Computation and Language
Beyond Poetry: Can Large Language Models Generate Classical Arabic Maqamat?
AbdulRahman A. Morsy and Aya Zirikly
Large
language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored. Prior work has focused largely on modern
language varieties and poetry, while classical prose traditions such as maqama remain largely unstudied. The maqama is a classical literary genre characterized by rhymed prose (saj), dense rhetorical ornamentation, and episodic narrative structure, making it a challenging testbed for evaluating whether LLMs can move beyond surface fluency toward deeper literary competence. In this paper, we present the first controlled evaluation study of maqama generation with LLMs, comparing five models under zero-shot, few-shot, and rule-based prompting, and evaluating outputs through both human annotation and an LLM-as-a-judge framework across dimensions such as rhetorical richness, saj density, structural coherence, and stylistic authenticity. Our results show that prompting strategy plays a strong role in stylistic quality: few-shot prompting most consistently improves saj density, while its effects on rhetoric and coherence vary by model, with the strongest models (GPT-4o and GPT-5.4-mini) benefiting most from rule-based prompting on these dimensions, though zero-shot prompting yields the highest aggregate scores across all five models. We further observe systematic differences between models in stylistic
alignment with Arabic maqama conventions, and corroborate our findings with a second independent LLM judge, paired statistical significance testing, and non-LLM proxy measures of saj.
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Systems and Control
Modularity is Not Enough: Demonstration of a Solderless 400 V DC, 2.5 kW Three-Phase Inverter
Luc Imperiali, Aaron Griesser and Jonas Huber
This paper presents the design and experimental evaluation of a fully solderless realization of a 400 V, 2.5 kW GaN-based variable speed drive (VSD), using screw-clamped resin molds and rubber compression pads instead of soldered interconnections. The power stage uses 650 V GaN power transistors and is operated at a switching frequency of 200 kHz. The solderless demonstrator is compared to a soldered reference realization using an identical printed
circuit board (PCB). Over 120 thermal cycles with heatsink temperatures up to 90 {\deg}C, the solderless contacts show no degradation in effective on-state resistances (including contact resistances). Separately, open-loop vibration sweeps from 5 Hz to 2 kHz with acceleration amplitudes above 10 g were performed on the solderless assembly and left the continuously powered demonstrator electrically intact; subsequent resistance and nominal-power checks likewise indicate no contact degradation. An initial life-cycle assessment (LCA) indicated a higher
embodied carbon footprint for the solderless realization due to 3D-printed resin molds, whereas a prospectively evaluated injection-molding scenario reduces the carbon footprint to near that of the soldered reference. The solderless assembly furthermore enables non-destructive component replacement, as demonstrated after a power transistor failure, as well as component re-use. These results support the feasibility of repair-oriented, industrially relevant kilowatt-class solderless power converters.
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Computational Complexity
Ideal Membership in Polynomial Calculus: Complexity and Reductions
Alex Bortolotti and Monaldo Mastrolilli
The Ideal Membership Problem (IMP) asks whether a
polynomial f belongs to an ideal <p_1, ..., p_m> of Q[x_1, ..., x_n].
Polynomial Calculus (PC) certifies membership by deriving f from the generators, and a degree-d derivation needs at most n^O(d) steps. We write PC-IMPd for the problem of producing a degree-bounded PC certificate, and call it solvable when one is guaranteed to exist and can be found in time n^O(d). Over Q, unlike over finite fields, a derivation may need exponentially many bits. We study PC-IMPd on instances arising from constraint satisfaction problems, and ask for which constraint
languages L it is solvable. Our main contribution is a reduction framework for PC-IMPd, based on pp-definitions, pp-interpretations, and pp-encodings, that mirrors the algebraic approach to CSP complexity. Solvability is preserved by these constructions and, in the
language of
algebras, by passing to subalgebras, finite direct powers, and homomorphic images. We obtain new tractable classes over ternary and larger domains: every
language closed under the median operation on a finite chain has solvable PC-IMPd, by reduction to the Boolean majority
algebra, and in particular so does every
language over {0, 1, 2} closed under a fixed-value majority. This also places IMPd(L) in P for such
languages, advancing the classification of IMPd over ternary domains. In the process, we settle the last open case of the Boolean dichotomy for IMPd(L) and complete the Boolean classification of PC-IMPd(L) with an unconditional lower bound for an instance of PC-IMP1. A recent PC-to-SoS simulation reduces degree-automatability of Sum-of-Squares (the open problem of finding a degree-d SoS proof in time n^O(d) when one exists) to solvability of PC-IMPd. Each new tractable class therefore yields a family of constraint systems on which SoS proofs are degree-automatable.
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Numerical Analysis · Cross-listed from math.ST
Bayesian statistical inverse problems for a coupled Fokker-Planck-Darcy system
Grigorios A. Pavliotis, Andrew M. Stuart and Andrea Zanoni
We study the nonparametric statistical inverse problem of recovering the space-dependent permittivity in a coupled Fokker-Planck-Darcy system from discrete, noisy observations of the Fokker-Planck solution. We consider the coupled
parabolic-
elliptic system on a bounded domain with the physically natural no-flux boundary condition for the Fokker-Planck equation and homogeneous Dirichlet boundary conditions for the Darcy equation. The inverse problem is indirect, since the unknown coefficient enters only through the
elliptic equation and is observed only through its effect on the density in the
parabolic equation. We first develop the analytical theory of the forward problem required for the statistical analysis, establishing well-posedness and a priori bounds uniform over the admissible set of permittivities. We then derive two stability estimates for the inverse problem, namely a Lipschitz-type forward estimate and a generalized backward estimate. Placing a rescaled Gaussian process prior on the log-shifted permittivity, we show that the posterior contracts around the truth at an explicit
polynomial rate in the number of observations, with the posterior mean converging at the same rate. Numerical experiments in one and two dimensions, using preconditioned Crank-Nicolson and ensemble Kalman filter
algorithms, complement our theoretical results.
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Systems and Control
Reward-Rate Congestion Games and Replicator–Dinkelbach Dynamics
Hassan Abdelraouf, Vaibhav Srivastava and Vijay Gupta
Reward rate is a key performance criterion in cyber-physical and robotic systems where time, workload, and coordination costs are limiting resources. We introduce reward-rate congestion games, where
agents seek to maximize reward per unit execution time. The direct reward-rate game is generally not an exact potential game. We develop a Dinkelbach-based framework in which, for every fixed Dinkelbach parameter, the transformed game is an exact potential game. This yields a potential-level Dinkelbach iteration that terminates finitely at the optimal potential reward rate when the inner potential maximization problem is solved globally. We also provide a sufficient condition under which an
equilibrium of the transformed game is an
equilibrium of the original reward-rate game. To optimize aggregate performance, we introduce marginal externality corrections that make the corrected potential coincide with the Dinkelbach-transformed social reward-rate objective, thereby enabling optimization of the social reward rate. Finally, we develop a continuous-time replicator–Dinkelbach dynamics for reward-rate population games coupling fast replicator dynamics with a slow reward-rate update. We establish convergence of the fixed-parameter replicator dynamics, global asymptotic and local exponential stability of the reduced Dinkelbach dynamics, and local exponential stability of the coupled system for sufficiently slow Dinkelbach updates. The framework is illustrated on a continuous task-allocation problem.
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Computer Vision
ODPure: Backdoor Purification for Object Detection via Ensemble Corruption Consensus
Li Zeng, Mingcheng Duan, Longfei Fan et al. (+5 more)
With the development of applications like autonomous driving, object detection has gained significant
attention, while also highlighting critical vulnerabilities like
backdoor attacks that severely compromise model integrity. Specifically, such attacks involve altering the categories of objects (i.e., object misclassification), removing bounding boxes (i.e., object disappearance), or generating bounding box proposals for non-existent objects (i.e., object generation) when a predefined trigger is present in the input. Although
backdoor defenses for image classification are well-established, the research for object detection remains comparatively underexplored. Existing defenses address these threats by scanning outputs or models for potential
backdoors but require discarding either malicious data or models. This remedy fails to enable a continuous and accurate perceptual stream for the object detection pipeline. To address such limitations, we propose ODPure, a novel input-stage black-box defense for object detection, which is based on input purification that ensures stable perception flows. Tailored to the dense prediction nature of object detectors, our Corruption-Reconstruction-Selection (CRS) paradigm operates by neutralizing triggers through a diverse portfolio of corruptions to generate a massive pool of redundant proposals, then recovering fine-grained structural cues via generative priors, and finally employing voting to reach a consensus on the resulting detections. Comprehensive experiments demonstrate that our method provides robust defense against diverse
backdoor attacks and trigger types while preserving baseline accuracy. Our code is available at https://github.com/Alex66366/ODPure.
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All authors Li Zeng, Mingcheng Duan, Longfei Fan, Hangtao Zhang, Xianlong Wang, Yanchun Li, Xia Wen, Leo Yu Zhang
Computer Vision
EmbodiedMemory-Bench: Benchmarking Embodied Memory for Long-Horizon Embodied Tasks
Lizhou Liang, Xinyu Zhong, Miao Pan et al. (+7 more)
Long-horizon
embodied interaction requires
agents to retain and continually update information about the environment as they observe, act, and encounter change. Yet current
agents struggle to maintain such
memory reliably. Our analysis traces this limitation to four key deficiencies: weak fine-grained visual
memory, unreliable dynamic world-state tracking, failing to record world state revealed by interaction outcomes, and limited generalization from prior experience. However, existing
benchmarks do not directly assess these
memory capabilities during long-horizon
embodied interaction. To address this gap, we introduce EmbodiedMemory-Bench (EMem-Bench), comprising 2,554 interactive episodes across four task families. EMem-Bench requires
agents to build and update
memory from interaction history, then use it to complete a later task by acting in the environment. We further present
Embodied-Memorizer (EMem), an external
memory system that organizes
embodied experience into spatial, event, and scene
memories. We also train EMem-8B, an 8B policy that manages and uses these
memories. We evaluate a diverse range of open-source and proprietary MLLMs and representative
multimodal memory systems. Results show that current models remain weak and uneven across the four challenges. Under matched backbones, EMem achieves the best overall performance among the evaluated
memory systems and improves both open-source and proprietary models, while EMem-8B further improves over its backbone. Project page: https://zju-omniai.github.io/EmbodiedMemoryBench/
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All authors Lizhou Liang, Xinyu Zhong, Miao Pan, Xiaohe Zhou, Xuanyu Liu, Qinfeng Li, Peng Li, Jintao Chen, Xuhong Zhang, Wenqi Zhang
Computer Vision
Diff-RF: Mutually Reinforced Image Registration and Fusion via Degradation-Aware Learning
Xunpeng Yi, Zaixi Du, Qinglong Yan et al. (+3 more)
Image registration and fusion aim to establish spatial correspondences from misaligned multi-modal source images, and integrate complementary information. However, in real-world imaging scenarios, source images are often affected by complex and diverse degradations, such as low illumination, noise, etc., which severely hinder the effectiveness of registration and fusion. To address this issue, we propose a mutually reinforced image registration and fusion
diffusion framework via degradation-aware learning, termed Diff-RF. It explores the intrinsic coupling between registration-fusion and information restoration in the degradation conditions, enabling high-quality fusion of unregistered images under complex degradation conditions. First, the intra-modal restoration module is designed to alleviate modality-specific degradations by leveraging information within each modality, thereby providing more reliable structural representations for registration and facilitating subsequent cross-modal fusion. Second, we develop a cross-modal
diffusion registration and fusion module that establishes bidirectional interaction between registration and fusion. By integrating fusion-derived visual cues and correspondence-based
geometric conditions into the
diffusion process, the proposed framework progressively refines spatial
alignment and exploits cross-modal complementary information to achieve collaborative enhancement. Rather than treating them as independent components, degradation-aware information restoration and the collaborative optimization of registration and fusion are tightly coupled, achieving overall performance improvements. Extensive experiments on multiple extended
datasets demonstrate that Diff-RF achieves superior registration accuracy and fusion quality under various degraded scenarios, exhibiting strong robustness and generalization ability.
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All authors Xunpeng Yi, Zaixi Du, Qinglong Yan, Yibing Zhang, Han Xu, Jiayi Ma
Computer Vision
Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification
Ilán Carretero, Pablo Meseguer, Rocío del Amor et al. (+1 more)
Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PFMs provide rich, discriminative representations for whole slide image (WSI) analysis, enabling tasks such as slide-level classification under multiple instance learning (MIL). However, these representations may also encode non-biological signals associated with acquisition centers, potentially introducing spurious shortcuts into downstream predictions. In this work, we evaluate center-associated robustness in WSI classification using a controlled training setting with increasing class-center correlations quantified by Cramér's V. We
benchmark six PFMs across four
datasets and two MIL aggregators, while evaluating ComBat as a robustification strategy. We further introduce the Area Under the Cramér's V Curve (AUCC) to jointly capture absolute classification performance and its degradation as spurious correlation increases. Results show that center-related information encoded by PFMs propagates to WSI-level predictions, with robustness depending on both the PFM representation and MIL aggregation strategy. Additionally, ComBat harmonization does not provide consistent robustness gains across
datasets.
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All authors Ilán Carretero, Pablo Meseguer, Rocío del Amor, Valery Naranjo
Computer Vision
A Unified Framework and Dataset for Oriented Object Visual Grounding in Remote Sensing
Zeyu Ding, Yong Zhou, Jiaqi Zhao et al. (+5 more)
Visual grounding in remote sensing images aims to locate objects described by referring expressions. Most existing methods predict horizontal bounding boxes, which are often inaccurate for objects with arbitrary orientations. To address this limitation, we introduce O$^2$-VG, a family of models for oriented object visual grounding with three complementary designs. Specifically, O$^2$-VG-Trans is a cross-modality
transformer for oriented object visual grounding. It establishes a strong discriminative foundation for the model family. Building upon it, O$^2$-VG-Uni predicts universal oriented proposals for possible foreground objects without specific text
prompts. It also supports object
retrieval through cached proposal
embeddings. Using these universal oriented proposals as input
prompts, O$^2$-VG-VLM is an autoregressive
vision-
language model. It generates oriented box
token blocks in parallel through multi-
token prediction. In addition, we construct DIOR-R-RSVG, a
dataset for oriented object visual grounding in remote sensing images. It provides image, expression, and oriented box triplets for training and evaluation. Together, the O$^2$-VG family provides a flexible framework that spans discriminative
transformers and generative
vision-
language models. It achieves superior performance across multiple
benchmarks. Code is available at https://github.com/wokaikaixinxin/ai4rs and https://github.com/wokaikaixinxin/Eagle_o2_vg.
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All authors Zeyu Ding, Yong Zhou, Jiaqi Zhao, Wen-Liang Du, Xixi Li, Hancheng Zhu, Rui Yao, Abdulmotaleb El Saddik
Cryptography and Security
MimicSat: A Reconfigurable Cyber-Physical Testbed For Small Satellite Systems and Cybersecurity Research
Nisha Vinayaga-Sureshkanth, A H M Nazmus Sakib, Mahsin Bin Akram et al. (+2 more)
MimicSat provides a common experimental environment for examining how changes in satellite subsystem behavior propagate to mission outcomes across software-based and hardware-based execution. Its design is motivated by controlled spacecraft cybersecurity studies involving attacks, faults, and defensive responses. In MimicSat, a mission encompasses the spacecraft and ground activities required to achieve defined objectives, and an experiment consists of one or more mission runs used to study selected conditions or interventions. To support such studies, MimicSat offers software-based and hardware-based execution environments that implement the same mission functions and data exchanges, while allowing specific functions to be realized differently. A shared mission definition preserves command and telemetry semantics across environments, and collected observations retain provenance about the originating participants and acquisition paths. As a result, mission behavior can be compared across execution configurations without redefining the surrounding mission. This paper presents the architectural principles of MimicSat, its software and hardware execution forms, and their integrated operation. MimicSat also supports satellite systems engineering, mission operations, resilience studies, and related experimental use cases.
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All authors Nisha Vinayaga-Sureshkanth, A H M Nazmus Sakib, Mahsin Bin Akram, David R. Silva, Murtuza Jadliwala
Cryptography and Security
Pinpointing Super-Quadratic Quantum Enumeration Speedups: Exact and Certified Evaluation of the Guessing-Moment Exponent under Product-Distribution Advice
Carsten Schubert, Niklas Paskarbeit, Maximilian J. Kramer et al. (+2 more)
Grover's
algorithm gives an optimal quadratic query advantage for black-box search. In cryptanalysis, however, the search often comes with additional probabilistic advice over the candidates, frequently of product form, e.g. from side-channel leakage on independent key coordinates. Classically, guessing in likelihood order is optimal in expectation. In the
quantum setting, Montanaro showed how to achieve an optimal expected query complexity, beating plain Grover on every non-uniform advice distribution (up to a constant overhead factor). What has been missing so far is a finite-size method for evaluating the
quantum-classical guessing-moment separation induced by a given advice distribution. We provide such a method for product-distribution advice, thereby sharpening the previous
entropy-based estimate of Bashiri et al. We reduce the classical and
quantum guessing moments to functionals of the one-dimensional surprisal distribution, obtained for product advice by convolving the per-coordinate surprisal laws. When the surprisals lie on a common arithmetic grid (the commensurate case), the logarithmic moments and hence the speedup exponent can be evaluated as finite sums without discretization error; exponential tilting makes this computation numerically stable. For general product advice, we discretize the surprisals onto a common grid and derive an a-posteriori bound on the resulting binning error. We apply the framework to cold-boot leakage on seeds and block-cipher keys, to template-attack posteriors, and to synthetic i.i.d. Bernoulli posteriors calibrated to average residual ranks reported for Keccak side-channel attacks on ML-KEM and ML-DSA. The resulting exponents substantially exceed 2 in several skewed-advice settings, reaching 3.92 for cold-boot advice and 3.12 in the synthetic models, and include cases where the previous
entropy-based bound did not establish an exponent above 2.
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All authors Carsten Schubert, Niklas Paskarbeit, Maximilian J. Kramer, Jean-Pierre Seifert, Marian Margraf
Robotics
Large-Scale Geometric Map-Based Localization of UAVs in GNSS-Denied Urban Environments
Garth Terlizzi and Kaveh Fathian
Unmanned aerial vehicles (
UAVs) operating in GNSS-denied urban environments require alternative methods for position estimation. Existing approaches based on satellite image
retrieval or learned descriptors are sensitive to appearance variation and degrade rapidly as the search area grows. We present a
vision-based localization system that matches building patterns observed from a downward-facing
UAV camera against a reference building footprint database. Our approach detects buildings in aerial imagery, accumulates observations across frames into a unified map, and matches local building arrangements against reference footprints using a novel geometry-driven descriptor that augments local triangle structure with per-building shape features. By encoding spatial relationships between nearby buildings rather than visual appearance, the system is robust to appearance variations and remains discriminative over large search areas. Evaluations on seven flights across four municipalities in a large metropolitan area demonstrate 100% Recall@1 at search areas of approximately 113 km$^2$ and 254 km$^2$, and 71.4% Recall@1 when expanded to approximately 452 km$^2$, encompassing up to 277,000 buildings. In contrast, baseline methods degrade rapidly and achieve 0% Recall@1 at 254 km$^2$ and 452 km$^2$.
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Computer Vision
Xueqi Qiu, Xingyu Miao, Jingjing Deng et al. (+3 more)
Unified 3D
vision-
language systems must combine complementary geometry, scale, and appearance cues while supporting tasks from instance
segmentation to
language-guided
reasoning. Existing methods often process point clouds, voxel grids, and multi-view images independently; directly combining these heterogeneous representations may leave substantial feature discrepancy unresolved, while subsequent unconstrained adaptation may distort their internal geometry. We propose an align-then-fuse framework that first applies triple pairwise cosine
alignment to establish segment-level correspondence across the three representations and then retrieves task-conditioned features with a
prompt-guided query decoder. Before fusion, representation-specific query features are transformed by learnable mappings constrained to the special orthogonal group. These mappings preserve inner products and Euclidean distances within each representation, permitting controlled representation-specific re-parameterisation without arbitrarily distorting its internal geometry. The transformed features are subsequently combined through Adaptive Fusion under downstream task supervision. Experiments cover eight
datasets for instance
segmentation, visual grounding, question answering, and dense captioning. Compared with PQ3D, the model improves average precision by 3.2 points on ScanNet200 and grounding accuracy by 2.9, 10.6, 4.6, and 4.1 points on ScanRefer, Nr3D, Sr3D, and Multi3DRefer, respectively, while also improving performance on ScanQA, SQA3D, and Scan2Cap. Ablations further support the complementary roles of
alignment and orthogonal re-parameterisation and the effectiveness of Adaptive Fusion.
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All authors Xueqi Qiu, Xingyu Miao, Jingjing Deng, Haoran Duan, Yang Long, Ling Shao
Networking
Load Balancing with Partial Queue Information - Threshold Optimality and Indexability
Sathwik Chadaga and Eytan Modiano
We consider the problem of load balancing in a system with one dispatcher and $N$ parallel servers. The dispatcher must select one server to dispatch new jobs at every time-step and each server buffers incoming jobs in a queue. However, the dispatcher does not know the servers' backlogs and must make dispatching decisions based on previous observations. The dispatcher's objective is to dispatch jobs to the shortest queue. This problem can be formulated as a restless multi-arm bandit (RMAB) problem where each arm's state is its corresponding belief vector. Our goal is to verify Whittle indexability for this problem and derive a low complexity Whittle index policy. Previous Whittle indexability results cannot be directly applied due to the multi-dimensional nature of the belief vector. To overcome this issue, we define the RMAB state as the tuple of the most recent backlog observation and the time since this observation. We consider two model variations, a standard finite queue model and a blocking queue model. We show that the single-arm decoupled problems of both these models have threshold optimal solutions under some assumptions. For the standard finite queue model, we prove indexability and derive the Whittle index policy in closed form. For the blocking queue model, we derive a sufficient condition for indexability under threshold optimality and use it to show indexability for some special cases.
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Software Engineering
From Agent Output to Authorized Transition
Christopher Koch
Agentic engineering systems can edit repositories, run tools and tests, build firmware, synthesize schematics, and prepare deployable or manufacturable artifacts. The assurance problem is therefore shifting from whether an
agent can produce an output to whether an engineering lifecycle is justified in acting on claims about that output. Current products and standards provide sandboxes, approvals, hooks, traces, policy enforcement, attestations, bills of materials, and assurance representations, but these capabilities remain fragmented. This paper presents the Agile-V Assurance Spine, a cross-domain transition contract for software, firmware, and PCB engineering. Evidence is admitted only when it establishes required properties through an authoritative source profile, is bound to the exact artifact and frozen policy baseline, remains current with respect to declared dependencies, and satisfies risk-appropriate independence and authority. Gate decisions are recorded as receipts; approvals and exceptions are exact-scope and time-bounded; and authorization is rechecked at the effect boundary before merge, deployment, flashing, release, or fabrication. A bounded review of contemporary research, commercial platforms, open-source infrastructure, and standards positions the model relative to evidence-gated lifecycle control, continuous assurance, runtime admission, provenance, and AI/ML inventories. The paper contributes a precise vocabulary, compositional architecture, domain profiles, mapping to open-source implementations, and an
adversarial evaluation agenda. It does not claim regulatory conformity or demonstrated production superiority.
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Machine Learning
Log-Depth Recurrent Language Modeling
Yiqin Wang, Nuri Cingillioglu and Charles Pert
Language modeling using
Transformers has become commonplace despite their fixed computational depth and quadratic runtime with respect to input
tokens. Recurrent models on the other hand offer linear depth but no parallel execution. In this work, we extend balanced-tree recursive operators from sequence encoding to autoregressive prediction, enabling all prefix representations to be computed with logarithmic depth and linear runtime. Our experiments provide an initial characterization of this model class, demonstrating robust length extrapolation and performance approaching that of ALiBi-based
Transformers, highlighting its potential as an alternative architecture for
language modeling.
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Cryptography and Security
Do Electromagnetic Side-Channel Attacks Threaten Electronic Polling Stations? Scenarios and Recommendations
Lucas Brito, Leonardo Teodoro, Pedro Tomaz et al. (+4 more)
This paper investigates the threat to ballot secrecy in the Brazilian electronic voting machine (UEB) posed by electromagnetic side-channel attacks, also known as TEMPEST attacks. In these attacks, screen content can be reconstructed remotely by intercepting electromagnetic emanations associated with the target device's
video signal. This work is motivated by a recent ruling by a Brazilian electoral court concerning an attempt to violate ballot secrecy using electronic equipment. Based on publicly available information about the electoral system, attack scenarios against polling stations are proposed. Experiments using software-defined
radio show that the effectiveness of TEMPEST attacks strongly depends on the lack of oversight resulting from public unawareness of the threat. Finally, awareness guidelines are proposed for voters, poll workers, and party representatives to mitigate attack risks within a polling station.
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All authors Lucas Brito, Leonardo Teodoro, Pedro Tomaz, Alyson Isaluski, Leandro Hyeda, Antonio Oliveira-Jr, Saulo Queiroz
Machine Learning
Support-Compiled Feature Folding: More Evidence at Lower Memory Across Tabular Foundation Models
Tian Zhou, Beverly Jin, Xue Wang et al. (+6 more)
Wide tables offer tabular foundation models more evidence, but accessing it can exhaust their
memory: full-width pairwise mixing grows quadratically with the number of columns, while feature selection makes inputs affordable by discarding evidence. We ask whether using more features requires interacting over all of them at once. We introduce Support-Compiled Feature Folding (SCFF), a training-free inference framework that encodes wide tables through bounded calls to a frozen backbone. SCFF organizes support-ranked features into a strong Core and a candidate Tail, folds them into narrow feature groups, and support-checks the Tail's added evidence before a single contextual prediction. This converts quadratic feature-interaction work into linear-in-width work with a bounded local working set, without ensembling predictions or training new parameters. On the exhaustive 18-
dataset wide-table slice of fixed AMLB-29, TabZilla, and TabArena snapshots, SCFF improves
dataset-macro accuracy and NLL on all six evaluated backbones. All four matched-width comparisons retain favorable 95%
dataset-bootstrap intervals on locked folds, with relative error reductions up to 26.1%. Median paired GPU-
memory savings are 2.09-2.36x, and the ratio of separately observed maximum peaks reaches 34.3x. Under a measured peak-
memory ceiling, SCFF uses the saved budget to preserve more support-selected evidence, improving accuracy by 4.06 and 3.72 points over the widest feasible single leaf on predeclared wide-Core strata of TabICLv2 and TabPFN-3.
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All authors Tian Zhou, Beverly Jin, Xue Wang, Linxiao Yang, Wenwei Wang, Bingqing Peng, Mengni Ye, Jinjie Gu, Liang Sun
Discrete Mathematics
Pimp my fixpoint: sofic realization of multidimensional substitution-based shift spaces
Antonin Callard, Pierre Guillon, Léo Paviet Salomon et al. (+1 more)
In symbolic dynamics, the fixed-point construction from arXiv:0910.2415 defines shift spaces of finite type whose configurations embed infinite hierarchies of tilings. This article provides a "black box" abstraction of this method phrased in terms of substitutions and $S$-adic limit spaces operating over sequences of increasingly large alphabets. By quantifying the amount of information computed by the substitutions at each level, and using a suitable parallel model of computation, we provide a simple positive criterion of multidimensional soficity that generalizes classical examples from the literature.
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All authors Antonin Callard, Pierre Guillon, Léo Paviet Salomon, Pascal Vanier
Sound · Cross-listed from eess.AS
UNITE-AUDIO: Joint Learning of Continuous Tokenization and Latent Flow Matching for Text-to-Audio Generation
Runwu Shi, Kai Li, Yujin Wang et al. (+8 more)
Text-to-audio (TTA) generation aims to synthesize realistic audio that faithfully reflects natural-
language descriptions. Most TTA systems adopt a two-stage latent paradigm: an audio tokenizer is optimized for reconstruction and then frozen, after which a generative model is trained in the resulting latent space. However, reconstruction-oriented representations may be suboptimal for generation, motivating joint representation and generative learning. To this end, we introduce Unite-Audio, to our knowledge, is the first to jointly learn continuous audio representations and latent flow matching for TTA. By coupling reconstruction with self-supervised generative prediction, Unite-Audio allows the generative objective to directly shape the latent space rather than treating it as a fixed intermediate representation. We further employ Flow-GRPO post-training to improve text-conditioned generation. Experiments show competitive TTA performance with a compact latent flow model, while ablation studies confirm the benefit of jointly learning the audio representation and generative model. Audio samples are available at https://runwushi.github.io/Unite-Audio.
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All authors Runwu Shi, Kai Li, Yujin Wang, Dong Yang, Jiahui Li, Jiang Wang, Chang Zeng, Benjamin Yen, Ashizawa Takeshi, Chunxiang Jin, Kazuhiro Nakadai
Cryptography and Security
GUIAuditor: Enabling Post-hoc Child Safety Forensics via Action-Guided GUI Provenance on Mobile Devices
Junlin Liu, Yifeng Cai, Shuai Wang et al. (+8 more)
The proliferation of smart devices exposes children to online risks like grooming and financial scams that are deeply embedded within legitimate applications. Current approaches rely on automated prevention and detection, a paradigm that is fundamentally limited by its inherent fallibility. Whether rule-based or AI-driven, they inevitably produce false positives and negatives, failing to provide reliable protection. In this paper, we argue for a complementary, human-in-the-loop, post-hoc forensic paradigm. We present GUIAuditor, the first system designed to realize this
vision by creating GUI Provenance: a queryable, semantic record of a child's interaction sequence. To generate this, GUIAuditor leverages a
Multimodal Large
Language Model (MLLM) to translate the temporal sequence of GUI events into a human-understandable narrative. To make this practical on mobile devices, a novel evidence
distillation pipeline reduces the data requiring analysis by over 89.2% compared to periodic sampling approaches adopted by industry standards, with negligible impact on accuracy. On a new
dataset of 295 interaction clips, GUIAuditor achieves a 95.23% Macro-F1 Score in logging significant events and, crucially, its two-stage forensic query engine successfully retrieves the correct evidence as the top result for over 90.20% of natural
language questions. An end-to-end evaluation on three modern smartphones shows that the full pipeline, including on-device MLLM inference, adds 2.1W of power draw and 7.4s of per-event latency, with a peak
memory footprint of ${\sim}$3.1GB. These results show that post-hoc GUI forensics can run on modern mobile devices and provide useful context for guardian-led safety review.
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All authors Junlin Liu, Yifeng Cai, Shuai Wang, Zhineng Zhong, Shaofei Li, Jiacheng Liu, Yuanchun Li, Ziqi Zhang, Xiangqun Chen, Ding Li, Yao Guo
Machine Learning
Transferable Evidence Reconstruction for Longitudinal Glucose Representations
Tian Zhou, Bingqing Peng, Linxiao Yang et al. (+6 more)
Long physiological recordings contain many routine measurements, while predictive information often lies in rare events, sustained burden, and recurring patterns. These properties can be computed as label-free evidence, but directly using them as features leaves limited labeled data to separate reproducible associations from sample-specific ones. Learning to reconstruct evidence can exploit unlabeled recordings, yet joint reconstruction does not explicitly require the decoding rule to transfer across individuals. We introduce transferable evidence reconstruction (TER): a Ridge regressor fits evidence from representations in one group and predicts it in an identity-disjoint group without refitting. The transfer error trains the encoder through the differentiable fit. For continuous glucose monitoring (CGM), clock-aware encoding preserves the multi-day content and timing needed for evidence recovery. Matched interventions connect the gains to reduced fitting-group sensitivity, with structured targets improving on raw recovery. Across ten leading CGM and time-series baselines, TER sets a new best metric on 12/14 phenotype tasks and exceeds the strongest prior overall PR-AUC/ROC-AUC/Macro-F1 by 4.95/4.43/0.66 percentage points; the PR-AUC and ROC-AUC gains are $2.6\times$ and $2.2\times$ the respective gaps between the two strongest baselines. Meal-response and future-CGM studies further demonstrate predictive utility. TER thus uses meaningful signal properties to supervise not only what a representation preserves, but how reliably it can be read across individuals.
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All authors Tian Zhou, Bingqing Peng, Linxiao Yang, Wenwei Wang, Mengni Ye, Beverly Jin, Zuyi Zhu, Jinjie Gu, Liang Sun
Artificial Intelligence
PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety
Jiapeng Sun, Yujin Zhou, Han Zhu et al. (+4 more)
As Large
Language Models (LLMs) evolve into autonomous
agents that alter real-world states, ensuring operational safety across multi-step workflows has become a critical challenge. While recent work has moved beyond single-turn evaluation toward multi-turn paradigms, key limitations persist: step-level methods treat actions in isolation, missing how risks accumulate, while trajectory-level evaluations operate post-hoc, offering no opportunity for timely intervention. To address these limitations, we formalize Decoupled Proactive Safety Monitoring along three dimensions: whether to intervene, when to intervene, and what the risk is. We introduce PASTABench, a
benchmark of 1,139 multi-turn trajectories spanning 5 risk categories and 13 subcategories. We further propose the Optimal Intervention Window (OIW), anchored by annotated Earliest-Signal and Trigger turns, to quantify intervention timeliness. Evaluation of 16 LLMs reveals that proactive intervention remains largely unsolved, with the best model achieving only 40.74% optimal-timing interventions. Fine-grained diagnosis further uncovers pervasive lexical overfitting: competitive safety scores of smaller models mask keyword hypersensitivity rather than genuine risk comprehension, as their proactive capability largely collapses once hazard vocabulary is neutralized.
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All authors Jiapeng Sun, Yujin Zhou, Han Zhu, Pengcheng Wen, Jiayi Zhou, Sirui Han, Yike Guo
Information Theory
Capacity Analysis and Joint Gaussian Beam Pattern Optimization for Positioning-Assisted Communications
Yuanbo Liu, Rui Wang, Han Zhang et al. (+3 more)
Positioning-assisted beamforming is a novel enabling technology for massive multiple-input multiple-output in modern wireless communications, as it provides reliable beam steering without requiring explicit channel state information. However, the channel capacity analysis under positioning error remains mathematically intractable, which limits both performance characterization and beamforming design. In this work, we derive instantaneous and ergodic channel capacity approximations in closed forms for both two-dimensional and three-dimensional positioning-assisted beamforming systems. Based on the new expressions, we provide closed-form optimal joint Gaussian beam pattern that maximizes the asymptotic ergodic capacity. Numerical results verify the theoretical capacity expressions and the optimal beam pattern. The derived expressions enable efficient beam design to maximize channel capacity and provide a theoretical basis for positioning-assisted beamforming design.
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All authors Yuanbo Liu, Rui Wang, Han Zhang, Shuojin Huang, Zaichen Zhang, Bingcheng Zhu
Machine Learning
Geospatial embeddings detect old-growth forests but buffered spatial validation narrows their advantage over Sentinel features
Thomas Ratsakatika, Mihai Zotta, Srinivasan Keshav et al. (+1 more)
Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, protecting them requires mapping that is accurate for individual forest parcels yet deployable continent-wide. Geospatial foundation model (GFM)
embeddings enable label-scarce land classification, but their value for old-growth detection remains unknown. Here, we map old-growth forests across 211,893 ha of Romania's Southern Carpathians, a beech-spruce landscape typical of the Alpine Biogeographic Region. We construct high-confidence, expert-informed reference labels for old-growth and non-old-growth parcels. We add AlphaEarth, TESSERA v2 and Sentinel-1/2 features to a common baseline of topographic and human-access predictors, then compare them under spatially blocked validation with and without 10 km train-test buffers to limit residual autocorrelation. With buffering, GFM and Sentinel-1/2 predictors increase precision-recall AUC by 0.21-0.25 [95% CIs: 0.15-0.34] relative to baseline, indicating spectral data contain a spatially robust old-growth signal. With a PR-AUC of 0.84 [0.79-0.88], TESSERA outperforms Sentinel-1/2 (+0.08 [+0.05 to +0.11]) and AlphaEarth (+0.08 [+0.04 to +0.12]) under unbuffered spatial validation. At a 10 km buffer, however, this advantage narrows to +0.04 [-0.01 to +0.11] and +0.03 [-0.04 to +0.10], intervals consistent with no difference. At 10 m resolution, convolutional neural networks add no benefit over pixel-based XGBoost. Comparisons with four national- and continental-scale products show the importance of non-old-growth labels, and reveal 81% agreement between our predictions and a field-calibrated map. We conclude that buffered spatial validation is vital when transferring old-growth detection models to unseen landscapes, and provide our labels and predictions for future work.
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All authors Thomas Ratsakatika, Mihai Zotta, Srinivasan Keshav, Emily R. Lines
Computer Vision
From Change Captions to Change Detection: Semantic-Appearance Agreement Framework for Remote Sensing Change Detection
Yuan Qian and Jie Ma
Remote sensing change detection (RSCD) is essential for monitoring land-cover changes and urban development. However, most methods demand pixel-level change masks, which are costly and time-consuming to annotate. Weakly supervised methods reduce this cost by using image-level change labels. Yet these labels indicate only whether a change occurs, leaving models to recover the location of the change and semantic meaning through additional and complex mechanisms. This missing information can be supplied directly by change captions, which describe what changes, what it becomes, and where it occurs. Therefore, we introduce change-caption-guided RSCD, using change captions as the sole task-specific supervision to learn change masks without manually annotated change masks. Our framework has two components: a caption-driven generation pipeline that produces bi-temporal remote sensing image pairs at scale with controlled changes matching each caption, and a change detector guided by the caption's transition semantics. The detector uses our Semantic-Appearance Agreement Framework (SAAF) to combine caption-grounded semantic responses with RGB differences for change localization, while text conditioning guides dense prediction. Experiments on our newly constructed Flair-RSGen
dataset and WHU-CDC show that SAAF outperforms the closest reproduced limited-supervision baselines in macro-averaged IoU and F1 under the evaluated protocols. Code is publicly available at https://github.com/qianyuancs/SAAF.
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Multiagent Systems
Connectivity Preservation and Graph Stretching in Range-Only Swarm Dispersion
Ariel Barel
We study connectivity-preserving finite-jump dispersion of anonymous, identical, and oblivious
agents under an idealized range-only sensing model. Each
agent measures only the distances to its visible neighbors, without bearings, identifiers, communication,
memory, or a shared coordinate system. We derive the largest isotropic displacement certifiable as safe from these measurements alone. The resulting rule requires only the distance to the farthest visible neighbor: each
agent selects a random direction and moves by half of its remaining visibility margin. The rule preserves every existing visibility edge under synchronous finite motion and therefore preserves connectivity. For two
agents, we prove positive conditional drift in squared distance, almost-sure convergence to the visibility boundary, and finite expected time to reach any fixed neighborhood of that boundary. A one-million-run Monte Carlo experiment agrees with the exact first-round moments and estimates approximately 9.5 rounds to reach distance 0.97V from coincident initial positions; an independent Bellman-equation computation gives the same estimate. For general swarms, 1,000 runs across five initial-
topology classes reproduce the deterministic safety guarantee at implementation level and reveal a consistent
topology-dependent ordering of attainable diameter under the tested protocol. These results provide a theoretical foundation for connectivity-preserving multi-
robot dispersion under minimal sensing, while isolating the guarantees achievable from anonymous range measurements alone.
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Computer Vision
Two Global Crops Suffice: Locating Semantic Emergence in DINO-Style Self-Supervised Learning
Basavaraj Sunagad, Artur Jesslen and Adam Kortylewski
Self-supervised
vision transformers trained with DINO-style objectives exhibit striking emergent semantic representation quality across visual tasks, yet the mechanisms underlying this behavior remain unclear. We present a systematic empirical dissection of the DINO family and show that semantic representations arise primarily from enforcing consistency between geometrically distinct global views of the same image instance. This instance-specific global
alignment acts as the semantic anchor of DINO-style learning. Across controlled retraining experiments evaluated on semantic correspondence and a diverse suite of 2D and 3D downstream tasks, we find that patch-level masking objectives enhance semantics only when trained jointly with this global
alignment, indicating that the iBOT objective refines and densifies existing semantic structure rather than creating it independently. In contrast, local-to-global view
alignment does not substantially improve semantic qualities at fixed compute beyond a purely global
alignment. Beyond training design, we revisit how semantic representation quality should be evaluated: while classification accuracy is the standard validation score, semantic correspondence provides a complementary axis that more reliably predicts downstream task performance. Together, these findings provide a functional decomposition of DINO-style learning and represent an important step toward understanding how semantic representations emerge in self-supervised
vision models.
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Robotics
VLMs Can Describe, But Not Measure: Object-Centric Scene Understanding for Robotic Manipulation
Enrico Saccon, Tommaso Faraci, Iñigo De La Ossa Zarzuelo et al. (+3 more)
Robotic operation in previously unseen environments requires both semantic understanding and reliable metric information. While
vision–
language models (VLMs) provide strong semantic capabilities, their
geometric estimates remain less reliable. In this paper, we propose a VLM-driven, modular perception framework for scene understanding using off-the-shelf approaches. Starting from a single RGB-D observation, the scene is segmented into object-level regions, annotated by a VLM, and grounded with depth information to construct a task-independent object-centric representation. Experiments on 151 tabletop scenes show that the proposed decomposition preserves strong semantic performance while substantially improving localization and depth estimation over direct VLM inference. The resulting representation is also integrated with a task-planning framework for robotic execution.
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All authors Enrico Saccon, Tommaso Faraci, Iñigo De La Ossa Zarzuelo, Luigi Palopoli, Marco Roveri, Matteo Saveriano