Artificial Intelligence
Refuse, Decompose, Refresh: A Claim-Safe Protocol for Closed-Loop AI Evaluation
Peiying Zhu and Sidi Chang
An AI evaluation can be perfectly reproducible and still support the wrong claim. This risk is acute in closed-loop systems: policy determines visited states, observable components, and which failures leave a measurable trace. We propose a claim-safe protocol with three actions. Refuse: abstain when a clean reference stream or matched runtime comparison lacks support. Decompose: report protocol execution, operational false admission, and structural hypotheses separately rather than as one PASS/FAIL label. Refresh: treat distribution-shift alarms as requests to invalidate and recompute a reference map, not as fault evidence. We instantiate the protocol in an aggregate-only simulator with 24 policy components, three demand regimes, two fault-mask families, and independent development and heldout seeds. The preregistered heldout contains 1,440 cases and 21,600 partition rows. Only 55/72 regime-component units were reference-admitted and 54/55 remained runtime-admitted, making abstention part of the result. Stable false admission was 0/20 represented components, with a one-sided exact 95% upper bound of 0.1391 under a frozen 0.20 rule. Within admitted units, affected clean traffic outpredicted nominal fault-
cell fraction: across 540 unit-arm rows nested in 20 component clusters, the
cell-minus-traffic negative-log-likelihood difference was 0.1264 nats per row, with a 95% component-cluster interval of [0.0593, 0.1918]. A drift log shows why "null" must be reference-relative: clean fault-null streams triggered 15/15, 0/15, and 14/15 alarms across three regimes, while only the middle regime matched the frozen detector reference. Rather than a universal threshold, we contribute an executable contract linking observable support, statistical calibration, and justified claims.
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Artificial Intelligence
FreqCondNorm: Towards Cross-domain Predictive Maintenance through a Frequency-Conditioned Transformer Foundation Model
Zaynab Raounak, Camille LHermine and Zhiguo Zeng
Deep learning predictive maintenance models suffer from poor transferability across machines and operating conditions, especially when labelled data are scarce and signals span five orders of magnitude in sampling frequency (1 Hz to ~100 kHz). We propose FreqCondNorm, a
Transformer-based architecture that introduces a FiLM-style frequency-conditioned normalization layer to unify heterogeneous time-series within a single model. The architecture is pretrained on five public predictive maintenance
datasets (CWRU, MFPT, UOC18, PRONOSTIA, CMAPSS) using masked auto-encoding and contrastive learning with balanced domain sampling. On fault diagnosis, the model achieves 99.2% accuracy on CWRU (+6.4 pp over CNN) and 82.1% zero-shot accuracy on MFPT, demonstrating strong transfer across sampling frequencies. However, the approach does not improve remaining useful life prediction, suggesting a mismatch between pretraining and RUL objectives that warrants future investigation.
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Cryptography and Security
Towards TEE-Certified DP: Verifiable Differentially Private Training on Legacy GPUs
Li Ge, Wenjie Qu, Weitao Feng et al. (+4 more)
Wide adoption of machine learning has created growing policy and regulatory demand for protecting sensitive training data, with differential
privacy (DP) emerging as a key mechanism. Yet a less-studied problem is how to certify the faithful execution of DP during training: an external verifier should be able to check that a released model was trained with proper DP protection, without accessing the private training data. Existing cryptographic approaches, such as zero-knowledge proofs, provide strong guarantees but often incur prohibitive overhead, in some cases by orders of magnitude. Trusted Execution Environments (TEEs) offer a more efficient alternative, but the multi-GPU TEE support needed for training and fine-tuning large
language models remains limited to recent platforms and is absent or inefficient on legacy GPUs. To address this, we propose a practical framework for verifiable DP training using CPU-side TEEs together with untrusted GPUs. Our design addresses a fundamental efficiency-security tension: training entirely inside a CPU TEE is too slow, while unrestricted GPU offloading can allow malicious deviations from DP. We therefore offload expensive gradient computation to GPUs, while using the CPU TEE to efficiently verify the correct enforcement of DP on gradients through probabilistic checking. Our framework detects frequent full deviations from DP with high probability; for the utility-oriented forged-gradient attacks evaluated in this work, sparse deviations provide limited utility benefit and show no measurable additional membership leakage. Experiments further show that our approach nearly achieves a “free lunch”: it incurs only modest overhead compared with standard GPU-based DP training, while effectively constraining malicious deviations from the claimed DP execution.
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All authors Li Ge, Wenjie Qu, Weitao Feng, Yi Zeng, Jiaheng Zhang, Xiaofeng Wang, Wei Dong
Computation and Language
Relational Attention for Data-Efficient Language Modeling
Adrian Brasoveanu, Ece Takmaz and Jakub Dotlačil
We present Relational BabyLM, a system submission to the BabyLM 2026 challenge that combines two cognitively motivated inductive biases in a single decoder-only
Transformer. Architecturally, we replace standard self-
attention with a Dual
Attention Transformer (DAT), which separates the routing of object-level ("sensory") lexical features from structural/relational information (Altabaa and Lafferty, 2025; Altabaa et al., 2024; Webb et al., 2024; Kerg et al., 2022; Webb et al., 2021). Relational
attention (RA) disentangled from self-
attention greatly increases data efficiency and out-of-training-sample generalization on purely relational tasks, but
language modeling requires object-level and relational information to be integrated as well as disentangled, and RA-based LMs have remained largely unexplored. BabyLM's data-constrained training and comprehensive evaluation is an ideal testing ground for whether that data efficiency transfers. As a training intervention, we add a Next-Latent Prediction (NextLat; Teoh et al. 2026) objective that encourages hidden states to compress history incrementally into a dense belief state. Architecture is the dominant factor for structural linguistic generalization; the objective is secondary but still significant. DAT's three relational
attention types (full RA vs. the simpler RCA and DisRCA variants) are largely interchangeable at 10M words; full RA pulls ahead at 100M. We also introduce a novel symbol-
retrieval mechanism (RoPE-based, as opposed to learned, relative symbols) that matches learned symbol libraries while adding no parameters. On the strict (100M-word) track, our best model ranks 6th of 55 overall and 3rd of 55 on the leaderboard's NLP-task subset at the time of writing; our two strongest models outperform the GPT-2 baseline on most
benchmarks, with one attaining the highest EWoK score among strict-track entries.
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Graphics
S4R: Scaling for Rigid-Body Interpenetration Resolution
Zhiyang Dou, Ang Zhao, Chen Peng et al. (+8 more)
Rigid-body interpenetration frequently occurs in procedurally assembled and generated scenes and must be removed before downstream applications such as physical simulation. We present S4R (Scaling for Rigid-Body Interpenetration Resolution), a scale-continuation method for static interpenetration repair. S4R first uniformly shrinks each body about a fixed reference center to a small initial scale, at which the layout is penetration-free, and then restores full scale through a sequence of minimum-norm convex contact quadratic programs (QPs) that target the linearized separation margin during continuation. Resolution thereby replaces one deep correction with a sequence of shallow-contact subproblems. A conservative scale-event bound and frozen-witness gap predictions cut the number of exact mesh queries; the continuation then ends with a full-scale evaluator check and bounded tail refinement. We evaluate S4R on Kubric, HY3D-Bench, and Thingi10K using a shared mesh-level evaluator and a unified per-scene timing protocol. In the main comparisons on all three
benchmarks, up to N=5000 bodies, S4R reaches zero reported penetration with displacement that stays small and nearly independent of scene size, and at the lowest wall time within each hardware tier among the compared methods. A GPU implementation extends these results to large-scale scenes. Our code and data can be found on our project page: https://frank-zy-dou.github.io/projects/S4R/index.html.
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All authors Zhiyang Dou, Ang Zhao, Chen Peng, Minghao Guo, Haixu Wu, Cheng Lin, Yuan Liu, Junfeng Yao, Xiaohu Guo, Wenping Wang, Wojciech Matusik
Machine Learning · Cross-listed from quant-ph
Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learning
Bo Tang, Zixuan Liao, Hao Li et al. (+9 more)
Quantum communication underpins secure information processing and scalable
quantum networks. In particular, remote state preparation (RSP) enables efficient
quantum state transfer, but accurately estimating target states under complex noise remains challenging. Here, we propose a
Transformer-based
Quantum State Characterizer (TQSC) model for noisy RSP experiments. Our model reconstructs experimentally prepared pure and mixed photonic polarization states from noisy measurements in complex scattering environments, while its
attention patterns provide physically grounded insights into correlations among the measured observables. The method achieves a mean
estimator-target fidelity exceeding 99.999% under complex scattering and dynamic Gaussian noise, while its robustness and generalization are further examined using Qiskit-simulated Bloch-ball states. Furthermore, in a practical MNIST image transmission task with held-out states, the decoded bit error rate is reduced from 50.34% to zero after TQSC post-processing. The TQSC model enables accurate tomographic characterization under dynamic noise and provides physically grounded post-hoc insights, holding promise for intelligent
quantum information processing applications.
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All authors Bo Tang, Zixuan Liao, Hao Li, Yilin Yang, Jiani Lei, Zengya Li, Jing Qiu, Zhaohui Dong, Zhengyang Mao, Yuanhua Li, Yuanlin Zheng, Xianfeng Chen
Artificial Intelligence
SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
Haozhe Liu, Tian Ye, Sensen Gao et al. (+11 more)
As coding
agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of
reasoning, tool use, and feedback.
Token efficiency therefore becomes important for scaling recursive self-improvement. We take an RSI-inspired approach at the harness layer, scaling auto-research loops across increasingly numerous and diverse environments for harness rollouts. At this scale, the process yields reusable improvements that transfer beyond their development setting, moving automated harness discovery toward production-level outcomes. Four mechanisms survive selection and form SoL-Pi, spanning action execution, context compaction, observation handling, and delegated reading. On the 51-task EdgeBench evaluation, SoL-Pi achieves performance comparable to Pi across GPT-5.6 Sol and Opus 5 while reducing recorded
token traffic by 44.7-49.0% and API cost by about one third. In other words, estimated hourly savings are \$8.75-\$13.50 relative to native Codex and Claude Code harnesses, and \$4.36-\$5.71 relative to Pi.
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All authors Haozhe Liu, Tian Ye, Sensen Gao, Qihang Cao, Yitong Li, Mingchen Zhuge, Duomin Wang, Ruihua Zhang, Ping Luo, Jiawang Bian, Lei Zhu, Ligeng Zhu, Enze Xie, Song Han
Systems and Control
Advances in Modeling Techniques for Ventricular Assist Devices: A Comprehensive Review and Future Directions
Muhammad Adel Yusuf, Nezar M. Alyazidi, Hamna Saleem et al. (+2 more)
Ventricular Assist Devices (VADs), particularly rotary Left Ventricular Assist Devices (LVADs), are essential for patients with advanced heart failure who are ineligible for transplantation. Despite advances in cardiovascular modeling and control, clinical translation of proposed LVAD control methods remains limited. This review examines the
evolution of LVAD modeling and control and argues that the gap results not from insufficiently sophisticated
algorithms, but from mismatches between modeling assumptions, sensing limitations, and real-world cardiovascular variability. A systematized review of more than 100 peer-reviewed studies covers mathematical cardiovascular models, lumped-parameter and reduced-order representations, classical and advanced control strategies, and emerging data-driven and machine learning approaches. The literature is synthesized using a problem-driven framework linking modeling and control choices to clinical challenges, including physiological observability, parameter identifiability, patient variability, and prevention of adverse events such as ventricular suction and thrombosis. The analysis shows that high-fidelity models and intelligent controllers can perform well in simulation, but their dependence on unmeasurable states, extensive parameter tuning, and dense sensing limits clinical implementation. Simpler control approaches often provide greater robustness under clinical constraints. Adaptive and data-driven techniques may help bridge this gap, but only if they account for implantable sensing limitations and interpretability requirements. By identifying structural barriers to adoption, this review synthesizes LVAD modeling and control paradigms and outlines research directions for physiologically adaptive, clinically implementable, and patient-safe LVAD systems.
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All authors Muhammad Adel Yusuf, Nezar M. Alyazidi, Hamna Saleem, Ali Nasir, Mojeed Oyedeji
Information Theory · Cross-listed from math.PR
Copula Operad and Copula Entropy
Xuexing Lu
We construct a symmetric operad $\mathfrak{C}$ on the class of all multivariate copulas, where operadic composition is given by Sklar substitution. We prove that the absolutely continuous subclass $\mathfrak{C}^{ac}$—which coincides with the $L^1$ class of copula densities—forms a suboperad; under composition, the density of the composite copula is given by the explicit Sklar substitution density formula $g(v)=\phi\big(\Psi_1(v^{(1)}),\dots,\Psi_n(v^{(n)})\big)\prod_{k=1}^{n}\psi_{k}(v^{(k)})$. Furthermore, we show that copulas with finite copula
entropy—identified with the $L\log L$ class of copula densities—are closed under substitution and hence constitute a suboperad $\mathfrak{C}^{L\log L}$. On this suboperad, copula
entropy is strictly additive: $H(\gamma(\Phi;\Psi_{1},\ldots,\Psi_{n}))=H(\Phi)+\sum_{k=1}^{n}H(\Psi_{k})$.
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Machine Learning
When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation
Yuxiao Yang, Tianrun Yu, Shangzhe Li et al. (+6 more)
We study length inflation in on-policy
distillation (OPD), where student responses can become excessively long and even exhaust the generation budget. We identify termination-
token mismatch between base students and post-trained teachers as an important source of this behavior. Across Qwen3, Llama, and Gemma, the two models can place their stopping probability on different EOS
tokens, even when their declared stopping sets are identical. This mismatch can suppress the student's preferred termination action without reliably transferring the teacher-preferred alternative. We show that aligning the decoding stopping set alone is insufficient, while treating functionally equivalent EOS
tokens as a shared semantic stopping action substantially mitigates mismatch-induced length inflation across all three model families. To further understand how termination behavior evolves over training, we study OPD across different K2-Horizon training stages. This stage-wise analysis shows that termination preferences can shift substantially during training, while also revealing a distinct length inflation late in the OPD run that persists beyond termination
alignment. Together, these results identify termination mismatch as an important, but not exhaustive, source of OPD length dynamics. We release an implementation incorporating the proposed termination-handling corrections.
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All authors Yuxiao Yang, Tianrun Yu, Shangzhe Li, Kaixiang Zhao, Xuchao Zhang, Chetan Bansal, Huaxiu Yao, Taylor W. Killian, Weitong Zhang
Machine Learning · Cross-listed from physics.chem-ph
Truncated automatic sparse differentiation for machine learning interatomic potentials
Marcel F. Langer, Adrian Hill and Michele Ceriotti
Machine learning interatomic potentials (MLIPs) learn the mapping from atomic positions to potential energy. The forces, the negative gradient of this energy, drive molecular dynamics and are readily obtained using automatic differentiation. Higher-order derivatives, most notably the Hessian, describe collective motion and allow the direct prediction of experimental observables, but are considered computationally inaccessible for large systems. We suggest a solution: in physical systems, interactions decay with distance, and most MLIPs build on this locality through message passing up to a finite receptive field. This implies both sparsity of higher-order derivatives and their decay with distance. This structure can be exploited using automatic sparse differentiation (ASD). We explain how to compute the sparsity pattern for MLIP derivatives and demonstrate that, for multiple foundation MLIPs, ASD computes full Hessians of large porous materials exactly, but with modest speedups at best. The larger gains come from truncated ASD: discarding small, but nonzero, Hessian entries between distant atoms yields order-of-magnitude speedups with negligible impact on predicted observables.
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Computer Vision
Automated Goldsmith's Mark Retrieval in Silverware
Atmik Tiwari, Vincent Christlein, Mark Fichtner et al. (+5 more)
For art historians, goldsmith marks play a critical role in the identification and dating of artifacts. In practice, experts must manually compare a query mark against hundreds of documented examples, a process that is both tedious and highly dependent on specialist knowledge. To address this, we present an AI-assisted
retrieval pipeline that combines mark localization with metric-learning fine-tuning across three backbone architectures: an ImageNet-pretrained ResNet-50, a supervised ViT-S/16, and a self-supervised DINOv2 ViT-S/14. We conduct a systematic evaluation of cropping strategies, where we measure the impact of no cropping, manual ground-truth cropping, and learned detection-based cropping, and assess their interaction with each backbone. Our strongest configuration, DINOv2 ViT-S/14 with manual crop and metric-learning fine-tuning, achieves an mAP of 62.63% and a Top-1 accuracy of 73.74%. Our experiments show that self-supervised pretraining and mark localization are the two most impactful factors, with learned cropping recovering the majority of the gain from manual cropping without requiring ground-truth annotations at inference time. To enable reproducibility and adoption in the digital humanities, we release our manually annotated
dataset and codebase, and deploy the system via a public web interface.
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All authors Atmik Tiwari, Vincent Christlein, Mark Fichtner, Freya Gohlke, Birgit Schübel, Theresa Witting, Heike Zech, Mathias Zinnen
Computer Vision
Grounded Product Understanding in Livestream Videos
Xinyu Zhang, Junjie Chen, Jiawei Ge et al. (+4 more)
E-commerce livestreams have emerged as an important channel for presenting products to online consumers, often featuring multiple products with relevant information distributed across different moments. This poses significant challenges for downstream product understanding applications, such as product-centric livestream clipping, where models need to identify the product and its relevant segments for information gathering. However, existing
benchmarks for general product understanding typically evaluate product
retrieval and temporal localization in isolation, leaving the critical correspondence between product identity and temporal evidence largely unassessed. To address this limitation, we introduce GPUB, a large-scale
benchmark comprising 3,000 real-world e-commerce livestream instances with quality-controlled multi-moment temporal annotations and a catalog of over 31K fashion products. GPUB supports three evaluation tasks: given a livestream
video and a candidate product set, the main task Grounded Product Understanding (GPrU) requires jointly identifying the product being presented and localizing its supporting moments; Product
Retrieval and Product Moment Localization serve as two complementary subtasks. Evaluation of existing
multimodal models shows that GPrU remains highly challenging, with the best-performing off-the-shelf baseline achieving only 10.13% Pair mAP@.3. To narrow the performance gap, we further develop UniPro, a unified product understanding model that derives product-aligned and temporally structured representations from shared
multimodal encoding, improving Pair mAP@.3 to 24.58% while achieving 38.81% Joint R@1@.3 on GPrU.
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All authors Xinyu Zhang, Junjie Chen, Jiawei Ge, Qianlong Li, Libin Ma, Baokun Pan, Yahui Luo
Machine Learning
Radio Frequency Detection and Classification of Microplastics in Water
Jaden Tolbert, Md Saiful Islam and Pingshan Wang
Micro- and nano-plastic particles (MPs/NPs) are ubiquitous environmental contaminants whose increasing abundance and potential health impacts have created an urgent need for rapid, label-free detection methods. As particle size decreases to the low-micrometer range, conventional optical and spectroscopic techniques become increasingly challenging because of limited throughput and/or complex sample preparation. In this work, we present a machine learning (ML)-assisted
radio-frequency (RF) dielectric spectroscopic cytometry (DiSC) platform for the label-free detection and classification of MPs. Eight types of $ 10 $ {\mu}m nominal-diameter MP particles suspended in deionized (DI) water were characterized at four frequencies spanning $ 0.2\text{-}9\text{ GHz} $. The measured alterations in RF scattering parameters (S-parameters), referenced to the carrier medium, were used to train supervised ML models for material classification, including the identification of MPs in mixed samples and saline-water environments. For eight MP classes suspended in DI water, the proposed method achieved macro-average F1-score, precision, and recall values exceeding $ 0.71 $. Furthermore, PET classification performance was largely maintained in saline carrier media containing $3.3\% $ and $ 6.6\% $ sea salt. These results demonstrate the feasibility of ML-assisted RF DiSC for rapid, single-particle MP classification in aqueous environments. Future work will focus on improving classification performance through enhanced RF calibration, increased spectral coverage, larger training
datasets, and validation using environmentally aged and biologically contaminated microplastics.
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Artificial Intelligence · Cross-listed from eess.AS
Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain
Aakash Singh, Lakshmi Pedapudi, Chandrashekar M S et al. (+3 more)
FarmerChat is Digital Green's AI-powered agricultural advisory assistant for smallholder farmers, who access it in their own
language through text, voice, or photographs. Voice is a critical channel for this population, yet field-recorded
speech is challenging for general-purpose automatic
speech recognition (ASR) because recordings frequently contain machinery noise, background media, competing speakers, and domain-specific agricultural vocabulary. These conditions disproportionately affect crop, pest, chemical, and quantity terms that carry the meaning of a farmer's query. We present a modular, model-agnostic pipeline for improving ASR quality in FarmerChat without fine-tuning or replacing the underlying ASR model. The pipeline combines gated audio enhancement, speaker diarization and target-speaker selection, ASR, domain-aware correction using a weighted agricultural lexicon, and a quality gate for detecting unreliable transcripts. Only the diarization stage is fine-tuned; all other stages use off-the-shelf models behind common interfaces. We evaluate the pipeline on human-annotated FarmerChat recordings in Hindi, Telugu, and Odia using word error rate (WER) and a domain-weighted error rate that gives greater importance to agricultural terminology. The largest improvements occur on multi-speaker recordings, where target-speaker selection prevents competing
speech from entering the transcript. Across the full corpus, the pipeline reduces WER by 16-23% relative on three cloud ASR models and by 5% on an on-device model. On multi-speaker recordings, the reductions are 32-42% for the cloud models and 16% for the on-device model. All reported reductions are statistically significant. These results show that targeted preprocessing, speaker selection, and domain-aware post-processing can substantially improve agricultural
speech transcription while preserving the underlying ASR model.
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All authors Aakash Singh, Lakshmi Pedapudi, Chandrashekar M S, Sanyam Singh, Naga Ganesh, Vineet Singh
Machine Learning
Distributionally Robust Federated Learning with Multi-Source Data
Yingzhu Liu, Zhongkui Li, Pengcheng You et al. (+1 more)
Federated learning trains a shared model from private client data. In practice, data-generating distributions may differ, and the true mixture across clients is often unknown, making the underlying group distribution difficult to specify. Existing approaches address cross-client mixture uncertainty by optimizing against the worst-case mixture, yet assume accurate client-wise distribution estimates. However, these estimates can be unreliable when based on finite samples. To handle both cross-client mixture uncertainty and within-client distributional ambiguity, we construct a global ambiguity set as the union of admissible mixtures of local ambiguity sets. The construction allows client-specific ambiguity radii and admits a client-wise separable reformulation. Leveraging this structure, we establish a high-probability out-of-sample performance guarantee. We further develop a
federated algorithm for a penalty-based reformulation and prove its convergence under milder regularity conditions. Simulations validate the
algorithm's effectiveness.
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All authors Yingzhu Liu, Zhongkui Li, Pengcheng You, Ashish Cherukuri
Robotics
Towards AI-enhanced control: a numerical technique for trajectory smoothing of a parallel robot for pancreatic surgery
Iosif Birlescu, Alexandru Pusca, Bogdan Gherman et al. (+4 more)
The paper presents a numerical approach for the end-effector trajectory smoothing of a parallel
robot designed for minimally invasive pancreatic surgery. The approach is tailored for real-time master-slave control architecture and uses a 3D space mouse for command input for velocity control. The trajectory smoothing is achieved by generating S-curves in the end-effector velocity fields, thus controlling the accelerations, which in turn reduces tissue trauma in the minimally invasive procedures. Real-time control is enabled by segmenting the S-curves based on the command inputs from the 3D space mouse. A special case is considered where the acceleration time is constant for all command inputs. Numeric results demonstrate stable transitions (without abrupt changes) in both the end-effector parameter space and in the active joints parameters, thereby validating the proposed approach. Further work aims to test the approach on an experimental model and integrate it into AI-based training modules.
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All authors Iosif Birlescu, Alexandru Pusca, Bogdan Gherman, Calin Vaida, Ionut Zima, Damien Chablat, Doina Pisla
Distributed Computing
A Kubernetes-Native Request Router for Quality-Aware Inference Serving in the Computing Continuum
Ignjat Karanovic, Pantelis A. Frangoudis, Ivan Čilić et al. (+2 more)
We introduce Adaptive Score-based Routing Balancer (ASRB), a dynamic, score-based request routing mechanism for Kubernetes-based service deployments over the computing continuum. ASRB jointly considers infrastructure-level information, response time measurements, and application-level quality indicators, with a particular focus on serving Machine Learning (ML) workloads. For these workloads, ASRB balances requests over service instances deployed in the continuum, following service provider-defined policies encoded as weighted combinations of QoS criteria to flexibly address latency-accuracy trade-offs. To drive routing decisions and swiftly adapt to changes in the operating environment, ASRB monitors a range of runtime metrics across multiple system layers. To deal with the associated monitoring overhead, particularly important for large-scale deployments, it selectively and adaptively controls monitoring intensity without sacrificing on routing quality. ASRB is implemented without requiring any modifications to Kubernetes, making it straightforward to deploy and operate in existing cluster environments. Our testbed experiments demonstrate the versatility of ASRB: When tuned for latency reduction, it achieves at least 10 ms lower mean response time compared with latency-oriented state-of-the-art routing mechanisms, while it achieves higher accuracy when this is prioritized through specific configurations, thus enabling flexible and operator-controllable trade-offs. At the same time, it attains reduced failure rates, higher responsiveness to changes in the operating environment, and up to ~70% less monitoring cost than relevant state-of-the-art solutions, at the potential expense of only a modest latency penalty in some configurations.
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All authors Ignjat Karanovic, Pantelis A. Frangoudis, Ivan Čilić, Ivana Podnar Žarko, Schahram Dustdar
Human-Computer Interaction
TeamCAMS: An Open-Source Research Platform for Studying Human Behaviour in Human-AI Teams
Amos Brocco, Alain Chavaillaz, Andreas Sonderegger et al. (+1 more)
In this article, we present TeamCAMS (Cabin Air Management System), a collaborative work environment for simulating human-AI (artificial intelligence) interaction for scientific research. The article outlines how several psychological theories guided the development of this multiple-task simulation. Modelling a process control environment, previous versions of TeamCAMS have already been used in empirical studies to address a wide range of research questions (e.g., comparing different forms of automation, evaluating impact of automation reliability, effects of stress on multiple-task performance). Outlining the technical possibilities offered by TeamCAMS, the article points out how its latest version offers researchers the possibility of addressing a set of new research questions including problems associated with teamwork (e.g., within-team conflict, distributed teamwork) and human-AI interaction. Finally, we will outline how this simulation environment can be enhanced further still to address research questions in new fields (e.g., automation of leadership). To promote transparency, reproducibility, and further development, TeamCAMS is made available to the research community under an open-source license.
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All authors Amos Brocco, Alain Chavaillaz, Andreas Sonderegger, Juergen Sauer
Databases
Resolution limits for process comparison from event data
Antony R. Lee, Peter Tiňo and Iain B. Styles
One hospital runs bloods and imaging at the same time. Another runs them one after the other, in either order, equally often. Knowing which actually happened, and how it is recorded in data, is critical for all operational managers. In process mining, the standard approach is to construct an event log, and attempt to discover concurrent and sequential processes in a data-driven way. We show this standard approach, built on the
stochastic language of an event log, reports only the assumptions of its discovery
algorithm, because every such log is explained equally well by a model with no concurrency at all. Further, before any data is acquired, we characterise when data can and cannot distinguish concurrent behaviour. Where it cannot, the distinction is recoverable from evidence the
stochastic language discards, such as the times at which activities start and end, or object-centric records that fix an order within an execution. The remedy is therefore a choice of what is recorded, rather than a larger sample. This impacts decision making, as planning resource for truly concurrent services is very different from sequential services.
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Computation and Language
Edustories: A Collection of Real-world Case Studies from Classroom Practices
Michal Štefánik, Jan Nehyba, Jirina Karasova et al. (+4 more)
Despite the widely recognized potential of AI in education, most prior work has focused on individualized student assistance. In contrast, the majority of educational practice worldwide still takes place in collective classroom settings. To enable researchers to study AI assistance in collective teaching, we introduce Edustories, a
dataset of 1,492 teacher-written case studies describing real elementary and high-school classroom situations involving challenging student behavior, pedagogical interventions, and their outcomes. Among many other applications, Edustories enables evaluating LLMs' ability to predict the success of teacher interventions, crucial for providing practicing teachers with useful feedback. Comparing the latest models from four
language-model families against expert assessments, we find that current models fall short of human expertise in predicting classroom outcomes; the strongest models reach 58% accuracy compared to 64% of human experts. This gap highlights both the limitations and the emerging potential of AI as assistants for practicing teachers.
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All authors Michal Štefánik, Jan Nehyba, Jirina Karasova, Martin Fico, Lucie Škarková, Markéta Košatková, David Kosatka
Performance
Scaling Fourier-Based Sparse Matrix Analysis on GPUs
Ruifeng Zhang, Sai Krishna Teja Varma Manthena, Jiajia Li et al. (+1 more)
Sparse computations are important workloads in applications such as scientific computing, graph neural networks (GNNs), and machine learning. While many sparse operations can benefit from modern GPUs, the sparsity pattern remains important to performance because it affects
memory coalescing, block organization, and load balancing. Previous studies show that spectral signatures can help analyze the global structure of sparse
matrices. The fast
Fourier transform (FFT) is commonly used to extract spectral signatures, and efficient GPU FFT libraries are available. However, sparse
matrices, especially adjacency
matrices for large graphs, tend to be very large and sparse. Existing dense-
matrix-based FFT implementations are difficult to scale up, making the spectral patterns of these
matrices difficult to obtain. We therefore propose a three-fold research approach comprising a lossless Binary-Sparse FFT (BS-FFT) and two compression methods: Elastic BS-FFT, which reuses the BS-FFT pipeline on a sampled frequency grid, and density-map-based spatial compression. Experiments show that BS-FFT reduces GPU
memory use by 2.9–11.6 times relative to dense cuFFT and completes all 15 GNN adjacency
matrices where dense cuFFT completes 6 on a 40 GB A100. Elastic BS-FFT and Density Map compression reduce GPU computation time by 2.0–1466.4 times relative to BS-FFT with spectral feature error of only 0.16% to 11.56% across the sampling rates from 6.25% to 0.0061%.
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All authors Ruifeng Zhang, Sai Krishna Teja Varma Manthena, Jiajia Li, Xipeng Shen
Digital Libraries
greCAPTCHA: Assessing Understanding as Evidence of Research Authorship Under Generative AI
Justin Payan, Bálint Gyevnár, Atoosa Kasirzadeh et al. (+1 more)
Conferences, journals, funders, schools, and universities are struggling with a surge of potentially AI-generated submissions from ostensibly human authors, who may not have exercised sufficient human oversight for their manuscripts. In turn, institutions evaluating submissions can no longer reliably credit expertise based solely on authors' names on submitted work. To address this problem, we propose greCAPTCHA, a proctored assessment approach that measures authors' understanding of research manuscripts via the construct of capacity to verify, which we define as the knowledge and
reasoning required to critically assess the contents underlying one's contributions to a manuscript. greCAPTCHA generates questions assessing multiple levels of understanding and provides an evaluative report based on authors' responses. Using a prototype implementation, we conduct a user study and semi-structured interviews with $31$ researchers to evaluate greCAPTCHA. Its automated scores predict which papers were or were not authored by study participants with an AUC of $0.90$. Participants reported positive overall experiences with the system and remarked on the appropriate construct validity for author understanding, while also suggesting important changes to be made before deployment. Our results provide initial evidence that greCAPTCHA can assess manuscript-specific understanding under proctored conditions.
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All authors Justin Payan, Bálint Gyevnár, Atoosa Kasirzadeh, Nihar B. Shah
Robotics
Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions
Ruichen Tan, Zengxiang Lei and Satish Ukkusuri
Occlusion creates fundamental uncertainty in autonomous driving. Existing methods often propagate frame-wise hypotheses or optimize ego behavior against prescribed hidden-
agent predictions, leaving the worst history-consistent interaction unexplored. We introduce History-Conditioned Minimax Trajectory Search (HC-MTS), which combines temporal occlusion
reasoning with response-aware search. First, HC-MTS constructs finite hidden-state modes, each certified by a backward witness satisfying multi-frame visibility, occupancy, semantic-map support, and class-specific kinematic constraints. It then solves a bilevel minimax problem: an inner finite oracle maximizes the ego driving score over destination attainment and ride comfort, while the outer search selects the legal hidden-vehicle trajectory that minimizes this best-response value. Across eight Waymo Open Motion
Dataset scenarios, increasing the visibility-
memory horizon from K=1 to K=20 reduces the mean per-scenario vehicle, pedestrian, and total retained hidden-seed counts by 18.12%, 21.67%, and 18.45%, respectively. HC-MTS identifies six avoidable counterexamples, while no legal collision-producing attacker is found in the remaining two scenes within the finite search budget.
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Logic in Computer Science
String Diagrams for Process Mining
Antony R. Lee, Peter Tiňo and Iain B. Styles
Run two process-discovery
algorithms on the same event log and they return two different pictures of the same process. Petri nets, causal nets, process trees and BPMN each rely on routing machinery of their own, and their only common ground is the traces they generate. A trace lists activities one after another, so it discards the concurrency the notations exist to express, and two models with identical trace
languages can describe genuinely different processes. Whether two discovered models mean the same thing therefore has no notation-independent answer. We show that all four notations admit one canonical presentation, a signature recording a model's activities and the typed interfaces along which they compose, and nothing of the routing machinery. Four construction
theorems establish this presentation notation by notation, so two models are compared in a form that each notation determines on its own. Signature equality implies trace equivalence and is strictly finer, separating genuine concurrency from interleaved choice, which a trace comparison cannot, and signature inclusion implies trace inclusion. In a recovery study on one object-centric log, the discovered causal net has exactly the ground-truth signature, while the discovered Petri net's signature strictly contains it and locates every behaviour the Petri net adds in its silent structure. The gain is largest for object-centric data, where a single log supports several notations at once. The four notations become one calculus, in which a translation between them is a claim that can be checked.
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Robotics
Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation
Tao Sun, Beining Han, Patrick Yin et al. (+5 more)
Rebar insertion is among the most repetitive and physically demanding tasks on construction sites, and a contact-rich problem at 1.4 mm clearance. The parts, however, vary at two levels: a nominal design per structural member, and fabrication tolerance around each nominal design. Real-world data therefore has to be re-collected as designs and batches change. We present RebarSim, a visual sim-to-real system trained entirely in simulation. A privileged state-based teacher is trained with reinforcement learning over procedurally generated rebar geometries, then distilled into a multi-view student that maps raw RGB and proprioception directly to actions under extensive domain randomization. The student transfers to the real world zero-shot, seating rebars taken from a real factory production run in 91.3% of real-
robot rollouts. Underlying that result, geometry diversity and pretraining both bring benefits. Training across a diverse set of nominal designs rather than one lifts the zero-shot success of both the teacher and the student on unseen designs, and the student policy outperforms a single-design specialist on that specialist's own design. A pretrained student then adapts to a new design with 4–6x fewer
distillation samples than one trained from scratch. Visual sim-to-real transfer depends on appearance randomization and the DAgger mixture: removing either one sharply lowers success.
Videos, code, and task assets are available at https://rebarsim.github.io.
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All authors Tao Sun, Beining Han, Patrick Yin, Rui Xu, Harry He, Abhishek Gupta, Szymon Rusinkiewicz, Yi Shao
Computer Vision
SenseFuse: Label-Free Fusion of Image and Shape Encoders for Open-Vocabulary 3D Instance Segmentation
Euiseok Han, Tri Ton, Hwanhee Kim et al. (+2 more)
Open-vocabulary scene understanding is fundamental for robotics, laying the groundwork for spatial
reasoning and object manipulation. While closed-vocabulary 3D instance
segmentation heavily leverages 3D shape information, state-of-the-art open-vocabulary methods remain predominantly restricted to 2D image features or image-distilled representations during mask labeling. In this paper, we propose SenseFuse, a label-free fusion method that balances 2D image and 3D shape encoders for robust open-vocabulary 3D instance
segmentation, refining only the mask-labeling stage of existing pipelines. We reveal that 2D image and 3D shape encoders exhibit largely disjoint failure patterns and rarely share identical wrong labels, whereas two 2D image encoders frequently repeat the same errors. This distinct behavior makes the 2D and 3D pair inherently complementary. We introduce an adaptive mechanism that selects a scene-level fusion weight to maximize a label-free sensitivity measure, estimated directly from a single scene's unlabeled proposals in milliseconds. SenseFuse improves labeling accuracy in every evaluated setting across ScanNet200, Replica, and ScanNet++, recovering 67-100% (median 93%) of the gain achievable with an oracle weight, and it raises instance AP in 21 of 22 reported settings. Code is available at https://github.com/hanes1207/SenseFuse.
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All authors Euiseok Han, Tri Ton, Hwanhee Kim, Seungyeon Ryu, Chang D. Yoo
Artificial Intelligence
How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents
Yukun Zhang, Kemu Xu and Yishen Chen
Agent harnesses supply planning guidance, organize execution, and check completion. We study how these components affect success, erroneous acceptance, and cost in two Retail experiments and an Airline pilot in $\tau^2$-bench. The primary comparison pairs prewritten task-specific plans (Fixed) with shuffled policy text matched in word count (Sham), isolating the contribution of guidance content. Across 265 matched
cells, Fixed improves oracle-verified success by 7.17 percentage points (90% task-clustered bootstrap interval, 1.15–13.36 points), with gains concentrated in higher-complexity tasks. A read-only terminal verifier rejects 61% of Retail oracle-invalid episodes while withholding 17% of correct ones, at less than one cent of additional cost per episode. Which component matters more depends on the loss assigned to erroneous acceptance: at low liability the planning gain dominates; at high liability the verifier's avoided false passes dominate—and a standalone verifier captures nearly all the false-pass benefit of the full planning-plus-verification stack at a fraction of its cost.
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Machine Learning
Deep Learning-Based Classification of Cognitive and Resting States Using Electroencephalography Signals
K. A. Januka S. Fernando and Harshit Srivastava
The categorization of cognitive and resting states derived from electroencephalography (
EEG) signals is crucial for comprehending fluctuations in
brain activity linked to various mental states.
EEG provides a non-intrusive approach for documenting
brain function in both resting and task-oriented cognitive conditions, whilst deep learning techniques enable the automatic extraction of significant patterns from intricate
EEG data. This study presents a deep learning framework to distinguish between resting and cognitive states through
EEG records. The proposed framework integrates a Convolutional Neural Network (CNN) stacked with a Gated Recurrent Unit (GRU) for the extraction of features from
EEG signals. Time-frequency analysis is conducted to explore the salient aspects of signals, and the derived features are then assessed utilizing conventional deep learning and machine learning classifiers, including the suggested 2D-Net architecture. The proposed approach and feature extraction strategy outperform the evaluated comparative methods, achieving accuracies of 83.177% for resting-versus-mathematical task classification, 76.107% for resting-versus-
memory task classification, and 83.432% for resting-versus-music task classification. The findings illustrate the efficacy of integrating signal processing with deep learning methodologies to discriminate resting from cognitive states utilizing
EEG signals.
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Machine Learning
Training Neural Networks to Approach the Optimum Bayes Estimator in Dense Multi-Emitter Localization
Yi Sun, Mona Sharifi and Muzna Yumman
We train neural networks on synthesized frames to approach the optimum Bayes
estimator for dense emitter localization. The result justifies the future work on training neural networks to achieve high-throughput large-FOV super spatiotemporal resolution SMLM.