Quantum Physics
The refractive index of the medium within the volume of deep inelastic $ep$ scattering: I. Group velocity of interaction carriers
B. B. Levchenko
We propose a new method for measuring several characteristics of a
fermion-
boson medium formed during deep inelastic particle scattering. This method involves measuring the group and phase velocities of the accompanying wave process. The phase and group velocities of a wave process are related by the Rayleigh differential condition. The refractive index of a medium is determined by the phase velocity, while the group velocity characterises the medium's dispersion properties. In the case of deep inelastic $ep$ scattering under consideration, the wave process in an individual event is understood to be the exchange between an electron and a
proton of an effective, non-perturbative mediator of interaction (${\cal B}$) with the
quantum numbers of a neutral current. In the first report, we present new data on the group velocity of interaction carriers ${\cal B}$. We extracted the group velocity modulus values, $U_{_{\cal B}}^*$, from the HERA
collider data using a formula derived from modified Heisenberg uncertainty relations and applying the indirect measurement method. The HERA data demonstrate that in the selected kinematic domain the speed of the mediator ${\cal B}$ exceeds the speed of light $c$ in free space, $U_{_{\cal B}}^* > c$. To compare with the obtained data on group velocity, we analyse a simplest model of dispersion relation occurring due to the relativistic relationship between the energy, mass and momentum of a particle, supplemented by de Broglie relations.
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Algebraic Geometry
Symmetric differentials on weighted hypersurfaces
Wan-Yuan Xu
We study symmetric differentials on weighted hypersurfaces. Let $k$ be an algebraically closed field and let $$ X_d\subset \mathbb{P}_k(a_0,\ldots,a_N),\qquad N\ge3, $$ be a weighted hypersurface, with $a_{\min}:=\min_i a_i$. In characteristic zero we prove twisted vanishing in the range $t<m a_{\min}$ for quasi-smooth hypersurfaces of degree $d\ge2a_{\min}$, with the corresponding reflexive statement on the coarse space. We also prove a tame positive-characteristic analogue; in particular, reflexive symmetric differentials of every positive order vanish. In the second part we specialize to $k=\mathbb{C}$ and to surfaces in weighted projective three space. Using the quotient-singularity criterion of Asega–De Oliveira–Weiss together with weighted Kummer and Segre constructions, we produce singular weighted hypersurfaces whose minimal resolutions have big cotangent bundle. Combining the vanishing and bigness results with simultaneous resolution yields deformation-equivalent smooth projective surfaces for which the symmetric plurigenera jump from zero in every positive order to cubic asymptotic growth.
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Cryptography and Security
Agent Name Collision Attacks in Multi-Agent Systems
Adithyan Arun Kumar
Multi-
agent hosts turn remote
Agent Cards into local
agents, tools, workflow targets, and broker routes. A2A defines the card's name as human-readable metadata, not as a stable identity, and specifies no collision semantics. The security failure begins when a host nevertheless uses that remote name as a local routing identifier. We traced registration through dispatch and ran isolated regression tests at seven pinned open-source revisions. Six client-style integrations selected an attacker-controlled peer's client or loopback endpoint for a request addressed to a trusted peer's name. A seventh, brokered implementation collapsed both peers onto one name-derived route; queue and access-control state determine whether the result is interception or denial. The common result is wrong-peer dispatch, not universal privilege inheritance. Synthetic credential and tool tests found no A-specific credential transfer in the tested client bindings and no direct transfer of A-owned tools. The broker path forwards a caller-configuration object; delegated identity or
tokens reach B only if present and B can consume the route. Two other paths expose a later, model-mediated decision rather than direct execution authority. The necessary conditions assign different responsibilities to the protocol, implementations, and deployments. Hosts should route by an origin-bound stable identity, keep names presentational, and reject ambiguous aliases. The evidence establishes a recurring implementation vulnerability class, not a universal A2A protocol exploit or a count of vulnerable deployments.
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Game Theory
Cost Sharing with Hidden Time Flexibility
Mohsen Pourpouneh and Farzaneh Rajabighamchi
Customers often specify acceptable time intervals for receiving a service. When service on a date entails a fixed activation cost, overlapping intervals allow joint service and cost sharing. Customers may nevertheless conceal flexibility by reporting a smaller interval, affecting both operating cost and its allocation. We ask whether an operator can select a core allocation at every reported profile without making concealment profitable. We establish sharp population boundaries for preventing such coalitional reductions. When intervals may be narrowed from either end, a core-selecting rule preventing such reductions exists if and only if there are at most three customers. With four or more customers, impossibility holds even under Pareto contraction-proofness, which excludes deviations making every contracting customer weakly better off and at least one strictly better off. The impossibility persists when each contracting customer conceals only an arbitrarily small fraction of her feasible interval. The boundaries change when either the earliest or latest acceptable date of every customer is verifiable. With common verified dates, equal sharing is core-selecting and prevents such reductions for any population size. When verified dates may differ, the boundary rises to five: such a rule exists for up to five customers, whereas with six or more no core-selecting rule prevents the contracting customers from reducing their combined payment. Under Pareto contraction-proofness, however, core selection is possible for every population size on either one-sided domain. Finally, the relaxations needed for approximate core selection and coalitional contraction-proofness must grow with population size.
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Earth and Planetary Astrophysics
Probing Exoplanetary Chemistry with Ariel: Scientific Priorities and Observational Strategies
Olivia Venot, Yamila Miguel, Robin Baeyens et al. (+30 more)
Over the past two decades, increasingly precise observations have revealed that
exoplanet atmospheres are chemically diverse and often far from
equilibrium, with processes such as vertical mixing, photochemistry, and atmospheric circulation producing significant departures from thermochemical expectations. As large surveys across a wide range of
planets begin to uncover population-level chemical trends, a coherent interpretation of these patterns remains elusive, particularly for gas
giants and Neptune-like
planets where disequilibrium processes likely dominate. The ESA Ariel mission will provide the first homogeneous, statistically significant atmospheric
dataset for nearly a thousand
exoplanets, offering an unprecedented opportunity to investigate the origins of chemical diversity across planetary populations. This white paper highlights central scientific questions for understanding atmospheric chemistry and recognized as priorities for the community. These questions span the mechanisms driving disequilibrium chemistry, the role of sulfur- and phosphorus-bearing species, the influence of
stellar activity, the formation of haze precursors, and the chemical
evolution of atmospheres around
stars of different ages and types. Because these topics connect chemistry, physics, and planetary
evolution, they form the core focus of the Ariel Chemistry Working Group, which synthesizes current knowledge and identifies the diagnostics best addressed by Ariel's spectral capabilities. For each theme, we outline observational strategies and representative targets already included in the Mission Candidate Sample, illustrating Ariel's ability to address these questions. By linking large-scale observations to predictive atmospheric models, this work supports refinement of Ariel's target selection and enables population-level studies that will transform our understanding of planetary atmospheres.
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All authors Olivia Venot, Yamila Miguel, Robin Baeyens, Stefano Bellotti, Giuseppe Cassone, Quentin Changeat, Ryan Cloutier, Athena Coustenis, Dwaipayan Dubey, Billy Edwards, Kaustubh Hakim, Eric Hébrard, Christiane Helling, Helgi Rafn Hrodmarsson, Leoni Janssen, Adam Yassin Jaziri, Gaia Lacedelli, Panayotis Lavvas, Jorge Lillo-Box, Amy Louca, Adrien Masson, Zita Martins, Karan Molaverdikhani, Benjamín Montesinos, Harrison Nicholls, Enric Palle, Paul Rimmer, Donna Rodgers-Lee, Jonathan Tennyson, Shang-Min Tsai, Roméo Veillet, Sergey N. Yurchenko, Maria Zamyatina
Artificial Intelligence
SHRAV: State-Hypothesis-Reason-Action-Verify Framework for Physical Modeling and Inverse Design
Ziheng Guo and Yang Bu
Physical modeling and inverse design require computation that can continue from reusable state. We introduce SHRAV, an architecture-independent computational framework organized around State, Hypothesis, Reason, Action, and Verify. Its central mechanism is a state-continuation core with declared reuse boundaries and explicit roles for learned
evolution and numerical quantities. Forward configurations evolve predictive state and read out physical responses; inverse-design configurations additionally generate target-directed modifications and consume evaluator feedback. Electromagnetic world-model studies are mapped to forward configurations, with selected readout and reuse diagnostics reported here. Computational lithography demonstrates an inverse-design configuration: four fixed-weight design updates improve thresholded aerial-image intersection-over-union from 0.5313 to 0.8153 under independent scalar-pupil replay, with a maximum absolute IoU difference of approximately 0.000824 between predictor estimates and independent replay.
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Computer Vision
InGuard: Towards Generalized Inner Guardrail for Safe Text-to-Image Generation
Zeyu Wang, Xiaodan Li, Zhiwen Li et al. (+2 more)
Modern text-to-image (T2I) models generate high-quality images from arbitrary user
prompts, yet they can just as easily produce not-safe-for-work (NSFW) content. Conventional outer guardrails consist of two components: a
prompt classifier that checks for risk before generation, and a post-hoc image classifier that checks the fully generated image. In this design, both classifiers operate outside the generation pipeline and do not use the model's own representations. This separation can limit
prompt-screening accuracy, while the image-side check runs only after the full generation cost has been spent. Moreover, a flagged
prompt can only be rejected, even when it could be adjusted to produce a safe image. In this work, we propose the Inner Guardrail (InGuard), a safety framework that works inside the pipeline on the model's own representations, leaving base-model parameters untouched. First, a risk classifier grades each
prompt as unsafe, risky, or benign based on the text encoder's
embeddings, with no external
language model. Second, SAGE (Soft-gated Asymmetric Guardrail for
Embeddings) modifies the
embeddings of risky
prompts, aiming to return a safe image instead of a refusal. Third, a latent detector checks the one-step clean latent estimate midway through denoising, reaching nearly image-level performance and halting generation when risk is detected. We also construct the RevGen Safety
Benchmark to evaluate T2I safety under realistic conditions: 10,000
prompts built through real-image reverse generation, with a rewriting step that supplies controlled intellectual-property (IP) characters, covering graded porn/gore risks, categorical IP risks, and benign negatives. Across five open-weight T2I models, InGuard reaches 97.9-98.8% safety rate, matching or exceeding the outer guardrail, with 57.5-73.5% less benign disturbance, ~3.7x fewer parameters, and 50-55.6% of denoising steps skipped.
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All authors Zeyu Wang, Xiaodan Li, Zhiwen Li, Yuefeng Chen, Hui Xue
Statistical Methodology
Computer code validation via mixture model estimation
Negar Soleimani, Pierre Barbillon, Kaniav Kamary et al. (+1 more)
When computer codes model complex physical systems, calibration alone is insufficient; one must also assess whether a discrepancy term is needed. In this paper, we study computer code validation through a
Bayesian mixture approach that compares a pure-code model with a discrepancy-corrected model. The method relies on the posterior distribution of a mixture weight, which measures the relative support of the two competing distributions. Under the assumption that the code is linear in the calibration parameters, or can be well approximated by a linear surrogate, we show that mixture component-shared parameters can be used to combine flexible modeling, even when noninformative priors are assigned to some common parameters. Inference is performed using a Metropolis-within-Gibbs
algorithm. In addition, we introduce a thresholded allocation rule that complements the global mixture weight by providing a local diagnostic of where the discrepancy-corrected component is truly needed along the input domain. Beyond global model comparison, the proposed approach is also able to perform local model discrimination by identifying where the discrepancy-corrected component is truly needed along the input domain.
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All authors Negar Soleimani, Pierre Barbillon, Kaniav Kamary, Merlin Keller
Applied Physics
Thermoreflectance-Based Techniques for Micro- and Nanoscale Thermophysical Property Measurements: Principles, Methods, and Recent Advances
Puqing Jiang, Shanshan Chen, Xiaobo Li et al. (+1 more)
As
semiconductor devices shrink toward the nanoscale, heat transport and thermal-energy-storage behavior in materials and interfaces become increasingly dependent on length scale, structure, and interfaces. Accurate characterization of thermophysical properties, including thermal conductivity, interfacial thermal conductance, and volumetric heat capacity, is therefore essential for functional-material design and thermal management of advanced electronic devices. Photothermal thermoreflectance techniques provide noncontact measurements with high spatial and temporal resolution and a broad measurement range, making them important tools for micro- and nanoscale thermophysical characterization. This review examines the physical principles and technical characteristics of transient thermoreflectance (TTR), time-domain thermoreflectance (TDTR), frequency-domain thermoreflectance (FDTR), steady-state thermoreflectance (SSTR), spatial-domain thermoreflectance (SDTR), and the square-pulsed source (SPS) method. A unified heat-
diffusion framework is established to compare these methods in terms of parameter sensitivity, measurement uncertainty, and applicability. Representative applications illustrate their suitability for low-thermal-conductivity materials, anisotropic films, crystalline materials, and multilayer
heterostructures, as well as their complementary capabilities for characterizing thin-film thermal conductivity, interfacial thermal conductance, and in-plane/cross-plane heat transport. Finally, emerging directions are discussed, including ultrahigh spatiotemporal resolution, multiphysics coupling, in-line industrial inspection, intelligent data processing, and natural-
language-driven analysis.
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All authors Puqing Jiang, Shanshan Chen, Xiaobo Li, Ronggui Yang
Information Theory
Finite-Sample Binary Hypothesis Testing via Rényi Divergences: Strong Converse and Local Privacy
Roberto Bruno, Adrien Vandenbroucque and Amedeo Roberto Esposito
We study asymmetric simple binary hypothesis testing between $H_0:P_0^{n}$ and $H_1:P_1^{n}$, based on $n$ independent and identically distributed observations. Leveraging a variational representation of Rényi divergence of order $\alpha$, we derive our main result: a finite-sample converse with $\alpha>1$. The bound uses both directions of the divergence $D_\alpha(P_1\|P_0)$ and $D_\alpha(P_0\|P_1)$, tensorises under product measures, and contains familiar data-processing converses as boundary cases. For comparison, we apply the same variational approach to general $f$-divergences and specialise it to total variation, $E_\gamma$, Hellinger, and Kullback Leibler divergences, thereby recovering familiar converses within a unified framework. Together with an achievability bound involving Rényi divergence with $\alpha\in (0,1)$, the main converse recovers the phase transition of the optimal Type II error under the exponentially decaying Type I error constraint $\varepsilon_n=e^{-nr}$. Under regularity conditions, the optimal Type II error vanishes exponentially when $r<D(P_1\|P_0)$ and converges exponentially fast to one when $r>D(P_1\|P_0)$. We also derive sample-complexity bounds and extend both the converse and achievability analyses to locally differentially private observations, quantifying the cost of
privacy and recovering the non-private achievability bound as the
privacy constraint vanishes.
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Materials Science
High-Throughput Photovoltaic Screening and Spacer-Dependent Shift Current in Two-Dimensional Hybrid Perovskites
Vikrant Chaudhary, Fu Li, Yue Zhao et al. (+5 more)
High-throughput first-principles screening of structurally diverse two-dimensional hybrid organic-inorganic perovskites (2D HOIPs) provides a route to systematically explore their photovoltaic properties. Here, we develop a computational workflow with two distinct objectives: spectroscopic limited maximum efficiency (SLME) screening to identify materials with promising photovoltaic potential and nonlinear shift current calculations to characterize their bulk photovoltaic response. Applying this workflow to a curated
dataset of experimentally reported 2D HOIPs, we identify more than 25 compounds with SLME values above 25% and more than 25 compounds exhibiting shift current responses exceeding 10~$\mu$A/V$^2$. We then focus on the Pb-I-based 2D HOIPs with $n=1$ to investigate the influence of spacer chemistry on the shift current response, revealing substantial spacer-dependent variations in both its magnitude and spectral peak position. Overall, these results highlight the potential of high-throughput first-principles calculations to accelerate the discovery of experimentally relevant 2D HOIPs with promising photovoltaic properties while revealing how organic spacer chemistry influences their nonlinear shift photocurrent response.
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All authors Vikrant Chaudhary, Fu Li, Yue Zhao, José A. Márquez, Wei Xie, Claudia Draxl, Zhihua Sun, Hongbin Zhang
Artificial Intelligence
BiCFlow-MER: Orchestrating Discriminative and Generative Multimodal Emotion Recognition via Conditional Transport
Yanbing Wang, Shenyue Wang and Chunyang Yu
In
multimodal emotion recognition (MER), human affective states are inferred by integrating complementary cues from multiple modalities. In audio-text MER, affective cues are often entangled with speaker style and lexical content, while cross-modal disagreement further complicates how the evidence should be integrated. Under conventional discriminative fusion,
multimodal evidence is compressed into a terminal prediction, with modality-specific cues and conflict information insufficiently preserved. In large generative affective models, by contrast, affective
reasoning is typically embedded in
language decoding, leaving
emotion evidence implicit and difficult to verify in a structured space. To address these limitations, BiCFlow-MER (Bidirectional Conditional Flow for
Multimodal Emotion Recognition) is proposed as a conditional-flow framework in which audio-text MER is formulated as generative evidence transport within a structured
emotion space. Within BiCFlow-MER,
emotion-oriented evidence is disentangled from speaker-style and lexical-content factors to construct a conflict-aware affective condition. Guided by this condition, each utterance is transported to an explicit
emotion-space endpoint through a bidirectional rectified flow. Candidate
emotions are jointly verified through adaptive prototype-cloud scoring of the transported endpoint and backward class-to-condition consistency with the original
multimodal condition, enabling conflict-aware recognition. BiCFlow-MER is shown to outperform all compared methods across IEMOCAP, MELD, and the zero-shot CASE
benchmark. By orchestrating discriminative recognition and generative evidence modeling through conditional transport, BiCFlow-MER defines a new MER paradigm.
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Statistical Methodology
Model-agnostic noise reduction for high-dimensional time series data
Bram Wouters and Cees Diks
We develop a model-agnostic framework for noise reduction in high-dimensional time series that explicitly targets optimal recovery of a low-dimensional latent dynamic component contaminated by observational white noise. Under the assumption that the latent dynamics live in a low-dimensional linear dynamic subspace, we characterize the optimal linear projection onto the dynamic subspace and provide a
geometric description of the residual error in terms of the relative orientation of the signal and noise spaces. We propose
estimators for the dynamic subspace and the optimal projection based on lagged covariance
matrices, bootstrap dimension selection, and a low-rank representation of the structured noise. Under mild conditions, the resulting denoised series is shown to converge to its population target at the usual parametric rate. Simulations show that the proposed method can substantially improve subspace estimation, reconstruction error, and one-step-ahead forecast accuracy compared with both orthogonal projection-based denoising and the raw data. The approach is illustrated by empirical applications to high-dimensional stock returns and to a 20-variate time series of macroeconomic indicators.
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Combinatorics
On Arc-Transitive Regular Covers of Cubic Edge-Primitive Graphs
Feng Deng, Jing Jian Li, Yu Wang et al. (+1 more)
We determine, up to isomorphism of the covering graphs, the connected arc-transitive regular covers of cubic edge-primitive graphs whose covering transformation group is cyclic or elementary abelian of order $p^2$, where $p$ is a
prime. Combining the known classifications for the base graphs ${\rm K_{3,3}}$ and ${\rm DC_{14}}$ with new arguments for ${\rm F30A}$ and ${\rm F102A}$ gives the full list in these two classes of covering groups. In the cyclic case, the covers of ${\rm F30A}$ and ${\rm F102A}$ are ${\rm F90A}$ and ${\rm F204A}$, respectively. In the elementary abelian case, neither ${\rm F30A}$ nor ${\rm F102A}$ admits an arc-transitive regular $\mathbb{Z}_p^2$-cover, so the base graph is ${\rm K_{3,3}}$ or ${\rm DC_{14}}$.
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All authors Feng Deng, Jing Jian Li, Yu Wang, Hao Yu
Robotics
RegenHarness: A Robot Agent Harness with Evidence-Gated Recursive Self-Improvement
Kailin Wang, Haoxiang Jie, Yaoyuan Yan et al. (+2 more)
Long-horizon
robot execution requires a clear distinction between a model's proposal, a controller's termination, and verified task completion. We present RegenHarness, an evidence-gated
robot-
agent harness connecting task planning to heterogeneous
robot skills. Its execution architecture couples a model loop for context-conditioned proposals with an
agent loop for dispatch, observation, verification, commitment, and bounded recovery. Four role-isolated contexts separate planning, supervision, verification, and recovery inputs. Versioned
memory distinguishes observed facts from accepted task progress, while an identity- and version-bound commit gate controls updates to trusted task state. The runtime combines duplicate-dispatch control, resource leases, and recovery budgets under explicit backend contracts, and checks the original user goal before reporting completion. To our knowledge, we are the first to introduce an evidence-gated recursive self-improvement (RSI) protocol for
embodied robotic
agents. Across missions, execution records motivate candidate changes to context rules, task templates, routing, and recovery policies; fixed regression checks and release authorization govern their acceptance; versioned rollout and rollback preserve configuration traceability. This RSI protocol revises the harness configuration without online model-weight updates or permission to weaken the commit gate. A real quadruped deployment documents voice-triggered warehouse
navigation, panoramic inspection, visual analysis, message delivery, return, and spoken reporting through linked audio, images, trajectories, and receipts. A separate
circuit demonstrates why completion depends on execution history rather than endpoint proximity alone. Together, the cases demonstrate integrated perception, physical execution, communication, and history-dependent completion in real-world
robot tasks.
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All authors Kailin Wang, Haoxiang Jie, Yaoyuan Yan, Zhiyou Heng, Zhaosong Li
Computer Vision
A generalizable structural brain MRI foundation model via dual-priority federated pretraining
Zhen Yu, Yang Liu, Jianfeng Feng et al. (+2 more)
Foundation models hold promise for generalizable analysis of structural
brain magnetic resonance imaging (MRI) scans across diverse populations and imaging conditions. However, existing models typically rely on centralized pretraining using data pooled across cohorts, which is constrained by
privacy and data-governance requirements. Moreover, larger cohorts can dominate optimization, underutilizing anatomical information from smaller cohorts and limiting generalization. Here we present BrainFedFM, a structural
brain MRI foundation model federatively pretrained on 164,707 three-dimensional scans drawn from diverse real-world data distributions and organized across 42
federated sites. BrainFedFM uses dual-priority
federated pretraining, in which spatial-priority masking emphasizes informative anatomical regions during local training, while site-priority aggregation adaptively weights contributions during global aggregation. Across 20 downstream
datasets spanning 17 classification, regression and
segmentation tasks, BrainFedFM achieved the state-of-the-art performance across seven foundation models, including four centralized models, while showing consistent advantages in classification and regression tasks and strong robustness under underrepresented populations and challenging imaging conditions. These findings demonstrate the generalizability of BrainFedFM and highlight
federated pretraining as a practical strategy for developing neuroimaging foundation models from distributed data without pooling raw images.
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All authors Zhen Yu, Yang Liu, Jianfeng Feng, Xiahai Zhuang, Qingchao Chen
Algebraic Geometry
On the local theory of non-archimedean psh functions
Lyuhui Wu
The purposes of this paper are two-folded. First, we define a class of local psh functions on Berkovich spaces, and study their properties. In particular, we show that this class of psh functions is equivalent on the one hand to the one defined by Chambert-Loir–Ducros, and is equivalent on the other hand to the class of global psh functions of Boucksom–Favre–Jonsson on projective varieties satisfying the envelope property. Second, making use of our local theory of non-archimedean psh functions, we prove the envelope
conjecture for projective normal varieties over a discretely or trivially valued non-archimedean field of residue characteristic zero.
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Number Theory
Construction of vector-valued adelic automorphic forms
Oliver Stein
We construct an adelic counterpart of Scheithauer's lifting map from scalar-valued modular forms to vector-valued modular forms transforming with the Weil representation. More precisely, we introduce suitable spaces of vector-valued adelic automorphic forms and define an adelic lifting map between the corresponding scalar-valued and vector-valued automorphic forms. We then prove that this adelic construction is compatible with Scheithauer's classical lifting map under adelization. In this way, the relation between scalar-valued and vector-valued modular forms is transferred naturally to the adelic setting.
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High Energy Astrophysical Phenomena
Broad-band spectral-timing: simultaneous NICER and HXMT observations reveal an anticorrelation between the softest and hardest X-ray fluxes in MAXI J1820+070
Niek Bollemeijer, Phil Uttley and Bei You
Black hole X-ray binaries (BHXRBs) in the hard state show variable X-ray emission across a wide range of energies. Hard X-rays are thought to be produced through Compton-upscattering of seed
photons from the
accretion disc by the hot X-ray corona, leading to a spectral component roughly shaped like a power-law with a high-energy cut-off. A complete study of the coronal variability requires a full view of the spectrum, from the low-energy cut-off at the seed
photon energy to the high energy cut-off, which may be related to the coronal temperature. We present a detailed spectral-timing study of strictly simultaneous observations by NICER and Insight-HXMT of the bright BHXRB MAXI~J1820+070, covering the 0.3-250 keV energy range. The high data quality allows us to study the lags and coherence across the full X-ray band. We find that at low
Fourier frequencies ($\lesssim0.02$ Hz), the soft X-ray flux below 1 keV and hard X-ray flux above 100 keV are anticorrelated, i.e. phase lags of $|\pi|$ rad. No anticorrelation is observed when comparing energy bands above 1 keV. We consider several explanations for the observed complex behaviour, such as a modulation of the coronal cut-off energy by the disc, power-law pivoting and a contribution from non-thermal electrons. Future, more detailed modelling efforts based on the observed effects may place new constraints on the dynamics of the X-ray emitting regions around accreting black holes.
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Computation and Language
Can Jev Judge Radiology Reports? Evaluating a System One Model for Clinical Factuality
Jiaju Huang, Hao Yang, Xinyu Ma et al. (+6 more)
An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its presence. Measuring these factual differences is essential for evaluating report generators. We study Jev, a System One decision model, as a simple, low-cost judge of agreement with physician-written reference reports. Our evaluator checks whether each statement is supported by the other report and combines these judgments in both directions to capture unsupported claims and omissions. A single-question configuration reaches Kendall correlations of 0.573 on RadEvalX and 0.398 on RadEvalExpert with expert error counts, outperforming an open natural
language inference judge under matched decomposition and aggregation. One support question per statement retains similar expert agreement to seven while using 43-45% fewer judgment input
tokens. At the documented API price, judgments cost under three cents per hundred report pairs, excluding local decomposition. In a separate controlled-error test, Jev detects false negation with an AUROC of 0.977. Local RadMatch achieves stronger agreement on clinically significant errors in both expert
datasets and on total errors in the shared RadEvalExpert subset. Finding-count and error-scope analyses show that
benchmark agreement reflects report size and error definitions as well as medical error detection. These results support Jev as a practical judgment component for measuring factual differences in generated radiology reports and identify where more elaborate evaluation remains valuable.
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All authors Jiaju Huang, Hao Yang, Xinyu Ma, Xinglong Liang, Kunyan Cai, Junqiang Ma, Shaobin Chen, Yue Sun, Tao Tan
Artificial Intelligence
State-Grounded Conditioning: Wrapping User-Facing LLM Agents Where Direction Depends on Live State
Qi Liu, Xiaoyang Yuan, Yubin Ruan et al. (+8 more)
We introduce State-Grounded Conditioning (SGC), a design principle for user-facing LLM
agents that must condition on live user state (game state, session history, live inventory), and a distinct failure class we call direction drift: task-complete responses whose chosen direction misaligns with the current state. SGC externalises state-dependent control into rule kernels over structured inputs and three primary state slices, via Perception, Grounding, and Interaction wrappers with explicit conditioning dependencies. We evaluate SGC on a 200-session anonymised
benchmark ($\approx$1,000 assistant model turns) from an in-game conversational coaching
agent that guides players through consecutive competitive matches, reporting mean first-
token latency and five human-annotated dialogue-quality metrics that jointly cover factual grounding and coach-like guidance progression. The Perception wrapper holds mean first-
token latency at 1.5s (vs. 6.1s for PE-
Agent inside a production tool-use harness); enabling all three wrappers lifts turn-level grounded accuracy from 61.1%/69.8% (Prompting / PE-
Agent) to 96.7% and session-level grounded accuracy from 20.0%/26.5% to 83.5%; session-level grounding-failure incidents drop by $\approx$78% relative to the strongest baseline. A cumulative ablation shows complementary incremental gains as the wrappers are added. These results inform approximate state-slice orthogonality, without establishing independent per-wrapper effects.
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All authors Qi Liu, Xiaoyang Yuan, Yubin Ruan, Zhuomeng Zhang, Wenjin Wang, Di Wu, Mingye Xu, Xinyi Mou, Xingxi Yin, Ke Feng, Zixun Sun
Combinatorics
Symmetric polyhedral scenes and parallel redrawings
Signe Lundqvist, Bernd Schulze and Klara Stokes
Liftings and parallel redrawings are classical topics in applied discrete geometry, concerned respectively with vertically lifting a $(d-1)$-picture – a realisation of a vertex-hyperplane incidence geometry in $\mathbb{R}^{d-1}$ – to a $d$-dimensional polyhedral scene, and with redrawing a $d$-picture (or hyperplane arrangement) within $\mathbb{R}^d$ while preserving prescribed hyperplane normals. In this paper, we develop a unified framework for the analysis of forced-symmetric liftings and forced-symmetric parallel redrawings. In particular, we show that the classical duality for these theories also appears in the forced-symmetric setting. We establish the orbit lifting
matrix and the orbit concurrence geometry
matrix, and show that they are the appropriate
symmetry-adapted analogues of the standard lifting
matrix and concurrence geometry (or parallel redrawing)
matrix. We also make explicit the relationship with the orbit version of Whiteley's parallel design
matrix for graphs. Using these tools, we derive necessary conditions for symmetric pictures to be forced-symmetric flat, and for symmetric hyperplane arrangements to be forced-symmetric robust, expressed as sparsity counts on the group-labelled quotient graphs associated with the symmetric incidence geometries. Finally, we discuss
conjectures regarding the sufficiency of these conditions for generic configurations.
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Mesoscale and Nanoscale Physics
Observation of the orbital Nernst effect
Yuto Masuda, Takamasa Hirai, Daegeun Jo et al. (+5 more)
The Nernst effect, which converts a temperature gradient into a transverse charge current, is fundamental to thermoelectrics. Its
spin analogue, the
spin Nernst effect, enables thermal generation of transverse
spin currents and is central to
spin caloritronics. Recently, the discovery of orbital currents, the orbital counterpart of
spin currents, has extended angular-momentum transport beyond
spin, leading to the prediction of the orbital Nernst effect, in which a temperature gradient drives a transverse orbital current. However, experimental evidence for this effect has been lacking. Here, we report the observation of the orbital Nernst effect in Ti. Using Ni electrodes on Ti, we detect a thermally induced voltage that depends on the magnetization direction and scales linearly with the temperature gradient. This voltage is strongly suppressed both when Ni is replaced with Ni$_{81}$Fe$_{19}$ and when Ti is replaced with Cr, providing strong evidence that the signal originates from the orbital Nernst effect rather than the anomalous Nernst or
spin Nernst effect. These results establish thermally driven orbital transport, opening a pathway toward orbital caloritronics.
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All authors Yuto Masuda, Takamasa Hirai, Daegeun Jo, Naoki Yano, Peter M. Oppeneer, Hossein Sepehri-Amin, Ken-ichi Uchida, Kazuya Ando
Computation and Language
When Context Misleads: In-context Learning with Jurisdiction in Large Language Models
Pei-lin Li, Qingle Liu, Junyang Feng et al. (+4 more)
In-Context Learning (ICL) has become a cornerstone of modern LLM deployment. However, existing ICL post-training methods have a critical blind spot: they excel at extracting patterns from demonstrations while often neglecting context authority, the ability to determine whether contextual information should govern the final answer. To
benchmark this capability, we introduce FakeContextBench, which contains pseudoscientific claims across seven domains. Our evaluation of commercial and open-source models shows that large-scale pre-training alone is insufficient for reliable context-authority discrimination. Moreover, prevalent ICL fine-tuning methods can increase susceptibility to misleading context, reducing reality accuracy by up to 14.95 percentage points relative to the base model. To address this trade-off, we propose Jurisdiction In-Context Learning (J-ICL), a post-training framework that incorporates context validation into the training objective. Across four model backbones, J-ICL improves ICLEval by an average of 5.84 percentage points and reality accuracy by 9.20 points over the corresponding base models. It also raises the Reality Rate by an average of 18.09 points relative to MetaICL and Symbol Tuning. These results demonstrate that ICL capability and resistance to deceptive context can be improved together. The
benchmark is available at https://github.com/peilin717/FakeContext-Bench.
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All authors Pei-lin Li, Qingle Liu, Junyang Feng, Siyu Li, Sunqi Fan, Xin-Sheng Chen, Shuojin Yang
Numerical Analysis
Efficient Geometry Representation Strategies for the Shape Optimization of Profile Extrusion Dies
Jana Sasse, Maximilian Esser, Markus Mügge et al. (+1 more)
The design of profile extrusion dies remains a challenging task due to the complex rheological behavior of
polymer melts and the
geometric intricacies of flow channels. Traditional manual optimization approaches, which rely heavily on human experience, are inefficient and often employ unvalidated heuristics. To address these challenges, we present a deterministic and explainable framework for automatic die design based on adjoint-based shape optimization. This approach enables the computation of sensitivities that directly indicate beneficial modifications to the flow channel geometry. A major difficulty in such optimization processes lies in generating boundary-conforming meshes that evolve consistently with changing geometries. To overcome this issue, we employ non-boundary conforming geometry representation methods that eliminate the need for an explicit surface representation along physical boundaries. A dedicated reconstruction technique is developed to recover accurate sensitivity information at the virtual interface between fluid and solid regions. The proposed
algorithm is demonstrated on 3D geometries with varying complexity, including realistic extrusion die flow channels. Several objective functionals relevant to industrial applications, such as flow balance at the
outflow, are considered. The results highlight significant improvements in performance metrics while maintaining numerical robustness. This work showcases the potential of adjoint-based techniques for automated die design in a domain still largely governed by manual trial-and-error procedures, establishing a foundation for data-efficient, sustainable manufacturing workflows using computational rheology.
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All authors Jana Sasse, Maximilian Esser, Markus Mügge, Stefan Turek
Quantum Physics
Scalable integration of silicon carbide color centers into nanophotonic structures
Raphael Wörnle, Jonas Schmid, Dennis Grüner et al. (+7 more)
Color centers in silicon carbide are a promising platform for
quantum technologies, offering long-coherence electron and
nuclear spins for storing and manipulating
quantum information, as well as single-
photon emission for
quantum communication. Low
photon collection efficiency is commonly addressed by integrating color centers into nanophotonic devices. Here, we report a scalable method for the deterministic integration of color centers into silicon carbide nanopillars, using the same nanoscale mask for both implantation of oxygen-related color centers (PL5, PL6) and subsequent nanopillar fabrication via dry etching. This approach solves the issue of low color center creation yield in nanostructures, with figures of merit comparable to bulk samples. We demonstrate count rate enhancements of up to one order of magnitude for PL5 and four times for PL6, while preserving
spin coherence times. Our method is directly applicable to other solid-state platforms, offering a scalable route to efficiently integrate color centers into nanostructures.
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All authors Raphael Wörnle, Jonas Schmid, Dennis Grüner, Jonah Heiler, Flavie Davidson-Marquis, Georgy V. Astakhov, Vadim Vorobyov, Florian Kaiser, Jonathan Körber, Jörg Wrachtrup
Graphics
ARS-Avatar: Animatable and Relightable Surfel Avatars with Learnable Ambient Occlusion
Jiateng Liu, Hao Gao, Junxin Sun et al. (+4 more)
Creating animatable and relightable human avatars from multi-view images remains challenging, as pose-dependent deformation, materials, and light visibility are tightly coupled in images. In this paper, we present ARS-Avatar, a novel method using surfel representation for high-quality, animatable, and relightable human avatars from multi-view images captured under unknown illumination. For faithful surfel estimation, we extract deformation displacement and body model deviation two priors from the mesh, which jointly capture local deformation complexity and serve as auxiliary. We employ deferred shading to estimate BRDF materials and further introduce a differentiable screen-space ambient occlusion formulation that enables gradient-based optimization of body-part specific occlusion radii through finite differences, providing an efficient approximation of light visibility that can be jointly optimized with the avatar. Extensive experiments show that ARS-Avatar achieves competitive or improved radiance reconstruction on multiple metrics and consistently outperforms the evaluated PBR relighting baselines, while enabling realistic animation and relighting under novel poses and illuminations.
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All authors Jiateng Liu, Hao Gao, Junxin Sun, Mengqi Liu, Jiu-Cheng Xie, Jucheng Song, Feng Xu
Audio and Speech Processing
EmphTTS: an emphasis-control TTS with reinforcement learning
Zirui Li, Rech Silas, Lauri Juvela et al. (+2 more)
Generating controllable and human-like emphasis remains an open challenge in text-to-
speech, even when explicit emphasis control signals are provided in the text input, limiting the communicative accuracy of synthetic
speech in real-world applications. Reinforcement learning has recently shown promise for post-training TTS systems to align with human preference, yet existing methods have not been applied to word-level prosodic control. We present EmphTTS, a non-autoregressive TTS system that applies Group Relative Policy Optimization (GRPO) to the duration predictor with an emphasis localization reward, enabling direct optimization for word-level emphasis. Evaluations show that EmphTTS achieves the best emphasis controllability and performs the best in emphasis objective evaluation. In subjective preference tests, EmphTTS is significantly preferred over synthetic groundtruth and most baselines. Ablation studies show that GRPO improves emphasis realization beyond supervised-finetuning-based duration modeling and simple speaking-rate adjustment, while alleviating the mismatch between the independently trained duration predictor and TTS model.
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All authors Zirui Li, Rech Silas, Lauri Juvela, Tom Backstrom, Mikko Kurimo
Audio and Speech Processing
DriftAudio: Marginal Drifting for Distributional Post-Training of One-Step Text-to-Audio Generators
Xingyu Chen, Fei Ma and Sipei Zhao
Recent one-step text-to-audio (TTA) models substantially reduce inference cost, yet their generated distributions can still be improved through post-training. We propose DriftAudio, a distributional post-training method that adapts Drifting to pretrained one-step TTA generators. Applying Drifting condition-wise is challenging under free-form text conditioning, where only one or a few real samples are typically available for a particular condition. DriftAudio instead performs Drifting on the marginal audio distribution while retaining text-conditioned generation. The drifting field is estimated in a frozen audio feature space using resampled real samples, a rolling bank of generated samples, and the current batch of generated samples. The resulting Drifting field provides a detached training target for updating only the generator, keeping the original one-step inference procedure. On AudioCaps, starting from MeanAudio, DriftAudio reduces FAD and FD by 33.9% and 17.6%, respectively, while also improving KL and CLAP. Starting from FdAudio, it further reduces FAD, FD, and KL, with trade-offs in IS and CLAP. These results demonstrate the effectiveness of marginal distributional post-training for one-step TTA generation.
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Robotics
Gray-Box Model Predictive Control for Articulated Dump Trucks via Gaussian Process Learning of Sideslip
Arash Shahirpour, Jens Ahlers, Christopher Schulte et al. (+1 more)
The growing demand for automation in the mining industry, particularly for the autonomous operation of articulated dump trucks (ADTs), has drawn increased
attention to accurate vehicle modeling. The importance of such models lies in their use in model predictive control (MPC), model-based estimation methods, and vehicle simulation. While dynamic modeling offers a viable solution for these purposes, it is associated with complex setup and parametrization and may require recalibration in changing operating environments. As a result, kinematic models have dominated ADT modeling, especially in MPCs, at the expense of reduced prediction accuracy. In this work, we propose an approach using Gaussian Process Regression (GPR) to learn the sideslip angle of the vehicle, which is identified as the primary contributor to the reduced accuracy of kinematic models. The learned GPR function is augmented into the kinematic model to form a gray-box model that aims to reduce the gap to dynamic models. We show that the gray-box model can predict the sideslip angle and, consequently, the vehicle's lateral velocity, thereby improving the MPC's prediction performance. The resulting gray-box MPC is compared against two white-box MPCs in a simulation environment. The results indicate an improvement in terms of maximum lateral tracking error from over 2 m to 0.56 m.
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All authors Arash Shahirpour, Jens Ahlers, Christopher Schulte, Tim Reuscher