arXiv:2607.17747v1 Announce Type: new
Abstract: The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management. We propose a world model-based approach for network control that enables adaptive configuration of crucial parameters. The world model is trained from historical data and predicts the impact of its actions on future network states. Our controller leverages the model's uncertainty estimate to robustly find optimal network configuration changes. Furthermore, the optimization objective can be changed dynamically without model retraining. We demonstrate the effectiveness of the approach in simulated closed-loop control of a mobile network energy-saving feature. Our results show improved performance in balancing energy savings with quality of service, compared to traditional methods and reinforcement learning approaches. Finally, we show the world model performance on real network data from, and evaluate counterfactual actions proposed by the controller under various throughput constraints.
Science Journals
arXiv:2507.04384v4 Announce Type: replace
Abstract: Achieving safe, efficient, and kinematically feasible planning in dynamic environments remains a significant challenge, as planners must simultaneously handle moving obstacles, sensor uncertainty, and strict motion constraints. To address this problem, we propose an energy-parameterized diffusion planning framework that learns a conservative energy field to realize safe and stable generalization across diverse scenarios. The energy-parameterized diffusion formulation enables flexible integration of multiple constraints, allowing the planner to generalize to previously unseen environments without retraining. To ensure real-time safety during deployment, we further incorporate a lightweight safety filter that enforces safety and kinematic feasibility constraints in real-time. Additionally, we develop a scene-agnostic, MPC-based data generation pipeline to produce large-scale, dynamically feasible training trajectories. In simulation, the proposed method achieves real-time performance with a mean planning time of 0.21s and a low planning failure rate of 0.57%. Real-world experiments on the F1TENTH platform further validate the effectiveness of the proposed framework. Under sensor uncertainty in previously unseen dynamic environments, the planner consistently generates collision-free trajectories, which remain safe after being tracked by a simple controller, maintaining a mean obstacle clearance of 0.26 m, demonstrating strong robustness and practical applicability. Project page: https://rstp-comp-diffuser.github.io.
arXiv:2607.17290v1 Announce Type: new
Abstract: In this work, we investigate the effect of lookahead branching strategies in neural network verification. We present a general recipe to integrate lookahead into any branch-and-bound verifier and demonstrate how one of the current state-of-the-art branching heuristics, FSB, can be viewed as a special instantiation of the lookahead branching strategy. We also describe how, in addition to improving the quality of branching decisions, lookahead can generate additional lemmas that accelerate verification. We instantiate the method in two representative branch-and-bound-based verifiers (Marabou and $\alpha$-$\beta$-CROWN), and demonstrate that lookahead leads to consistent speedups in verification time and up to $57\%$ more solved instances. Code is available at https://github.com/ai-ar-research/lookahead-branching.
arXiv:2607.17511v1 Announce Type: new
Abstract: Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application to regional climate forecasting faces practical challenges: model weights are often proprietary, local training records are limited, and computational budgets are constrained, making traditional fine-tuning approaches infeasible. To address these constraints, we introduce a lightweight, black-box adaptation framework (requiring no access to backbone parameters and no backbone fine-tuning) that enhances frozen TSFMs at inference time through two plug-and-play wrappers: \textbf{SMR\textsuperscript{2}} (Stationarity aware multi-resolution Residual), which decomposes the input into multi-resolution temporal views, learns stride specific residual corrections that capture regional dynamics, then adaptively ensembles them into a single forecast, and \textbf{MBB} (Moving Block Bootstrap), which preserves temporal dependencies through block resampling and ensembles over temporally coherent residual perturbations to stabilize the point forecast. Both wrappers instantiate the same bagging style principle: they build diverse views of the input or its residuals, forecast each with the same frozen backbone, and aggregate, so all adaptation comes from inference time ensembling rather than any weight update. Evaluated on one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) prediction across multiple sites in South Australia, our framework consistently improves forecasting performance across several backbone models, demonstrating up to 26\% mean squared error (MSE) reduction over the corresponding frozen backbone while enabling practical deployment in resource constrained regional forecasting systems.
arXiv:2607.17851v1 Announce Type: new
Abstract: We present a numerical construction of cubature rules for area integrals of analytic functions over planar domains with rectifiable Jordan boundary. The starting point is the Cauchy--Green identity. Given a weight $w$, we choose a $\bar\partial$-antiderivative $W$ and reduce the area integral to a contour integral involving the boundary values of $W$. These values are then approximated by a rational function with free poles, computed by the AAA algorithm. The poles inside the domain become cubature nodes, the corresponding residues become weights, and the boundary residual controls the error through an a posteriori estimate, rigorous once the continuous boundary residual is bounded. The same rule admits a dual reading, as the exact integral of a rational interpolant to the integrand, the area analogue of the one-dimensional interpolatory viewpoint. The numerical examples recover the disk mean-value rule and the focal-segment rule of the ellipse to machine precision, reproduce the exact finite quadrature identities of quadrature domains with both separated and confluent nodes, and evaluate logarithmic and Cauchy volume potentials from boundary data alone. The interior poles trace analytic skeletons that we identify tentatively with the mother bodies of potential theory, along with image points that appear without being imposed; for the square the observed convergence is root-exponential.
arXiv:2607.17369v1 Announce Type: new
Abstract: In a previous paper, we began the study of sequence prediction algorithms adapted to stringological word complexity measures. One measure we considered was left-to-right (most-significant-digit-first) automaticity. Here, we show a statistically and computationally efficient algorithm adapted to the ``dual'' right-to-left (least-significant-digit-first) automaticity, which turns out to be substantially different for our purpose. We also demonstrate a prediction algorithm for a more expressive measure that we call ``arithmetic repetition complexity''. In particular, the latter can be used for predicting the so-called mix-automatic sequences.
arXiv:2510.13634v2 Announce Type: replace
Abstract: Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints. This work introduces a gate-based QRC for multivariate time series (MTS-QRC) that pairs injection and memory qubits and uses a Trotterized nearest-neighbor transverse-field Ising evolution optimized for current device connectivity and depth. On Lorenz-63 and ENSO, the method achieves a mean square error (MSE) of 0.0087 and 0.0036, respectively, performing on par with classical reservoir computing on Lorenz and above learned RNNs on both, while NVAR and clustered ESN remain stronger on some settings. On IBM Heron R2, MTS-QRC sustains accuracy with realistic depths and, interestingly, outperforms a noiseless simulator on ENSO; singular value analysis indicates that device noise can concentrate variance in feature directions, acting as an implicit regularizer for linear readout in this regime. These findings support the practicality of gate-based QRC for MTS forecasting on NISQ hardware and motivate systematic studies on when and how hardware noise benefits QRC readouts.
arXiv:2508.01438v3 Announce Type: replace
Abstract: The capacity of bandlimited direct-detection channels is challenging to compute or approach due to the receiver non-linearity. A generalized vector approximate message passing (GVAMP) detector is designed to achieve high rates at a reasonable level of complexity. The rates increase by using multi-level coding and successive interference cancellation. The methods are applied to fiber-optic channels with intersymbol interference caused by spectrally efficient pulse shapes, chromatic dispersion, and receiver sampling at twice the baud rate. Bipolar modulation operates within 0.26 bits per channel use (bpcu) of the real-alphabet coherent capacity for optically amplified links, reducing the best-known theoretical gap of 1 bpcu. Remarkably, bipolar modulation achieves 6 dB and 3 dB of power gain over unipolar modulation with and without optical amplification, respectively. Simulations with polar-coded modulation confirm the gains. The GVAMP complexity, measured in multiplications per information bit (mpib), is proportional to the number of iterations and to the logarithm of the block length, and is substantially lower than that of other equalizers. For example, a system with 64-ary bipolar modulation and a root-raised cosine pulse with a 1% roll-off factor was simulated over 4 km of optically amplified standard single-mode fiber in the C-band. The GVAMP receiver requires 93 mpib to achieve 5 bpcu at 300 gigabaud.
arXiv:2607.17778v1 Announce Type: new
Abstract: Class-agnostic 3D instance segmentation is critical for robotic systems operating in unknown environments, enabling perception of previously unseen objects for reliable manipulation and navigation. Existing approaches typically project per-frame 2D instance masks into 3D and merge them, which often breaks object identities across time and yields fragmented 3D instances. We introduce Cross-Dimensional Class-Agnostic 3D Instance Segmentation (CDIS), a zero-shot framework that explicitly tracks 2D instance masks across frames and associates them with 3D superpoints, creating a feedback loop between 2D and 3D. This cross-dimensional reasoning links temporally stable 2D tracks with spatially coherent 3D regions, producing globally consistent 3D instance labels without any 3D-specific training. Experiments on benchmark datasets demonstrate that CDIS achieves higher accuracy and consistency than state-of-the-art zero-shot methods, while remaining efficient and scalable to diverse real-world environments.
arXiv:2510.15936v3 Announce Type: replace
Abstract: The study explores the role of large language models (LLMs) in the context of the architectural design studio, understood as the pedagogical core of architectural education. Traditionally, the studio has functioned as an experiential learning space where students tackle design problems through reflective practice, peer critique, and faculty guidance. However, the integration of artificial intelligence (AI) in this environment has been largely focused on form generation, automation, and representation-al efficiency, neglecting its potential as a pedagogical tool to strengthen student autonomy, collaboration, and self-reflection. The objectives of this research were: (1) to identify pedagogical challenges in self-directed, peer-to-peer, and teacher-guided learning processes in architecture studies; (2) to propose AI interventions, particularly through LLM, that contribute to overcoming these challenges; and (3) to align these interventions with measurable learning outcomes using Bloom's taxonomy. The findings show that the main challenges include managing student autonomy, tensions in peer feedback, and the difficulty of balancing the transmission of technical knowledge with the stimulation of creativity in teaching. In response to this, LLMs are emerging as complementary agents capable of generating personalized feedback, organizing collaborative interactions, and offering adaptive cognitive scaffolding. Furthermore, their implementation can be linked to the cognitive levels of Bloom's taxonomy: facilitating the recall and understanding of architectural concepts, supporting application and analysis through interactive case studies, and encouraging synthesis and evaluation through hypothetical design scenarios.
arXiv:2607.17970v1 Announce Type: new
Abstract: The global competition for developing robotic foundation models is intensifying. Among the data collection systems used for dual-arm robots, ALOHA is representative of being low-cost and open-source, and is widely adopted by researchers as a de facto standard. However, due to its limited ability to generate high forces and speeds, it is difficult to handle heavy objects or perform fast manipulations. To address this, we developed MEVION, a low-cost and open-source dual-arm robot data collection system capable of generating greater force and speed. All parts of this robot can be sourced through e-commerce, and by extensively utilizing sheet metal welding, its large body structure is constructed with a small number of components at low cost, while also simplifying assembly. MEVION is equipped with four 6-DoF arms with parallel grippers. Each arm weighs 7.0 kg and has a maximum torque of 60 Nm, and the entire system can be constructed for about USD 14,000. The elbow joint adopts a closed-link mechanism similar to those used in quadruped robots, which reduces the distal mass and enables higher force and speed output at the end-effector. We demonstrate that MEVION enables data collection for object manipulation tasks not previously possible and supports imitation learning-based motion generation. All hardware and software of this work are included in the Supplementary Materials or https://github.com/haraduka/mevion.
arXiv:2607.17939v1 Announce Type: cross
Abstract: Importance splitting (ISPLIT) evaluates the probabilities of rare events in non-Markovian models. It requires a heuristic importance function (IFUN) that estimates the distance to the target. While including timer evaluations in the IFUN can substantially improve the effectiveness of ISPLIT, the existing time-sensitive IFUNs evaluate simulation states with respect to single sampled timer values. Thus, reaching highly important states requires simultaneously sampling specific combinations of timer values, yielding several unproductive simulation runs. In this paper, we revisit time-sensitive ISPLIT with the goal of steering simulation runs towards important states. First, we study how timer values can be resampled conditioned on the elapsed time. The importance can be evaluated by considering the set of feasible timer values, decoupling importance estimation from timer samples. Second, we exploit the global age of a simulation to identify and prune the executions that can no longer reach the target within the remaining time budget. Together, these ideas lead to a taxonomy of distance metrics clarifying the role of timer bounds, resampling, and the global age. In particular, for models with unbounded timers, we show that time-sensitive IFUNs collapse to ordinary IFUNs under resampling. Experiments demonstrate that the proposed formulations substantially improve the accuracy of ISPLIT estimators.
arXiv:2607.17536v1 Announce Type: new
Abstract: The rely/guarantee approach supports the stepwise development of concurrent programs. Our goal is to develop a theory for reasoning algebraically about concurrent programs in a rely/guarantee style, where rely and guarantee conditions are encoding as commands within our theory. As for mathematics, distributive laws are essential for algebraic manipulation of concurrent programs. In this paper we investigate distributive laws for parallel composition and show how these can be applied to rely/guarantee concurrency. The most general distributive laws are only refinements in a single direction, however, by restricting the form of the command being distributed, one can devise stronger equality laws, which are applicable to guarantee commands as well as to suitable combinations of rely and guarantee commands. Our approach is to develop the distributive laws in a more abstract synchronous atomic algebra, and then apply them to an instance of that algebra supporting rely and guarantee commands. The theory has been formalised in Isabelle/HOL along with proofs of the lemmas presented here.
arXiv:2607.18153v1 Announce Type: new
Abstract: Dynamic object segmentation plays a critical role in many visual applications such as static scene reconstruction from dynamic videos. However, existing optical flow-based methods fail to ensure consistent static/dynamic segmentation along object boundaries, while 3D reconstruction-based approaches are highly sensitive to reconstruction errors. To address these limitations, we present a dynamic object segmentation framework that can generate both precise and complete dynamic masks by integrating multimodal cues including 2D point tracks, 3D reconstruction, and semantic information. We design a network combining Transformer architectures with feature clustering aggregation modules to perform static/dynamic classification of multimodal feature trajectories. It enables the model to adaptively determine which type of feature should dominate based on the characteristics of each scene, while also mitigating the impact of feature degradation. Additionally, we introduce a novel point-query-based SAM post-processing method capable of handling multiple objects within a single mask. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in both dynamic object segmentation and static scene reconstruction tasks.
arXiv:2607.17897v1 Announce Type: new
Abstract: Distributional soft policy iteration (DSPI) provides an important framework for combining distributional reinforcement learning (DRL) with maximum-entropy control, in which the policy evaluation step is governed by a distributional soft Bellman operator acting on entropy-regularised returns. Theoretical analysis of such an evaluation step requires a probability metric under which Bellman updates can be controlled, typically by showing that the operator contracts the distance between any two candidate return-distribution estimates. In this paper, we focus on the Cram\'er geometry, a cumulative distribution function (CDF)-based metric with an $L^2$ structure, and study whether the fixed-policy distributional soft Bellman operator has this contraction property and hence a unique fixed point under this metric. Working directly on an admissible CDF field domain, we formulate the CDF-level distributional soft Bellman operator, prove that it is a $\sqrt{\gamma}$-contraction, and obtain the corresponding unique fixed point together with convergent iterative policy evaluation. The CDF formulation also shows that this finite-Cram\'er-domain property follows from a uniform first-moment condition on the combined one-step reward entropy shift, rather than from separate uniform boundedness assumptions on the reward and entropy terms. We then transport the same evaluation problem to the spectral domain by conjugation, obtaining an equivalent Hilbert-space representation of the same decision process. Taken together, these results identify the Cram\'er-geometric Bellman fixed point associated with the policy-evaluation step of DSPI, providing a reference point for studying approximate critics, evaluation error, and critic-loss design in DSPI-style algorithms.
arXiv:2607.17408v1 Announce Type: new
Abstract: Hilbert-space techniques are widely used not only for quantum theory, but also for classical physics. Two important examples are the Koopman-von Neumann (KvN) formulation and the method of ``classical'' wave functions. As this paper explains, these two approaches are conceptually distinct. In particular, the method of classical wave functions was not due to Bernard Koopman and John von Neumann, but was developed independently by a number of later researchers, perhaps first by Mario Sch\"onberg, with key contributions from Angelo Loinger, Giacomo Della Riccia, Norbert Wiener, and E. C. George Sudarshan. The primary goals of this paper are to explain these two approaches, describe the relevant history in detail, and give credit where credit is due.
arXiv:2607.18225v1 Announce Type: cross
Abstract: We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG). We formulate RAG-based action selection under the potential outcome framework. In the two-step method, vector search retrieves action-specific neighboring evidence in an embedding space, the generator estimates conditional expected outcomes or their contrasts, and a plug-in rule selects an action. This formulation connects action-specific vector search with nearest-neighbor matching in causal inference. We decompose the regret of the two-step method into candidate-generation regret and within-candidate choice regret, and we bound the latter using prediction-error guarantees for nearest-neighbor estimators and transformers. We evaluate the one-step method directly as a policy because its intermediate computation is unobserved.
arXiv:2607.18059v1 Announce Type: cross
Abstract: Quantum materials experiments increasingly rely on microwave, electrical, thermal, optical, and structural probes, but these capabilities are typically assembled from custom hardware that limits reproducibility and scalability. Here we show that a commercial 65-nm CMOS process can be repurposed as a passive, foundry-manufacturable characterization platform by functionally partitioning its metal stack into microwave, thermal, and electrical subsystems within a 1 mm2 footprint. The integrated RF architecture enables cryogenic magnetic susceptibility measurements of Fe3GeTe2 heterostructures at 1.75 K without sample-specific fabrication. We further demonstrate NV-center optically detected magnetic resonance (ODMR) with >20% contrast at 4-9 dBm microwave power, reducing power requirements by 20-25 dB relative to conventional antenna-based approaches while maintaining sensitivities of 2-3 uT/sqrt(Hz). We additionally confirm compatibility with in-situ electron-beam imaging, showing no measurable degradation in image quality upon device operation. These results establish a scalable, foundry-manufacturable platform for multimodal quantum sensing and materials characterization.
arXiv:2508.08368v2 Announce Type: replace
Abstract: Molecules with heavy, radioactive nuclei promise extreme sensitivity to fundamental nuclear and particle physics. However, these nuclei are available in limited quantities, which challenges their use in precision measurements. Here we demonstrate the gas-phase synthesis, cryogenic cooling, and high-resolution laser spectroscopy of radium monohydroxide, monodeuteroxide, and monofluoride molecules ($^{226}$RaOH, $^{226}$RaOD, and $^{226}$RaF) in a tabletop apparatus by combining novel radioactive target production protocols, optically driven chemistry in a cryogenic buffer gas, and low-background spectroscopic detection methods. The molecules are cooled in the lab frame, creating conditions that are the same starting points as many current molecular precision measurement and quantum information experiments. This approach is readily applied to a wide range of species and establishes key capabilities for molecular quantum sensing of exotic nuclei.
arXiv:2510.19244v3 Announce Type: replace
Abstract: Deep reinforcement learning (RL) achieves remarkable performance but lacks interpretability, limiting trust in policy behavior. The existing SILVER framework (Li, Siddique, and Cao 2025) explains RL policy via Shapley-based regression but remains restricted to low-dimensional, binary-action domains. We propose SILVER with RL-guided labeling, an enhanced variant that extends SILVER to multi-action and high-dimensional environments by incorporating the RL policy's own action outputs into the boundary points identification. Our method first extracts compact feature representations from image observations, performs SHAP-based feature attribution, and then employs RL-guided labeling to generate behaviorally consistent boundary datasets. Surrogate models, such as decision trees and regression-based functions, are subsequently trained to interpret RL policy's decision structure. We evaluate the proposed framework on two Atari environments using three deep RL algorithms and conduct human-subject study to assess the clarity and trustworthiness of the derived interpretable policy. Results show that our approach maintains competitive task performance while substantially improving transparency and human understanding of agent behavior. This work advances explainable RL by transforming SILVER into a scalable and behavior-aware framework for interpreting deep RL agents in high-dimensional, multi-action settings.
arXiv:2607.17858v1 Announce Type: new
Abstract: Reservoir computing exploits nonlinear dynamical systems to encode temporal inputs into high-dimensional state space representations. Although reservoir performance is often characterized through memory, nonlinearity, and their tradeoff, such aggregate measures do not reveal how task-relevant information is organized within the state space. Here, we introduce an eigen-spectral decomposition framework linking the degree-wise information processing capacity to the corresponding state space modes. As a result, we are able to quantify the degree-wise representation energy, and show that in some cases, substantial amounts of information processing capacity may reside in low-energy modes that are vulnerable to experimental noise. These results suggest that useful reservoir computation depends not only on dimensionality expansion, but also on the geometric organization of task-relevant information, with direct implications on physical reservoir computers.
arXiv:2607.17996v1 Announce Type: new
Abstract: Terahertz (THz) wireless communication offers a promising alternative to traditional wired links in data centres (DCs), enabling ultra-high data rates, low latency, and greater scalability. However, THz signals suffer from high path loss, necessitating the use of directional antennas (DAs). While DAs enhance signal strength, they introduce challenges such as deafness and synchronisation, typically addressed through receiver-initiated MAC protocols. Most existing THz MAC protocols use fixed beamwidths, which results in a key performance trade-off: narrow beams improve gain for long-range links but reduce throughput for short distances due to increased alignment overhead, while wide beams benefit short links but degrade performance over longer distances. To overcome this limitation, we propose DBS-ADAPT, a dynamic beamwidth selection-based MAC protocol that adjusts the antenna beamwidth according to the distance between nodes, maximising throughput without compromising link range. We also introduce an enhanced version, EDBS-ADAPT, which further reduces beamwidth switching and control overhead while preserving throughput gains. Both protocols are evaluated using the NS-3 THz module. Simulation results show that DBS-ADAPT improves average throughput by up to 22% and reduces delay up to 10% compared to the baseline ADAPT-3 protocol. EDBS-ADAPT further cuts beamwidth switching overhead by 95%, making it more efficient for scalable and high-performance wireless DC environments.
arXiv:2509.00930v2 Announce Type: replace
Abstract: Large language models (LLMs) exhibit strong general reasoning, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve these abilities. We present SATQuest, a verifier that generates diverse SAT-based reasoning tasks directly from Conjunctive Normal Form (CNF) instances and checks answers objectively with PySAT. SATQuest factorizes evaluation along three orthogonal dimensions: instance, problem type, and question format, enabling fine-grained, multi-dimensional analysis and reinforcement fine-tuning. Randomized CNF generation mitigates memorization and supports reproducible experiments. Using SATQuest, we benchmark a range of open- and closed-weight LLMs and uncover persistent gaps in logical reasoning, particularly on higher-complexity tasks and in transfer beyond familiar mathematical notation to machine or narrative formats. We further show that reinforcement fine-tuning with SATQuest rewards substantially boosts targeted performance and generalizes to larger instances, while cross-format robustness remains challenging. Collectively, SATQuest provides verifier-backed infrastructure for controlled, scalable, and reproducible empirical research on LLM logical reasoning and its training.
arXiv:2510.19789v2 Announce Type: replace
Abstract: This paper introduces OmniMotion-X, a versatile multimodal framework for whole-body human motion generation, leveraging an autoregressive diffusion transformer in a unified sequence-to-sequence manner. OmniMotion-X efficiently supports diverse multimodal tasks, including text-to-motion, music-to-dance, speech-to-gesture, and global spatial-temporal control scenarios (e.g., motion prediction, in-betweening, completion, and joint/trajectory-guided synthesis), as well as flexible combinations of these tasks. Specifically, we propose the use of reference motion as a novel conditioning signal, substantially enhancing the consistency of generated content, style, and temporal dynamics crucial for realistic animations. To handle multimodal conflicts, we introduce a progressive weak-to-strong mixed-condition training strategy. To enable high-quality multimodal training, we construct OmniMoCap-X, the largest unified multimodal motion dataset to date, integrating 28 publicly available MoCap sources across 10 distinct tasks, standardized to the SMPL-X format at 30 fps. To ensure detailed and consistent annotations, we render sequences into videos and use GPT-4o to automatically generate structured and hierarchical captions, capturing both low-level actions and high-level semantics. Extensive experimental evaluations confirm that OmniMotion-X significantly surpasses existing methods, demonstrating state-of-the-art performance across multiple multimodal tasks and enabling the interactive generation of realistic, coherent, and controllable long-duration motions.
arXiv:2510.23060v4 Announce Type: replace
Abstract: Industrial control systems (ICS) form the operational backbone of critical infrastructure networks (CIN) such as power grids, water supply systems, and gas pipelines. As cyber threats to these systems escalate, regulatory agencies are imposing stricter compliance requirements to ensure system-wide security and reliability. A central challenge, however, is enabling regulators to verify the effectiveness of detection mechanisms without requiring utilities to disclose sensitive operational data. In this paper, we introduce zkSTAR, a zero-knowledge based cyberattack detection framework that leverages zk-SNARKs to enable regulatory compliance while delivering provable detection guarantees with complete data privacy. Our approach builds on established residual-based statistical hypothesis testing methods applied to state-space detection models. Specifically, we design a two-pronged zk-SNARK architecture that enforces (i) temporal consistency of the state-space dynamics and (ii) statistical consistency of the detection tests, enabling regulators to verify correctness and prevent suppression of alarms without visibility into utility-level data. We formally analyze the soundness and zero-knowledge properties of our framework and validate its practical feasibility through computational experiments on real-world ICS datasets. Our work demonstrates that zkSNARKs can provide a compliant, scalable, privacy-preserving alternative for detecting data-driven cyberattacks on ICS driven critical infrastructure networks.