data, in both magnitude and spatial distribution. These results demonstrate that clustered SN feedback is sufficient to sustain
galactic-scale
turbulence, which shapes disk structure and influences
galaxy evolution.
Read the full abstract ▾
All authors Fanyi Meng, Chao-Wei Tsai, Jingwen Wu, Sihan Jiao, Mordecai-Mark Mac Low, Zhi-Yu Zhang, Amélie Saintonge, Hui Li, Zongnan Li, Jie Wang, Lile Wang, Haitao Xu, Yanbin Yang, Kai Zhang, Rouyu Li, Di Li