arXiv:2606.19821v1 Announce Type: new Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, existing machine learning (ML) approaches face significant limitations in scalability and explainability, restricting their effectiveness in real-world deployments. We propose TelcoAgent, a foundation model-based framework that enables accurate, scalable, and explainable forecasting of multiple KPMs across diverse network cells without the need for site-specific training. Specifically, the framework comprises three key components: (i) an automated three-agent pipeline that constructs a 3rd Generation Partnership Project (3GPP) knowledge graph directly from specification documents, (ii) a scalable, time-series foundation model (TSFM)-based prediction pipeline to deliver accurate, zero-shot forecasting, and finally (iii) a reasoning and explanation pipeline that provides actionable, domain-grounded diagnostics. Evaluated using a 3-month, real-world, city-scale 5G KPM dataset from a U.S.-based network operator, TelcoAgent demonstrates high forecasting accuracy for all 7 considered KPMs per cell across 200 cells, while delivering explainable insights and actionable instructions to address network degradations.
Science Journals
arXiv:2606.19822v1 Announce Type: new Abstract: We present the AL*RTA algorithm for learning alternating real-time automata (ARTAs) using membership and equivalence queries. AL*RTA combines ideas from AL*for learning alternating finite automata and NL*RTA for learning nondeterministic real-time automata. We first define ARTAs and show that alternation improves succinctness, although it does not increase expressive power. We then present AL*RTA and show its termination. Our empirical evaluation suggests that AL*RTA generally learns smaller automata than NL*RTA at the cost of more queries.
arXiv:2606.20361v1 Announce Type: new Abstract: The performance of high-tech systems is often dictated by a few performance objectives shared among the many closed-loop controlled subsystems operating in the machine, such as synchronization, coordination, and alignment, which necessitates control methods that explicitly address them to achieve optimal performance. The aim of this paper is to introduce a framework that improves system performance through system-level controllers designed to be implemented as add-ons to the existing subsystem control structure. The developed method parametrizes all stabilizing system-level add-on controllers using the Youla framework, enabling a convex formulation of the sparse $\mathcal{H}_2$ synthesis problem. The result is a sparse add-on controller that achieves the optimal trade-off between combined performance and interconnection complexity, as demonstrated through numerical simulations.
arXiv:2606.20363v1 Announce Type: new Abstract: Explicit skill libraries make computer-using agents easier to inspect, but it remains unclear whether such libraries can be mined from interaction data in a way that improves downstream policies. We study this question through a three-stage pipeline that segments GUI trajectories, clusters segments into candidate skills, and trains a skill-aware policy from the resulting annotations. The mined clusters are readable on the source benchmark: five of eight clusters have at least 0.95 purity against InteraSkill Workflows labels. However, readability does not imply transfer. GRPO improves IW skill-step accuracy only from 18.5\% to 20.5\%, leaves BrowseComp+ essentially unchanged, and underperforms trivial frequency priors on key source-domain metrics. We therefore present the method as a diagnostic study: trajectory mining can expose inspectable skill structure, but the current boundary detector, orderless segment representation, and offline reward model are insufficient for reliable cross-domain policy improvement.
arXiv:2507.22524v3 Announce Type: replace Abstract: We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction. Featuring four GCN architectures (O-GCN, T-GCN, TP-GCN, TE-GCN) across the GCNConv and GraphConv layers, our toolkit integrates multiple graph representations of event sequences with different choices of node- and graph-level attributes and in temporal dependencies via edge weights, optimising prediction accuracy and stability for balanced and unbalanced datasets. Extensive experiments show that GCNConv models excel on unbalanced data, while all models perform consistently on balanced data. Experiments also confirm the superior performance of HGCN(O) over traditional approaches. Applications include Predictive Business Process Monitoring (PBPM), which predicts future events or states of a business process based on event logs.
arXiv:2606.18436v2 Announce Type: replace-cross Abstract: Sparse point observations are increasingly available for precipitation nowcasting, but it is unclear how much they improve dense radar-field forecasts. We partially address this question with a multimodal graph neural network nowcasting system over the Nordic radar domain. The model predicts rain rate every five minutes up to two hours ahead and is trained with different combinations of radar history, MEPS numerical weather prediction, Netatmo surface observations, MSG satellite channels, stochastic noise, and CRPS-based ensemble losses. The study is designed as an ablation of operationally relevant information sources and training objectives. We compare radar-only, NWP-informed, station-informed, satellite-informed, noise-augmented, and CRPS-based configurations using complementary diagnostics on the radar grid, at station locations, for rain onset, and through oracle, displacement, and amplitude scores. The results show that each source improves a different part of the forecast problem. MEPS stabilises radar-only extrapolation, Netatmo observations improve local station and onset diagnostics, and satellite predictors reduce some station-level biases but may activate rain too early when used deterministically. CRPS-based configurations provide the most consistent radar-grid gains, while the combined satellite and CRPS setup gives the best overall oracle/DAS score. These results do not support the conclusion that point observations are uninformative for nowcasting, but they show that local observational skill and spatially coherent radar-field skill are distinct targets. The practical implication is that sparse observations can provide useful local constraints, but their benefit for radar-like fields depends on the training loss, uncertainty representation, and how observation support is encoded in the model.
arXiv:2606.20467v1 Announce Type: new Abstract: Mathematicians understand a PDE solution through mathematical structures rather than tables of computed values. Historically, this has been the product of mathematical analysis, carried out by hand for each problem individually. Neither numerical simulation nor neural networks produce those structures directly. We propose Agentic Symbolic Search (ASYS), a prior-guided framework in which an agent translates PDE theory, public problem constraints, and accumulated search experience into testable differentiable symbolic programs. The mathematical forms are refined under evolutionary search, while their continuous parameters are fit by gradient-based optimization. This makes the search an automated form of inductive-bias injection rather than blind symbolic regression. For problems with known analytical forms, ASYS recovers these forms naturally; for other problems, ASYS constructs analytical approximations which can guide mathematicians toward further analysis. In our experiments, across five problems spanning bounded dynamics, finite-time blow-up, and free-boundary focusing, ASYS produces interpretable representations, including a geometric interface formula for Allen-Cahn 2D dynamics and a nine-parameter contraction law for Keller-Segel chemotactic blow-up, in settings where no closed-form description was previously available. ASYS shows the possibility of a new paradigm for characterizing PDE solutions, beyond handcrafted analytical solutions, mesh-based numerical solutions, and neural network approximations.
arXiv:2606.19431v1 Announce Type: cross Abstract: Cellular automata are discrete dynamical systems defined on a lattice, in which each site carries a finite set of states that evolve in time according to local deterministic rules. An important application of cellular automata is in lattice gas models of fluids, where the cellular automaton framework provides a particle-based microscopic description of hydrodynamic behavior. The macroscopic fluid equations emerge after coarse-graining over many lattice sites and time steps, offering a bottom-up route to hydrodynamics. A celebrated example is the Frisch-Hasslacher-Pomeau (FHP) model, an automaton defined on a two-dimensional triangular lattice that yields the two-dimensional Navier-Stokes equations upon coarse-graining. In this work, we construct a parity-breaking generalization of the FHP model through two modifications: introducing chiral two-body collision rules and systematically rotating particle velocities to mimic the effect of a background magnetic field. We show that this automaton yields a hydrodynamic model with odd viscosity, a transverse transport coefficient that is a hallmark of odd fluids. We verify the analytical transport coefficients using Poiseuille-flow simulations of the chiral FHP automaton. Our results demonstrate that the chiral automaton introduced here provides a bridge between microscopic parity-breaking scattering processes and macroscopic odd-fluid hydrodynamics.
arXiv:2606.19899v1 Announce Type: new Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks. As these systems enter real research workflows, decision-makers increasingly face evaluation results whose meaning depends on underlying design choices that are often implicit or under-documented. We synthesize current evidence on AI-enabled biological risks and introduce biological agentic evaluations as a promising, but interpretation-sensitive, tool for assessing these systems. Our central contribution is a set of practical, experience-grounded considerations -- drawing from our own evaluations -- that show how choices around defining, designing, running, scoring, and documenting evaluations materially shape what results do and do not imply about risk. The analysis is intended to help policymakers interpret biological evaluation outputs with appropriate caution; guide public and private funders toward high-leverage investments in AI-biology evaluation research; and support biosecurity practitioners assessing emerging AI systems. A secondary audience includes researchers designing or conducting agentic evaluations within frontier AI labs, AI providers, scientific institutions, and third-party evaluation organizations.
arXiv:2606.19926v1 Announce Type: new Abstract: MLLM-based mobile GUI agents have made substantial progress on short-horizon tasks, yet remain unreliable on long-horizon tasks that require retaining intermediate facts across many steps and app transitions. We attribute this limitation to ReAct-style prompting, which passively accumulates per-step records, leading to prompt explosion and dilution of critical cross-app facts. To address this, we introduce MemGUI-Agent, an end-to-end long-horizon mobile GUI agent with proactive context management. MemGUI-Agent is built on Context-as-Action (ConAct), which casts context management as first-class actions emitted by the same policy that selects UI actions. Instead of passively appending history, ConAct maintains three structured context fields: folded action history, folded UI state, and recent step record, preserving critical UI facts while keeping context compact. To make proactive context management learnable across model scales, we construct MemGUI-3K, a 2,956-trajectory dataset with full ConAct annotations for supervised training and offline analysis. Training an 8B model on MemGUI-3K produces MemGUI-8B-SFT, an 8B MemGUI-Agent that achieves the best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark. Code, data, and trained models will be released at https://memgui-agent.github.io/.
arXiv:2606.20151v1 Announce Type: new Abstract: This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to enable high-performance spiking neural networks (SNNs). The architecture couples a pretrained EfficientNet encoder with a CoLaNET spiking classifier. We convert the encoder's activations into spike trains via rate-coding and train the subsequent SNN classifier using local, biologically inspired learning rules, bypassing end-to-end gradient propagation. This approach achieves 99.09% accuracy on a 64-class ImageNet benchmark, demonstrating performance on par with conventional deep networks. The work presents a biologically plausible and efficient framework for adapting powerful pretrained encoders to downstream spiking neural network tasks.
arXiv:2606.20212v1 Announce Type: new Abstract: We present CzechDocs, a multiway parallel dataset of formatted documents (HTML, DOCX, and PDF) covering Czech and minority languages used in Czechia-primarily Ukrainian and English, with smaller portions of Vietnamese, Russian and other languages. The dataset is designed to support the evaluation of machine translation systems that aim to preserve document formatting during translation. We provide a comparison of the most common approaches to format-preserving machine translation on a validation subset of the dataset. This validation split, together with the evaluation toolkit, is publicly released for further research. A held-out test split will be reserved for a future shared task focused on document-level translation with formatting preservation.
arXiv:2606.16725v2 Announce Type: replace Abstract: The widespread adoption of microservice architectures has introduced new challenges in aligning software modularity with the structure of development organizations. Although prior research has extensively examined technical properties such as service coupling and dependency structures, comparatively little attention has been paid to how contributor activity reflects or diverges from service boundaries. In this paper, we introduce the notion of organizational cohesion in microservice ecosystems and propose a quantitative approach to measure it. Building on the Sensitive Class Cohesion Metric (SCOM), we define Pairwise Team Cohesion (PTC), a metric that captures the balance and focus of developer contributions within individual microservices. We analyze the evolution of organizational cohesion using a longitudinal case study of the Spinnaker microservice platform and replicate the analysis across six additional open-source microservice systems. Our results reveal systematic differences between core and peripheral services and show that PTC and Average Organizational Coupling (AOC) exhibit only a weak correlation across projects. This finding shows that team cohesion and cross-service developer activity suggest distinct and weakly associated organizational dynamics. By extending the "high cohesion, low coupling" principle to the organizational level, our study provides a quantitative perspective for assessing the socio-technical structure of microservice development.
arXiv:2606.20197v1 Announce Type: new Abstract: Actor-Critic Model Predictive Control (MPC) effectively addresses complex, non-convex control problems, but guaranteeing the closed-loop stability of sequence-based learning models within these pipelines remains challenging. This paper introduces a novel Transformer-Actor-Critic MPC architecture with formal robustness guarantees. First, we prove that Transformer networks can satisfy global incremental Input-to-State Stability ($\delta$ISS). We then leverage Riemannian contraction theory to analyze the interconnected dynamics between the physical plant and the predictive neural network. Finally, we integrate these theoretical bounds as a training regularizer to yield a certifiably robust policy. The framework is validated on a nonlinear 3D drone model executing target-reaching and obstacle-avoidance maneuvers.
arXiv:2606.19957v1 Announce Type: new Abstract: Machine learning is often touted to improve the efficiency of ICT, but that small gain is overwhelmed by the enormous carbon, water, and land footprints of data centers and ML-ready devices. We survey the electricity consumption of ML applications in training and inference, focusing on electricity-intensive image generation. Our team of a computer engineer, a media scholar, and an artist explore solutions including inexact computing; tiny language models; low-precision hardware architectures; hardware with limited capacity; and anticipating and mitigating energy demands at the design phase. We will sketch our work in progress of an ethical and aesthetically sophisticated tiny image generator using non-scraped data. Looking to the economic context, we will propose a true-cost accounting for the environmental impact of machine learning and suggest that the criterion of efficiency is driven by the shareholder-capitalist framing of ICT.
arXiv:2606.19529v1 Announce Type: new Abstract: We argue here that traditional network models, which are overwhelmingly based on the mathematical construct of a simple graph, are fundamentally insufficient for capturing the complexity of modern distributed systems. Such systems are characterized by heterogeneous agents with diverse capabilities, high-dimensional and multi-modal data streams, and intricate, context-dependent relationships that cannot be adequately described by a simple connection or a scalar weight. The limitations of these classical models necessitate a new mathematical language, one with far greater expressive power. We have found that sheaf theory provides us with such a language. Moreover, we show that the sheaf Laplacian is a suitable mechanism for data fusion and establishing consensus within distributed sensing networks.
arXiv:2606.19541v1 Announce Type: new Abstract: Modeling the circadian impact of seasonal clock changing requires precise synchronization between solar and social time. This report critiques a recent study that associated disease prevalence in the United States with seasonal clock exposure. We identify a fundamental computational error in which a sign reversal of the longitudinal offset effectively inverted the US East-West axis, cross-correlating local health data with the circadian burden of hypothetical locations on the opposite side of a time zone. We outline the methodology for a correct modelization of the circadian process in the context of US geography.
arXiv:2606.19555v1 Announce Type: new Abstract: Quadruped robots are increasingly expected to navigate through narrow passages, cluttered indoor scenes, and large-scale 3D unstructured environments. Existing local planners commonly approximate the robot using isotropic geometric inflation or rely on planar and elevation-map representations, leading to conservative motion in tight spaces and limited reasoning about overhanging structures. This letter presents SCAN-Planner, a spatial collision-aware local planning framework for long-range quadruped navigation. A yaw-aware twin-cylinder footprint is used to model the elongated robot body, enabling whole-body collision evaluation through sparse queries in an inflated 3D occupancy map. We further introduce a projected A* search that generates collision-free guidance on an interpolated ground-following surface, with z-gradient suppression to avoid obstacles horizontally while maintaining vertical stability. For large-scale deployment, a robot-centric sliding map with boundary fallback provides high-resolution local collision checking and recovery from local dead ends. Simulation and real-world experiments demonstrate that SCAN-Planner generates safe, smooth, and efficient trajectories in dense clutter, 3D unstructured scenes, stair traversal, and long-range navigation tasks.
arXiv:2606.19556v1 Announce Type: new Abstract: Predictive simulation of melt pool dynamics in laser-based processing of metals, e.g., laser beam welding or laser powder bed fusion additive manufacturing, requires accurate resolution of thermo-hydrodynamic interactions at the melt-gas interface. Here, evaporation-induced recoil pressure and temperature-dependent surface tension govern the flow. Because these mechanisms depend sensitively, often exponentially, on the interface temperature, reliable predictions demand highly accurate heat transfer models. Popular diffuse-interface formulations smear the extreme thermal gradients as typical for laser-metal interactions, leading to interface temperature errors that critically degrade the accuracy of interface force predictions and melt pool dynamics. We present a hybrid sharp-diffuse interface approach for high-fidelity modelling of melt pool thermo-hydrodynamics with rapid evaporation. The heat transfer problem is represented using a sharp-interface unfitted finite element (CutFEM) formulation, enabling accurate prediction of the temperature field. The multi-phase flow problem, characterized by large density ratios and complex interface dynamics, is accurately captured using a robust level-set-based one-fluid diffuse-interface finite element formulation. Consistent coupling is achieved by extending the sharp-interface temperature into a narrow interface region to evaluate temperature-dependent interface forces within the diffuse-interface flow framework. In practically relevant benchmarks, the sharp-interface thermal model exhibits second-order spatial convergence, enabling finite element sizes two orders of magnitude larger than the diffuse-interface approach for 1 accuracy. In a novel coupled thermo-hydrodynamic benchmark representative of laser-metal interactions, the hybrid approach is one order of magnitude more accurate than a purely diffuse-interface model on the same mesh. Robu
arXiv:2606.18611v2 Announce Type: replace Abstract: We propose a parameter-efficient speech enhancement framework, Quaternion Conformer GAN (QC-GAN), which combines a Quaternion Conformer generator with MetricGAN-based training. The Hamilton product encodes the magnitude and phase via structured weight sharing, reducing the number of layer parameters while preserving their interdependencies. A metric-learning discriminator was employed to maximize perceptual quality by optimizing the approximate perceptual evaluation scores. On the VoiceBank+DEMAND dataset, QC-GAN achieved a Perceptual Evaluation of Speech Quality (PESQ) score of 3.48 with only 0.89M parameters, delivering a performance comparable to state-of-the-art models at less than half their size. A 35K-parameter variant achieved a PESQ score of 3.23, surpassing conventional methods with significantly fewer parameters. Evaluation on the DNS-Challenge 3 dataset further confirmed generalization to real-world conditions.
arXiv:2606.08892v2 Announce Type: replace Abstract: AI models deployed in critical domains, such as AI safety research, may subtly sabotage our efforts due to misalignment. Diffuse AI Control is a subfield of AI safety concerned with mitigating risks from AI sabotage distributed over long deployment horizons (diffuse threats). These risks are particularly pernicious on fuzzy tasks, i.e. tasks which are hard to grade or require intuition. To understand diffuse threats on fuzzy tasks, we introduce a framework that considers AI control as an adversarial game between a blue team and a red team. The blue team uses a weak trusted model to construct a weak score against which they would train a strong, potentially subversive model to remove the subversion propensity if it were present. The red team then tries to find model behaviors that are rated highly by the weak score, and thus might not be trained out, but actually correspond to poor performance. We test our framework on the task of writing experimental proposals for research questions from recent ML papers. We use a language model with access to the original paper as a proxy "ground-truth" scorer. Our red team discovers subversive behaviors using multi-objective evolutionary prompt optimization. We show that Opus~4.6 can write proposals that are worse according to the ground truth proxy than those of GPT-OSS-20B, while the weak scorer rates them as highly as the best proposals from Opus 4.6. We then propose an adversarial optimization algorithm for the blue team that discovers more robust prompts for the weak model. This algorithm produces a blue team prompt that our red team optimization fails to exploit.
arXiv:2601.21081v2 Announce Type: replace Abstract: Multimodal models for text-to-image generation have achieved strong visual fidelity, yet they remain brittle under compositional structural constraints, notably generative numeracy, attribute binding, and part-level relations. To address these challenges, we propose Shape-of-Thought (SoT), a visual CoT framework for process-supervised progressive shape assembly in the rendered 2D domain, without external engines at inference time. SoT trains a unified multimodal autoregressive model to generate interleaved textual plans and rendered intermediate states, helping the model capture shape-assembly logic without producing explicit geometric representations. Unlike text-only CoT, each decision is grounded in a rendered state, making counts, attachments, topology, and intermediate part-addition errors inspectable across the trajectory. To support this paradigm, we introduce SoT-26K, a large-scale dataset of grounded assembly traces derived from part-based CAD hierarchies, and T2S-CompBench, a benchmark for evaluating structural integrity and trace faithfulness. Fine-tuning on SoT-26K achieves 88.4% on component numeracy and 84.8% on structural topology, outperforming direct generation by +24.2 points on component numeracy and +19.3 points on structural topology. SoT establishes a transparent testbed for rendered-domain structure-aware generation. The code is available at https://github.com/yuhuo03/Shape-of-Thought.
arXiv:2601.22300v3 Announce Type: replace Abstract: We propose a deep photonic neuromorphic network (PNN) architecture based on phase-change material (PCM) synapses and local optical feedback for online, unsupervised Hebbian learning. The proposed architecture combines optical vector-matrix multiplication, non-volatile PCM synaptic weighting, and local coincidence-driven synaptic adaptation within a multilayer photonic crossbar framework compatible with photonic integrated circuits. Unlike conventional PNNs that rely on externally computed gradients, repeated optical-electrical-optical conversions, or global backpropagation, the proposed framework employs local Hebbian learning governed directly by correlated pre- and post-synaptic optical activity. To investigate the feasibility of the proposed learning mechanism, we implemented the PNN design using fiber-optic components, programmable variable optical attenuators, and real-time software control that incorporates PCM thermal dynamics. Supervised and unsupervised learning behaviors were experimentally evaluated under both offline and online learning conditions using representative image-recognition tasks. The experimental results demonstrate adaptive synaptic evolution, successful optical inference, and autonomous pattern encoding through local Hebbian learning under realistic fiber-optic hardware conditions. These results establish a pathway toward future integrated photonic neuromorphic systems capable of scalable and energy-efficient online Hebbian learning.
arXiv:2512.01538v3 Announce Type: replace Abstract: In this work we introduce an effective approach to quantize the electromagnetic response of plasmonic metallic nanostructures. Their shape is arbitrary and they feature a realistic description of the frequency-dependent metal dielectric function that is based on experimental data. The derived quantum modes correctly reproduce the linear response macroscopic polarization of the nanoparticle upon external drive according to classical macroscopic Maxwell equations in the quasistatic limit. We further investigate the coupling of these modes to a quantum-chemical molecular description. The presented methodology paves the way for accurate modeling of plexcitonic system, where strong plasmon-molecule coupling and/or strong-driving fields call for a quantized description of the plasmonic response.
arXiv:2606.19987v1 Announce Type: new Abstract: This data report introduces PolSeT (Polish Semantic Timbre), a dataset designed to facilitate research in psychoacoustics and Music Information Retrieval (MIR) in Polish and cross-cultural contexts. The dataset contains data from two sequential experiments. Experiment 1 (N=60) was a free-verbalization task aimed at creating a lexicon of Polish semantic descriptors. Using 11 stimuli, a total of 1901 descriptors (701 unique) were gathered. Experiment 2 (N=105) utilized this lexicon to conduct a semantic differential study, where participants rated 18 instrument sounds on 8 bipolar scales, with repeated trials for reliability analysis. The released dataset includes raw listener responses, comprehensive demographics (experience, gender, age), audio stimuli, and extracted acoustic features with Python extraction code. This dataset addresses a gap in open timbre research data, providing both the qualitative linguistic groundwork and the quantitative ratings necessary for psychoacoustic research and the training of multilingual semantic embedding models.