Computation and Language
TTLab at StanceEval-2026: A Cloze-Style Prompting Approach for Arabic-Language Stance Detection (CLASP-Ar)
Bhuvanesh Verma, Ali Abusaleh and Alexander Mehler
Arabic-
language stance detection remains challenging, and previous shared-task systems have largely relied on multitask learning and ensembles. While these systems achieve state-of-the-art performance, their applicability and transferability are limited by the additional complexity introduced by multitask learning.To reduce this complexity, we introduce $\texttt{CLASP-Ar}$, which reformulates the task as cloze-style masked
language modeling. In this approach, the target, predicted sentiment, and text are combined into a single
prompt whose $\texttt{[MASK]}$ prediction is restricted to a verbalizer-constrained label vocabulary.
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Distributed Computing
Reusing Spare Vehicle Computing Capacity: Is It Viable, Profitable and Sustainable?
Rosario Patanè, Nadjib Achir, Andrea Araldo et al. (+1 more)
Vehicular Cloud Computing (VCC) exploits computing hardware already embedded in vehicles for purposes unrelated to offloading and puts its idle cycles to work executing end-users' offloaded tasks, avoiding the deployment of new computation infrastructure. Despite its conceptual appeal, adoption is hindered by the lack of quantitative evidence that sharing spare vehicular capacity is viable, profitable and sustainable. A management scheme for task offloading sustains deadline-constrained execution by decoupling deadline-aware allocation from ex-post incentive design, making the approach viable. A robust coalitional game and a vehicle-selection mechanism keep the network operator and the vehicle owners profitable even under unfavorable outcomes of the random environment, e.g.,mobility,
radio conditions, or vehicles overstating their spare capacity. A simulation campaign on realistic urban mobility, using Simulation of Urban MObility (SUMO) traces of Rome,together with a CO2 life-cycle assessment against edge computing infrastructure, whose footprint, is attributed pro rata to the share of its capacity devoted to offloading shows the approach to be sustainable. Vehicles absorb most of the offloaded traffic within tens of milliseconds; participation yields up to 157 km of monthly driving range per vehicle; and life-cycle emissions drop by over 99% when using VCC compared to that edge infrastructure.
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All authors Rosario Patanè, Nadjib Achir, Andrea Araldo, Lila Boukhatem
Artificial Intelligence
The Gold in Bias: Maturing the AI Design Process through Verification
Samira Maghool and Paolo Ceravolo
Bias in AI systems is typically framed as a flaw to be minimized, yet it also serves as a critical indicator of underlying weaknesses in data, modeling assumptions, and system design. Existing approaches often treat bias as an isolated problem rather than as evidence that can strengthen verification and governance across the AI lifecycle. This paper aims to reconceptualize bias as a diagnostic tool that supports rigorous AI verification. We seek to develop a multidimensional framework to analyze bias, demonstrate how biases emerge in both Traditional and Generative AI, and provide a structured pathway for verification-driven mitigation. We present a multidimensional framework analyzing bias across four dimensions: origin sources, emergence points throughout the AI modeling lifecycle, technical and methodological causes, and validation approaches for detection and mitigation. Through a comprehensive typology spanning traditional and generative AI systems, we demonstrate how biases manifest and propagate across development stages. Our analysis encompasses 30 distinct bias types, 16 verification methods, and 20 countermeasures, providing an actionable roadmap for practitioners. We introduce a hierarchical evidence framework that distinguishes internal validity (mechanistic integrity of AI systems) from external validity (contextual reliability in deployment environments). The framework reveals how biases manifest and propagate across modeling stages, enabling systematic mapping between bias types, verification techniques, and effective countermeasures. The proposed evidence hierarchy clarifies how different verification strategies contribute to mechanistic integrity and contextual reliability. We advocate for ”Ethics by Design” principles that integrate bias verification throughout the development lifecycle, enabling the construction of fairer, more robust, and trustworthy AI systems.
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Hardware Architecture
VQ-LIC: Shared Vector-Quantized Learned Image Compression on a Resource-Constrained FPGA
Muhammad Fahd Ibrahim Bhatti, Abdullah Bin Faisal, Ahsan Usman et al. (+2 more)
Learned image compression (LIC) is hard to deploy on severely resource-constrained FPGAs, since how fast it actually runs depends not just on arithmetic count, but also on
memory traffic, imbalance between different operations, and how the hardware batches its work. We present VQ-LIC, an asymmetric edge-cloud codec in which a compact INT8 depthwise (DW)-pointwise (PW) analysis transform and multi-codebook vector quantization (VQ) run at the edge on a reusable DW/PW engine pair, while reconstruction is handled by a larger cloud decoder. Since VQ codeword matching is expressible as a dot product, it is mapped directly onto the same PW engine, removing the need for a separate VQ compute array, to our knowledge a first for FPGA LIC. A novel latency model, derived from deterministic RTL cycle counts of an FPGA's read, DW, PW, and write costs, predicts an analysis transform's per-block latency; since VQ shares the same PW datapath, the model applies to VQ as well. Validated directly against silicon, the model predicts deployed analysis and VQ latency within 0.26% and 0.05%, and guides the selection of a three-block $16$-$48$-$64$ transform. Post-training codebook reduction then cuts VQ arithmetic and codebook storage by $4\times$ and shrinks the fixed-width latent representation. On a 220-DSP Zynq-7020, VQ-LIC's mid-rate preset reaches 0.1398 bits per pixel at 28.69 dB PSNR and 13.06 dB MS-SSIM on CLIC~2017, outperforming a similarly sized neural encoder and reaching a rate-distortion range comparable to a codec three orders of magnitude larger. The complete 0.1945-kMAC/pixel analysis-VQ pipeline runs at 47.98 frames per second and 42.84 mJ per frame on silicon, using an order of magnitude fewer DSPs than comparable FPGA LIC accelerators while achieving lower bitrate, higher throughput, and lower energy per frame at a modest PSNR tradeoff.
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All authors Muhammad Fahd Ibrahim Bhatti, Abdullah Bin Faisal, Ahsan Usman, Naveed Anwar Bhatti, Muhammad Ali Siddiqi
Computer Vision
A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinoma
Anh-Tien Nguyen, Mawuko Tettey, Jacqueline Michelle Metsch et al. (+14 more)
Survival research in pancreatic ductal adenocarcinoma (PDAC) is limited by the scarcity of
datasets linking whole-slide histology with clinical, molecular, and long-term outcome data. We present a retrospective single-centre cohort of 302 patients who underwent PDAC resection at University Medical Center Gottingen. The
dataset comprises 446 H&E whole-slide images, clinicopathological variables, targeted sequencing data for 154 patients, and overall-survival outcomes. During follow-up, 253 patients died, and the median follow-up was 76 months. To establish initial reference values, we evaluated fourteen survival-prediction configurations using identical five-repetition Monte Carlo cross-validation partitions. Ridge Cox regression using numeric clinicopathological variables achieved a mean concordance of $0.649 \pm 0.042$ and $0.652 \pm 0.046$ after adding KRAS and TP53 mutation status. The image-only
attention model achieved $0.603 \pm 0.030$, while
multimodal fusion achieved $0.619 \pm 0.025$, the highest concordance among the neural models. These results establish promising initial
benchmarks for future research using this pancreas-specific
multimodal dataset, paving the way for external validation.
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All authors Anh-Tien Nguyen, Mawuko Tettey, Jacqueline Michelle Metsch, Teresa Zimmer, Niklas Ullrich, Mario Duker, Sandra Rungeling, Kirsten Reuter-Jessen, Tessa Rosenthal, Lena-Christin Conradi, Michael Ghadimi, Alexander Konig, Elisabeth Hessmann, Volker Ellenrieder, Philipp Strobel, Hanibal Bohnenberger, Anne-Christin Hauschild
Artificial Intelligence
A General Framework for Budgeted Threshold Incentives on Request
Zhuolin Wu, Chengrui Zhu, Wenhua Nie et al. (+9 more)
On-demand delivery platforms pay riders through incentive activities whose tiers are set from recent completions of riders with a similar history. Operators request such plans for changing periods, rider populations, payment rules and budgets, often for holidays or bad weather, where randomized trials are scarce and take months to collect. We present a request-driven framework that composes four stages (conditional prediction, population reduction, trajectory integration and budget allocation) through seven replaceable modules that exchange conditional trajectory laws, whose award probabilities and award-marked moments give payment and uplift for any activity rule. A response-correction step reweights trajectories from abundant no-offer history to match the moments of a short pilot. We prove that, on a fixed plan menu and given the stage errors, the end-to-end value loss is bounded by the sum of four stage terms, and that for every stage there are instances on which omitting it leaves an error floor the others cannot remove. On 3,000 riders over 45 weekly origins, all 127 windows of a week are answered 11.04x faster with identical scenarios and at most 0.92% value lost by the allocation. On 24 new controlled response laws, the response correction with a one-week pilot lowers regret by 51.2% relative to a trial with the same nominal randomized rider-weeks, and a four-week pilot with exact summation comes within +0.007 of an 18-week trial. In registered studies where windows, populations, rules and binding budgets change from request to request, the framework's regret is below that of a trial with the same nominal rider-weeks and below dose interpolation of the same pilot data, and reusing its one-off preparation answers 60 requests 14.1x and 2.70x faster with identical answers. Against a nine-offer trial fitted with the framework's own dose curve, one-week regret is 0.055 lower.
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All authors Zhuolin Wu, Chengrui Zhu, Wenhua Nie, Kenny Ye Liang, Junming Lin, Haiyang Li, Zhilin Li, Wenjia Geng, Zeyu Wu, Yinan Wu, Jinghua Hao, Renqing He
Computer Vision
SALI: Shot-Aware Late Interaction for Cross-Shot Relation Matching in Text-to-Video Retrieval using Film-Grammar Knowledge
Toya Oyama, Rainer Lienhart and Shin'ichi Satoh
Text-to-
video retrieval usually represents a
video clip by a single
embedding. This
embedding often loses important relations between people. E.g., an interaction "Anna confronts Mark" is regularly filmed as alternating shot and reverse shot of both (Fig. 1a). No single shot or averaged
embedding over clip shots captures this relation. Thus, we propose SALI (Shot-Aware Late Interaction). It extracts the subject and object from a single-sentence query, and matches the query, its subject and object text
embeddings against each visual shot
embedding of a
video clip. The matching operator is greedy max or optimal transport. A film-grammar penalty in fine-tuning adds a small, consistent shift. Built on CLIP4Clip-meanP, SALI keeps overall recall on par on Condensed Movies and ActivityNet while raising R@1 on multi-shot relation queries by 3 and 12 points, the most among all compared methods, and improves such queries on MSR-VTT at a cost of 1.4 R@1 overall.
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Cryptography and Security
Automated Abstraction Refinement for Information Flow Security in Embedded Systems
Jonas Becker-Kupczok, Lukas Ernst and Paula Herber
Information flow analysis (IFA) is a powerful technique for verifying confidentiality and integrity and is therefore highly desirable for security-sensitive embedded systems. However, as these systems are inherently concurrent and time-dependent, existing IFA for embedded systems tend to be either imprecise or expensive. In this paper, we propose an approach to tackle this problem using automatic abstraction refinement. The key idea is to heuristically choose abstraction levels based on information about dependencies between states and detected potential information leakage. Our approach builds on previous work, where we leverage symbolic execution to precisely capture data, control, timing, and event dependencies between processes within an IFA. To capture values symbolically, this analysis uses abstract interpretation. While the existing approach requires manual definition of abstraction levels, our novel contribution in this paper is using carefully designed heuristics to select these levels automatically. The aim is to keep analysis times acceptable while also retaining enough information to decide whether or not illegal information flow is possible. We have implemented our approach for the system design
language SystemC and demonstrate its feasibility with experimental results on several shared bus architectures.
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Robotics
Markerless Multi-Modal Autonomous Robotic Inspection of Large Space Structures
Juan De Dios Alfaro, Arturo Ríos, David Rodríguez-Martínez et al. (+1 more)
Future orbital infrastructures, such as deployable
antennas,
solar farms, and large orbital platforms will require autonomous inspection systems able to operate with limited prior knowledge and without cooperative markers. Current on-orbit servicing approaches often rely on predefined trajectories, standard interfaces, fiducial markers or accurate target models, which limits scalability for large, heterogeneous or partially unknown structures. This paper presents a markerless autonomous robotic inspection pipeline in which 3D reconstruction is used as an inspection-support representation. The system integrates a Kinova Gen2 manipulator with an end-effector-mounted
multimodal sensor head composed of an RGB-D camera, a thermal camera and a 2D
LiDAR. The pipeline estimates an approximate inspection volume, generates viewpoints, plans collision-free motions with MoveIt, and synchronously records RGB-D images, thermal data, and
robot poses in ROS2. Candidate reconstruction methods were evaluated to select a practical method for this pipeline, with Nerfacto used for
geometric reconstruction and Thermal-Nerfacto used to demonstrate thermal-aware rendering for inspection. Validation in a Gazebo-based simulator and preliminary laboratory tests reveal that the proposed system can autonomously acquire spatially coherent inspection data and produce reconstructions suitable for visual and
geometric assessment, representing a step towards inspection of large non-cooperative space structures.
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All authors Juan De Dios Alfaro, Arturo Ríos, David Rodríguez-Martínez, Carlos Pérez-del-Pulgar
Computation and Language
TTLab at AlexandriaX-2026: A Fine-Tuned Surface Tagger for Arabic Machine-Translation Error-Span Detection and Classification
Ali Abusaleh, Bhuvanesh Verma and Alexander Mehler
We present TTLab's submission to the AlexandriaX-2026 Subtask~3 on Arabic MT error span detection and classification. Our system frames the task as
token-level classification over surface forms, preserving character offsets to ensure exact
alignment with the evaluation metric. To handle severe label imbalance, we employ a focal loss with class weighting and dialect-specific decoding thresholds. Among six Arabic pre-trained encoders, MARBERTv2 achieves the best overall performance of 40.8 and 40.91 on the development and test set, respectively, ranking $\nth{3}$ out of all participating teams. While our system localizes error spans effectively, classification of rare error types remains challenging, highlighting the need for data augmentation for tail categories. The code is available at ${\href{https://github.com/ENTAILab/arabic-dialectal-mt-error-span-detection}{\faGithub~ TTLab at AlexandriaX-2026}$
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Emerging Technologies · Cross-listed from physics.app-ph
Dynamical Diversity for Reservoir Computing in Reconfigurable Nanomechanics
Humayun Ahmed, Inês S. Garcia, Filipa C. Mota et al. (+7 more)
Physical reservoir computing uses nonlinear dynamics and a trained linear readout to process information. Nanoelectromechanical (NEMS) resonators combine
geometric Duffing nonlinearity with fading
memory, but most electromechanical implementations use a single resonance mode. Here, we demonstrate reservoir computing with two interacting modes of a single NEMS resonator measured through one readout port. We introduce dynamical diversity through complementary modal drive settings: the same input sequence is replayed under different allocations of drive amplitude between the modes, and the responses are concatenated into a single feature
matrix. This multiplexing expands the representation available to the readout without additional devices or training of internal parameters. On NARMA-2, it reduces variance-normalized test error more than 28-fold relative to single-mode operation and more than threefold relative to the best individual two-mode setting. Linear
memory-capacity measurements show that accessible recall spans only a few symbols at the tested symbol duration. Its rapid decline with delay, consistent with mechanical dissipation, accompanies rising NARMA error and the eventual loss of multiplexing gains at higher orders. We also use electrical feedthrough as an internal reference for assessing the computational contribution of the NEMS response. Separate linear readouts are trained on feedthrough features and features derived from the measured NEMS response, using the same recordings and matched drive settings and processing. On challenging nonlinear mapping tasks, the multiplexed NEMS features yield substantially lower errors than the feedthrough features. These results demonstrate how dynamical diversity through variations in modal drive amplitudes expands the computational capability of a single multimode NEMS resonator.
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All authors Humayun Ahmed, Inês S. Garcia, Filipa C. Mota, Muhammad Asim, Ghaith Mahook, Suleyman Celik, Yunus Selcuk, Filipe S. Alves, Alper Demir, M. Selim Hanay
Software Engineering
CodeGraph: Open-Taxonomy Knowledge Graph for Source Code with Wikidata Grounding
Federico Pennino, Andrea Gurioli, Stefano Zacchiroli et al. (+2 more)
Public software repositories, like GitHub and Software Heritage Archive, store billions of files, yet extracting their implicit engineering knowledge —i.e., the
algorithms they implement, the paradigms they follow, the patterns they instantiate, and the application domains they serve— remains challenging, as current tools are constrained to syntactic and
token-level analysis. We present a pipeline for building an open-taxonomy semantic annotation of source code using a code-specialised Large
Language Model. The extracted entities are grounded in Wikidata through a three-stage linking procedure: a deterministic SPARQL stage handles unambiguous entities, a Deep Research
Agent resolves the residual long tail, and a hierarchy-rollup stage imports the parent-of closure of each resolved Wikidata identifier. The resulting annotations are materialised as a source-code-specific open-taxonomy knowledge graph. We further introduce a calibrated quality-assurance protocol that quantifies annotation precision by combining a small human gold set with an LLM-as-a-judge filter. We applied our pipeline to the 167 million files of the Stack-Edu corpus, creating the first known large-scale open-taxonomy knowledge graph for source code. Our graph, named CodeGraph, contains approximately 158 million nodes, which include around 145 million files, about 63,000 extracted concept entities (such as
algorithms, paradigms, design patterns, and application domains), and roughly 19,800 grounded Wikidata entities. Furthermore, CodeGraph features approximately 1 billion typed edges that connect files to their respective concepts, link these concepts to their grounded Wikidata identifiers, and relate them to their parent categories, covering 14 programming
languages.
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All authors Federico Pennino, Andrea Gurioli, Stefano Zacchiroli, Maurizio Gabbrielli, Paolo Ferragina
Software Engineering
Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning
Qusay H. Mahmoud
Generative artificial intelligence has moved from a disruptive novelty to a recurring part of software-development and computing-education workflows, while software
agents are beginning to act across repositories, command lines, browsers, tests, and other tools. The educational problem is no longer whether students should be allowed to generate code, but whether software-engineering (SE) programs can preserve and assess human understanding while preparing students to work responsibly with increasingly capable AI systems. This paper presents a structured integrative review of research and practice from 2023 through 23 September 2026, supplemented by established work on AI literacy, technical debt, and human-AI collaboration. The evidence supports a conditional conclusion: GenAI can improve access to explanations, feedback, practice, and short-term task completion, but learning outcomes depend on prior knowledge, scaffolding, verification, task design, and assessment. We examine student help-seeking and authorship, faculty assessment and policy demands, and the wider SE risks created when generated artifacts persist across repositories, teams, architectures, and deployed systems. We extend the concept of comprehension debt: the deferred learning and maintenance cost that arises when AI-assisted production outpaces a learner's or team's ability to explain, test, modify, and justify the resulting software. We then refine the AI-Augmented Software Engineering Education (AASEE) framework into five non-linear integration levels and four cross-cutting evidence obligations: explain, verify, modify, and account. The framework links delegation to evidence, governance, and recovery mechanisms appropriate to its consequences.
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Systems and Control
Hierarchical Edge Computing in SAGSIN: Multi-Layer Network Architecture and Multi-Level Information Processing
Jiajie Xu, Zhengying Lou and Mohamed-Slim Alouini
Maritime Internet of Things (IoT) deployments increasingly rely on the space-air-ground-sea integrated network (SAGSIN) to connect
underwater sensors with terrestrial and space backbones. However, the heterogeneous links along this path, ranging from bandwidth-limited and energy-hungry
underwater acoustic channels to high-capacity optical links above the sea, make the transport of massive raw sensing data costly in both latency and energy, and difficult to sustain for unattended, battery-powered nodes. This article presents an edge-computing paradigm for SAGSIN built on two coupled ideas: a Multi-Layer Network Architecture (MLNA) that organizes the
underwater, surface, aerial, and ground/space tiers, and Multi-Level Information Processing (MLIP) that progressively refines data from raw measurements toward compact, event-level representations as they ascend the network. We characterize the computation, transmission, and storage energy at each tier and show how distributing refinement across layers trades local processing cost against transmission and storage savings. We then discuss how MLNA-MLIP reduces latency, strengthens data
privacy, improves service reliability, and manages energy to prolong network lifetime. A case study on offshore monitoring quantifies the resulting lifetime gains and identifies the optimal processing depth. Open challenges and future directions are outlined.
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Machine Learning
Direct Message Approximation (DMA): A Consistency-Based Framework for Tractable Approximate Inference on Factor Graphs
Ralf Herbrich, Rainer Schlosser, Jan Lemcke et al. (+11 more)
Approximate message passing on factor graphs underlies two dominant families of probabilistic inference
algorithms: expectation propagation (EP) and variational message passing (VMP). Both methods approximate the marginal at each factor edge, forcing an iterative round-robin schedule, risking negative-precision messages, and, for VMP, collapsing to point estimates at Dirac-delta factors. We introduce Direct Message Approximation (DMA), which approximates factor-to-variable messages directly rather than the marginal. For normalisable factors, we define a consistency condition (requiring exactness when all other incoming messages are Dirac deltas) to guide message construction. We prove a master
theorem (proper messages, any graph) bounding marginal KL from message KL, with three structural corollaries: Dirac-input consistency, no EP-style inner-loop iteration, and no negative-precision messages. Further, we prove a complementary $O(1/r^2)$ guarantee for the inherently improper backward message of the product factor, whose closed-form treatment has resisted prior work. As a concrete instantiation, we derive explicit DMA messages for the product and leaky-ReLU factors and assemble a
Bayesian neural network (BNN) inference
algorithm with one forward/backward sweep per training example and no gradient learning-rate hyperparameter, validating that the structural guarantees translate to predictive uncertainty that widens in data-sparse regions, including under model mismatch.
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All authors Ralf Herbrich, Rainer Schlosser, Jan Lemcke, Johann Ukrow, Anna Kazachkova, Nicolas Alder, Leonhard Hennicke, Theo Bardey, Nico Grimm, Luca Kleinschmidt, Philipp Kolbe, Cezary Kujath, Johanna Schlimme, Karl Matti Schütz
Artificial Intelligence
SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories
Jiajun Wu, Leixin Sun, Zihan Tan et al. (+12 more)
Large
language model based coding
agents have made substantial progress on repository-level software engineering tasks. Existing repository
benchmarks, however, usually start from a human-identified issue and evaluate whether a patch satisfies a functional signal. We present SWE-Prometheus, a
benchmark for the broader task of improving repository engineering governance. Each task provides a fixed snapshot and an open-ended objective, requiring the
agent to identify risks, prioritize interventions, and verify the resulting changes. SWE-Prometheus evaluates six governance dimensions through paired evidence, clean-environment probes, behavior gates, and two independent teacher ratings of the same evidence. The
benchmark contains 60 repositories; ten models are evaluated on a shared 22-repository public subset, where mean Normalized Governance Improvement ranges from 0.0568 to 0.5760 and observed behavior-breakage rates range from 0% to 23%. On a frozen ten-repository batch, a repository-blind template obtains mean NGI 0.272, but its gains concentrate in Tests & CI, Quality Gates, and Documentation; it improves Reproducible Environment and Dependency & Security on none of the repositories. This baseline makes the distinction between adding governance artifacts and producing execution-backed improvements measurable. The no-op condition has median NGI zero and standard deviation 0.073; two teachers agree exactly on 57 of 60 dimension scores for the same no-op evidence. For the two highest conditional-mean systems, common-valid NGI is similar, while full-pool comparisons that include behavior failures favor Kimi-K3. These results show why repository-governance evaluation should report improvement, behavior preservation, evidence quality, and coverage together.
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All authors Jiajun Wu, Leixin Sun, Zihan Tan, Yitao Liu, Zhenjun Zhao, Danyang Zhang, Shuo Li, Jiaru Qian, Yuxin Wu, Shanghaoran Quan, Chuangxin Zhao, Yangxu Liao, Yang Liu, Bin Chong, Guancheng Wan
Sound
TS-OPD: Reconciling ASR and QA in Speech Language Models via Task-Specific On-Policy Distillation
Yujie Guo, Hongjie Chen, Jian Kang et al. (+3 more)
Speech Language Models (SLMs) inherit strong instruction-following capabilities from pretrained
language models, yet ASR specialization can substantially degrade them. To address this ASR–QA trade-off, we propose Task-Specific On-Policy
Distillation (TS-OPD), which leverages models before and after ASR specialization as complementary QA and ASR teachers. The student generates separate task-conditioned trajectories for ASR and QA, each supervised only by its corresponding teacher, thereby reducing direct competition between the two supervision signals. Experiments on basic ASR, contextual ASR, and QA demonstrate that TS-OPD improves recognition while preserving QA capability. Moreover, TS-OPD remains robust across different balancing coefficients and continues to benefit from increased
distillation data.
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All authors Yujie Guo, Hongjie Chen, Jian Kang, Jie Li, Yongxiang Li, Yong Qin
Computational Engineering
Dynamic production control and deadlock prevention in conveyor equipped manufacturing systems
Claudio Castiglione, Erica Pastore and Arianna Alfieri
Flexible CONWIP approaches enable the dynamic production control to align production performance with production targets in the presence of high-fluctuating demand and system variability. However, frequently changing the WIP level might generate nervousness and, thus, can be practically infeasible. This paper investigates the combined use of job sequencing and routing to enable dynamic control in a CONWIP system comprising six unreliable workstations interconnected by conveyor carousels. Job sequencing and routing have been investigated using scenario analysis and discrete-event simulation. The results shed light on the effectiveness of the proposed approach, which improves throughput and enables dynamic production control without changing the WIP level. The results also provide managerial insights for system design, considering the impacts of the job-handling system (transport) and the minimum digital and technological requirements from Industry 4.0 for implementing the proposed approach.
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Computer Vision
AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture
Shengjie Guo, Xin Li, Borjana Arsova et al. (+2 more)
Accurate counting and localization of plants and their organs support phenotyping and yield estimation, yet target appearance, scale, and density vary widely across species and imaging conditions. Exemplar boxes specify the target without category-specific retraining, and point predictions identify the individual instances contributing to the count. We introduce AgriCountDINO, a parameter-efficient exemplar-guided framework for joint counting and localization. It conditions frozen multiscale DINOv3 features on exemplar appearance and size, then progressively decodes them into target points. Missed-object recovery extends supervision to targets overlooked by initial matching, and exemplar-adaptive point NMS filters duplicate predictions according to exemplar scale. With 8.4M trainable parameters, approximately one-tenth of TasselNetV4's, AgriCountDINO achieves a three-shot MAE of 11.92 on the TPC-268
benchmark, reducing counting error by 9.7% while providing individual target locations. Trained only on TPC-268, it achieves a zero-shot MAE of 14.25 on unseen generic object categories in FSC-147, improving upon the best compared zero-shot method by 6.0% without target-domain training or fine-tuning.
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All authors Shengjie Guo, Xin Li, Borjana Arsova, Hanno Scharr, Silvio Salvi
Machine Learning
Precise Convergence Speed of Clipped SGD
David A. R. Robin
We present a tightened convergence analysis of clipped gradient descent on $(L_0, L_1)$-smooth functions, with quantitative constants. Building on the ideas of Koloskova et al (2023), we refactor several case disjunctions to reveal the central role of a control of the bias derived from fundamental properties of $\ell_2$-projection, simplifying proofs. We also extend the domain of validity from $\eta \leq 1 / (9 \beta)$ to $\eta < 1 /\beta$ where $\beta = L_0 + c L_1$ for clipping constant $c$, which matches the more traditional analysis of smooth functions. We strengthen the convergence criterion from $\left( \min_{t < T} \mathbb{E}[\lVert \nabla f(x_t) \rVert_2] \right)$ to $\left( \frac{1}{T} \sum_{t < T} \mathbb{E}[\lVert \nabla f(x_t) \rVert_2] \right)$ with matching speed, and lower the final achievable loss from $\mathcal{O}(\min(\sigma^2/c, \sigma))$ to the more precise $6 \min(\sigma^2 /c, 3 \sigma)$.
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Computer Vision
Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space
Jaron Yeh, Yen-Wei Chang, Jiang Liu et al. (+1 more)
Industrial anomaly detection (IAD) is evolving beyond conventional detection and localization toward
multimodal inspection systems that can describe, explain, and reason about fine-grained defects. Although recent
multimodal large
language model (MLLM)-based methods improve anomaly understanding through textual
reasoning and visual guidance, they face two limitations in fine-grained inspection. First, their visual refinement often requires iteratively revisiting local image regions or augmenting with additional tools. Second, the resulting local defect evidence may not be reliably preserved throughout subsequent
reasoning. To address these, we propose Anomaly-LR, a defect-grounded latent
reasoning framework that first forms a global understanding of the input and then progressively refines anomaly-relevant representations directly in the visual latent space. We further construct IAD-LR-22K, the first IAD instruction
dataset designed for latent
reasoning, containing 22,228 image-question instances from 4,523 industrial images, with global textual
reasoning traces and region-level visual annotations. Extensive experiments show that Anomaly-LR achieves state-of-the-art performance among comparable-scale methods across multiple IAD
benchmarks, without requiring external references or tools. The code and data will be released at https://github.com/Yen666/Anomaly-LR.
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All authors Jaron Yeh, Yen-Wei Chang, Jiang Liu, Shao-Yuan Lo
Computer Vision
Dense Coverage, Sparse Refinement: Byte-Constrained Cooperative Perception
Melih Yazgan, Timon Müller and J. Marius Zöllner
Collaborative perception improves autonomous perception by sharing intermediate Bird's-Eye-View (BEV) features across connected
agents, but dense feature exchange is difficult to deploy under strict Vehicle-to-Everything (V2X) bandwidth limits. Existing efficient methods typically either compress the full feature map uniformly, spending bits on low-value background, or sparsify communication, risking the loss of useful context. We propose a coverage-refinement design for byte-constrained cooperative perception: each
agent transmits a highly compressed coarse layer over the full BEV map and allocates the remaining budget to selected high-resolution patches. A Task-Aware Benefit Selector ranks
cells by estimated downstream utility, enabling deterministic budgeted refinement and zero-retraining adaptation to changing bandwidth. The receiver reconstructs a dense BEV tensor compatible with standard fusion modules. Experiments on DAIR-V2X and OPV2V show strong accuracy-payload trade-offs at kilobyte-scale budgets. On DAIR-V2X, our method reaches 0.60 AP@0.7 at only 1.87 KB per non-ego
agent, compared with 0.52 at 4.61 KB for uniform SimVQ compression. Controlled diagnostics further show that the gain arises from coverage-refinement allocation rather than quantization alone. Code will be published.
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Information Retrieval
A Systematic Multi-Domain Evaluation of Document Retrievers
Valentin Velev and Andreas Spitz
Document
retrieval is a crucial component of many modern AI systems, directly influencing their effectiveness, robustness, and
fairness in downstream tasks. While recent years have seen a growing number of retrievers, comparative studies in the literature are typically limited in scope or focused on singular
benchmarks, domains, or model families. This fragmentation makes it difficult to draw reliable conclusions about the relative strengths, weaknesses, and trade-offs of document retrievers. To address this gap, we conduct a large-scale empirical evaluation of document retrievers, covering three families (sparse, dense, and expansion-based) and evaluating 33 retrievers across seven IR
datasets, analyzing
retrieval quality, runtime, and failure points. Rather than tuning each model individually, we evaluate every retriever off the shelf, under the configuration reconstructable from its public documentation and a uniform compute budget. Our results show that NV-Embed-v2 achieves the strongest performance on four of the seven
datasets, albeit at the cost of substantial query latencies. Among sparse retrievers, we find that SPLADE-v3 rivals the top-performing approach despite much lower latency, and even achieves top scores on MS MARCO. On instruction-following
datasets, GritLM delivers the best performance. Finally, an analysis of the retrievers' failure points reveals contrasts between models and families that indicate potential for unrealized gains in
retrieval performance.
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Software Engineering
Demystifying Agent Skills for Smart Contract Auditing: Design, Effectiveness, Behavioral Impact
Cuifeng Gao, Juantao Zhong, Jiachi Chen et al. (+2 more)
LLM
agents, notably Claude Code and OpenAI Codex, are emerging as versatile tools beyond coding
agents only. These
agents can be enhanced with skills—reusable artifacts that package domain knowledge, workflows, and tool-use instructions. To date, however, little is known about how such skills are designed or how they affect
agent effectiveness and behavior in practice. In this paper, we investigate these questions in smart contract security
auditing, a domain in which
agents have shown substantial promise. We systematically collect 83 smart contract audit skills from the wild and evaluate them on EVMBench across seven
agent–model configurations. Our study examines three dimensions: (i) the design characteristics of audit skills, including their structure, knowledge representations, workflows, and tool dependencies; (ii) their effectiveness in improving vulnerability detection; and (iii) their influence on
agent execution trajectories. We find that audit skills are mostly lightweight but heterogeneous in design, covering a broad yet imbalanced range of vulnerability types. Their effectiveness is determined primarily by the model rather than the
agent harness: Codex/GPT-5.5 achieves the largest gains, improving detection score by 22.8% and captured award by 43.2%. We further find that skill triggering is a key bottleneck. When triggered, skills preserve a shared six-stage audit workflow while exhibiting distinct loading patterns and differential effects on
agent behavior across configurations. We release our skill corpus and artifacts to support future research.
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All authors Cuifeng Gao, Juantao Zhong, Jiachi Chen, Shuai Wang, Daoyuan Wu
Machine Learning
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
Sam Urmian, Qinyi Liu and Mohammad Khalil
We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-
privacy guarantee passes through selection and its audit trace by post-processing. End-to-end
privacy holds only when selector inputs are public or independent, previous private outputs, or separately
privacy-accounted; fixing raw state or candidate information instead yields only a conditional guarantee. Second, we derive a logged margin certificate: bounded score-induced objective movement below half the smallest greedy decision margin guarantees that the ordered slate is unchanged. Controlled fixed-margin tests show near-linear exponent scaling, with an empirical slope of $-0.220$ (95% CI $[-0.231,-0.210]$) against the independent-noise reference $-1/4$. Real-anchor experiments on OULAD, MovieLens-25M, and Amazon Musical Instruments show that greater anchor weight reduces score-noise-induced ranking churn. OULAD and EdNet certificate checks validate the implementation of the logged inequality, while closed-loop simulations show bounded target drift and setting-dependent downstream utility. The contribution is therefore a
privacy-scope contract and a certifiable score-to-slate stability mechanism, not a universal utility claim.
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Human-Computer Interaction · Cross-listed from eess.AS
Voice Agents under Acoustic Stress: From Signal Degradation to Interaction and Action
Amir Ivry, Kai-Wei Chang, Lin Zhang et al. (+2 more)
Voice
agents must complete users' tasks despite noise, reverberation, and competing
speech. Evaluating
agents' robustness therefore requires following how acoustic conditions affect the conversation and the actions taken on the user's behalf. This overview examines what existing
benchmarks reveal about
agents' ability to complete tasks under acoustic stress and where further task-based evaluation is required. We then introduce TRACE, a practical workflow for designing, running, and interpreting evaluations of acoustic robustness in task-oriented human-
agent interactions: the same
agent attempts a specified task with an original recording and an acoustically stressed copy, and the resulting conversations are scored for task completion, wrong actions, recovery, and user effort. Finally, we explain how results from these evaluations can guide changes to an
agent to prevent wrong actions and improve recovery.
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All authors Amir Ivry, Kai-Wei Chang, Lin Zhang, Sharon Gannot, Carlos Busso
Data Structures and Algorithms
On Kernels and Leaves: Searching for Bare and Lush Trees
Jesse Beisegel, Ekkehard Köhler, Robert Scheffler et al. (+1 more)
We study a variation of the classical Maximum (Minimum) Leaf Spanning Tree problem. In many applications, Depth-First Search (DFS) is used to compute a spanning tree of a graph. Such a search tree is constructed by connecting each vertex $v$ with the last vertex the search has visited before $v$ and we call this a last-in tree. By restricting the Maximum (Minimum) Leaf Spanning Tree problem to last-in trees of a graph search, we ask for a search ordering that leads to the largest (smallest) number of leaves in its search tree. Recently, Bergougnoux et al. [Journal of Computer and System Sciences 154 (2025)] have studied the parameterized complexity of these problems for DFS. They showed that the minimization problem is para-$\mathsf{NP}$-hard and the maximization problem is $\mathsf{W}[1]$-hard when parameterized by the number of leaves. When parameterized by the number of internal vertices, both problems have
polynomial kernels. Here, we examine whether these results also hold for the variant Lexicographic DFS (LDFS). We show that the hardness results of DFS can be transferred to LDFS. We also present exponential kernels for the number of internal vertices as the parameter. We complement this by showing that
polynomial kernels do not exist, unless $\mathsf{NP} \subseteq \mathsf{coNP} / \mathsf{poly}$. We also consider last-in trees of searches that do not follow the DFS scheme. In contrast to (L)DFS, minimizing the number of internal vertices is para-$\mathsf{NP}$-hard for several searches including Breadth-First Search.
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All authors Jesse Beisegel, Ekkehard Köhler, Robert Scheffler, Martin Strehler
Computation and Language
YODAS v3: Over 1 Million Hours of High-Bandwidth, Stereophonic, Multilingual Speech
William Chen, Shinnosuke Takamichi, Sayaka Shiota et al. (+3 more)
We present YODAS v3, a weakly-labeled
speech corpus containing over 1.1 million hours of 48kHz multi-channel audio in 147
languages, released under a CC BY 3.0 license. YODAS v3 is not only the largest open
speech dataset to date, but also the first truly large-scale
speech corpus with high-fidelity stereo audio. We first provide the collection methodology for the corpus, where we introduce new techniques for gathering
language-balanced
speech data. The effectiveness of our approach is shown by the
language distribution of the crawled data: 22
languages in YODAS v3 have over 10K hours and 73
languages have over 5K hours of data. We then conduct extensive analyses on the composition of the data, such as the distribution of
languages, audio quality, and transcription quality. Finally, we train baseline
speech recognition and neural codec models to show the effectiveness of the
dataset. Download at https://huggingface.co/
datasets/espnet/yodas3.
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All authors William Chen, Shinnosuke Takamichi, Sayaka Shiota, Satoru Fukayama, Samuele Cornell, Shinji Watanabe
Computer Vision
Frame-to-Panorama Localization and Context-Aware Sampling for Scene-Specific Ship Detection in a Smart Marina Testbed
Ignat Romanov, Andreas Hadjipieris and Neofytos Dimitriou
Smart maritime infrastructures provide continuous access to heterogeneous sensing streams, enabling repeated experimentation, digital-twin development, and AI-based maritime services. However, sensing hardware alone is not sufficient for scene-specific model development: historical
video streams must also be spatially indexed, contextualized, and reduced to informative subsets for annotation. This paper presents a frame-to-panorama localization and context-aware sampling pipeline for ship detection in historical PTZ maritime
video lacking reliable pan, tilt, and zoom metadata. The main contribution is an end-to-end data-curation approach that recovers camera-view information from historical PTZ
video and combines it with environmental context and visual diversity to construct compact, scene-specific training sets. Specifically, frames are localized on a reference panorama using SuperPoint and LightGlue, enriched with weather and
solar-state metadata, and selected through diversity sampling to preserve variation across camera view and environmental conditions. A second context-aware stage targets under-represented distant-vessel cases near the horizon using tile-level visual
embeddings and Gaussian Mixture Model clustering. Applied within the CMMI MDigi-I Smart Marina testbed, the proposed pipeline reduces 40,718 candidate frames to 220 images for annotation, corresponding to a 99.5% reduction. A YOLO26-m detector fine-tuned on this subset achieves a mean AP50 of 94.78% $\pm$ 0.51% and a mean AP50-95 of 75.10% $\pm$ 1.73% under sequence-grouped five-fold cross-validation. These results demonstrate that highly redundant infrastructure
video streams can be transformed into compact, spatially and contextually diverse training sets for scene-specific detector adaptation while substantially reducing annotation effort.
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Machine Learning
SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM
Mengli Wei, Mengkai Zhu, Jiawen Chen et al. (+2 more)
Reducing communication in derivative-free decentralized learning requires controlling the disagreement accumulated over multiple local updates. This paper develops SPADE-DFL, a primal–dual method that allows the number of local function-value updates between neighbor exchanges to grow with the computation budget while preserving the nonprivate convergence order. For smooth nonconvex objectives under uniform query-moment bounds, the prescribed nonprivate schedule achieves a time-averaged stationarity and consensus bound of $\mathcal{O}(T^{-1/3})$ using only $\Theta(T^{2/3})$ communication rounds, where $T$ is the number of local updates per client. For private training, the accumulated data-dependent increment is isolated from the graph correction, allowing one protected state per client and round to generate all outgoing messages. We prove client-level differential
privacy for the full interactive transcript and quantify the resulting optimization error over a finite horizon. Experiments on four classification tasks show that SPADE-DFL achieves higher mean test accuracy than existing decentralized learning methods.
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All authors Mengli Wei, Mengkai Zhu, Jiawen Chen, Wenwu Yu, Duxin Che