Computer Vision
Stop My Dancing! Understanding, Detecting and Attributing Motion-Aware Deepfake Videos
Fazhong Liu, Yan Meng, Tian Dong et al. (+2 more)
Pose-guided
diffusion models can now synthesize entire human figures in motion, spawning a new class of deepfakes: Motion Aware Deepfake (MAD) that have already reached hundreds of millions of viewers. To better understand this emerging threat, we construct the first MAD-specific
benchmark and measurement framework, containing over 1.5 million frames that mix 1,363 real and 30,122 synthetic
videos from six controllable generators, with realistic perturbations and open-world evaluation splits. Then, we dissect MAD and discover that, despite their global coherence, these
videos betray faint yet reliable cues: because the model relies on limited input frames for motion synthesis, it must predict and simulate coherent movement at motion boundaries, thereby producing high-frequency artifacts along with model-specific spectral fingerprints. Based on the observations obtained from analysis on
dataset, we propose MoDA, the first defense framework tailored to detect and attribute MAD
videos. MoDA couples spatial semantics with steganalysis-rich frequency features via cross-domain
alignment and multi-scale aggregation, achieving 94.8% in-distribution and 89.1% cross-
dataset detection accuracy gains of 10% to 25% over prior work and 91.5% model attribution accuracy. MoDA achieves 81.94% accuracy on 200 clips produced by two unseen commercial MAD platforms, indicating promising zero-shot transfer, and 78.13% detection accuracy on 1,200 unseen MAD
video clips (55k frames in total) collected from the open Internet. Under white-box, gray-box, and black-box adaptive attacks, MoDA maintains relatively stable detection and attribution performance while the accuracies of the baselines drop rapidly.
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All authors Fazhong Liu, Yan Meng, Tian Dong, Guoxing Chen, Haojin Zhu
Quantum Physics
Topological Superradiant Universality Classes in Cavity-Mediated Spin Chains
Yunhao Liu, Sheng Yang, Mingyuan Chen et al. (+1 more)
Superradiant phase transitions are fundamental phenomena in light-matter coupled systems. Here, we uncover a novel class of superradiant criticality that is topologically distinct from previously known superradiant transitions. Using cavity-mediated cluster-Ising chains as an illustrative example, we numerically demonstrate that the critical line separating the superradiant phase from the cavity topological phase hosts robust
symmetry-protected edge degeneracies, a feature absent in conventional superradiant transitions. Remarkably, although its local bulk critical properties are indistinguishable from those of the previously identified topologically trivial superradiant transition, the two criticalities cannot be smoothly connected without breaking the protecting
symmetries or passing through a multicritical point. We further show that such topological superradiant criticality is generic, also occurring in cavity-mediated $\mathbb{Z}_{n}^{}$ cluster-qudit chains, as supported by complementary
lattice simulations.
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All authors Yunhao Liu, Sheng Yang, Mingyuan Chen, Xue-Jia Yu
Soft Condensed Matter
Laser fragmentation in liquid - constructing a generic reaction map
Anton Plech, Yogesh Pokhrel, Meike Tack et al. (+2 more)
Excitation of absorbing colloids in
liquid by pulsed
laser irradiation creates highly non-
equilibrium states of matter that relax via defined pathways of thermal and non-thermal dissipation channels. The structural fingerprint of these channels can be probed by ultrafast in situ x-ray scattering methods and classified as a function of temporal delay of excitation excitation density, or specifically
laser fluence. We study the example of photo-excitation of a gold colloid by picosecond
laser pulses at the interband absorption band at 400 nm in water. By quantifying
lattice temperature, crystalline fraction, particle sizes and the structural response of the water around the nanoparticles a reaction map is constructed that allows to pinpoint heating, particle melting, water bubble formation and finally particle fragmentation to form nanoclusters of predominant sizes of < 3 nm for applications in theranostics, photonics or catalysis.
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All authors Anton Plech, Yogesh Pokhrel, Meike Tack, Matteo Levantino, Sven Reichenberger
High Energy Physics (Theory)
Quantum Gravity in Asymptotically Flat Spacetimes from Unstable D-Branes
Roji Pius
We present a non-perturbative formulation of
quantum gravity in asymptotically flat spacetimes using Witten's cubic open string field theory (OSFT) on unstable D-branes. Given that the massless
spin-2 sector of the closed string spectrum inherently encodes the degrees of freedom of
quantum gravity, we establish this connection by mapping on-shell closed string excitations directly to gauge-invariant operators in OSFT, realizing a concrete flat-space analogue of the AdS/CFT correspondence. By evaluating the OSFT correlation functions of these operators perturbatively around the tachyon
vacuum, we systematically reproduce the all-orders genus expansion of the closed string S-
matrix for the associated states. This demonstrates that closed string theory-and thus
quantum gravity-emerges dynamically from the open string tachyon
vacuum, establishing Witten's OSFT as a fully calculable, non-perturbative framework for flat-space
quantum gravity.
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Networking
Energy-Aware Receiver-initiated MAC Protocol for Energy Harvesting IoT
Sohail Sarang, Praveen Kumar Donta and Dejan Vukobratović
Energy harvesting (EH) technology has emerged as a key approach for extending the operational autonomy of Internet of Things (IoT) devices. In this context, medium access control (MAC) protocols for EH-IoT make use of the harvested energy efficiently to enhance the application performance. However, many existing MAC protocols rely on stored energy the battery in devising energy-aware strategies, which can interrupt the device operation and hinder the execution of ongoing tasks in dynamic and low EH conditions. To address this, this paper presents an energy-aware receiver-initiated MAC protocol for EH-IoT devices, called EARI-MAC, that adjusts the duty cycle of the receiver device based on stored energy and current harvesting power using the mathematical formulation. This approach supports sustained receiver operation under low EH conditions and improved application performance when harvested energy is abundant. The performance of the EARI-MAC has been evaluated in simulation using the real
solar data gathered in Novi Sad, for three consecutive days. The simulation results demonstrate that the EARI-MAC utilizes the available energy resources efficiently and improves the packet delivery ratio and network throughput by more than 10%, reduces the receiver energy consumption by 5.6% and 8.2% and energy consumption per bit by 9.6% and 13.9% compared with QPPD-MAC and energy-unaware MAC, respectively.
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Optimization and Control
On error bounds and noncritical Lagrange multipliers
Matúš Benko and Patrick Mehlitz
Critical Lagrange multipliers are known to be responsible for the potentially slow convergence of Newton-type methods, and noncriticality of a given multiplier has been tied to an error bound which estimates the distance of a primal-dual pair associated with a perturbed problem to the primal solution and the multiplier set of the unperturbed one, and is decisive for a local convergence analysis. We investigate this relation in an abstract setting where merely a set-valued mapping assigning primal-dual pairs to a parameter is available, and characterize the error bound of interest in terms of noncriticality of the underlying multiplier and two calmness-type properties of the associated multiplier mapping, one of which is a novel weak inner calmness in the fuzzy sense. No structural assumptions on the underlying problem are needed for that, and the two calmness-type conditions are not only sufficient but also necessary. Afterwards, we specify these findings for composite optimization problems. Whenever the subdifferential of the outer function is a polyhedral mapping, both conditions hold automatically, which explains the equivalence of noncriticality and the error bound observed in the polyhedral setting. Whenever the outer function is $C^2$-decomposable, they are secured by calmness of a restricted multiplier mapping together with a closedness condition, which recovers and extends the known characterization for $C^2$-cone reducible constraint systems.
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Cryptography and Security
A Zero-Knowledge Signature Framework for Efficient Post-Quantum Message Authentication in Cooperative Automated Driving
Takahito Yoshizawa, Aysajan Abidin, Edoardo Pena-Gonzalez et al. (+1 more)
Connected and Automated Vehicles (CAV) rely on authenticated Vehicle-to-Everything (V2X) communications to exchange safety-critical information among vehicles and roadside infrastructure. As the automotive industry transitions toward post-
quantum cryptography (PQC), the significantly larger public keys and signatures of standardized PQC digital signature
algorithms introduce substantial communication overhead, which challenges the scalability of certificate-based V2X authentication, particularly for high-frequency cooperative awareness messages (CAM). This paper presents ZKS-PQC, a zero-knowledge signature framework that enables communication-efficient post-
quantum message authentication for cooperative V2X systems. Instead of transmitting complete post-
quantum public keys and signatures, the proposed framework replaces this authentication material with a compact Zero-Knowledge Proof (ZKP) while preserving compatibility with existing certificate-based trust architectures. This approach supports incremental migration and backward compatibility with the legacy ECDSA. The framework is implemented using the Open
Quantum Safe (liboqs) and ZKP (Bulletproofs) libraries, and we evaluated the performance of standardized NIST PQC signature
algorithms and additional candidate
algorithms. Experimental validation on both a Linux platform and a commercial On-Board Unit (OBU) demonstrates substantial reductions in message size exceeding 95% for all
algorithms, while limiting additional processing overhead by staying within the order of milliseconds at both sender and receiver in many
algorithms, consistent with the latency requirements of real-time V2X operation. By decoupling communication overhead from the size characteristics of post-
quantum signature
algorithms, ZKS-PQC offers a practical migration strategy for scalable,
quantum-resilient message authentication in future CAV systems.
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All authors Takahito Yoshizawa, Aysajan Abidin, Edoardo Pena-Gonzalez, Bart Preneel
Computer Vision
Beyond Resolution: Object-to-Image Ratio Mismatch in Instance Retrieval
Boaz Meivar, Ofir Kedem, Amit Edenzon et al. (+2 more)
Visual instance
retrieval often fails when the same object appears at different apparent sizes in the query and gallery. We show that the dominant cause is usually not resolution loss but object-to-image (O2I) ratio mismatch: the object occupies different fractions of the two images. On a controlled
benchmark of 3,021 Objaverse objects rendered at five camera distances, more than 80% of the cross-distance degradation is attributable to O2I mismatch rather than resolution for 9 of 12 pretrained backbones; multi-scale architectures cut the resolution-only effect to single digits yet remain equally susceptible. The failure is also asymmetric: tight queries retrieve more reliably against wide gallery images than the reverse. Guided by this analysis, query-side scale augmentation and an OWLv2 crop reranker reach state of the art on ILIAS 100M (29.2 mAP@1000 before reranking, 42.0 after) without training or modifying the precomputed gallery index, and a LoRA fine-tune matches the query-side gains at a single forward pass, showing that O2I robustness is learnable.
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All authors Boaz Meivar, Ofir Kedem, Amit Edenzon, Gal Chechik, Shai Avidan
Cryptography and Security
MARC: Multi-Bit Watermarking for Autoregressive Audio Generation against Codec Attacks
Liaoran Xu, Weizhi Liu and Zhaoxia Yin
Generated audio is now used in a range of applications, creating a need to verify its origin after distribution and signal processing. This task is particularly challenging for autoregressive audio generation because codec processing can alter the
token sequence recovered from the waveform. Such changes reduce the reliability of watermark detection and payload decoding. Existing methods construct
token groups using either intrinsic
token representations or substitution patterns caused by transformations. As a result, intrinsic
token relationships and codec induced substitutions are modeled separately. In addition, most methods support only zero bit detection. They can determine whether a watermark is present but cannot distinguish individual generated outputs. We propose MARC, a multi-bit generative watermarking method for autoregressive audio generation. MARC integrates intrinsic
token representations with confusion patterns obtained through retokenization and multiple codecs, forming a codec-aware
token-cluster space. Within this space, payload-driven cluster scheduling is used to embed a multi-bit watermark, while detection and payload decoding are performed on retokenized observations. Experiments on
speech, dialogue, and music generation show that MARC achieves an average of 97.3% bit extraction accuracy on unmodified watermarked audio and the watermark can still be extracted under diverse codec attacks. MARC also demonstrates robustness to overwriting attacks.
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Number Theory
Discorrelation of the Möbius function with linear phases in short intervals
Javier Pliego and Mengdi Wang
We introduce a new approach to studying correlations between arithmetic functions and linear phases with respect to highly irrational frequencies over short intervals. As an application, we prove that \[ \Big|\sum_{x<n \leq x+H}\mu(n) e(n\alpha)\Big| \ll H(\log x)^{-1/3+\varepsilon } \] whenever $x^{0.58+\varepsilon }\leq H\leq x$. In the same range of $H$ we also show that \[ \Big|\sum_{x<n \leq x+H} \Lambda (n) e(n\alpha)\Big| \ll H(\log x)^{-A} \] for arbitrarily large $A>0$ unless there exists an integer $1\leq q\ll (\log x)^{O_A(1)}$ such that $\|q\alpha \| \ll x (\log x)^{O_A(1)}/H^2$. This breaks the $3/5$ barrier in
Theorem 1.5 of arXiv:1911.09076v2 and
Theorem 2 of [T. Zhan, "On the representation of large odd integer as a sum of three almost equal
primes," Acta Mathematica Sinica 7.3 (1991), 259-272]. Moreover, by our method, any improvement in large value estimates for character-twisted Dirichlet
polynomials on the critical range would lead to a corresponding improvement in the lower bound for $H$.
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Machine Learning (Statistics)
PSI-SINDy: Post-Selection Inference for Sparse Identification of Nonlinear Dynamics
Ashraful Islam, Shuichi Nishino, Tomohiro Shiraishi et al. (+1 more)
Sparse identification of nonlinear dynamics (SINDy) is a data-driven framework for discovering governing dynamics from time-series data by identifying a sparse subset of candidate dynamical terms from a prespecified library. In this work, we develop a statistical inference framework for quantifying the reliability of dynamical terms selected by SINDy through hypothesis tests and confidence intervals. A key difficulty is that using the same noisy trajectory for both selecting dynamical terms and assessing their statistical significance can introduce selection bias. Post-selection inference provides a principled framework for addressing such bias, and we propose PSI-SINDy, a post-selection inference method tailored to SINDy. Direct application of existing post-selection inference techniques is challenging because SINDy involves measurement error in the candidate terms and shared noise between the response and design. To address these challenges, PSI-SINDy uses data thinning to decompose a single observed trajectory into four mutually independent views with distinct roles in selection and inference. This construction enables inference for selected dynamical terms while accounting not only for selection bias but also for measurement-error and shared noise effects. We establish the theoretical validity of PSI-SINDy under stated conditions and evaluate its performance through numerical experiments on simulated and experimental dynamical-system data.
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All authors Ashraful Islam, Shuichi Nishino, Tomohiro Shiraishi, Ichiro Takeuchi
Differential Geometry
An optimal pinching theorem on compact minimal submanifolds in the Euclidean spheres via eigenvalues of fundamental matrices II
Huimin Liu and Ling Yang
Let $M^n\subset S^{n+m}$ be a compact minimal submanifold of the unit sphere, and let $\lambda_1\geq\cdots\geq\lambda_m\geq0$ be the
eigenvalues of its fundamental
matrix. We prove that $\sum\limits_{\alpha=1}^{\min\{n,m\}}\lambda_\alpha+\lambda_2\leq n$ forces $M$ to be a totally geodesic subsphere, a generalized Clifford
torus, or a Veronese
manifold. We first show that equality everywhere in the pinching condition forces the second fundamental form to be parallel. Next, by computing the
eigenvalues of the fundamental
matrices of irreducible symmetric $R$-spaces, we obtain a complete classification of the equality cases among minimal submanifolds with parallel second fundamental form.
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Superconductivity
From band reconstruction to Bogoliubov dispersion: How dz2-band enhances iron-based superconductivity
Jingming Yan, Shendong Su, Guihao Jia et al. (+6 more)
Conventional understanding of iron-based superconductivity is usually established based on electronic bands at the
Fermi level, leaving deeper bands overlooked as potential factor. Here, we expand this view by demonstrating that a deep-lying dz2 band below the
Fermi level can be employed to enhance the superconducting paring strength. By using scanning tunneling microscope to mechanically change the
crystal lattice of an iron-based
superconductor, we observe an upward shift of the deep-lying dz2 band related feature, leading to hybridization with the primary superconducting bands at the
Fermi level. We visualize the novel evidence of this band hybridization and resulting enhanced superconducting gap through quasiparticle interference imaging, providing conclusive evidence that engineering such correlations between deep and pairing-active bands is a potent mechanism for amplifying the superconducting gap. This work refines the conventional view of pairing-relevant electronic structures and establishes band hybridization as an effective way to optimize superconducting materials.
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All authors Jingming Yan, Shendong Su, Guihao Jia, Yucong Peng, Xuanyu Long, Zheng Liu, Pei Ouyang, Qi-Kun Xue, Wei Li
Artificial Intelligence
BridgeGuard: Explicit Safety Drift for Diffusion-based Autonomous Driving
Zhenjun Qiu, Jianing Huang, Dongang Liu et al. (+6 more)
Diffusion-based driving planners capture diverse behaviors but can generate unsafe trajectories under distribution shift. We propose BridgeGuard, a safety-constrained
diffusion planning method that progressively strengthens a constraint term during denoising to drive intermediate trajectories toward a scene-dependent safety domain. Corrections operate in a low-dimensional curve space, promoting
geometric coherence. A learned module, DistanceFieldNet, predicts a time-dependent distance field from bird's-eye-view features. Value and spatial-gradient supervision at queries sampled beyond expert trajectories teaches this field about both safe and unsafe regions. The learned field supplies the constraint term through safety injection while the pretrained perception backbone and planner remain frozen. We further establish sufficient conditions for terminal safety in an idealized continuous-time bridge. On Bench2Drive, BridgeGuard improves driving score/success rate from 87.99/74.99% to 90.88/76.36% for BridgeDrive and from 80.79/58.18% to 90.46/74.09% for $\text{DiffusionDrive}^{\text{geo}}$, demonstrating cross-model generalization.
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All authors Zhenjun Qiu, Jianing Huang, Dongang Liu, Baiyu Du, Yixun Niu, Hao Yang, Xinyu Huang, Chuan Hu, Shu Liu
Software Engineering
Evaluating Local Language Model Agents for Reproducible Data Engineering: An Empirical Software Engineering Study of Mobility Workflows
Jorge García-Carrasco, Javier Sanchis, Alejandro Reina-Reina et al. (+2 more)
Context: Large
language model (LLM)
agents are increasingly used as software and data-engineering assistants, yet evidence about locally deployable open-weight
agents remains limited. Existing evaluations often emphasize textual responses or isolated code generation rather than the validity of complete engineering artifacts. Objectives: We evaluate whether local LLM
agents can produce correct and reproducible data-engineering artifacts, quantify the effect of a closed-loop workspace condition, and examine trade-offs in model scale, architecture, quantization, runtime, tool use, and failure. Methods: We introduce a
benchmark of fifteen mobility-workflow tasks covering data discovery, connectors, transport-feed processing, semantic enrichment, feature engineering, validation, visualization, and reporting. Deterministic checkers assess generated scripts, tables, structured files, figures, and reports. Ten local configurations are evaluated in one-shot and closed-loop conditions, with five repetitions per model, mode, and task, yielding 1,500 scored attempts on a consumer-grade GPU. Results: Among models larger than two billion parameters, the workspace condition increases pass rates by 26.7-52.0 percentage points over one-shot generation. The strongest configuration reaches 85.3% artifact-level success, and a quantized 9-billion-parameter model reaches 69.3% with an approximately 6.5 GB
memory footprint. Gains are largest when intermediate artifacts expose errors the
agent can inspect and repair. Conclusion: Local open-weight
agents can support a meaningful subset of software-intensive data-engineering work, but reliability depends on model capability, task verifiability, and deterministic validation. The
benchmark provides a reproducible method for evaluating complete
agent configurations before adoption in engineering workflows.
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All authors Jorge García-Carrasco, Javier Sanchis, Alejandro Reina-Reina, Alejandro Maté, Juan Trujillo
Data Structures and Algorithms
A QPTAS for Stochastic Scheduling of Bernoulli Jobs
Junho Hwang
We study the classical problem of scheduling jobs with random processing times on $m$ identical machines to minimize the expected sum of completion times, for Bernoulli jobs: job $j$ takes time $p_j$ with probability $q_j$ and time $0$ otherwise, and its outcome is revealed when it starts. The
benchmark is an optimal adaptive policy. We give a quasi-
polynomial-time approximation scheme for every number of machines. Previously, quasi-
polynomial time was known to give an $O(\log N)$-approximation, and approximation schemes were known only for a constant number of distinct sizes. The scheme rests on a simple observation: it suffices to round the times at which machines become free, rather than the times at which jobs start, to a grid whose width is proportional to the job size, and an optimal policy stretched by a factor close to one already respects such grids. We also show that every policy that fixes the order of the jobs in advance loses a factor $\Omega(\log N)$, already on two machines, so a constant factor requires adapting the order to the observed outcomes. Further results include a
polynomial-time adaptive rule with ratio $\min\{m,1+\sum_j q_j\}$, a simpler approximation scheme for a constant number of sizes, and #P-hardness of computing the optimal expected cost on two machines.
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Robotics
RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes
Bohan Zhou, Xingbei Chen, Emily Huang et al. (+17 more)
Embodied coding
agents can combine modular
robot skills with frozen end-to-end policies, yet effective composition requires anticipating which policy family will succeed in the current physical state. We present RoboAware, which builds on coding
agents' skill orchestration by learning only a state-conditioned responsibility coordinator from counterfactual outcomes. Inspired by the success of REPL, we propose the $P^5$ schema and formulate a hierarchical MDP based on it. $P^5$ organizes skills uniformly into five semantic stages, defining where responsibility can be compared. To address the lack of counterfactual branch outcomes in existing work, we introduce State-Locked Counterfactual Branching (SCB), which restores the same training state to generate and execute a code block from each admissible family, exposing outcomes that selected-branch experience leaves unobserved. Building on this, we propose Execution-Aware Learning (EAL), which combines Monte Carlo tree search with Q-learning to distill these outcomes into family-conditioned values. At deployment, the coordinator selects the policy family according to observable context, and the frozen coding
agent generates the next local code block. Comprehensive single-episode evaluations on 100 tasks show that RoboAware reaches a 77.0% overall success rate, with SOTA averages of 90.0% on RoboSuite, 73.8% on diverse LIBERO-Pro task clusters, and 90.0% on challenging RoboTwin bimanual tasks, outperforming existing code-as-policy and VLA-harness baselines.
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All authors Bohan Zhou, Xingbei Chen, Emily Huang, Weilin Ruan, Haojian Huang, Yehang Zhang, Zexi Li, Wenqian Li, Qize Yu, Zetian Song, Leyi Wu, Jinghao Li, Mingxuan Song, Xinrun Xu, Zongyang Qiu, Yangkai Wei, Tianyi Zhang, Kaiwen Zhou, Yinchuan Li, James Cheng
Computer Vision
Conditional Residual Prediction: Improving Autoregressive Video Diffusion without a Bidirectional Teacher
Bowen Zheng, Zhiguang Liu, Jiarong Ou et al. (+2 more)
Causal
video diffusion models generate
video autoregressively, which suits streaming, interactive, and long-
video generation. Under standard training, however, they often yield lower generation quality than bidirectional models of the same size. Many existing approaches address this gap by initializing from or distilling a pretrained bidirectional teacher. We instead train a causal model from an image-model initialization, with no bidirectional
video model at any stage. Because this path requires neither a large bidirectional teacher nor a complex
distillation pipeline, it is simpler and more scalable. On this path, we find that a causal model trained on ground-truth history becomes strongly dependent on it, so that at inference errors in its own generated history propagate forward. We hypothesize that much of this dependence is unnecessary, because the current input already determines much of what the history provides. We propose Conditional Residual Prediction (CRP), a simple recipe for reducing a model's reliance on a condition: the model first predicts the target without the condition, and the condition may only add a residual on top of this prediction. Applied to history, CRP makes the model predict each chunk from the present as far as it can and use the past only for what the present cannot supply. In controlled experiments, CRP nearly closes the 6.14-point gap to a bidirectional model trained under the same setup. Scaling this recipe, we train Optica, a 2B-parameter causal
video model that autoregressively generates 5-second 480p
videos and reaches 82.78 on VBench with only about 15M training
videos.
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All authors Bowen Zheng, Zhiguang Liu, Jiarong Ou, Rui Chen, Tianyang Hu
Metric Geometry
Doubling, Poincaré and Gromov-Hausdorff precompactness for tamed spaces
Mathias Braun, Chiara Rigoni, Christian Rose et al. (+1 more)
We establish local volume doubling and a local $L^2$-Poincaré inequality for metric measure spaces tamed by a measure in the extended Kato class that satisfy the bounded interpolation property. To this end, we construct a bi-Lipschitz time change to a metric measure space satisfying a uniform lower
Ricci curvature bound. As an application, we obtain pointed measured Gromov-Hausdorff precompactness for such spaces.
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All authors Mathias Braun, Chiara Rigoni, Christian Rose, David Tewodrose
Quantum Physics
Quantitative Rules for Parallel Calibration and Drift-Resilient Maintenance of Large-Scale Superconducting Quantum Processors
Zhihao Wu, Kechengqi Zhu, Shichuan Xue et al. (+13 more)
As superconducting
quantum processors scale toward hundreds of
qubits, manual calibration becomes increasingly costly and difficult to sustain. Here we address two system-level factors governing automated calibration, namely, crosstalk induced by parallel operations and temporal drift of control parameters. On a 66-
qubit superconducting processor, cross-
entropy benchmarking shows that single-
qubit calibration tolerates full-chip concurrency with minimal fidelity loss, whereas CZ calibration requires a minimum coupler-graph distance of four to avoid elevated control errors. Continuous monitoring of representative
qubit and coupler parameters further yields quantitative refresh rules that assign refresh priority by error-budget consumption. These results turn two conventionally heuristic choices, the concurrency density of calibration tasks and the refresh priority of each control parameter, into measurable operating rules. Implemented within an automated calibration framework, the rules support a six-
qubit Greenberger-Horne-Zeilinger state with a zero-noise-extrapolated fidelity of 89.64%, distance-dependent logical-error suppression in a single-cycle repetition-code
benchmark, and a reduction of the mean single-
qubit error from 0.023 to 0.011 on a 337-
qubit processor, providing an experimentally grounded reference for the automated calibration and maintenance of current and future large-scale superconducting
quantum systems.
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All authors Zhihao Wu, Kechengqi Zhu, Shichuan Xue, Xinfang Zhang, Zhaohua Yang, Zhida Zhang, Shun Hu, Cong Li, Dingdong Liu, Kangding Zhao, Peng Luo, Lingling Lao, Xiang Fu, Mingtang Deng, Junjie Wu, Kai Lu
Materials Science
Generation, Accumulation, and Utilization of Data in the Automatically Controlled End-Station of a Soft X-ray Beamline
Yasunobu Ando, Masashige Miyamamoto, Keita Hiromori et al. (+4 more)
AI is rapidly transforming materials exploration, yet reliable AI-assisted research depends on experimental data that retains their context, provenance, and machine-readable structure. High-throughput synchrotron measurements therefore require more than automated data acquisition: data generation, accumulation, and utilization must be connected within a continuous workflow. Here, we present a data-centric research framework that integrates the PIONEER system, the OMNES, and ML analysis. The PIONEER system automates sample handling,
vacuum transfer, and scanning XAS at the BL08U soft X-ray beamline of NanoTerasu, systematically generating large volumes of spatially resolved spectral data. Measurement and analysis files are stored in the cloud-based ARIM-mdx data system, while OMNES manages metadata describing samples, preparation procedures, measurement conditions, instrument settings, data locations, and analysis histories. Tabular and JavaScript Object Notation formats are used according to the metadata structure, with numerical values stored separately from units to facilitate machine processing. OMNES organizes these records based on causal relationships among experimental and analytical processes, linking samples, measurements, raw data, processed data, and analysis results through a relational data model and visualizing their relationships as a graph. This causality-based organization preserves experimental and analytical provenance and clarifies how each result was derived from the preceding processes and data. The accumulated spectra can subsequently be processed using ML methods, including dimensionality reduction and materials classification, and the resulting data can be registered back into OMNES. Previously reported ML analysis of BN XAS spectra is presented as an example of the analytical workflow that can be applied to data acquired at the beamline.
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All authors Yasunobu Ando, Masashige Miyamamoto, Keita Hiromori, Takeru Nakashima, Masafumi Horio, Masato Kotsugi, Iwao Matsuda
Machine Learning
Rare Gate Disagreements Can Limit Plasticity: When Gradient Flow Mispredicts Finite-Batch SGD
Ruoyu Zhao, Mingxuan Zhang, Jianbo Dai et al. (+3 more)
Population gradient flow is a common tool for
reasoning about how neural networks adapt, including after pretraining. We show that it can mispredict finite-batch
stochastic gradient descent (SGD) qualitatively, and we trace the discrepancy to a specific mechanism. In a two-unit ReLU regression, a source task drives the two
neurons toward positive proportionality and a target task rewards separating them. After source training for time $T$, gradient flow recovers on the target in time linear in $T$. Online SGD with batch size $b$ and step size $\eta$ in both phases instead fails with high probability throughout a horizon of order $e^{c/\eta}$ once $T \gtrsim \log(b/\eta)$, uniformly on an explicit set of initializations with Gaussian probability above one percent. For each fixed $T$, small-step SGD still recovers, so the failure requires the joint limit of small steps and long pretraining. At the target clone, the population instability is carried entirely by inputs on which the two ReLU gates disagree. For units at angle $\delta$ these inputs form a wedge of probability $\delta/\pi$, and weight decay shrinks the angle exponentially during pretraining. On every other input both units receive the same random linear update, which contracts their separation in conditional expectation. Bounding the cumulative probability of sampling the wedge along the exact online recursion, without a
diffusion approximation, shows that recovery with fixed probability from an identical source-gradient-flow checkpoint, within $e^{c/\eta}$ updates, requires $Nb \gtrsim e^{\lambda T}$ target samples and batch size $b \gtrsim \eta e^{\lambda T}$, where $N$ counts updates and $\lambda$ is the weight decay. In simulations, recovery is approximately a function of the disagreement budget $b\delta/\eta$ and saturates in the horizon.
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All authors Ruoyu Zhao, Mingxuan Zhang, Jianbo Dai, Jiaqi Wu, Chenyu Zhu, Tong Che
Fluid Dynamics
Net drag reduction in turbulent pipe flow by airfoil-section grids concentrated near the wall
Takuya Masuda
The frictional drag of turbulent pipe flow is responsible for enormous energy consumption in water and natural gas distribution systems. Flow straighteners can reduce this drag by suppressing
turbulence, but they generally induce a pressure loss that exceeds the reduction; similarly, large-eddy breakup devices installed in pipes have not achieved a net drag reduction. In this study, a grid of NACA0006 airfoils was adopted as a flow straightener to suppress the pressure loss, and its net drag reduction was evaluated by numerical simulations using the $k$-$\omega$ shear stress transport model of the axisymmetric Reynolds-averaged Navier-Stokes equations with periodic boundary conditions. The model was validated against fully developed turbulent pipe flow and a previous experiment on a large-eddy breakup device in a pipe. Under a pressure difference corresponding to a Reynolds number ($\mathit{Re}$) of approximately $10^5$, a grid pattern of concentric rings concentrated near the wall at an axial spacing of 0.5 m increased the flow rate by up to 9.0%, and the flow rate increase remained positive for $5 \times 10^{4} \leq \mathit{Re} \leq 2 \times 10^{5}$. The grid pattern reduced the area-integrated Reynolds shear stress by 29% and brought the velocity profile closer to a laminar one. The effectiveness of the near-wall placement is consistent with the Fukagata-Iwamoto-Kasagi identity, which weights the Reynolds shear stress more heavily closer to the wall.
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Data Structures and Algorithms
An ETH-based quasipolynomial lower bound for Dualization
Yasuaki Kobayashi, Kazuhiro Kurita and Kunihiro Wasa
Dualizing monotone Boolean functions (or equivalently, enumerating minimal transversals in hypergraphs) is a long-standing problem whose output-
polynomial-time solvability remains open. While various special cases have been extensively studied, the state-of-the-art
algorithm for the general case, due to Fredman and Khachiyan, runs in quasipolynomial time. This paper presents a subexponential-time reduction from 3SAT to the complement of Dual: Given a 3CNF formula with $n$ variables, the reduction constructs hypergraphs $H$ and $L$ of total size $2^{O(n^{2/3}(\log n)^{1/3})}$ such that $L \subseteq Tr(H)$ and the formula is satisfiable if and only if $L\neq Tr(H)$, where $Tr(H)$ is the set of minimal transversals of $H$. As a consequence of this reduction, assuming the Exponential Time Hypothesis (ETH), neither Dual nor Dualization admits an
algorithm running in $N^{o(\sqrt{\log N/\log\log N})}$ time, where $N$ is the input size for Dual and the combined input and output size for Dualization. In particular, Dualization cannot be solved in output-
polynomial time under ETH.
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Artificial Intelligence
From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers
Gerard Grau García, Arnau Padrés Masdemont, Niccolò Grillo et al. (+3 more)
Chain-of-thought (CoT)
reasoning often improves
language-model performance by giving models additional computation before answering. However, explicit CoT expresses this computation as a sequence of autoregressively generated
tokens. Latent
reasoning replaces these
tokens with compact continuous states, but most autoregressive latent-
reasoning methods retain a left-to-right dependency among latent vectors. We introduce LLoCoT: a looped latent-
reasoning framework that replaces left-to-right latent generation with iterative refinement of a compact latent workspace. A shared
transformer is reapplied for a small number of refinement iterations, jointly updating the latent slots based on the
prompt and the evolving workspace state. Using the refined state, a probabilistic head predicts a distribution from which latent
tokens are sampled in parallel and used to condition an autoregressive decoder for answer generation. Training uses continuous representations derived from explicit CoT together with a final-answer prediction loss and likelihood-based supervision of the latent states. Across HumanEval and MBPP, LLoCoT achieves the highest mean among the evaluated methods, performing on par in accuracy with
Reasoning SFT, our explicit-CoT baseline, while outperforming the base model, answer-only SFT and NF-CoT. Relative to
Reasoning SFT, LLoCoT reduces time to the first answer
token by approximately $36\times$ and
reasoning-phase latency by approximately $42\times$, while increasing end-to-end throughput by $9.2\%$. This design replaces serial thought generation with parallel latent-slot refinement while retaining probabilistic latent modeling and autoregressive answer decoding.
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All authors Gerard Grau García, Arnau Padrés Masdemont, Niccolò Grillo, Jordi Ros-Giralt, Arash Behboodi, Victor Conchello Vendrell
Cryptography and Security
A Survey of Security Research for Operating Systems
Masaki Hashimoto, Ruo Ando, Toshiyuki Maeda et al. (+1 more)
In recent years, information systems have become the social infrastructure, so that their security must be improved urgently. In this paper, we introduce the results of the survey of virtualization, operating system verification and access control technologies in association with the design requirements of the reference monitor. Additionally, we show the prospects and challenges for each technology.
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All authors Masaki Hashimoto, Ruo Ando, Toshiyuki Maeda, Hidehiko Tanaka
Human-Computer Interaction
SpheriColor: Colormaps for Spherical Geospatial Input Topographies
Julius Rauscher, Johannes Fuchs, Daniel A. Keim et al. (+1 more)
Multivariate geospatial data visualization often relies on multiple coordinated views, where color can be used to either link views or encode data attributes. Encoding spatial locations through color can reveal patterns in non-spatial visualizations, yet most applications of colormaps focus on high-dimensional attribute encodings instead. While 2D colormaps have been studied extensively, color encodings designed for spherical geospatial data have received much less
attention, even though all locations lie on a sphere. To address this, we propose SpheriColor, a colormap generation approach for mapping geospatial references guided by the design goals of distance preservation and colorspace exploitation. We project a geospatial distance function into a perceptually linear colorspace, followed by two different gamut-constrained optimization strategies. We perform a quantitative evaluation using both real-world and synthetic
datasets, demonstrating superior performance over 2D colormaps and HSLuv double cone encodings. The simplex-based optimization excels at distance preservation, whereas the ray-based approach provides better colorspace exploitation.
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All authors Julius Rauscher, Johannes Fuchs, Daniel A. Keim, Frederik L. Dennig
Computer Vision
SAGE: Sink-Aware Guided Emphasis for Visual Grounding in Vision-Language Decoders
Jeonghyo Song and YoungJoon Yoo
Recent large
vision-
language models (VLMs) pair a visual encoder with a large
language model (LLM) and perform well on diverse image-text tasks, yet their reliability is often limited by decoder
attention pathologies that suppress visual evidence and exacerbate
hallucinations. In this paper, we revisit visual
attention sinks and uncover a structured, layer-dependent behavior: across
prompts, early and late decoder layers exhibit
prompt-invariant
attention collapse onto the same few image regions, which we term PIS (
Prompt-Invariant Sinks), whereas mid layers become
prompt-conditioned and drive
vision-
language alignment. This split suggests that treating sinks as a uniform effect is incomplete. Building on this insight, we propose SAGE (Sink-Aware Guided Emphasis), a lightweight intervention that steers decoder
attention away from PIS and toward query-dependent regions of interest (ROIs) using
token-aligned ROI masks derived from standard
vision backbones such as CLIP, ViT, and DINOv3. Evaluated on diverse
vision-encoder + decoder-only LLM VLM families, SAGE improves visual grounding, reduces
hallucinations, and yields consistent gains across public downstream
vision-
language benchmarks, including fine-grained visual discrimination settings where localized evidence is crucial, when instantiated with backbone-derived ROI masks.
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Number Theory
Invariant Measures on Smooth Algebraic Groups over Higher Local Fields
Xuecai Ma and Jingwen Zhu
We construct a positive, finitely additive, left-invariant measure on the congruence cylinder ring of a smooth $d$-dimensional algebraic group over an $n$-dimensional local field. The measure takes values in $\mathbb{R}((X_2))\cdots((X_n))$. A normalized cylinder of depth $(i_1,\ldots,i_n)$ has volume $q^{-di_1}\prod_{\ell=2}^nX_\ell^{di_\ell}$, where $q$ is the final residue-field cardinality. We also obtain coordinate-change and quotient integration formulas for this measure.
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Cryptography and Security
GROB: A Multi-Agent Architecture for Public-Trace Investigation of Candidate Agentic Activity
Chiara Bonfanti and Cataldo Basile
We present GROB, a multi-
agent architecture for investigating candidate autonomous-
agent activity through public Internet traces when privileged telemetry is unavailable. The system performs controlled, read-only collection of public traces and preserves selected observations for later resolution. In a frozen September 2026 corpus, several collected traces became more informative as additional public evidence emerged. The strongest result concerns Census-labelled identifiers captured on 9 September. Public revision records later resolved these identifiers to specific Census requests from 16 - 17 June. Other results show weaker links between traces collected by GROB and evidence reconstructed or reported later. These links vary in strength, and only some can be tied to specific public records. The results show that sparse public traces can remain useful even before their significance is fully understood. Such evidence can support later reconstruction, but public traces alone do not establish organizational attribution. Execution identity presents a separate problem, as continuity of
agent identity remains an active research question for autonomous
language-model
agents.
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