Computation and Language
DuplexDrama: A Synthesized Dialogue Dataset with Scenarios, Full-Duplex Behaviors, Expressive Speech, and Sound Events
Qingxiang Guo, Wenke Fan, Shuofeng Zhao et al. (+9 more)
We present DuplexDrama, the first synthesized spoken dialogue
dataset that simultaneously covers four dimensions: (i) complete persona and scenario settings; (ii) three full-duplex behaviors (interruption, backchannel, incomplete); (iii) expressive
speech with persona-aligned
emotion labels; and (iv) script-aware sound events. DuplexDrama is built via a 4-stage pipeline; quality validation on both scripts and synthesized audio confirms its quality. We have produced more than 2,000 hours audio data with a 64-voice timbre pool spanning 13 personas and 5 age buckets; 3.8% of all turns carry at least one full-duplex behavior. This data has been validated through internal full-duplex model training. We will release a curated subset of 6,400 bilingual dialogues (800 h, Chinese ~500 h + English ~300 h) to advance full-duplex spoken dialogue model research. Data samples are available at our demo page and LLM-judge evaluation
prompts will be released with the
dataset.
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All authors Qingxiang Guo, Wenke Fan, Shuofeng Zhao, Dawei Yang, Zhiyang Zhou, Yingxin Shang, Hongwei Cai, Zhou Wang, Weixu Wang, Lin Yang, Shuran Zhou, Yang Song
Robotics
A Multi-Vehicle Dataset with Camera, LiDAR, and Radar Sensors and Scanned 3D Models for Custom Auto-Annotation using RTK-GNSS
Philipp Berthold, Bianca Forkel and Mirko Maehlisch
Datasets are a crucial element in the development of perception
algorithms. They relate sensor measurement data to annotated reference information and allow for the deduction of sensor and object characteristics. In autonomous driving, the reference data commonly consist of semantic image
segmentation, point-wise associations, or bounding box annotations. The
dataset proposed in this work, however, aims to dig deeper into the evaluation of measurement principles and provides scanned 3D models of all vehicles together with a pose and continuous kinematics reference obtained by RTK-GNSS. Combined, the state of the complete dynamic surrounding of the sensor vehicle is known for any point in time. Subsequent reference formats can be easily computed in user-defined granularity. This
dataset involves single-object and multi-object recordings with seven target vehicles. In particular, measurement effects such as occlusion, as well as reflections, can be evaluated, as the normals of the shape of the target vehicles are known. We describe the
dataset, discuss the technical background of its development, and briefly present exemplary evaluations.
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Numerical Analysis
Partition of unity a posteriori error estimators for the linear elasticity eigenvalue problem
Joscha Gedicke and Karen Petersen
In this paper we consider the linear elasticity
eigenvalue problem with different boundary conditions and examine two a posteriori error
estimators, that compute solutions to local residual problems using a partition of unity and localized residuals. These a posteriori error
estimators are known for the source problem, and in this paper we investigate their reliability and efficiency for the given
eigenvalue problem with Dirichlet, Neumann and gliding boundary conditions. In numerical experiments we test the performance of the
estimators on different domains, with different boundary conditions, and with different
polynomial degrees. The numerical experiments show efficiency indices closer to one compared to the standard residual-based a posteriori error
estimator.
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Machine Learning
GenOR-Twin: A Semantic Middleware for Integrating Operational Discourse with Mathematical Optimization
Rahimeh Neamatian Monemi, Shahin Gelareh, Lubin Cui et al. (+1 more)
We introduce GenOR-Twin, a neuro-symbolic framework that bridges the translation gap between unstructured operational logs and rigorous mathematical optimization. Our architecture uniquely positions Large
Language Models as semantic translators rather than direct solvers, ensuring that the system retains the feasibility guarantees of exact combinatorial methods. { \color{red}We design a dynamic constraint injection mechanism (the runtime translation of qualitative disruption events into formal mathematical constraints) that allows the system to structurally modify the optimization problem's feasibility region in real-time based on qualitative human inputs. The resulting bidirectional coupling—where operational observations update the virtual model state and optimized decisions are reflected back into the Knowledge Graph—satisfies the synchronization requirement of a proper Digital Twin. The framework features an adaptive decision policy} that automatically selects between low-complexity schedule repair and full re-optimization by analyzing the available system slack. Finally, we demonstrate the generalization of this approach across six distinct optimization domains, {\color{red}turning static models into resilient systems that adapt to the operational uncertainty and variability of real-world environments.}
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All authors Rahimeh Neamatian Monemi, Shahin Gelareh, Lubin Cui, Nelson Maculan
Computer Vision
3D CT-to-PET Translation via Latent Brownian Bridge Diffusion
Sarita Mourya, Francesco Di Feola, Pierangelo Veltri et al. (+1 more)
Computed
tomography (CT) and positron emission
tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and treatment planning. However, the widespread use of PET is limited by high radiation exposure, elevated costs, and restricted availability. To address these limitations, deep learning-based CT-to-PET translation has emerged as a promising approach for synthesizing PET-like information directly from CT images, although accurately modeling the large cross-modal gap remains challenging. In this work, we propose a 3D CT-to-PET translation framework based on latent Brownian Bridge
Diffusion (BBDM). The method consists of two stages. First, a Variational Autoencoder (VAE) is trained on paired CT-PET patches, integrating contrastive learning to improve latent
alignment between anatomical and metabolic representations. Second, a BBDM is trained in the latent space to translate CT latent representations into their corresponding PET counterparts. The translated PET latents are then decoded and stitched to reconstruct the final 3D PET volume. We evaluate the proposed approach on two publicly available
datasets. Quantitative results based on image fidelity and lesion-level PET-specific metrics demonstrate improved performance compared with competing methods. In particular, the proposed approach improves PET signal fidelity, better preserves clinically relevant uptake patterns, and shows improved performance in preserving small-lesion metabolic activation, paving the way for virtual imaging applications.
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All authors Sarita Mourya, Francesco Di Feola, Pierangelo Veltri, Paolo Soda
Information Theory · Cross-listed from quant-ph
Optimal Hamiltonian Parameter Estimation in the Presence of Nuisance Parameters
Zhiyao Hu, Haidong Yuan, Liang Jiang et al. (+1 more)
In many sensing applications, the quantity of interest is not the only unknown, there are also additional unknown parameters, known as nuisance parameters, that affect the precision of estimation. While the ultimate local precision limit for a target parameter is well understood in the absence of nuisance parameters, the problem becomes significantly more challenging when they are present. In this work, we develop a framework for optimal
Hamiltonian parameter estimation in the presence of nuisance parameters. We introduce an effective generator that captures the influence of nuisance parameters on the target precision, providing an explicit characterization of the ultimate precision limit for estimating the target parameter. Finally, we provide explicit optimal protocols, including probe state, control, and measurement that saturate this fundamental limit.
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All authors Zhiyao Hu, Haidong Yuan, Liang Jiang, Zain H. Saleem
Systems and Control
Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models
Fabian Raisch, Felix Koch, Zack Xuereb Conti et al. (+2 more)
The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for individual buildings. Transfer learning (TL) has consequently gained increasing
attention for target building modeling, as it reduces data requirements and modeling effort by reusing pretrained source models. However, these TL models are typically evaluated only on prediction accuracy in the target, without testing downstream control performance. To address this gap, we apply a state-of-the-art TL approach - pretraining a generalized model on multiple source buildings using standard operational data - within an MPC setup in a target building. We show that this approach is insufficient to achieve satisfactory control performance. As a solution, we introduce generalized models pretrained on excitation-based operational source data - purposefully probed inputs that explore the building's state-action space. For evaluation, we apply the generalized models via zero-shot (i.e., without fine-tuning) to 32 simulated target buildings and assess MPC performance. Our results show that excitation-based generalized models achieve the strongest control performance among all
benchmarks, outperforming an online linear model-based MPC and a PI controller by 6.4% and 36.9%, respectively. By combining strong control performance with the ability to generalize across multiple buildings, without requiring any target-specific data, our approach reduces MPC setup cost and simplifies its widespread deployment in the building sector.
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All authors Fabian Raisch, Felix Koch, Zack Xuereb Conti, Christoph Goebel, Benjamin Tischler
Artificial Intelligence
MedRoundsQA: A Persona and Difficulty Aware Evaluation for Multi-Turn Medical Consultations
Youssef Mohamed, Ahmed Heakl, Qinrong Cui et al. (+13 more)
Medical
benchmarks are dominated by single-turn, multiple-choice clinical cases that poorly reflect real consultations. Practically, clinicians elicit evidence interactively and patient communication varies widely. We introduce MedRoundsQA, a multi-turn diagnostic
benchmark derived from 1,387 board-exam cases across 17 specialties. Each case is converted into a structured 24-slot clinical record, and then instantiated as controlled doctor-patient dual-
agent dialogues under varying patient personas, with the underlying clinical content held fixed. We further classify cases by difficulty using model-based uncertainty to enable easy-to-hard analysis. Evaluations of fifteen LLM doctor
agents show that (i) moving from a single-turn diagnosis on the standardized records to multi-turn consultations causes large degradations of roughly 13-39 points; (ii) more turns reliably improves question relevance, but diagnostic accuracy exhibits diminishing returns and typically plateaus after 6-12 turns; and (iii) patient persona differences can shift diagnosis accuracy by about 7-8 points (lowest to highest education), highlighting equity risks that single-turn
benchmarks miss.
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All authors Youssef Mohamed, Ahmed Heakl, Qinrong Cui, Junhong Liang, Rafiq Ali, Bdour Babillie, Nazira Dunbayeva, Lang Gao, Omar Hussein, Ahmed Nada, Ahmed Mohamed Magdy Mohamed, Jinghui Liu, Salman Khan, Imran Razzak, Yuxia Wang, Xiuying Chen
Computer Vision
MGAvatar: Mesh-Bound Gaussians for Head Avatar Geometry and Appearance Modeling
Lei Shi, Sen Peng, Zhiyang Deng et al. (+4 more)
Accurate head modeling requires a stable yet expressive
geometric representation. Existing Gaussian-based head avatars commonly rely on parametric templates (e.g., FLAME) for Gaussian initialization and deformation, but these templates lack personalized priors and struggle to represent structures such as hair and clothing. To address this issue, we propose MGAvatar, a Gaussian-mesh hybrid representation that jointly models geometry and appearance through two Gaussian-mesh binding modes. Specifically, we introduce vertex-bound Gaussians and constrain their learnable parameters, enabling progressive mesh deformation to represent complex head geometry, while a pose-dependent offset module accounts for non-rigid deformations. Once geometry is stabilized, MGAvatar switches to face-bound Gaussians for appearance modeling. To improve appearance consistency across novel poses and viewpoints, we introduce a view-conditioned neural color field that alleviates artifacts caused by independently optimized Gaussian colors. In addition, we design a Gaussian offset network to predict Gaussian offset maps in the observation space, providing greater flexibility for face-bound Gaussians to capture dynamic facial textures. Extensive experiments on multi-view and
monocular videos show that MGAvatar outperforms existing methods in rendering quality, producing high-fidelity head avatars with rich texture details.
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All authors Lei Shi, Sen Peng, Zhiyang Deng, Zhonggui Chen, Xiaohu Guo, Baorong Yang, Xiao Dong
Computer Vision
Pre-Trained Low-Rank Tensor Decomposition for Multi-Dimensional Image Recovery
Bing-Zhang Fu, Zhi-Long Han, Ting-Zhu Huang et al. (+2 more)
Recently, tensor decompositions are prevalent for multi-dimensional image representation, which learn the instance-specific structure of each image from scratch. However, tensor decompositions neglect the common structure across different images, leading to limited semantic modeling capability, high computational cost, and a large number of learnable parameters. To address this challenge, we suggest the first pre-trained low-rank tensor decomposition (PLTD) framework, which organically integrates the pre-trained large
vision model into the classical tensor decomposition framework. Beyond the shallow and untrained deep tensor decomposition, the suggested PLTD achieves an unprecedented balance among higher recovery fidelity, fewer learnable parameters, and smaller carbon footprint. Specifically, PLTD factorizes the target tensor into a latent tensor and a learnable transform that maps the latent tensor back to the original data domain. The latent tensor consists of two indispensable and complementary terms, i.e., a fixed pre-trained latent tensor and a learnable low-rank latent tensor. The fixed pre-trained latent tensor is distilled from a pre-trained large
vision model (i.e., DINOv3) to capture the common structure of the target tensor, while the learnable low-rank latent tensor characterizes the instance-specific structure of the target tensor. To examine the potential of PLTD, we develop the corresponding multi-dimensional image recovery model and theoretically justify the advantages of this framework. Additionally, we discuss the connections between PLTD and classical tensor decomposition frameworks. Extensive experiments on multi-dimensional image recovery demonstrate that PLTD consistently achieves superior performance compared with state-of-the-art methods.
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All authors Bing-Zhang Fu, Zhi-Long Han, Ting-Zhu Huang, Xi-Le Zhao, Deyu Meng
Information Retrieval
MIMA: Multi-Interest Recommendation via Multi-Positive Exclusive Assignment
Xingyuan Mao, Alin Fan, Shichao Nie et al. (+4 more)
Multi-interest recommendation represents each user with multiple interest vectors for fine-grained candidate matching, yet it often suffers from interest collapse, where the learned interests converge to similar representations. We highlight the prevailing single-positive paradigm as one important factor behind this issue. Since each instance provides only one positive item, intents are optimized independently, potentially causing the same best-matching interest to be repeatedly updated toward different positives while leaving the others under-supervised. Moreover, existing methods rarely model how strongly a user activates each interest, leaving scores from different interest channels incomparable at inference. To address these problems, we propose MIMA, a Multi-Interest recommendation framework built on Multi-positive exclusive Assignment. MIMA groups items co-occurring within the same request into a positive set, generates complementary interests with a causal
Transformer decoder, and exclusively assigns each positive to supervise a distinct interest via Hungarian matching, so that interest differentiation emerges from the training objective itself rather than auxiliary regularization. A lightweight routing module further estimates user-interest activation probabilities to calibrate scores across interest channels. Experiments on three public
datasets and an industrial
dataset show that MIMA consistently outperforms state-of-the-art baselines, and an online A/B test yields significant business gains.
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All authors Xingyuan Mao, Alin Fan, Shichao Nie, Junfeng Zhang, Yan Xiao, Tao Luo, Xiaoyi Zeng
Cryptography and Security
A Graph-Based Approach for Mapping Kernel-Level Telemetry to MITRE ATT&CK
Matteo Lupinacci, Luigi Arena, Francesco Blefari et al. (+1 more)
Mapping observed system behavior to standardized frameworks such as MITRE ATT&CK is essential for threat-informed defense. Existing automated methods mostly depend on Cyber Threat Intelligence reports, which offer only retrospective accounts of attacks. Kernel-level telemetry, i.e., system calls, instead provides first-hand evidence of adversary behavior, but its volume and complexity have limited its use for automated mapping. Its content also makes it unsuitable for cloud-based analysis: command lines carry credentials, events carry user identifiers and network endpoints, and during an incident the telemetry reveals which defenses failed. In this work, we present Trace2ATT&CK, a framework that maps kernel-level telemetry to ATT&CK techniques entirely within the defender's infrastructure. Trace2ATT&CK collects events via eBPF, reconstructs the attacker's activity as a process-centric provenance graph, and condenses it into a compact execution graph. This graph is classified by locally deployed open-weight LLMs, using either pure prompting or
retrieval-augmented generation (RAG) grounded in the ATT&CK knowledge base, producing ranked technique candidates with supporting rationales. We evaluate Trace2ATT&CK on 347 Linux Atomic Red Team tests with seven open-weight LLMs. The best local configuration reaches an HR@5 of 66.57% at the technique level. RAG improves over base prompting in every model and setting, by up to $+15.44$ percentage points, and the execution graph substantially outperforms raw telemetry while never exceeding the LLM context window. Finally, a frontier cloud model used as an upper-bound reference quantifies the cost of local inference at 24–28 percentage points. These results show that ATT&CK mapping from kernel-level telemetry is feasible without disclosing sensitive evidence to third-party services.
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All authors Matteo Lupinacci, Luigi Arena, Francesco Blefari, Angelo Furfaro
Cryptography and Security
Evaluating Context Segmentation in Locally Deployable SLMs for Cybersecurity CTF Tasks
Sebastiano Nordio and Michele Lotto
The proliferation of highly capable open-weight Small
Language Models (SLMs) democratizes access to advanced cybersecurity capabilities, posing an escalating risk as these models can bypass proprietary API guardrails when deployed locally. However, SLMs deployed as autonomous
agents often struggle with long-horizon, exploratory tasks like cybersecurity Capture The Flag (CTF) challenges due to context bloat and cognitive degradation from accumulated tool-call outputs. To understand and mitigate this cybersecurity threat, we introduce context
segmentation, a two-level agentic framework that divides complex exploitation tasks into manageable, contextually isolated sub-problems. Evaluating on the picoCTF
dataset using
memory-constrained gemma-4 models, we demonstrate that for the E4B model, our strategy acts as an intelligent search, achieving competitive rewards with superior
token efficiency compared to brute-force retries, and successfully solving 18.52% of tasks that standard agentic execution fails to complete. Code is available at https://github.com/9xeb/context-
segmentation.
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Robotics
Parameter Sensitivity Analysis for Aerial LiDAR-Inertial Odometries in low-altitude flights
Robert Milijas, Jose Ramiro Martinez-de Dios and Stjepan Bogdan
LiDAR-based SLAM (Simultaneous Localization and Mapping) and LIO (
LiDAR-inertial odometry)
algorithms are often used for precise
navigation of unmanned aerial vehicles, especially during interactions with the aerial
robot's environment. However, the performance of these
algorithms is greatly dependent on the scenario,
LiDAR, and
robot motion characteristics, often requiring an intensive tuning process to achieve the desired performance. To aid these tuning efforts, this paper analyzes the influence on performance of the parameters of an EKF-based LIO
algorithm (FAST-LIO2) and the LIO module of a graph-based SLAM
algorithm (Cartographer) on aerial
LiDAR SLAM
datasets recorded using different
LiDARs in low-to-moderate-altitude flights in diverse environments. The analysis is conducted on the absolute trajectory error (ATE) resulting from processing the
datasets with the LIO
algorithms configured with each combination of parameters obtained in an exhaustive grid search. The relationship between individual parameters and the ATE results is assessed using Pearson's correlation, while the influence of each parameter is assessed using random forest permutation importance analyses with random forest models trained to predict the resulting ATE values based on the choice of parameters. The performed analysis obtains for Cartographer and FAST-LIO2: i) the identification of parameters with stronger influence in performance, ii) a simplified tuning procedure, and iii) tuning recommendations. Using the proposed tuning recommendations, both
algorithms obtain on the analyzed
datasets ATE values within 5 cm to the optimal performance found in the grid search procedure in 94% of the analyzed cases.
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Computer Vision
HemaHier: Chain-Conditioned Ordinal Hierarchies for Lineage-Aware Bone-Marrow Cytology
Afshin Bozorgpour, Peter Schüffler, Edgar Jost et al. (+1 more)
Bone-marrow cytology is inherently structured: each
cell belongs to a hematopoietic lineage, and many
cell types lie on ordered maturation trajectories. Standard flat classifiers ignore this structure, treating a mild same-lineage confusion the same as a severe cross-lineage mistake and predicting only discrete labels. We propose HemaHier, an ordinal-hierarchical prediction head for a frozen or lightly adapted cytology foundation model. Its central component is a chain-conditioned maturity score that reads a single maturity value under a per-chain query, supervised only on biologically valid healthy chains, while dysplastic and off-chain
cell types remain classes but are excluded from maturity supervision. Fine and lineage predictions are coupled through a shared posterior that guarantees hierarchical consistency, and a staged objective first stabilizes recognition, then adds lineage and maturity supervision. On three bone-marrow
datasets under a shared ontology, HemaHier achieves competitive recognition while reducing biologically severe errors and adding a within-lineage maturity ordering that flat classifiers lack. Code is available at https://github.com/xmindflow/HemaHier.
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All authors Afshin Bozorgpour, Peter Schüffler, Edgar Jost, Dorit Merhof
Computer Vision
Self-supervised Pre-training Helps Retinal Disease Progression Modelling Most When Data Is Scarce
Ifeoma Veronica Nwabufo, Julius Gervelmeyer, Sarah Müller et al. (+1 more)
Modelling how a disease progresses over time requires longitudinal imaging cohorts, which are scarce and small, whereas cross-sectional data – one image per participant – is abundant. Self-supervised pre-training on such data offers a way to bridge this gap, but it is unclear which strategy best supports progression modelling, or how that answer depends on the amount of labelled longitudinal data. We study this for age-related macular degeneration (AMD), pre-training encoders on the large cross-sectional NAKO cohort and predicting time to late AMD on the longitudinal AREDS
dataset. We compare in-house self-supervised encoders against a general-purpose (DINOv2) and a domain-specific (RETFound) foundation model, across contrastive, masked-autoencoding, and self-
distillation objectives, under frozen and fine-tuned protocols, and across labelled training sets from 100 to 32,250 examples. Which model performs best depends on how the encoder is used. When the encoder is frozen and labels are few – the regime typical of longitudinal cohorts – pre-trained representations reach clinically reasonable discrimination from a few hundred labelled samples, while models trained from scratch do not; this advantage fades under fine-tuning. Transfer is governed by the self-supervision objective rather than corpus scale or domain match, so that an encoder pre-trained on a modest cross-sectional cohort matches or exceeds a far larger in-domain foundation model. Together, these results offer a practical recipe for building progression models where longitudinal data is scarce: a frozen self-supervised encoder with a lightweight survival head.
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All authors Ifeoma Veronica Nwabufo, Julius Gervelmeyer, Sarah Müller, Philipp Berens
Robotics
VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search
Bojan Derajić, Sebastian Bernhard and Wolfgang Hönig
As the number of autonomous
robots continues to grow, safety becomes increasingly important. Control barrier functions (CBFs) provide a theoretically grounded framework for ensuring safety, but existing design methods often face limitations in effectiveness, scalability, or interpretability, and may result in overly conservative safe sets. In this paper, we propose VertexCBF, a framework for learning neural CBFs in a scalable, systematic, and explainable way. We approximate the stationary Hamilton–Jacobi value function using a neural network trained via a combination of physics-informed and sparsely supervised learning. By exploiting control-affine dynamics and a convex polytope control set, under which the
Hamiltonian is maximized at the control vertices, we efficiently generate supervision points via GPU-parallel vertex-restricted tree search, while a residual architecture guarantees that the learned CBF is never larger than the specified constraint function. We evaluate the method on 15 systems and compare it against relevant baselines, showing that it reliably recovers large safe sets where the baselines are conservative or fail completely. In addition, we perform a hardware experiment in which a mobile
robot safely avoids pedestrians using a neural CBF trained with our method.
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Computer Vision
Balancing Emotional Alignment and Semantic Consistency in Image Generation via Reinforcement Learning with Valence-Arousal Anchoring
Jisheng Dang, Zhenxuan Wang, Bin Li et al. (+3 more)
Continuous
emotion control in text-to-image generation requires a model to improve affective
alignment without changing the objects, layout, or scene described by the
prompt. Existing supervised
emotion-injection methods often optimize feature-space proxies and may therefore exhibit
emotion-semantic drift, in which stronger emotional conditioning is accompanied by unintended content changes. We address this problem with a flow-matching image-generation framework that combines continuous valence-arousal (VA) conditioning, Group Relative Policy Optimization (GRPO), and a neutral semantic anchor. The deterministic probability-flow ODE is converted into a marginal-preserving SDE, yielding non-degenerate transition densities for trajectory sampling and policy-ratio estimation. A frozen CLIP-based VA regressor supplies a terminal reward measuring the distance between the predicted and target VA coordinates, while an image generated from the same
prompt under zero VA conditioning provides a feature-space reference for semantic preservation. A reduced denoising schedule is used for online RL sampling, whereas the original schedule is retained at inference. Experiments on 3,300
prompt-
emotion combinations show substantially lower valence and arousal errors than the VA-conditioned baseline and an improved CLIPScore relative to EmotiCrafter, with a measurable trade-off in reference-free image quality. The results support anchor-regularized Flow-GRPO as a practical approach to balancing emotional
alignment and semantic consistency in continuous-affect image synthesis.
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All authors Jisheng Dang, Zhenxuan Wang, Bin Li, Ronghao Lin, Bin Hu, Tat-Seng Chua
Game Theory
Learning-Augmented Strategyproof Facility Location in $\mathbb{R}^d$ with $\ell_p$ Distances
Hau Chan, Jianan Lin and Chenhao Wang
We study learning-augmented mechanism design for locating a single facility in $\mathbb{R}^d$ to minimize the sum of the
agents' $\ell_p$ distances to the facility. We analyze the coordinate-wise median with predictions (CMP) mechanism, which adds $cn$ virtual
agents at a predicted optimal facility location to the $n$ reported locations and returns their coordinate-wise median. The parameter $c\in[0,1)$ represents confidence in the prediction; CMP is known to be strategyproof when both are fixed independently of the reports. We determine its approximation guarantees under correct predictions (consistency) and arbitrary predictions (robustness) in two settings. First, for $d=2$, we establish exact guarantees for every $p\in[1,+\infty]$. For $1<p<+\infty$, the consistency and robustness are respectively $\Bigl(1+\bigl(\frac{1-c}{1+c}\bigr)^{\frac{p}{p-1}}\Bigr)^{\frac{p-1}{p}}$ and $\Bigl(1+\bigl(\frac{1+c}{1-c}\bigr)^{\frac{p}{p-1}}\Bigr)^{\frac{p-1}{p}}$. We use the median condition in each coordinate to compare the mechanism's cost with the optimal cost, and establish tightness using instances with
agents at only three distinct locations. Second, for $1<p<+\infty$, we obtain dimension-independent consistency and robustness upper bounds valid for every $d\ge1$. For each fixed $p$ and $c$, we construct families of instances whose ratios approach the respective upper bounds as $d\to\infty$, proving asymptotic tightness. We also establish exact guarantees for $p=1$ in every dimension and asymptotically tight bounds for $p=+\infty$. Our high-dimensional results recover the prediction-free bounds of Gravin and Jia (STOC 2025) when $c=0$ and their learning-augmented bounds in arbitrary-dimensional Euclidean spaces when $p=2$.
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Computer Vision
A Dual Cross-Attention Framework for Colposcopic CIN Grading and Swede Score Prediction Using a New Multi-Center Dataset
Dania Khan, Nuzhat Aisha Shaikh, Asfina Hassan Juicy et al. (+3 more)
Cervical cancer is a major global health challenge, with disease burden falling disproportionately on low- and middle-income countries (LMICs) due to a shortage of trained specialists and the subjective nature of colposcopy-based screening. To address this challenge, we propose a novel deep learning framework for the automated grading of Cervical Intraepithelial Neoplasia (CIN) and the prediction of clinical Swede scores. We also introduce the BUET Multi-Center Colposcopy
Dataset, a novel, multi-center cohort designed and annotated for Swede score prediction and CIN grading. Our proposed dual-stream cross-
attention architecture mimics the visual
reasoning of an expert colposcopist by explicitly fusing paired
multimodal cervigrams to evaluate comparative tissue responses. Furthermore, we introduce a custom composite loss function to address severe class imbalances and scoring inconsistencies across the five Swede score components. The proposed framework achieved 71.85% accuracy and an 86.23% AUC-ROC for three-class CIN grading, outperforming existing methods. For Swede score component prediction, the architecture achieved AUC-ROC values ranging from 75.7% to 88.4%, with the composite loss function yielding consistent F1-score improvements. Finally, the total predicted Swede Score, which ranges between 0 and 10, shows a Mean Absolute Error (MAE) of 1.489. The results show that the proposed method can pave the way towards developing AI-assisted colposcopy screening tools to support risk-based triage in resource-limited healthcare settings. The
dataset and source code are publicly available(url: https://github.com/mHealthBuet/BUET-colposcopy)
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All authors Dania Khan, Nuzhat Aisha Shaikh, Asfina Hassan Juicy, Raiyun Kabir, S M Shahida, Taufiq Hasan
Computer Vision
CoralscapesV2: Panoptic and Fine-Grained Visual Scene Understanding in Coral Reefs
Jonathan Sauder, Thomas Ruckli, Gabrielė Strodomskytė et al. (+17 more)
In order to design conservation and restoration strategies to counter the global decline of coral reefs, ecological monitoring of reefs needs to be scaled up dramatically. Computer
vision methods are increasingly used to tackle the vast amount of data: as the paradigm of data collection in reefs shifts from highly standardized and constrained survey images to unconstrained imagery on scalable platforms, it is necessary to design machine learning methods that help to get a fine-grained understanding of reefs from general-purpose reef imagery. This paper provides CoralscapesV2, an extension of the Coralscapes
dataset for general-purpose visual scene understanding in reefs. CoralscapesV2 increases the
dataset size, scope, label completeness and quality for semantic
segmentation, and extends the number of classes from 39 to 95 fine-grained visual categories. Furthermore, CoralscapesV2 provides 65k exhaustive fish instance mask annotations, meticulously annotated to completeness by using the
video, revealing that annotation of fish based on only static images is insufficient. CoralscapesV2 is the first
dataset for panoptic
segmentation in coral reefs, capturing a wide range of scenarios in the wild, posing a challenging
benchmark for contemporary semantic
segmentation and instance
segmentation models. CoralscapesV2 is an important step towards general-purpose panoptic
segmentation in coral reefs, which has substantial implications for scaling up coral reef monitoring, as it can be employed in a wide range of applications from benthic cover mapping from
robot or handheld
videos to designing methods for automated quantification and understanding of fish behavior and fish-reef interactions.
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All authors Jonathan Sauder, Thomas Ruckli, Gabrielė Strodomskytė, Ibrahim Souleiman Abdallah, Rahma Hassan Abdi, Djama Goumaneh Awaleh, Mohamed Houssein Farah, Moustapha Nour, Osama Sharhubil Saad, Mustafa Mohammed Khalafallah Altaib, Maysoon Kteifan, Farah Alsoqi, Eyad Zgool, Jafar Al-Omari, Temesgen Gebremeskel Gebreluel, Zekaria Zekeria Abdulkerim, Meron Ghirmay, Teklehaimanot Beraki, Devis Tuia, Guilhem Banc-Prandi
Computer Vision
SCDM: Spatial-Contextual Disentanglement Mamba via Differential Inference for Efficient Image Classification
Mustafa Bora Çelik, Hayriye Aktaş Dinçer and Ayse Keles
State Space Models (SSMs), particularly VMamba, have emerged as efficient alternatives for modeling long-range dependencies in medical image analysis. However, distinguishing subtle pathological features from visually similar anatomical backgrounds remains a significant challenge. Existing SSM architectures often learn entangled representations, lacking explicit mechanisms to separate disease-specific signals from normal anatomy. To address this limitation, we propose Spatial-Contextual Differential Mamba (SCDM), an asymmetric dual-branch architecture designed for selective representational disentanglement. SCDM introduces a Positive Branch for extracting discriminative features and a Negative Branch that actively models and suppresses normal anatomical context. This separation is achieved through a similarity-driven repulsion gate and a differential inference rule, which promote competitive feature learning without requiring additional branch labels or increasing model capacity. Evaluated on the RSNA Pneumonia
dataset, SCDM achieves competitive classification performance (AUC of 0.858) while requiring significantly fewer parameters (29.4M) and FLOPs (1.44G) compared to standard VMamba and
vision transformer baselines. Furthermore, activation analyses demonstrate that our differential mechanism yields highly precise localization, effectively isolating lesions by inhibiting irrelevant anatomical distractors.
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Systems and Control · Cross-listed from math.OC
Continuous-Time Constrained Linear Quadratic Regulator for Switched Linear Systems
Pietro Gori, Michele Pierallini, Franco Angelini et al. (+1 more)
Switched systems, characterized by a family of subsystems governed by a switching rule, widely apply to complex real-world scenarios. However, their inherent switching dynamics pose significant challenges in phase analysis and control design. To address these challenges, we propose a framework for solving the Continuous-Time Constrained Linear Quadratic Regulator (CT-CLQR) problem for switched linear systems. Our approach partitions the time horizon into a finite number of intervals, each associated with a specific system mode. The duration of these intervals is parameterized by the switching instants, enabling a reformulation of the problem. We indirectly optimize the switching sequence by fixing the switching sequence and optimizing the interval durations. We derive analytical expressions for the cost function and its gradient, which are critical for efficient optimization. Unlike state-of-the-art methods that impose equality constraints on state
evolution, our approach inherently considers the state
evolution in the cost function. This not only simplifies the problem formulation but also reduces computational overhead by precomputing shared terms offline, enhancing efficiency during online operations. The proposed method significantly advances existing techniques, offering improved computational efficiency and flexibility. We demonstrate the effectiveness of our approach through comprehensive numerical examples, showcasing its potential for practical applications.
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All authors Pietro Gori, Michele Pierallini, Franco Angelini, Manolo Garabini
Artificial Intelligence
Scaling Clinical Judgment to Evaluate Medical AI
Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur et al. (+15 more)
Blinded physician evaluation has been considered by many to be the gold standard for assessing clinical
reasoning in large
language models (LLMs). This is difficult to scale; thus, prior studies typically rely on small physician panels, often from a single institution or specialty, which both limits the scientific questions investigated and makes it unclear whether findings would be reproduced with a different set of evaluators. To more rigorously and scalably study clinical
reasoning in AI models, here we introduce PrecepTron, an LLM fine-tuned for physician-level evaluation of open-ended responses. PrecepTron was trained using low-rank adaptation (LoRA) of a 32-billion-parameter model on a small number of physician examples. We also release GRAND-ROUNDS, a new large-scale physician-annotated
benchmark of 9,217 scored responses from 160 clinicians across seven studies. We show that frontier LLMs in typical "LLM-as-a-judge" approaches often disagree with physicians and with each other, but fine-tuning PrecepTron on a small number of cases enables physician-level consistent scoring across tasks. We use PrecepTron to reproduce headline findings from five influential studies assessing LLMs for clinical care in JAMA, Science, and Nature Medicine without new human grading. Using PrecepTron, we then pose new questions about how LLMs reason in medicine that would have been infeasible with human grading alone, including measuring the diagnostic accuracy of frontier LLMs when clinical cases are provided piecemeal, even
token by
token. Together, PrecepTron and GRAND-ROUNDS provide a foundation for reproducible, large-scale study of how LLMs reason in medicine. All code, data, and labels are made freely available for researchers.
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All authors Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur, Byron Crowe, Anthony M. Pettinato, Aashna P. Shah, Adrian D. Haimovich, Liam G. McCoy, Daniel Restrepo, Jason A. Freed, Ethan Goh, Jonathan H. Chen, Laura Zwaan, Katherine E. Goodman, Daniel J. Morgan, Raja-Elie E. Abdulnour, Adam Rodman, Arjun K. Manrai
Computer Vision
Online Video Agent Harness for Long Video Understanding
Sen Yang, Boqiang Duan, Jing Yang et al. (+7 more)
Long
video understanding often behaves like a visual needle-in-a-haystack problem: query-relevant evidence is sparsely distributed across long temporal spans, while packing dense frames into a single VLM context incurs context rot and high cost. Existing
video agents often rely on query-agnostic offline preprocessing or ad hoc tool sets, which can miss query-specific details and waste computation. In this work, we present VideoXAgent, a purely online
video-
agent harness for long
video understanding that starts from the given
video file and user query, plans and decomposes the task, invokes specialized expert tools on demand, and aggregates
multimodal evidence to produce a final answer while resolving conflicts among observations. To support this on-demand invocation, we design a suite of heterogeneous expert tools guided by a data-driven taxonomy of atomic capabilities, spanning scripts, VLMs, and domain models (e.g., detection, OCR, ASR, face recognition). The harness further enforces objective evidence prompting and budget-aware control to curb
hallucination and non-termination. Across
Video-MME-Long, LongVideoBench-Long, LVBench, and MINERVA, VideoXAgent is competitive with frontier LMMs and
video agents under a smaller context footprint—about 50k
tokens of
agent context per sample, even on hour-long
videos. In particular, on complex
video-
reasoning benchmarks such as MINERVA, it matches this level while using only about 15% of the context of a 1,024-frame dense-packing baseline. Notably, the harness remains effective with a visually weak or even text-only orchestrator, suggesting that strong long-
video understanding can emerge from progressive agentic evidence seeking rather than from packing the full
video into a single context. Project page: https://go-
agent-x.github.io/
video_
agent_harness/
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All authors Sen Yang, Boqiang Duan, Jing Yang, Weihao Bo, Jie Liu, Boyuan Tong, Ze Feng, Wenkang Zhang, Jingdong Wang, Hua Wu
Numerical Analysis · Cross-listed from math.OC
Controlling the energy jump of multistable structures using shape optimization
Arselane Hadj Slimane, Patrick E. Farrell, Alberto Paganini et al. (+1 more)
Multistable systems admit distinct stable
equilibria for the same loading and boundary conditions. Shape optimization of multistable structures offers a promising route to engineer the mechanical response of advanced materials and metamaterials. In this work, we develop a formulation and
algorithm for controlling the energy jump associated with snap-through behaviour in hyperelastic metamaterials by optimizing the shape of the domain. The key challenge is that classical shape optimization theory assumes a single-valued domain-to-solution map, which does not hold in the multistable setting. We address this by extending the theory to settings with non-unique solutions, treating the scaling of the domain deformation as a continuation parameter and applying the implicit function
theorem to give sufficient conditions for the continued existence of multiple solution branches as the shape varies. This theoretical foundation underpins a practical
algorithm that targets a prescribed energy jump between stable states, enabling the systematic design of structures whose snap-through response can be tuned on demand.
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All authors Arselane Hadj Slimane, Patrick E. Farrell, Alberto Paganini, Àlex Ferrer
Artificial Intelligence · Cross-listed from physics.ao-ph
4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
Deifilia Kieckhefen, Juan Pedro Gutiérrez Hermosillo Muriedas, Lars Helge Heyen et al. (+12 more)
We present BEAST, the first-ever
Bayesian Swin
Transformer for atmospheric
forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty. To overcome the associated computational bottlenecks, we devise an orthogonal 4D-parallelization scheme that introduces a unique domain-tensor-parallelism strategy and a novel uncertainty parallel method, enabling us to fully leverage GPU capacity and efficiently scale model training. For a 2.4-billion-parameter model, we achieve a peak performance of 3.96 EFLOP/s on 20,480 NVIDIA GH200 GPUs on the JUPITER supercomputer. We train BEAST as a 700-million-parameter model with 96 random weight samples on 384 nodes on 40 years of data for nearly one million gradient updates. This model achieves predictive skill scores competitive with state-of-the-art probabilistic atmospheric AI models and numerical models, and can predict extreme events with exceptional skill, while generating large ensembles 3 to 4 times faster than the current-best AI model. Our contribution unlocks the potential of high-fidelity uncertainty quantification in atmospheric AI models, heralding a new era for AI-based models in
climate and Earth system sciences.
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All authors Deifilia Kieckhefen, Juan Pedro Gutiérrez Hermosillo Muriedas, Lars Helge Heyen, Mathis Bode, Iida Hakulinen, Andreas Herten, Chelsea Maria John, Thorsten Kurth, Anni Moisala, Asena Karolin Özdemir, Kaleb Phipps, Oskar Taubert, Arvid Weyrauch, Markus Götz, Charlotte Debus
Machine Learning
RunningTensor: Generalizing Linear Attention to Higher-Order Recurrent States
Luca Herranz-Celotti and Vincent Guigue
Linear
attention and state-space models provide linear-time sequence modeling, but their recurrent
memory remains a second-order tensor (a
matrix), limiting the order of interactions that can be represented in the state. We introduce the RunningTensor, which generalizes this
memory to an order-$o$ tensor, updated by a rank-1 outer product and read by contracting against $o-1$ vector queries. Order $2$ recovers linear
attention; we study order $3$ as a proof of concept, retaining both recurrent and parallel forms while remaining linear in sequence length $T$ and improving working
memory capacity from $\mathcal{O}(W^2)$ to $\mathcal{O}(W^o)$. On synthetic multi-query associative recall, RunningTensor outperforms linear-
attention and SSM baselines. After pretraining, it also improves performance on
language-understanding and non-synthetic
retrieval tasks, suggesting that higher-order recurrent state can provide useful additional
memory capacity beyond
matrix-valued state.
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Cryptography and Security
Batten the Hatches: Cybersecurity with Military Mariners
Ryan Von Brock, Anna Raymaker, Animesh Chhotaray et al. (+3 more)
Cyberwarfare has become a key component of contemporary geopolitical conflict. However, there has been extremely limited systematic investigation into how cybersecurity is handled by military organizations and personnel. The military context is unique compared to other operational ones, with immense resource availability (U.S. military spending approached 1 trillion dollars in 2024), a rigid chain of command, and extraordinary consequences for its actions. Thus, military cybersecurity is a distinct yet understudied topic. In this paper, we take an early step at understanding military cybersecurity by investigating how service members understand, recognize, and respond to cyber risk. We focus on maritime services and carefully consider organizational barriers to design an unclassified study and conduct semi-structured interviews with 20 military mariners from U.S. Navy and Coast Guard vessels. Through our investigation, we identify unique consequences of compromising military systems, including weapon takeover and purposeful geopolitical escalation. We find that cybersecurity is organizationally abstract on ships, so mariners build cyber risk models from informal experience rather than formal instruction. They nonetheless make cybersecurity actionable by recognizing operational impacts and responding with a safety-oriented incident-response model that creates resilience but may delay cyber attribution and containment. These findings inform actionable recommendations to help military operators frame cyber threats, merge longstanding nautical doctrine with modern systems, and apply military insights to the civilian sector, all to secure the broader maritime environment.
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All authors Ryan Von Brock, Anna Raymaker, Animesh Chhotaray, Frank Li, Saman Zonouz, Raheem Beyah
Artificial Intelligence
K-Bench: A Benchmark for LLM Unlearning in Agentic Deployments
Guangsheng Yu, Yanna Jiang, Qin Wang et al. (+2 more)
Unlearning
benchmarks such as TOFU and MUSE certify forgetting by reading the model's final answer, where a model that refuses to answer already counts as having forgotten. We show that this model-level certificate does not transfer once the model is deployed as an
agent. We introduce K-Bench, a
benchmark that scores LLM unlearning under agentic deployment. K-Bench inspects all six channels a ReAct
agent exposes, including its chain-of-thought (CoT), tool calls and tool observations, and elicited summary. A query counts as leaked if the secret appears in any of them. Each experiment places the secret in exactly one of the
agent's three sources (the weights, the
prompt, or the
retrieval store). The K-Score is computed separately for each source and credits forgetting only when the
agent remains usable. Clearing the answer channel does not make the secret unrecoverable. On structured
retrieval, the secret stays verbatim in the tool-observation channel and the aggregate leak rate is unchanged. When the secret lives in the
prompt or the
retrieval store, TOFU and MUSE report no leakage, while the deployed
agent still leaks it on 22–86% of queries. When the secret is in the weights, none of the twenty evaluated published methods demonstrably removes it, and only an input-corruption intervention reaches selective forgetting under the evaluated observer. The top-ranked method changes across base models. A refusal-tuning method resists the evaluated extraction without verified knowledge removal.
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All authors Guangsheng Yu, Yanna Jiang, Qin Wang, Baihe Ma, Xu Wang