Cryptography and Security
CYBERFORT: A Compliance-Chain Platform Operationalising the Cyber Resilience Act for SMEs
Nikolaos Kekatos, Georgios Koutidis, Mihaela Curcă et al. (+8 more)
The EU Cyber Resilience Act (CRA) turns product cybersecurity into a lifecycle compliance obligation for manufacturers, importers, distributors, and integrators of products with digital elements on the EU
market, a load that falls largely on small and medium-sized enterprises (SMEs) that rarely have dedicated governance, risk, and compliance (GRC) capacity. We present CYBERFORT, an open-source CRA-first compliance platform developed under the EU Digital Europe Programme and one of twelve projects in the EU CRA cluster. CYBERFORT operationalises the CRA through a guided scope self-assessment, a question bank tied to Annex I and the vulnerability-handling obligations, and a compliance-checking engine that links every answer to controls, policies, and machine-attested evidence, reusing ISO/IEC 27001, NIS2, and GDPR controls only where they coincide with CRA obligations. Its central contribution is the compliance chain, a traceable structure linking each product risk through its controls and policies to the CRA obligations it satisfies, and onward through evidence to the technical-documentation file and EU declaration of conformity, so that every operational gap is traceable and can be closed before
market placement. Deployed at https://access.cyber-fort.eu/ for a first cohort of 43 organisations, the platform is presented with the engineering behind the chain, measured results from a completed end-to-end case study on a SIEM/XDR product with AI-driven remediation spanning the CRA obligation chapters, and the controlled effort study that remains in progress.
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All authors Nikolaos Kekatos, Georgios Koutidis, Mihaela Curcă, Angelos Fountoulakis, Marios Ioannou, Michael Ioannou, Elias Iosif, Paul Lacatus, Alexios Lekidis, Avgi Michael, Tom Nianios
Machine Learning
NeuralZip: Reusable Setup for Fast Lossless Compression
Martín Bravo, Samuel Horváth, Gonzalo Navarro et al. (+1 more)
Lossless compression can reduce the storage and movement of model weights without changing their floating-point values, but repeated statistical analysis and code construction add computational overhead. We study whether the statistical structure of exponents can be prepared once and reused. For this, we introduce NeuralZip, which groups chunks with similar exponent distributions, shares Huffman codes, and selectively represents recurring exponent tuples using packed exponents, thereby achieving additional moderate compression ratios. A setup chooses these representations before subsequent encodings, while every encoding still processes the current tensor values. In floating-point model checkpoints, post-setup compression is 1.81-21.33$\times$ faster than the baselines and achieves exact bit-to-bit reconstruction. We show that this setup can be precomputed and transferred from another compatible architecture, preserving similar compression ratios and avoiding the need to amortize setup costs. Therefore, compression adaptation is transferable and reusable. Training checkpoints demonstrate continued reuse as the weights evolve. Finally, GPU experiments reduce active
memory usage by up to 27.5$\%$ while reproducing the logits exactly.
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All authors Martín Bravo, Samuel Horváth, Gonzalo Navarro, Andrés Abeliuk
Machine Learning
RollVerify: Bridging Efficiency and Accuracy in Long-Tail Rollout Reinforcement Learning
Yongqiang Yao, Jinru Tan, Kaihuan Liang et al. (+5 more)
Reinforcement learning is crucial for improving large
language models'
reasoning and generalization. It relies on massive rollouts whose lengths become increasingly long-tailed as context windows grow. In on-policy training, these long-tail rollouts can result in GPU bubbles, reducing system utilization and limiting RL scalability. Asynchronous or partial-rollout methods improve throughput by relaxing synchronization, but inevitably introduce stale off-policy samples (trajectories) that may hurt final accuracy. Existing approaches mainly mitigate this off-policy issue by reweighting off-policy samples during training, yet they can still leave a performance gap compared to fully on-policy training. In this work, rather than passively reweighting samples during training, we propose RollVerify, a lightweight RL framework built on partial rollout that actively verifies and repairs samples before they enter training. Specifically, it introduces an off-policy shift metric OPS, to quantify the off-policy deviation of partially generated trajectories. Guided by the OPS constraint, RollVerify performs both sequence-level and
token-level verification to identify and truncate invalid suffixes of trajectories. This yields high-quality samples that protect the models' accuracy while preserving the efficiency gains of partial rollout. Experiments on mathematical and tool-assisted mathematical
reasoning show that RollVerify achieves accuracy comparable to on-policy training while reducing training cost. Additional code-generation results provide preliminary evidence beyond mathematics.
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All authors Yongqiang Yao, Jinru Tan, Kaihuan Liang, Zixin Yin, Yazhe Niu, Ruihao Gong, Dahua Lin, Ningyi Xu
Databases
The Case for Synthesized Data Compression
Ivan Iachnyk, Mihail Stoian, Luisa Neubauer et al. (+2 more)
Compression is a fundamental tool in data management. Today's compression schemes are generally designed by hand to apply to many different types of data (e.g., run length encoding or arithmetic encoding), leading to a zoo of general-purpose
algorithms which work well for many
datasets, but may not be optimal for any specific
dataset. Inspired by recent advancements in agentic coding, we propose synthesizing custom-tailored data compression
algorithms on a per-
dataset basis. Unlike prior work using
agents to synthesize database components, a data-specific compression
algorithm can be fully verified at construction time, alleviating most correctness concerns. Modern file formats, like AnyBlox and F3, allow including the synthesized code alongside the data itself. Experimentally, we show that our synthesis
agent can expand the compression-factor/decompression-speed Pareto front, outperforming general-purpose methods by nearly $2\times$ in compression factor.
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All authors Ivan Iachnyk, Mihail Stoian, Luisa Neubauer, Ryan Marcus, Andreas Kipf
Human-Computer Interaction
A Scale For Value Alignment In Human-AI Interaction
Lena Hegemann, Steeven Villa, Hyemin Bang et al. (+4 more)
Value
alignment is a central objective in AI and HCI research, yet no validated instrument measures how users perceive it. This gap hampers the comparison and accumulation of findings and limits the effectiveness of applications, where understanding users' viewpoints is critical. We construct and evaluate a 13-item psychometric scale that measures perceived value
alignment across two components: value understanding and value manifestation. It is based on a large item pool drawn from prior empirical studies, filtered by experts, and finally assessed by users (N=607) across diverse AI scenarios. Confirmatory factor analysis on an independent sample (N=259) confirmed the two-factor structure and high internal consistency for both subscales. Using optimization, we also derived a 6-item short form for quick administration. The scale is a reliable measure, providing HCI researchers with a common evaluation metric across contexts.
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All authors Lena Hegemann, Steeven Villa, Hyemin Bang, Mitchell L. Gordon, Antti Oulasvirta, Robin Welsch, Patrick Ebel
Software Engineering
Analysis and Visualization of the Linux Kernel's Software Evolution Using the City Metaphor
Malte Hansen, Lukas Damerau, Moritz Bugla et al. (+2 more)
The Linux kernel is one of the largest and longest-maintained open source projects in existence. With more than 40 million lines of code, understanding the kernel's internal structure and assessing its software
evolution is a great challenge. In this paper, we present an approach to visualize the Linux kernel using our software visualization tool ExplorViz. We analyze commits from the Linux Git repository using a custom analysis service. The web-based frontend utilizes the 3D city metaphor for visualization of the software structure. Metrics such as the number of lines are collected for each file and are accumulated for directories and commits. Calculated metrics can be mapped to the building's dimensions or be displayed via a heat map. A wide range of visualization, search, and filter options enable the interactive exploration of the visualized data. We present both a visualization of all files in the kernel repository and a visual analysis of the code
evolution of Rust files in the Linux kernel. Demo
video: https://youtu.be/3ODnERqD8g0
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All authors Malte Hansen, Lukas Damerau, Moritz Bugla, David Moreno-Lumbreras, Wilhelm Hasselbring
Numerical Analysis
UnCut FEM with PHT-splines on unfitted T-meshes
Falai Chen, Nuo Lei and Buyang Li
We propose a novel unfitted finite element method based on
polynomial splines over hierarchical T-meshes (PHT-splines) for
elliptic partial differential equations (PDEs) posed on domains whose boundaries are described implicitly by a level-set function. Exploiting the enhanced smoothness of the spline space, we formulate the method by minimizing the $L^2$ residual of the PDE over an extended neighborhood of the physical domain. This formulation allows the method to be implemented entirely on the background mesh using standard element-wise quadrature. It yields a symmetric positive-definite linear system and avoids the
geometric construction of curved cut subregions in boundary elements, as well as the associated nontrivial numerical integration. We establish well-posedness of the numerical scheme and derive optimal a priori error estimates in the $H^1$ norm. In addition, we employ a residual-based a posteriori error indicator to guide local adaptive refinement of the hierarchical T-meshes. Numerical experiments corroborate the theoretical results and demonstrate the accuracy and efficiency of the proposed approach.
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Computer Vision
FedSSMCoOp: SSM Encoders for light-weight Federated Prompt Learning for Few-shot Classification
Ankita Das, Ambarish Parthasarathy, Sumohana S. Channappayya et al. (+1 more)
Vision-
Language Models (VLMs) have shown strong performance across a wide range of downstream
vision tasks, thanks to the complementary information contained in the respective domains. Despite the performance gains, most of these approaches rely on aligning these domains using the cosine similarity metric, which fails to capture
token-level structure and cross-modal interactions prior to the classification stage. This is especially critical in biomedical applications under
federated constraints, where data sharing is restricted, labeled data is scarce at each site, and it differs widely across institutions, leading to substantial statistical heterogeneity. To overcome this issue, we propose FedSSMCoOp, a
federated few-shot image classification framework that enables
multimodal learning while preserving data
privacy. With the help of the SSM-based
Vision Mamba and Cross Mamba blocks, and by optimizing only the soft-
prompt and communication-
prompt updates in the
federated setting, the framework prioritizes both computation and performance. Importantly, this eliminates the need to use an external Large
Language Model (LLM) for feature
alignment. The framework is further trained and evaluated on various biomedical image
datasets, and its performance is assessed. The proposed framework delivers stable performance relative to the baselines and is, on average, 1.96 times lighter. The corresponding script will be made available soon.
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All authors Ankita Das, Ambarish Parthasarathy, Sumohana S. Channappayya, C. Krishna Mohan
Cryptography and Security
Constrained-Action AI Remediation for SIEM/XDR via a NeMo-Guardrails Proxy
Georgios Koutidis, Nikolaos Kekatos, Tom Nianios et al. (+1 more)
Security Operations Centers (SOCs) for information technology and operational technology share one incident-response problem: a flood of correlated alerts and too few analysts. Large
Language Models (LLMs) are increasingly proposed as
reasoning engines that triage alerts and, in autonomous deployments, issue commands that block IPs, kill processes, or quarantine files on production hosts. This coupling introduces a new risk: a single
adversarial alert can become a remote code path through the LLM's
reasoning, leading it to recommend an action the SOC then executes. We present a constrained-action architecture with two coordinated layers: (i) a SIEM/XDR control plane that grounds remediation in correlated host events and confines the LLM's output to a closed intent vocabulary whose templated commands are executed by thin endpoint
agents, backstopped by an argument validator; and (ii) a NeMo-Guardrails proxy that wraps the SOC-analyst LLM with input- and output-rail policies, evaluated out-of-the-box against a SOC-specific
adversarial corpus we release. The stock proxy lifts injection recall from 25.0% to 94.5% at a 0.1% false-positive rate, and a live red-team exercise confirms that the closed intent vocabulary and argument validator contain the observed LLM failure modes before any command crosses the trust boundary. As an architectural fit (not yet a measured operational-technology deployment), the constrained-action property suits critical-infrastructure settings where a wrong remediation has physical, not merely operational, consequences. The loop is best run human-in-the-loop or delayed: the measured rail latency keeps inline control out of scope.
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All authors Georgios Koutidis, Nikolaos Kekatos, Tom Nianios, Alexios Lekidis
Databases
ERP Event Logs: A Canonical Form for KPI Time-Series Extraction and Characterization
Sherri Hadian, Adrian Rebmann, Atacan Korkmaz et al. (+2 more)
Enterprise resource planning (ERP) systems record operations as transactions spread across hundreds of normalized relational tables. Extracting an operational key performance indicator (KPI) from this schema requires a separate, bespoke join for almost every metric. We show that an event log, a canonical (case, activity, timestamp) representation derived from the same tables, collapses this heterogeneity into one flat structure, from which KPI families reduce to reusable operations after a one-time mapping. Using standardized event logs built from hundreds of SAP S/4HANA customers under a shared activity ontology, we define three families of process-derived KPIs (volumes, durations, and rates) and extract them uniformly across customer systems for two common end-to-end organizational processes, order-to-cash and procure-to-pay. We normalize rates and screen series for minimum coverage to support comparison across customers of very different sizes. We then characterize heterogeneity across industries and tenants using descriptors derived directly from the canonical form, before analyzing the resulting KPI time series. The same process executes heterogeneously across customers and industries, yet this shared representation enables direct comparison of sales- and procurement-side signals, including lead times and order arrivals. Using this shared representation, we characterize which KPI families are forecastable and find a recurring ordering from volumes through durations to rates across both processes and a highly heterogeneous customer base; a pretrained time-series foundation model (Chronos) is competitive with classical baselines such as ARIMA and ETS on rate and duration series, while classical models retain an edge on volumes.
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All authors Sherri Hadian, Adrian Rebmann, Atacan Korkmaz, Gregor Berg, Ulf Brackmann
Software Engineering
A Chat Assistant for Software Exploration in a 3D Software Visualization
Malte Hansen, Karim Issa and Wilhelm Hasselbring
We present a chat assistant for interactive software exploration, embedded in the 3D software visualization tool ExplorViz. The assistant builds upon current Large
Language Models (LLMs) and enables users to ask questions about the currently visualized software system and trigger actions that change the visualization through natural
language. We integrate the chat assistant in our ExplorViz frontend using the CopilotKit libraries such that probabilistic LLMs are combined with deterministic and tool-based actions similar to implementations using the Model Context Protocol (MCP). The chat assistant is also enabled to restructure the software system by adding, removing, or modifying parts of the software system in the visualization. An empirical experiment with eleven participants evaluated both perceived comprehension support and the tool calls that were triggered by the chat assistant. Participants rated the assistant's generated summaries and explanations as largely correct. They also reported high usability for actions like highlighting entities in the visualization and the creation of new color themes. In contrast, open-ended chat-assisted software restructuring in the visualization showed mixed results. This suggests a need for stronger guardrails for the employed LLM and better incorporation of user feedback. Overall, the assistant was perceived as usable and promising for reducing interaction overhead during exploratory program comprehension tasks. We provide a
video presenting the chat assistant's use and a reproduction package of the software system that was used in our evaluation.
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Machine Learning · Cross-listed from stat.AP
Learning joint probabilistic weather forecasts from station observations alone
Chaeyeon Yi and Yun Am Seo
Assessing compound weather risks requires forecasts representing dependence between variables. CLARA (Calibrated Advection-Routing
Attention) learns joint Gaussian predictive distributions of five surface variables from station observations alone, without numerical weather prediction or reanalysis; the approximately 28,000-parameter model supports CPU training and prediction. Across six multi-year folds on 96 stations, its lead-mean energy score is 4.9% lower than that of a learned comparator with matched temporal inputs (4.7% with a similar parameter count) and 11-65% lower than those of statistical baselines. Holding marginal variances fixed, removing learned correlations worsens joint negative log-likelihood by 1.0-2.8 nats per station. A covariance-scale
estimator, proved consistent under stated assumptions, improves short-lead calibration but over-corrects at long leads. Synthetic interventions show an
attention-bias coefficient alone does not measure forecast influence. Retrained in ten regions on six continents, CLARA outperforms persistence in all 60 multi-year region-lead comparisons and a similarly sized learned model in 57 of 60.
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Artificial Intelligence
NL2Hull: A Natural Language-Driven Constrained Ship Design Decision Framework
Wenhua Huo, Fenglei Han, Wangyuan Zhao et al. (+2 more)
Ship-form design combines smooth
geometric representation, local shape editing, and constraints on the resulting hull. We present the Natural-
Language-to-Hull Framework (NL2Hull Framework), which formulates ship-form editing as a typed discrete decision problem and connects
language decisions to numerical geometry. Its Constrained Free-Form Deformation Engine (CFFD Engine) represents hull waterlines with non-uniform rational B-splines (NURBS), applies free-form deformation (FFD) to their control points, reconstructs the hull, and checks
geometric constraints. We construct the Ship Design Decision
Dataset (SDD
Dataset) with 134,558 cleaned records and evaluate compared models on its subset Ship Design Decision
Benchmark (SDDBench), containing 5,000 records and 43,496 typed questions. We propose Chip, a constrained ship-design decision model for processing natural-
language requests. Chip reaches 95.90% question accuracy and 99.32% FFD exact match, with a negative log-likelihood of 0.0951, an expected calibration error of 0.0032, and a Brier score of 0.0551. The NL2Hull Framework provides a reproducible interface for evaluating
language-based ship-form decisions while identifying the geometry and continuous-control components that require further development. Our code and
dataset is available at https://github.com/wenhuahuo/NL2Hull.
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All authors Wenhua Huo, Fenglei Han, Wangyuan Zhao, Jialin Wu, Jiayi Han
Human-Computer Interaction
Love for Believable AI: Artificial Partiality and Relationship Persistence as an Engineerable Stance
Sebastian Cochinescu
We study a behavioral mechanism for artificial partiality in conversational
agents. The paper reports no human-subjects data and makes no claim about attachment, trust, perceived mind, or machine interiority. Partiality has two components: caring, defined as allocation of a finite interaction surplus above a guaranteed per-user service floor, and particularity, defined as a per-user state estimate accumulated from relationship history on fixed inference machinery. We implement both as a persistence layer over one open-weight base model and evaluate them on constructed multi-session relationships with known hidden-state schedules. A four-channel divergence compares known-user and stranger conditions on identical probes with paired generation seeds; the stranger
prompt is not length-matched, and one channel (initiative) is an allocator output rather than generated behavior. The full mechanism reproduces the ordinal shape, but not the magnitude, of an analytically specified curve and is the only arm satisfying the protocol-defined joint signature on the calibrated battery. The initial mechanism fails probe-quality equivalence because relationship content reduces topical relevance. A guarded revision satisfies equivalence on the calibrated battery but not on a second battery not used in calibration, where it scores higher than the unmodified baseline. On that second battery, the specificity margin is 0.0198, below the 0.02 protocol threshold. The paired mean right-user–wrong-user estimation-accuracy contrast is 0.375 but is not positive in every run. The mechanism is therefore a candidate for a later perception study, not evidence that users perceive love. The versioned protocol record is not independently time-stamped and is not described as a preregistration. Ethical constraints include a stranger-treatment floor, a bounded surplus, disclosure, and de-intensification.
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Databases
QCATS: Query Context-Aware Transformer Slicing for Efficient Predictive Query Processing
Yueying Li, Zhongle Xie, Ke Chen et al. (+1 more)
In-database predictive query processing increasingly applies
Transformer-based models within relational pipelines. However, existing in-database inference typically exposes only tuple-level model inputs to the inference runtime, leaving relational predicates and metadata statistics invisible to neural execution planning. In this paper, we propose QCATS, a query context-aware
transformer slicing framework that enables efficient sparse inference inside database systems. QCATS executes at query granularity: instead of routing individual
tokens or tuples during inference, it uses query predicates and metadata statistics to pre-select context-aligned FFN slices before model execution. The framework comprises offline expert construction and lightweight query-level routing that dynamically selects experts during execution. QCATS further introduces system optimizations, including asynchronous CPU-GPU pipelines and routing-aware batching. Experiments on four predictive-query workloads with BERT-base and Qwen-0.6B show that QCATS achieves up to 4.42x latency reduction while preserving prediction accuracy comparable to dense baselines.
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All authors Yueying Li, Zhongle Xie, Ke Chen, Lidan Shou
Cryptography and Security
Defensive Sufficiency in a Stackelberg Model of AI Security
Subhabrata Majumdar and Rajlakshmi Chavan
Feedback from automated testing, human red teaming, and incident response can strengthen an AI system's defenses when discovered failures lead to effective repairs. We study when this feedback process provides sufficient protection and when investing in it is economically worthwhile. We begin by showing that an attack surface composed of finite number of inputs is defended with probability 1 if every unresolved attack has a persistent chance of discovery, repairs are effective, and subsequent updates preserve earlier protection. We derive completion-time bounds and extend the analysis to growing attack surfaces, repairs that generalize across related attacks, and multiple discovery mechanisms. These results distinguish eventual protection against each fixed attack from complete protection at a single time. We then formulate a defender-led Stackelberg game in which the defender invests in proactive discovery and reactive repair, anticipating the attacker's choice of search effort. We characterize the least-cost allocation that deters attack and the
equilibrium regimes in which the defender funds neither capability, one capability, or both. Numerical experiments illustrate these regimes and show how faster repair can reduce compromise duration without reducing compromise probability.
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Human-Computer Interaction
A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration
Alexandra Coroiu, Andrea Vogt, Viktor Werbilo et al. (+3 more)
Human-
Robot Collaboration (HRC) can facilitate mass customisation in Industry 4.0, with Reinforcement Learning from Human Feedback (RLHF) representing a promising approach for developing safe AI-based
robots. Practical challenges remain regarding safety during AI development, human feedback quality, and bidirectional human-
robot adaptation. We conducted a scoping review of RLHF in HRC systems, mapping methods that address these challenges. Following PRISMA guidelines, we screened 199 records and included 20 peer-reviewed publications (2020-2025) spanning multiple HRC domains. To our knowledge, this is the first review focused on the bidirectional, closed-loop design of RLHF. Our review found multiple feedback modalities enabling data collection in various feedback formats. Collected data can be integrated at different stages of AI training, resulting in a multi-step development process. Pilot experiments are commonly used to evaluate HRC systems based on both human and
robot metrics. To empirically test a key gap identified in the review, we conducted a between-subjects VR experiment comparing system- and user-initiated feedback on
robot proxemic behaviour for safe
navigation. Using
Bayesian models, we analysed the relation between the collected feedback and safety metrics: psychological safety (post-experiment questionnaire) and physical safety (inverse time-to-collision). Results show that user-initiated feedback captures perceived safety better than system-initiated feedback, indicating that feedback timing directly affects feedback quality. Our review and experiment findings show that RLHF relies on appropriate feedback methods to ensure AI safety in HRC, and future RLHF research should prioritise realistic HRC experiments evaluating the effects of feedback collection methods on relevant human and
robot metrics.
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All authors Alexandra Coroiu, Andrea Vogt, Viktor Werbilo, Andreas Poppele, Johann Christensen, Sven Hallerbach
Artificial Intelligence
Outperformance Inverse Optimization: Learning Objective Functions that Outperform Agent Decisions
Akira Kitaoka
Inverse optimization estimates the weights of an objective function that explain observed decisions as optimal solutions, and is used in a variety of fields. For mixed-integer linear programs (MILPs), existing methods aim to reproduce the observations as optimal solutions, and thus learn compromise weights when the observations are suboptimal. We propose outperformance inverse optimization, which instead seeks weights that induce, at each state, an optimal solution outperforming the observed action in every component. We give a loss function that can be evaluated with forward-problem oracles alone and is thus applicable to MILPs, together with gradient-based and DC optimization
algorithms for minimizing it. For weights inducing a unique outperforming optimal solution at all observations, we prove that the probability of failing to induce such a solution at a new state (the generalization error) is bounded by a quantity inversely proportional to the number of observations, and that this bound is tight in the number of observations up to logarithmic factors. In experiments on synthetic and real data, the proposed methods improve the prediction of solutions outperforming the actions over existing methods.
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Machine Learning
Identifiability of a dissipative knowledge-dynamics model: exact recovery under designed excitation, degeneration on observational data
Arman Kostanian and Armen Beklaryan
Human learning is a dissipative dynamical process: mastery accumulates through practice, decays through forgetting, and propagates across interdependent concepts. We model it as a nonlinear dissipative system of ordinary differential equations whose parameters are mechanistically meaningful (a transfer-of-practice
matrix, per-concept forgetting rates, and a saturating practice-response gain), and we study when those parameters can actually be recovered from data. For two concepts, we prove a constructive structural identifiability
theorem for the associated inverse problem under explicit excitation conditions, together with monotonicity, robustness and L-stability results. We derive an implicit L-stable scheme and a batched solver numerically equivalent to the per-trajectory formulation yet two orders of magnitude faster. The empirical study is two-sided. Under the
theorem's excitation conditions, synthetic recovery is exact. On large observational
benchmarks it is not. An apparently strong recovery, with forgetting rates correlating with topic error rates at Spearman $\rho = 0.83$, is refuted by four controls: it survives destroying the temporal order of the data, is matched by a classical
Bayesian baseline, and is unaffected by removing real timestamps. A single shared forgetting rate attains the same accuracy, and replaying the real schedule with identical true rates reproduces most of the correlation. A positive control shows that the controls detect forgetting where it is observable. We trace the effect to the stationary structure of the model and show that it is consistent with the degeneration anticipated by the theory in the absence of designed excitation.
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Computational Engineering
A dynamic pore-network model with discrete microbubble transport
Kristoffer Skjelanger, Anna Mareike Kostelecky, Maziar Veyskarami et al. (+1 more)
We develop a hybrid pore-network model (PNM) for discrete microbubble transport coupled to connected two-phase flow in porous media. We apply the model to simulate flow in the porous transport layer of a rotating
proton exchange membrane (PEM) electrolyser. At sufficiently large apparent gravitational forces, bubbles growing at the catalyst layer can detach at sizes smaller than the characteristic pore size and move through
liquid-filled pores via buoyancy and advection. The model couples a dynamic two-phase PNM to a Lagrangian bubble tracker. Pore-scale Stokes simulations in square throats provide closures for the bubble buoyancy velocity, the hydraulic resistance of bubble-occupied throats, and the reciprocal coupling between
liquid flow and bubble motion. Verification tests assess conservation, pressure-flow coupling, bubble growth, advection, merging, trapping, and transfer to the connected gas phase. The network simulations show that increasing apparent gravitational forces shifts a larger fraction of the produced gas toward discrete-bubble transport. The increase mostly comes from a reduced detachment radius, which decreases bubble size and lets them move more freely through the network.
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All authors Kristoffer Skjelanger, Anna Mareike Kostelecky, Maziar Veyskarami, Timo Koch
Machine Learning · Cross-listed from stat.ML
Sparsifying Stochasticity, Not Capacity: Partial Stochasticity via Deep Weight Factorization of Prior Scales
Marius P Linhard and Maurizio Filippone
Bayesian neural networks need not be fully
stochastic to be universal conditional density approximators, but it remains open which parameters should be
stochastic. We learn this split by applying deep weight factorization to the prior scales, which are the standard deviations of the parameter priors, while fitting the functional prior to a Gaussian process with a maximum mean discrepancy objective. A parameter whose prior scale falls below a cutoff becomes deterministic and is optimized during inference, so the regularizer sparsifies stochasticity rather than capacity. We give a certificate for universal conditional density approximation that is checkable in linear time, together with a minimal repair when it fails. We further show that the common hybrid scheme of sampling some parameters and optimizing the others is
stochastic approximation for a type-II maximum a posteriori objective, and that coupled step sizes can leave a tracking error that does not vanish as the step size shrinks. On a bimodal target, the learned split stays close to an unconstrained reference across all budgets and is insensitive to the cutoff, while random masks that distribute the same prior scales across layers are worse by up to two orders of magnitude. On UCI
benchmarks, our method performs on par with a fully
stochastic network while keeping about half of its parameters deterministic.
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Data Structures and Algorithms
Simulated annealing and Weak Poincaré inequalities with inverse-polynomial accuracy for the Sherrington-Kirkpatrick model
Zhe Hou, Jingcheng Liu and Yixiao Yu
We study sampling from the Gibbs distribution of the Sherrington-Kirkpatrick (SK) model with Glauber dynamics. For every fixed inverse temperature $0\leq\beta<1$ and every fixed $M,D>0$, we give a
polynomial-time simulated annealing
algorithm whose output distribution is within $n^{-M}$ total-variation distance of the Gibbs distribution, with probability at least $1-n^{-D}$ over the interaction
matrix. The
algorithm starts from uniform product
spins and uses Glauber dynamics along an increasing inverse-temperature schedule. The same approach also yields partition-function estimates with relative error $n^{-M}$. The key ingredient is a quantitative weak Poincaré inequality. Building on the
stochastic-localization approach to weak Poincaré inequalities [Davies, Lee, Sandhu, and Shi, arXiv:2607.08160, 2026], we use Gaussian isoperimetry to directly compare exact
stochastic localization paths. Our comparison tolerates failures of local Lipschitz continuity of the posterior mean by controlling their accumulated cost. The local Lipschitzness comes from approximating the posterior means by stationary points of the Thouless-Anderson-Palmer (TAP) free energy in locally strongly convex regions located by approximate message passing, a paradigm introduced by algorithmic
stochastic localization [El Alaoui, Montanari, and Sellke, 2025; Celentano, 2024]. The approximation errors are roughly characterized by the validity of the TAP gradient equations, but in a different coordinate. To show that the errors rarely accumulate too much, we need a strong concentration control. A direct argument would require solving a variant of an open problem by Talagrand [2010, Research Problem 1.7.9]. We bypass this open problem with an intermediate conditioning step and transfer the control back by establishing the stability of the TAP gradient under deletion of coordinates.
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Formal Languages and Automata · Cross-listed from nlin.CG
A repeating sequence of simple rules creates a universal computer
Riz Fernando Noronha
We show that the Turing complete rule 110 in elementary cellular automata can be emulated by alternating three simple symmetric rules in space. As the constituent rules themselves are trivial, this shows that diversity alone can generate emergent phenomena. The rules cannot distinguish left from right, and the chirality of rule 110 is created by their arrangement. Random initial conditions lock on to rule 110 over 90% of the time, explained through the motion of domain walls between desynchronized regions. Adding random noise creates errors that seed domain walls, whose fraction scales with the square root of noise strength, explained through a simple rate equation. This points to a route to complexity through repeated, perturbed units, common in biology, and shows that in cellular automaton models of
quantum field theory, broken
symmetries such as parity need not be present in the local rules.
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Machine Learning
Layerwise Error Attribution for Fast and Robust Mixed-Precision Post-Training Quantization
Samy Houache, Yann Traonmilin and Jean-François Aujol
Mixed-precision post-training quantization is a network compression method that assigns bits layer by layer, under a global
memory budget using a small calibration set. The main difficulties are to overcome the combinatorial nature of the allocation problem and to manage the sensitivity to small, potentially corrupted databases. Hence, an efficient allocation method should be fast to compute and preserve model quality when calibration data are corrupted. To design such a method, we derive a layerwise probabilistic analysis of the quantization error that separates propagated error from the local perturbation introduced at a given layer. We use this local term to build a separable score for a simple allocation
algorithm, that requires no external solver. The probabilistic nature of our approach brings robustness to corrupted data. On denoising tasks with DRUNet, with an average budget of 4 bits per weight, our method matches or improves state-of-the-art mixed-precision baselines under clean calibration, and is more robust to corrupted calibration, with PSNR gains of up to 7.5 dB under the tested corruptions. Experiments show bit-allocation speed-ups from 28x to 2,570x over the studied baselines. For quantized
diffusion models, our experiments show that a direct application of our framework also improves the state-of-the-art.
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Artificial Intelligence
Think Before You Paint: Recursive Latent Reasoning for Diffusion Models
Paweł Skierś, Małgorzata Grzanka, Wojciech Masarczyk et al. (+2 more)
Diffusion models generate realistic images but often fail on visual
reasoning tasks, such as filling in a Sudoku or drawing the path through a maze. When a discrete symbolic representation is available, recursive methods such as the Tiny Recursive Model (TRM) solve even hard instances of these puzzles. We ask how such
reasoning can be carried over to pixels, where no symbolic representation is available. We propose Painter-Thinker (PaTh): a small recursive network (the Thinker) reasons over a grid of learned
tokens that encode the noisy image and the conditioning, refines a latent state within every denoising step, and steers a frozen
diffusion model (the Painter) through ControlNet adapters. The Thinker is trained with the standard reconstruction loss alone, without symbolic targets, a solver, or a verifier. PaTh solves 92.5% of hard MNIST Sudoku puzzles (prior best 75%) and 71.2% of extreme ones (prior best 4.1%), with 10M parameters against 82M for a standard
diffusion model. It also improves on mazes, Queens, and CLEVR scenes with specified spatial relations, and its advantage grows with problem size. Diagnostic experiments show that PaTh recovers from injected mistakes that the
diffusion model cannot repair, especially when many
cells are wrong. Together, these results show that
reasoning mechanisms developed for symbolic data can be integrated into pixel-space
diffusion without symbolic supervision, opening a path toward generating data under increasingly complex constraints.
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All authors Paweł Skierś, Małgorzata Grzanka, Wojciech Masarczyk, Jan-Willem van de Meent, Kamil Deja
Computer Vision
SANet: Selective Attention Network for Infrared Small Target Detection
Yingmei Zhang, Wangtao Bao, Qin Xiao et al. (+5 more)
Infrared small target detection aims to accurately identify and locate dim targets in complex backgrounds and supports applications such as maritime surveillance and military search and rescue. However, the small size and weak contrast of infrared targets make it difficult to balance detection accuracy and false alarms. This paper proposes a selective
attention network (SANet) for infrared small target detection. A dual-path semantic-aware module combines standard and pinwheel-shaped convolutions to preserve local spatial consistency and capture broader contextual information. Spatial and channel
attention further refine the features and improve target-background discrimination. To address the limitations of static skip connections in U-Net, a selective
attention fusion module adaptively integrates features across scales using spatially varying weights. It selectively enhances salient regions and improves discrimination between true targets and false alarms. Experiments on three public
benchmarks, NUAA-SIRST, IRSTD-1K, and NUDT-SIRST, show that SANet achieves competitive performance in intersection over union (IoU), normalized IoU, detection probability, and false alarm rate. Its IoU exceeds that of the second-best method by 1.93, 4.32, and 2.21 percentage points, respectively. These results support the effectiveness of SANet in dim-target perception, discriminative feature representation, and background suppression.
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All authors Yingmei Zhang, Wangtao Bao, Qin Xiao, Yong Yang, Weiguo Wan, Yitao Luo, Xueting Zou, Lei Zhang
Computer Vision
Bringing BNNs to Fast Event Processing
Paul Longour, Julien Moreau and Franck Davoine
Binary Neural Networks (BNNs) enable efficient deep learning deployment on resource constrained devices with weights and activations compressed to one bit, substantially reducing model size and inference cost. Event cameras offer complementary advantages, including low latency, high dynamic range, and low power consumption, by capturing asynchronous streams of events rather than dense image frames. Despite their shared emphasis on efficiency, the combination of these technologies remains largely unexplored. This work aims at adapting and evaluating modern deep BNN architectures on event data. We also show that cross-modal pretraining from RGB data can improve the classification accuracy of BNNs on neuromorphic
datasets. We introduce the Polar-wise Binary Event Volume (PBEV), a binary representation that enables event-camera data to be processed directly by BNNs and represents a step toward fully binarized event-based
vision systems. Best evaluated BNN on N-Caltech101 classification
benchmarks shows 90.58% accuracy with 7.5x less operations than their full-precision counterparts.
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Artificial Intelligence
LiveMACE: Process-Aware Evaluation of LLM Agent Capabilities in Evolving Markets
Jun Zhao, Leiming Fu, Yanbo Wen et al. (+9 more)
Evaluating
agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, where outcomes reflect a closed-loop interaction between
agent behavior and changing external conditions. We introduce LiveMACEBench, a process-aware
benchmark that uses live financial
markets as a naturally evolving testbed for persistent LLM
agents. Five frontier LLMs operate along continuous trajectories under matched Tool Use, Persistent
Memory, Rule Following, and Multi-
Agent Collaboration configurations. We evaluate them through both realized outcomes and mechanism-specific diagnostics derived from complete decision traces. Across 30 days of live evaluation, we find a pronounced outcome-capability gap: realized returns often diverge from capability-specific measurements, and similar outcomes can arise from markedly different patterns of mechanism use. Trace-level diagnostics further expose distinct bottlenecks across capabilities, demonstrating that mechanism access, effective mechanism use, and downstream performance are not interchangeable measures of
agent capability. LiveMACEBench makes this distinction measurable, turning live
markets from a performance leaderboard into a diagnostic environment for
agent capability
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All authors Jun Zhao, Leiming Fu, Yanbo Wen, Yiding Wang, Xuantong Liu, Yang Shu, Yuyang Lu, Xuanran Xing, Jingqi Tong, Hao Xu, Qi Zhang, Xuanjing Huang
Artificial Intelligence · Cross-listed from physics.geo-ph
An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
Stefan Carpentier, Jan Diederik van Wees, Eva de Boever et al. (+5 more)
Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results. For optimal operation, these techniques require many well-constrained input data. In the framework of the Horizon Europe GO-Forward and MOOI WarmingUP GOO projects and to accelerate Implicit modeling, Machine Learning (ML) methods have been tested and implemented in a toolkit for the interpretation of (onshore) seismic data from the shallow to deep range (+- 300 - 3500 m). The goal is to rapidly characterise this depth domain by efficient interpretation of horizons and faults in seismic data. The first step is to improve the signal by applying AI techniques like self-supervised and semi-supervised contrastive learning CNN's for noise reduction and interpolation. Next, horizons and faults are interpreted with minimal use of human-generated training data by using (semi-) self-supervised methods. The resulting developed toolkit supports the application of the implemented
algorithms in an efficient workflow. As a first demonstration, the top of the Dutch Maassluis Formation has been interpreted in the Leeuwarden and Waalwijk 3D seismic cubes. Overall, this study demonstrates that AI-assisted interpretation workflows have reached a level of maturity that allows their integration into applied geological modeling and decision-making.
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All authors Stefan Carpentier, Jan Diederik van Wees, Eva de Boever, Jan Niederau, Camille Chapeland, Suzanne Atkins, Boris Boullenger, Jens Wollenweber
Networking
Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks
Bernardo A. C. Pereira, Marcos Carvalho, Fatih Temiz et al. (+5 more)
Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles. However, existing approaches often overlook network contention among co-located services with heterogeneous and dynamic latency requirements. While time-sensitive networking (TSN) provides bounded-latency communication, conventional and reinforcement learning-based schedulers struggle to adapt to highly dynamic vehicular environments and inter-queue dependencies. To address these limitations, we propose a multi-
agent reinforcement learning (MARL) approach for queue-level scheduling in TSN-enabled VEC. Each TSN queue is assigned an autonomous
agent that jointly learns the queue service order and time-slot duration to minimize deadline misses under speed-dependent latency requirements. We employ multi-
agent proximal policy optimization (MAPPO) to enable coordinated yet autonomous scheduling decisions. Evaluation against single-
agent, multi-
agent, and non-learning-based baselines shows that MAPPO provides robust performance across different traffic profiles. Compared with centralized single-
agent methods, it reduces service latency by up to 66.2% and improves reliability by up to 271.8%. Furthermore, unlike urgency-based heuristics, MAPPO ensures balanced scheduling while achieving lower inference times compared to other MARL methods.
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All authors Bernardo A. C. Pereira, Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Andreas Gavrielides, Johann M. Marquez-Barja, Daniel F. Macedo