Distributed Computing
Lightweight and Resource-Efficient Perception for Robotic Guide Dogs
Jinse Kwon, Yoojin Lim, Choonghan Lee et al. (+3 more)
Robotic guide dogs should understand their surroundings, objects, and potential risks. Prior research has focused on raw sensor data from cameras and 2D or 3D
LiDAR, which precisely measure distance points rather than provide a semantic understanding of the scene. While these physical measurements are effective for
robot-centric collision avoidance and
robot safety, they are not suitable for human-centric guidance. The system should recognize the type and relevance of obstacles and explain them, clearly and actionably, in terms of their spatial relation to the user. We present complete on-device perception modules that fuse a 360 camera and a 2D
LiDAR for reliable collision avoidance, with moving-object detection and tracking for human-centric guidance. Finally, in walking-impossible situations, a
vision–
language model delivers pathway explanations as a safety mechanism to reduce user anxiety. In experiments, verification of fused 360 camera–
LiDAR depth shows reliable near-range perception but inherent mid-range bias, while the system as a whole sustained real-time performance under 55 W. On the real-world
egocentric GuideDogQA
benchmark, our system achieved 83.8% accuracy, compared with 67.1% for GPT-4o. These results demonstrate that practical human-centric guidance with real-time on-device inference is feasible even on quadrupeds.
Read the full abstract ▾
All authors Jinse Kwon, Yoojin Lim, Choonghan Lee, Yongseung Yu, Yongin Kwon, Jemin Lee
Quantum Physics
Topological phase transition in a symmetric blockade structure
Johannes Mögerle, Simon Fell, Tobias F. Maier et al. (+4 more)
We present a numerical study of a blockade
Hamiltonian with a local $\mathbb{Z}_2$
symmetry. For such models, the existence of a topologically ordered ground state has been proven rigorously for weak Rabi driving [T. F. Maier et al., PRX
Quantum 6, 030340 (2025)]. However, the phase diagram beyond weak driving is still an open question. We perform large-scale infinite-size density
matrix renormalization group (iDMRG) simulations on a cylinder and map out the full phase diagram of the model. We find a single continuous phase transition between the topologically ordered phase and a trivial paramagnetic phase, and confirm numerically the stability of the topologically ordered phase up to Rabi drives comparable to the characteristic energy scale of the system. Furthermore, we compute the excitation gaps and show that the charge gap remains open across the phase transition and charges become confined in the trivial phase. On the other hand, the gap in the symmetric sector closes at the transition, consistent with the condensation of flux excitations.
Read the full abstract ▾
All authors Johannes Mögerle, Simon Fell, Tobias F. Maier, Ali Otaifi, Lode Pollet, Nicolai Lang, Hans Peter Büchler
Artificial Intelligence
Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective
Han Cui, Jianhao Yan, Yun Luo et al. (+3 more)
On-policy
distillation (OPD) has become an important approach to
language model post-training. However, despite its performance gains, OPD can also collapse into excessively long and repetitive generation, and the mechanism underlying these divergent outcomes remains poorly understood. We explain these outcomes through a reinforcement learning perspective: the teacher implicitly rewards student behaviors, even those it rarely exhibits itself. From this perspective, our experiments show that OPD improves performance without expanding the student's capabilities. When the implicit reward model is reliable, OPD makes correct responses easier to sample. In contrast, when the preference misaligns with quality, reward hacking happens: the implicit reward model amplifies overlong, repetitive student rollouts, even though it rarely generates such text itself. Guided by this diagnosis, we find that masking unhealthy responses during training and using SFT initialization can each effectively mitigate the collapse. Together, these findings show that OPD amplifies student behaviors favored by the teacher's implicit feedback, shifting the focus from how well the teacher generates to how reliably it evaluates student rollouts. Our code is available at https://github.com/HancCui/opd_hacking.
Read the full abstract ▾
All authors Han Cui, Jianhao Yan, Yun Luo, Hongbo Zhang, Zhizhang Fu, Yue Zhang
Numerical Analysis
Recursive Multilevel Decomposition of Pairwise Comparison Matrices: Algorithms and Inconsistency Diagnostics
Mert Çarboğa, Waldemar W. Koczkodaj and Yusuf Yayl{\i}
Positive reciprocal pairwise comparison
matrices are studied in entrywise logarithmic coordinates. Starting from the established consistent-residual split, we show that, for any fixed ordering, the residual
matrix admits a unique top-down decomposition into mutually Frobenius-orthogonal layers. The construction applies to every positive reciprocal PC
matrix of size $n \geq 3$, without additional spectral, sparsity, or genericity assumptions. An explicit active-
matrix recursion and an equivalent cumulative parameter-extraction procedure have computational complexities $O(n^3)$ and $O(n^2)$, respectively. The squared Frobenius norms of the layers define an ordering-conditioned residual-energy profile that provides structural information complementary to the
geometric-mean priorities and the Koczkodaj inconsistency indicator (Kii). Entrywise exponentiation yields an exact Hadamard-product reconstruction of the original reciprocal
matrix. Numerical experiments confirm agreement between the two computational procedures, exact reconstruction, mutual orthogonality, and numerical accuracy to machine precision.
Read the full abstract ▾
Lattice Field Theory
Looking at radiative corrections to the $\Sigma^\pm\to\Lambda e^\pm \bar{\nu}$ semileptonic decay-rate ratio
Constantia Alexandrou, Simone Bacchio, Antonio Evangelista et al. (+2 more)
We investigate the impact of radiative corrections on the ratio of the semileptonic decay rates $\Sigma^\pm\to\Lambda e^\pm \bar{\nu}$. Phenomenological analyses point to sizable radiative effects on the experimentally measured ratio. From a theoretical perspective, this observable is particularly appealing because several hadronic and electroweak contributions partially cancel in the ratio, providing a cleaner probe of electromagnetic effects. We identify the dominant contributions to the radiative corrections and develop the framework for a dedicated calculation. This represents a first step toward a precise theoretical description of
baryon semileptonic decays, helping to assess their potential as precision tests of the Standard Model.
Read the full abstract ▾
All authors Constantia Alexandrou, Simone Bacchio, Antonio Evangelista, Andreas Konstantinou, Nazario Tantalo
Cryptography and Security
Reversing the Clock: Layout-Aware Recovery of Design Intent from Clock Distribution Networks
Sascha Tommasone, Zehra Karadağ, Christopher Pawlowicz et al. (+6 more)
Hardware reverse engineering supports competitive analysis and hardware assurance by recovering information about an integrated
circuit (IC) from its physical implementation. While existing techniques primarily recover the gate-level netlist, which represents logical functionality, they often overlook physical design decisions such as placement, routing, delay insertion, and interconnect optimization. The clock distribution network encapsulates many of these decisions; however, no prior published work has recovered this network from a fabricated IC to infer design intent. We present a layout-aware methodology that recovers and analyzes the clock distribution network by integrating a recovered gate-level netlist with layout information extracted from scanning electron microscope imagery. Our four-phase pipeline recovers clock-tree
topology, buffering, gating and switching, interconnect delay, and crosstalk-mitigation measures. We demonstrate our methodology on a commercial 450 nm IC, recovering a global H-tree backbone with local X-tree-like branching, identifying an independent clock tree and global clock gating, quantifying latency, skew, and routing lengths, and confirming the absence of dedicated crosstalk mitigation. Together, these findings let us reason about the designer's intent. To encourage further research and support reproducibility, we release our clock tree recovery
algorithm as open source.
Read the full abstract ▾
All authors Sascha Tommasone, Zehra Karadağ, Christopher Pawlowicz, Michael Green, Bruno Machado Trindade, Eunsung Seo, Christof Paar, René Walendy, Steffen Becker
Statistical Methodology
Cellwise, Blockwise, and Casewise Robust Multiblock PCA for Sustainable and Inclusive Wellbeing in the EU
Arthur Daisomont, Fabio Centofanti and Mia Hubert
Gross Domestic Product (GDP) is widely used to guide economic and social decision-making, but it provides only a partial view of wellbeing. For this reason, the European Union (EU) has launched initiatives to monitor sustainable and inclusive wellbeing beyond GDP, developing indicator frameworks that cover dimensions such as health, education, environment, and social inclusion. These indicators are naturally grouped into thematic areas, and policymakers are interested in understanding how these areas contribute to global wellbeing and which indicators explain differences across countries. However, such data are high-dimensional, contain missing values, and may include anomalies affecting entire observations, specific thematic areas, or individual indicators. We introduce blockwise outliers and propose bloccPCA, a robust multiblock PCA method that simultaneously handles casewise, blockwise, and cellwise outliers, as well as missing values. The method provides robust global components to summarize the overall structure of wellbeing, while preserving thematic-area contributions through robust blockcomponents. It also yields diagnostic tools to identify whether anomalies arise at the case, block, or
cell level. Monte Carlo simulations and an application to the EU wellbeing
dataset show that bloccPCA provides valuable insights into sustainable and inclusive wellbeing.
Read the full abstract ▾
Statistical Mechanics
Probing fluctuating hydrodynamics through current fluctuations in hard rod gas
Tirthankar Mondal, Indranil Mukherjee and Anupam Kundu
We study the full counting statistics of the integrated current in a one-dimensional hard-rod gas in
equilibrium, employing two recently proposed fluctuating hydrodynamic frameworks: (i) Ballistic Macroscopic Fluctuation Theory (BMFT) and (ii) diffusive fluctuating hydrodynamic theory based on correlated Brownian motion approximation of hard-rod quasiparticles (CBFHD). We investigate how the predictions of these two approaches compare with each other and with the underlying microscopic dynamics. We show within BMFT that the current distribution admits a large-deviation form under ballistic scaling, and we derive an explicit expression for the large-deviation function, valid for an arbitrary inhomogeneous initial state factorized in point-particle coordinates. The corresponding current cumulants are also obtained. We subsequently analyze the current statistics within the CBFHD framework, which explicitly incorporates
stochastic noise and dissipative terms, and demonstrate that the current distribution likewise obeys a large-deviation principle in this approach. We find that, while BMFT predictions for the first few cumulants and the rate function are in excellent agreement with direct numerical simulations of the microscopic hard-rod dynamics, CBFHD prediction is not. We further verify the predictions of the CBFHD independently using a collection of correlated Brownian motions, which provides an approximate description of the dynamics of hard-rod quasiparticles. Our findings indicate that CBFHD does not accurately capture large-scale current fluctuations in the hard-rod gas, whereas BMFT yields predictions in quantitative agreement with the microscopic dynamics, at least in the context of full counting statistics.
Read the full abstract ▾
Functional Analysis
Basic Theory of Tower Systems
Quanfeng Liu and Nikica Uglešić
For a
Banach space $X$, we study the completed direct limit of its even iterated duals along the canonical
embeddings and the bounded inverse limit of the corresponding odd duals. We give explicit constructions, establish their duality relation, and describe the comparison maps associated with iterated dualization. We then examine several assertions in three preprints of Uglešić, giving counterexamples concerning dimension stability and the commutation of bidualization with direct limits. The finite quotient shape (the category whose objects are the finite-dimensional quotients, with all continuous linear maps between them) is classified by the algebraic dimension of the continuous dual. We also compute the dimensions of the finite iterated duals and the completed tower of $\ell^{\infty}(\mathbb{N})$, and prove that, inside the tower space, the canonical copy of a higher layer need not be the range of an $M$-projection. Throughout, uncompleted limits,
Banach completions, and the different meanings of isomorphism are distinguished explicitly.
Read the full abstract ▾
Instrumentation and Detectors
Residual AM and PM Noise of Selected X-Band Amplifiers
Archita Hati, Craig Nelson and Scott Littrell
In microwave systems design, amplitude modulation (AM) noise is often ignored relative to phase modulation (PM) noise. However, in high-performance applications requiring ultra-low PM noise, AM noise becomes a critical limiting factor due to AM-to-PM conversion mechanisms. Because AM noise specifications are rarely, if ever, provided for commercial microwave components, this study characterizes the residual AM and PM noise of several 10 GHz amplifiers. We present performance data for various Heterojunction Bipolar Transistor (HBT) amplifier designs and demonstrate the noise reduction achieved through power-summed configurations. For example, a discrete-die GaAs HBT amplifier achieves residual PM noise below $-160$ dBrad$^2$/Hz and AM noise of approximately $-155$ dB/Hz at a 1 kHz offset in a quad power-summed configuration. A notable finding is that the majority of amplifier designs surveyed in this study exhibit higher AM noise relative to their PM noise, with only a few exceptions. The impact of bias supply quality on residual noise is also quantified, demonstrating that the choice of voltage regulator is a first-order design parameter below 1 kHz offset.
Read the full abstract ▾
General Relativity and Quantum Cosmology
Semiclassical multi-mode squeezed states in harmonic cosmology
Bekir Baytaş
Harmonic cosmology is an algebraic framework based on the $\mathfrak{su}(1,1)$ Lie
algebra that underlies the effective dynamics of loop
quantum cosmology and group field theory. Focusing on the multimode bosonic realization of $\mathfrak{su}(1,1)$, we construct multimode squeezed states in harmonic cosmology and analyze their semiclassical properties by computing the relative uncertainties of the volume operator at the kinematical level. In contrast to single-mode squeezed
vacua, which invariably exhibit large
quantum fluctuations, we find that the relative uncertainty of the volume can be made arbitrarily small when the number of modes is large, even without displacement. Our results demonstrate that a kinematical squeezed
vacuum state with a large number of independent modes can behave semiclassically providing insights into the
quantum nature of bouncing cosmologies and the emergence of a classical universe.
Read the full abstract ▾
Computation and Language
Hindsight-Guided Rationale Distillation for Rare Disease Diagnosis
Aarav Singh, Animesh Pathak and Navyansh Singh
We study hindsight-guided
distillation for rare disease diagnosis on ZebraMap: a 1.5B student is fine-tuned on chain-of-thought traces from a 8B teacher that observes the ground-truth diagnosis during generation. Absolute accuracy remains low for all models - the task is hard at this scale - but within this ceiling a filtered variant (StudentF) achieves a small, statistically significant accuracy advantage over the teacher (p < 0.001), concentrated in better-represented diseases. The unfiltered student does not significantly outperform the teacher (p = 0.129), establishing that contamination filtering - not hindsight
distillation alone - drives the gain. The gap traces to an artifact we term GT
hallucination. Label-visible generation causes the teacher to embed "ground truth is X" phrases in its
reasoning chain; SFT copies the pattern. At inference, the unfiltered student reproduces the phrase in 33.9% of cases, with severe accuracy degradation when the hallucinated label is wrong. A regex filter removing these slots reduces contamination to near-zero, producing the observed gain - though the effect remains small. We precisely quantify this gain-cost tradeoff, document frequency-dependent knowledge transfer absent from the RL-trained teacher, and characterize a calibration gap that SFT does not close - identifying both as directions for future work.
Read the full abstract ▾
Differential Geometry
Twelve characterisations of Killing vector fields in Finsler geometry
Ioan Bucataru, Georgeta Cretu and Luciana Daniliuc
It is well known that infinitesimal isometries preserve many of the canonical
geometric structures of a Riemannian or Finslerian
manifold. In this paper, we identify those
geometric structures of a Finslerian
manifold that are preserved by an infinitesimal isometry (a Killing vector field) and whose preservation characterises such fields. Using the Frölicher-Nijenhuis formalism as a unified
geometric framework, we establish twelve equivalent characterisations of the Killing property in the Finslerian setting, utilizing the Finsler norm, the sphere bundle, contact and
symplectic structures, Reeb and
Hamiltonian vector fields, first integrals, the homogenized almost complex structure, and the Sasaki metrics. Furthermore, we derive the local expressions of these characterisations, yielding several equivalent forms of the Killing equations, including scalar, one-form and tensorial formulations. In our approach, none of these formulations is regarded as primary; each reflects a different
geometric aspect of the Killing condition.
Read the full abstract ▾
Multiagent Systems
FinNextAssist: Towards Professional Financial Deep Research Assistant
Xiangyu Li, Fengbin Zhu, Xuan Yao et al. (+9 more)
Deep Research (DR)
agents have demonstrated strong capabilities in complex, research-oriented tasks through autonomous planning, iterative
retrieval, multi-step
reasoning, and structured reporting. However, adapting DR
agents to finance introduces unique challenges: financial analysis demands the joint completion of heterogeneous sub-tasks spanning diverse data types, tools, and analytical workflows. We identify three key requirements for a professional financial DR
agent: integration of authoritative, heterogeneous financial data sources; specialized analytical tools and skills; and dedicated sub-
agents for domain-specific sub-tasks. Building on these principles, we propose FinNextAssist, an end-to-end deep research framework designed for professional financial analysis. FinNextAssist decomposes the research process into four stages: Task Planner, Evidence Compiler,
Reasoning Engine, and Report Assembler, and introduces two novel lightweight sub-
agents: TabAgent, for cross-
market financial table understanding, and HeteroAgent, for cross-modality heterogeneous financial data interpretation. Extensive experiments on FinDeepResearch, the Finance
Agent Benchmark, and FinTMMBench-Web show that FinNextAssist substantially outperforms both strong proprietary and open-source DR
agents, with ablation studies confirming the contribution of each component across diverse
markets and
languages.
Read the full abstract ▾
All authors Xiangyu Li, Fengbin Zhu, Xuan Yao, Siyu Liu, Xiaoluan Liu, Chao Wang, Huanbo Luan, Xiaofen Xing, Xiangmin Xu, Ke-Wei Huang, Richang Hong, Tat-Seng Chua
Systems and Control
SIS Epidemic Containment under Game-theoretic Rational Learning
Urmee Maitra, Keith Paarporn and Ceyhun Eksin
We consider a susceptible-infected-susceptible (SIS) epidemiological model in which individuals dynamically decide whether to follow social distancing or not. Driven by game-theoretic payoffs, their actions influence the spread of infection. Much of the existing work in this area considers individuals that are imitative learners, leading to the well-studied replicator equation. In this paper, we investigate how the dynamical outcomes may fundamentally shift when individuals are instead equipped with logit learning rules, i.e., when individuals possess bounded rationality. We analyze the local asymptotic stability of
equilibria that exist in different parameter regimes of the coupled dynamical system. We show that in regimes where the disease-free
equilibrium is unstable under imitative learning and sufficiently high-rationality logit learning, sufficiently low rationality can avoid such an undesirable outcome by stabilizing the disease-free
equilibrium. We also identify parameter regimes in which an unstable endemic
equilibrium stabilizes when rationality falls below a threshold. We numerically illustrate the convergence of infection state and response trajectories to a stable disease-free
equilibrium at a lower degree of rationality, whereas the same trajectories otherwise converge to a stable endemic
equilibrium as the rationality level rises. We also compare a specific case in which trajectories under replicator dynamics fail to converge, and illustrate convergence when rationality is below an analytically derived threshold.
Read the full abstract ▾
Computational Engineering
Domain-Adaptive Data Assimilation for Global AI Weather Forecasting
Minseok Seo, Noah Brenowitz, Doyi Kim et al. (+2 more)
AI weather
forecasting models are commonly trained on the ERA5 reanalysis, which is unavailable in real time. Operational deployment therefore relies on initial conditions produced by numerical or AI analysis systems that differ from those encountered during training. This mismatch can degrade forecast skill, while retraining for every analysis system is costly. Here, we present Domain-Adaptive Data Assimilation (DADA), an observation-guided framework that adapts external analyses to pretrained AI weather models. Starting from a background state, DADA optimizes only an initial-state perturbation while keeping the forecast model frozen. The perturbed state is propagated through the model, and its short-range trajectory is constrained by real-world observations through a learned observation operator. The resulting initial condition is shaped jointly by observational constraints and the dynamics learned by the target model. We evaluate DADA across five global AI weather models using backgrounds from the Global Forecast System and the AI-based HealDA. Across deterministic and probabilistic forecasts, DADA substantially reduces short-range skill loss caused by changes in the initial-condition source. More broadly, DADA turns observations into a common interface between independently developed analysis and
forecasting systems, enabling pretrained AI weather models to accommodate evolving operational initial conditions without reconstructing ERA5 or retraining the forecast model.
Read the full abstract ▾
All authors Minseok Seo, Noah Brenowitz, Doyi Kim, Hyesook Lee, Changick Kim
Materials Science
Coercivity-Aware Machine Learning Discovery of Rare-Earth-Free Soft Magnetic Alloys with First-Principles Magnetic Benchmarks
Avik Mahata and David Priefer
Coercivity-aware machine learning is developed to screen rare-earth-free soft
magnetic alloys using Curie temperature and coercivity as coupled design targets. A compiled experimental
magnetic-material
dataset is evaluated using composition-grouped partitioning to avoid overlap of identical compositions between training and testing, and elemental fractions and composition-weighted elemental descriptors are examined for prediction of the two
magnetic properties. Residual-based prediction intervals and distance-to-training filters are then incorporated to screen Fe-Co-Ni-Mn-Al-Si compositions. Composition-grouped evaluation shows lower predictive accuracy than conventional random partitioning, demonstrating the importance of evaluating transfer to previously unseen compositions. Combining weighted elemental descriptors with elemental fractions improves Curie-temperature prediction, whereas the additional descriptors do not improve coercivity prediction, consistent with the strong dependence of coercivity on processing and microstructure. Uncertainty-aware screening substantially narrows the candidate space and identifies Fe-Co-rich compositions for further evaluation. First-principles calculations independently show strong
magnetic polarization and magnetovolume effects in representative Fe-Co structures, while finite-temperature atomistic
spin simulations place the Fe-Co
benchmark systems on high
magnetic-ordering-temperature scales. The combined approach provides a computational framework for prioritizing rare-earth-free soft-
magnetic chemistries while distinguishing composition-level screening from candidate-specific physical validation.
Read the full abstract ▾
Computational Engineering
BRIDGE: Bridging Routine Inputs and Digital modelling for Geotechnical Engineering
Roshan Philip Saji, Waleed El-Sekelly, Tarek Abdoun et al. (+1 more)
The mechanical properties needed for geotechnical design require laboratory tests and are thus available for far fewer field samples than routinely measured basic (index) properties which are available for nearly every sample. We term this difference as the characterization gap. Purely data-driven models do not explicitly enforce physical laws and may be unreliable beyond their training domain, while direct constitutive calibration preserves physical consistency but requires advanced laboratory data and expert judgment. We therefore propose BRIDGE (Bridging Routine Inputs and Digital modelling for Geotechnical Engineering) a modular framework that predicts mechanical properties from routinely available basic properties. BRIDGE screens the
dataset for outliers and non-physical responses, bins the retained samples into
Cells, and selects a small calibration
dataset from each
Cell, while the remaining samples are used for validation. A mechanical model is then calibrated for each calibration sample, and the converged calibrations collectively define the mechanical surrogate of the
Cell together with its parameter ranges and the corresponding predictive envelope in the mechanical property space. A
Cell is validated when most validation samples fall within this envelope, whereupon the surrogate can predict the mechanical property ranges of new samples whose basic properties lie within the
Cell. In this work, we adopt a finite element implementation of the Extended Cam-Clay (ECC) constitutive model as the mechanical model, whose four parameters are calibrated in a multi-stage process. Applied to the Tokyo-CLAY/14/67760 database, BRIDGE predicts the mechanical properties of an entire
Cell from calibrations of just 10 to 20% of its samples. Finally, since the mechanical model, the binning criteria, and the sampling method are components of BRIDGE, the framework is easily extensible.
Read the full abstract ▾
All authors Roshan Philip Saji, Waleed El-Sekelly, Tarek Abdoun, Mostafa E. Mobasher
Disordered Systems and Neural Networks
Cup and Cap Topological Neural Network
Marta Niedostatek, Ferran Hernandez Caralt, Runyue Wang et al. (+4 more)
Topological Deep Learning (TDL) is designed to learn features associated with higher-dimensional simplices including not only nodes but also edges, triangles and so on. However, most TDL approaches, being based on boundary operators and Hodge
Laplacians, have the limitation that features and signals cannot be lifted or lowered across more than one dimension per layer. To overcome this limitation, in this work, we propose the adoption of the cup and cap products. Specifically, we formulate the Cup and Cap Topological Neural Network (CCNN), a topological deep learning architecture designed to learn node-based variables (or 0-cochains) by taking into account their many-body interactions (e.g. triangles) present in the data. We validate CCNN by assessing its performance on the TopoBench
datasets, revealing its competitiveness with respect to other simplicial complex neural networks.
Read the full abstract ▾
All authors Marta Niedostatek, Ferran Hernandez Caralt, Runyue Wang, Federica Baccini, Lorenzo Giambagli, Pietro Lió, Ginestra Bianconi
Quantum Physics
Self-testing ideal quantum measurements
Debashis Saha, Zhen-Peng Xu and Adán Cabello
A fundamental prediction of
quantum mechanics is that observables can be measured through ideal measurements, that is, measurements that give the same outcome when repeated and do not disturb any observable that is jointly measurable. In terms of
quantum instruments, such an ideal measurement is described by projective effects together with the associated Luders state-update rule. Here, we show that
quantum mechanics allows for self-testing any ideal measurement. Using solely the output statistics of bipartite Bell tests in which a non-demolition measurement is sometimes added, it is possible to certify in a device-independent manner whether the added measurement is ideal.
Read the full abstract ▾
Computer Vision
Geometry-Aligned Semantic Matching for Cross-Modal Planar Image Registration
Zhiwei Wang, Defeng He, Yuxing Li et al. (+2 more)
Cross-modal image matching establishes stable and accurate
geometric correspondences across modalities for planar registration. Existing semantic representations provide cross-modal consistency, but semantic similarity does not necessarily imply
geometric correspondence. Meanwhile, fine-grained CNN features provide accurate local details but lack global cross-modal semantic guidance for stable refinement. To address these issues, we propose CDPM, which first establishes geometrically consistent semantic representations and then preserves their dominant role in correspondence estimation during fine-grained localization. Specifically, we progressively adapt DINOv3 using geometrically consistent cross-modal patch pairs, enabling feature similarity to better reflect true cross-modal spatial correspondences. We then construct a DINO-Centric Feature Pyramid, where multi-scale DINO representations maintain stable cross-modal correspondences, while a lightweight CNN branch provides auxiliary structural details for precise local refinement. Extensive experiments on three cross-modal
datasets demonstrate the superior performance of CDPM. On VIS-IR, compared with the dense matcher RoMa, CDPM improves AUC@3/5/10/20 by 7.36, 13.40, 13.75, and 10.42 percentage points, respectively, and reduces mACE from 5.83 to 2.78 pixels. It also outperforms RoMa v2 across all metrics while requiring 45.6% fewer FLOPs. The online demo and
dataset are available, and the code will be released on our project page at https://warren-wzw.github.io/CDPM/.
Read the full abstract ▾
All authors Zhiwei Wang, Defeng He, Yuxing Li, Meilu Zhu, Edmund Y. Lam
Cryptography and Security
LiBRA: Detection-Aware Image Watermark Removal via Bidirectional Latent Optimization
Saibo Ye, Huajie Chen, Xin Guo et al. (+5 more)
Digital watermarking supports source attribution for AI-generated images, but its reliability depends on resistance to removal attacks. Some attacks attempt to remove watermarks by forcing the decoded watermark to differ from the original. However, this can produce an inverted watermark that remains detectable, causing removal to fail, while further attempts to alter the watermark may unnecessarily degrade image quality. To address these limitations, we present LiBRA (Latent In-band Bidirectional Removal Attack), which aims to make watermarks undetectable while preserving image quality. Instead of continually pushing the watermark toward inversion, LiBRA adjusts the image to conceal the watermark without encouraging further changes that could degrade image quality. Some attacks keep pushing decoded bits away from the original watermark, even when further changes preserve detectability and damage image quality. With access to the watermark key and decoder, LiBRA makes bounded changes in a public autoencoder's latent space. Unlike inversion-driven objectives that cannot correct excessive inversion, LiBRA guides average decoding confidence toward random guessing from either direction. This helps avoid an inverted but detectable watermark. Leaving individual bits flexible allows image-quality constraints to favor less damaging changes, while an optional frequency-guided mask limits their location. We verify removal using an exact two-sided binomial test rather than assuming the confidence target guarantees success.
Read the full abstract ▾
All authors Saibo Ye, Huajie Chen, Xin Guo, Le Yang, Chi Liu, Xiangyu Hu, Jingjing Guo, Tianqing Zhu
Machine Learning
Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds
Gabor Paczolay, Matteo Papini, Alberto Maria Metelli et al. (+2 more)
Several variance-reduced versions of REINFORCE based on importance sampling achieve an improved $O(\epsilon^{-3})$ sample complexity to find an $\epsilon$-stationary point, under an unrealistic assumption on the variance of the importance weights. In this paper, we propose the \algo (Defensive Policy Gradient)
algorithm, based on defensive importance sampling, which achieves the same rate without any assumption on the variance of ordinary importance weights. We also establish lower bounds in a generalized black-box policy-optimization model that hides states and actions and permits parameter-dependent rewards. In this model, the optimal rates are $\Theta(\epsilon^{-4})$ with bounded-variance one-policy feedback and $\Theta(\epsilon^{-3})$ with mean-square-smooth coupled two-policy feedback. Under standard policy-regularity conditions, REINFORCE and \algo realize the corresponding oracle conditions and attain the $O(\epsilon^{-4})$ and $O(\epsilon^{-3})$ upper bounds, respectively. Although the lower bounds do not apply directly to the classical MDP interaction model in which these
algorithms operate, this correspondence provides oracle-level evidence that the faster rate of \algo is optimal and genuinely separated from that of vanilla policy gradient.
Read the full abstract ▾
All authors Gabor Paczolay, Matteo Papini, Alberto Maria Metelli, Istvan Harmati, Marcello Restelli
Astrophysics of Galaxies
A two-stage settling of the Milky Way disk revealed by precise ChronoGal ages
David Mirabal, Carme Gallart, Tomás Ruiz-Lara et al. (+9 more)
The spatial distribution of
Galactic disk
stars of different ages and
metallicities encodes its dynamical
evolution history and formation pathways. The lack of precise ages for large, representative samples of
stars has traditionally been a drawback for securely differentiating among formation scenarios proposed to explain the vertical properties of the Milky Way disk (e.g. upside-down formation versus secular dynamical heating). In this Letter, we use CMD-fitting of
Gaia data to derive very precise age-
metallicity distributions for complete
stellar samples in the
solar neighborhood. We identify quasi-mono-age-
metallicity stellar populations with an age resolution better than $5 \%$ ($\sim 0.5$ Gyr at old age), and study their absolute vertical density profile. We observe a clear correlation between scale height and
stellar age, with younger populations consistently more confined to the plane. We find evidence that the scale height decrease rate changed drastically $10.85^{+0.13}_{-0.12}$ Gyr ago: at older ages, the
evolution is very steep, with $h_Z$ dropping from over $1.26$ kpc to $\sim 0.5$ kpc in merely $2$ Gyr; subsequently, the thinning is very gradual, with $h_Z \sim 0.1$ kpc for the youngest populations. The extremely rapid early vertical settling of the disk points to a fundamentally upside-down formation process at early times, when the thick disk component is basically formed. We thus strongly support an evolutionary scenario defined by two distinct temporal phases: an initial, highly turbulent epoch that gave birth to the thick disk, and a subsequent more quiescent period of secular
evolution and gradual gas settling which formed the thin disk.
Read the full abstract ▾
All authors David Mirabal, Carme Gallart, Tomás Ruiz-Lara, Emma Fernández-Alvar, Anna B. A. Queiroz, Guillem Aznar-Menargues, Chris B. A. Brook, Santi Cassisi, Yllari K. González-Koda, Julio F. Navarro, Pedro A. Palicio, Francisco Surot
Computation and Language
Predicting Steering Vectors and Adapter Weights for Few-Shot Author-Style Transfer
Leonard Popp, Danni Liu, Supriti Sinhamahapatra et al. (+1 more)
Adapting large
language models to an individual author's style from a few examples is challenging, and scientific writing sharpens the difficulty: formal conventions leave little surface variation, and authors write about their own topics, so extracted “style” easily entangles with content. We study style-conditioned abstract generation from a few example abstracts per author and propose three methods: (1) contrastive activation steering, (2) a network that predicts steering vectors, and (3) a hypernetwork that predicts LoRA adapters. We find a consistent trade-off between style imitation and output quality: fine-tuning buys most of the available style signal but forfeits fluency, while the hypernetwork achieves the best trade-off on both seen and unseen authors. Our steering operates at author level, contrasting an author's abstracts against style-neutral generations for the same content. This holds topic fixed, removes the need for a predefined style inventory, and outperforms inventory-based steering. % [EDIT 1a] softened "no single optimal axis" claim Moreover, our analyses demonstrate that manually extracted and predicted steering vectors are near-orthogonal yet score comparably, indicating that style conditioning here can admit at least two unrelated directions rather than requiring one particular axis.
Read the full abstract ▾
All authors Leonard Popp, Danni Liu, Supriti Sinhamahapatra, Jan Niehues
Graphics
Budgeted-GS: Real-Time Large-Scale Gaussian Splatting via Factoring LOD
Haipeng Wang
3D Gaussian Splatting achieves excellent visual quality with real-time rendering, but at the scale of entire cities it does not fit: a trained model carries millions of primitives and gigabytes of
memory, and real-time rendering at high quality on a consumer GPU remains out of reach. We introduce Budgeted-GS, a post-hoc method that turns any trained 3DGS model into a factoring tree, a multi-resolution hierarchy of moment-matched aggregates. After a construction pass of a few seconds, a single quality parameter selects, for each view, the level of detail that fits the
memory of the target device, so the same city-scale model serves GPUs with widely different
memory capacities. When a new scene is to be trained, the same theory applies: instead of growing a full-sized model and compressing it afterwards, budget-centered training first measures how many primitives the scene needs and then trains the model directly at that size, avoiding the wasted effort of optimizing primitives that are later discarded. Both methods are grounded in a measurable capacity floor, a budget-error law derived from optimal transport in phase space; selection rules certified by recent covering
theorems decide which primitives are redundant. The floor answers how many primitives a scene actually needs and how many can safely be given up. We validate the floor on 13 public scenes under a preregistered protocol, and exercise both methods from object scenes to an official city capture, rendering it at native 1920x1080, full SH, in real time on one consumer GPU.
Read the full abstract ▾
Quantum Physics
Landscape-Dependent Performance of Photonic Quantum Solvers in QUBO Feature Selection for Financial Risk Detection
Nirvik Sahoo and Paul Robert Griffin
Feature selection for imbalanced classification tasks such as credit card fraud and consumer default detection requires balancing predictive relevance, inter-feature redundancy, and computational feasibility. We
benchmark three computing paradigms, classical branch-and-bound optimization (Gurobi), photonic
entropy computing (QCI Dirac-3), and simulated photonic
boson sampling (Piquasso), across thirteen feature-selection methods on two
datasets: ULB Credit Card Fraud (30 features) and AmEx consumer default (159 features). Each method is routed to the solver matched to its mathematical structure. On ULB, Dirac-3 MI-Spearman matches the all-features model using 13 of 30 features (mean F1 0.873 +/- 0.023 over five runs, best run 0.896), and Piquasso is the best method at k=5. On AmEx, performance rises steadily with the feature budget and every paradigm approaches F1 = 0.80 only near the full feature set. Most differences between Gurobi and Dirac-3 on identical methods fall within run-to-run variation; the large gaps occur where the certified optimum generalizes poorly, most sharply for distance correlation on AmEx at k=25 (Gurobi F1 = 0.422 vs. a Dirac-3 mean of 0.746). At matched budgets, F1 varies about ten times more across methods on ULB than on AmEx, which we trace to how concentrated the predictive signal is in each feature space.
Read the full abstract ▾
Quantitative Methods
Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
Hantao Lou, Jianqing Zheng, Can Yue et al. (+21 more)
Discovering broadly neutralizing antibodies (bnAbs) from human natural immune repertoires remains a fundamental challenge in immunology, hindered by: the extreme rarity of bnAb, incomplete understanding of their cellular origins across pathogens, and the inability of existing computational tools to generalize across emerging viral threats. Here we present ImmuneAgent, a closed-loop AI system that integrates
multimodal reasoning with continual meta-learning and wet-lab feedback to overcome these barriers. Applied to screen the natural BCR repertoires from vaccinated or infected cohorts, the system achieves a ~55% neutralization antibody discovery rate (60 of 110 cloned candidates) and a ~11% bnAb yield (12 of 110), substantially outperforming a state-of-the-art sequence-based neutralization predictor or cofolding models evaluated at the same cloning budget. Five ImmuneAgent-discovered antibodies conferred 100% in vivo protection against lethal influenza challenge, comparable to the clinical-stage therapeutic MEDI8852. The system recovered the cellular and structural determinants of bnAb activity and identified FCRL5+CD27+ atypical
memory B
cells as a conserved bnAb reservoir and hydrophobic interface enrichment as a cross-viral structural signature, which generalized to unseen antigens, discovering human metapneumovirus (hMPV) cross-neutralizing and human papillomavirus (HPV)-neutralizing antibodies without antigen-specific sorting. These results validate that ImmuneAgent is a generalizable framework for rapid therapeutic antibody discovery against emerging viral threats.
Read the full abstract ▾
All authors Hantao Lou, Jianqing Zheng, Can Yue, Meihan Zhang, Yuanchao Bao, Yu Chen, Mengting Huang, Yupeng Yang, Qianyu Pan, Nana Fu, Yansong Shi, Hongli Li, Yangyang Chai, Ruyi Chen, Wansheng Li, Zhu Liang, Rongmei Yao, Yuanhan Mo, Lei Wang, Chunmei Wang, Yun Quan, Qiong Zhang, Xiangxi Wang, Xuetao Cao
Combinatorics
The Genus of Bipartite Kneser Graphs
Austin Ulrigg and Alexander Metzger
We determine the orientable genus of an infinite family of bipartite Kneser graphs. The graph $H(h,2)$ has two copies of the two-element subsets of $[h]$, with opposite-class vertices adjacent when the corresponding subsets are disjoint. For every
prime $h>3$ with $h\equiv3\pmod8$, we prove $$ \gamma(H(h,2))=1-\frac{h(h-1)}{2} +\frac{h(h-1)(h-2)(h-3)}{16}. $$ Euler's formula gives this lower bound, with equality for a quadrangulation. We construct a vertex-transitive orientable quadrangulation using an odd-order affine group that acts simply transitively on the two-element subsets. This gives an infinite family satisfying Pisanski's
conjecture on quadrilateral
embeddings of regular bipartite graphs.
Read the full abstract ▾
Systems and Control
A New Index for Quantifying Stability Adaptation Effort in Power Electronics-Dominated Power Systems Based on Frequency-Domain Impedance Identification
Luis A. Garcia-Reyes, Macarena Martin-Almenta, Edgar Nuño-Martínez et al. (+4 more)
The increasing use of converter based resources has made stability assessment in modern power systems more challenging, particularly because many devices are available only as black box models. Conventional
eigenvalue based methods require full dynamic representations, while frequency domain impedance techniques based on the Generalized Nyquist Criterion (GNC) provide stability insight but do not quantify how much a converter must be retuned to restore stability. This paper introduces the Adaptation Effort Index (AEI), a metric that measures the sensitivity of dominant modes to parameter variations and quantifies the effort required for a converter to reconfigure itself under unstable operating conditions. The AEI combines the Nyquist Stability Margin obtained from frequency domain screening with a modal sensitivity index derived from fitted state space models synthesized from black box impedance data. The method is applied to a system comprising a grid forming and a grid following converter. Results show that, although both units can be tuned to stabilize the system, the grid following converter requires significantly less adaptation effort. The AEI therefore provides a practical and model agnostic tool for identifying which converter should be reconfigured in converter dominated grids, offering valuable guidance for system operators and manufacturers.
Read the full abstract ▾
All authors Luis A. Garcia-Reyes, Macarena Martin-Almenta, Edgar Nuño-Martínez, Javier Renedo, Antonio Cordón-Rodríguez, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo