Papers

Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, distributed computing, artificial intelligence, cloud infrastructures, and Internet of Things (IoT) architectures, there remains no universally accepted definition of decentralization applicable across computer communication systems. This has become increasingly problematic with the emergence of decentralized AI and machine learning paradigms, including collaborative training, distributed inference, blockchain-based, and agentic AI, where decentralization is often treated as a core design objective. Meanwhile, existing approaches frequently conflate decentralization with related notions such as distribution of trust or specific implementation paradigms. Such ambiguity creates inconsistencies in system analysis, limits comparability between works, and weakens the rigor of formal reasoning surrounding communication architectures and protocol design. In this work, we define this research gap as the Decentralization Problem. We analyze the formal-semantic, epistemological, and pragmatic foundations of decentralization and introduce a graph-based ontology defining it as both relational and subject-specific property of computer communication systems. The framework formally distinguishes decentralization from distribution and supports evaluation through two novel metrics: Void Tolerance and Imperviousness. We also provide a browser-based implementation that enables automated classification and metric computation of arbitrary systems. Instantiations to federated learning and blockchain architectures show consistent, comparable assessments where existing definitions produce incomplete or contradictory conclusions, providing a domain-independent foundation for analysing decentralization across heterogeneous systems.

Observation tasking is critical to satellite operations, enabling observation of planetary phenomena, orbital debris monitoring, and space domain awareness. To improve the effectiveness of satellite operations, orbital maneuverability is introduced as a leading-edge concept of operations within constellation reconfigurability for response to dynamic events. Previous investigations regarding constellation reconfigurability consist of deterministic mission environments, in which the formulations consider a priori knowledge of the environment; however, observation objectives inherently involve uncertainty at any given time. As such, scheduling satellite operations must account for uncertainties to ensure adequate observation tasking. In response, we present a stochastic variant of the Multistage Constellation Reconfiguration Problem (MCRP) which is solved using Stochastic Dual Dynamic Integer Programming (SDDiP). We additionally explore other stochastic problem-solving techniques for solution quality comparison. To demonstrate each solution method, two computational experiments with stochastic target properties are conducted. The first concerns random orbital targets, and the second concerns simulated hurricanes. The results of the experiments demonstrate the effectiveness of the SDDiP solution approach over other stochastic problem-solving methods. Overall, the stochastic MCRP accounts for target stochasticity while obeying visible time windows and maneuver feasibility.

Collective opinions in social networks evolve through local interaction rules, yet how such local updates give rise to dynamic polarization–persistent oscillatory disagreement between opposing opinion clusters at the network level–remains unexplained. This paper proposes a Minimally-Nonlinear Opinion Dynamics (or M-NOD) framework that analytically characterizes dynamic polarization as a truly emergent collective behavior arising solely from local, agent-level opinion-update rules without externally imposed mechanisms. By introducing a minimal cubic nonlinearity, we rigorously prove that, as the reactivity rate exceeds a critical threshold, the network's consensus equilibrium loses stability via a supercritical flip bifurcation. In the post-bifurcation regime, this instability gives rise to a unique, locally asymptotically stable periodic orbit, thereby characterizing symmetric dynamic polarization with balanced bipartite opinion clusters. We further establish the structural robustness of this behavior by proving the existence and local asymptotic stability of asymmetric dynamic polarization under directed graphs with nonuniform influence weights. Finally, to resolve this undesirable cyclic deadlock, we develop local agent-level control strategies. We prove that anchoring the opinion of only a single agent is sufficient to eliminate network-wide oscillatory disagreement and restore asymptotically stable consensus. Numerical simulations substantiate the theoretical analysis, including the emergence of symmetric and asymmetric dynamic polarization, and demonstrate the efficacy of the proposed control interventions.

Reliability qualification of advanced semiconductor devices requires sequential stress decisions that balance characterization objectives against multiple competing failure mechanisms. Current practice relies on static test plans derived from population-level acceleration models, which cannot adapt to per-unit variability or real-time degradation observations. This paper presents a closed-loop adaptive test planning framework that formulates reliability qualification as a partially observable sequential decision problem and solves it using Monte Carlo tree search for seed-action simulators (MCTS-SA) coupled with extended Kalman filter (EKF) belief-state estimation. The framework models stochastic, per-device variability in bias temperature instability (BTI), electromigration (EM), and time-dependent dielectric breakdown (TDDB), and treats stress selection as a constrained sequential optimization, i.e., to maximize the probability of successful degradation characterization while respecting catastrophic failure constraints. Under the experimental assumptions used here (discrete stress actions, proxy damage observability, and cumulative degradation without recovery), we believe this to be a novel application of tree-search-based adaptive test planning to multi-mechanism reliability qualification. Across 5,000 planning iterations, the characterization yield (CY) improves from 20% in the first 500 iterations to over 54% in the final 500, with 39% cumulative success, while the best successful test sequence terminates with EM and TDDB damage fractions DEM=0.564 and DTDDB=0.537, well within safety margins. These results demonstrate that sequential Bayesian planning can synthesize damage-aware test policies that significantly outperform non-adaptive strategies for reliability qualification under competing failure modes.

Optimization theory is a widely used tool for intelligent decision-making. While classical optimization deals with fixed, time-invariant objective functions, many modern applications operate in dynamic environments where data arrive sequentially, and the learning objective evolves over time, often under decentralized data and communication constraints. Motivated by these trends, we study decentralized optimization from streaming data through a structured time-varying formulation in which the global objective is a temporally weighted average of losses observed across the network. We analyze multi-iteration decentralized first-order methods, including decentralized gradient descent. For strongly convex and smooth losses, we develop guarantees for the Euclidean-norm tracking error through a contraction-mapping viewpoint. The resulting bounds decompose the tracking error into a fixed-point tracking component and a bias term induced by decentralization and data heterogeneity. We specialize our analysis to uniform and exponentially discounted weights, as well as their finite-memory windowed counterparts. The bounds explicitly characterize the roles of the temporal weighting rule, per-step iteration budget, step size, and network connectivity. Uniform weighting yields a vanishing fixed-point tracking contribution of order $\mathcal O(1/t)$, whereas discounted and windowed strategies generally induce non-vanishing tracking floors governed by the discount factor and effective memory, respectively. In all cases, decentralization induces an additional non-zero bias floor under a constant step size. Numerical experiments illustrate the predicted trends.

Multi robot teams performing cooperative transportation face a fundamental challenge: maintaining stable control while keeping communications efficient. This paper investigates how adaptive sampling time adjustment informed by measured network delay and strategic leader rotation can distribute wireless load fairly across the team. We use physics based simulation in MuJoCo with realistic wireless modeling, including time division multiple access, medium access control, jitter, queueing, and packet loss, to evaluate three control approaches: fixed sampling with static leadership, dynamic sampling with static leadership, and dynamic sampling with rotating leadership. Our results reveal an important trade off: dynamic sampling effectively reduces communications overhead without compromising control performance, while rotating the leader role meaningfully improves how fairly airtime is distributed all with negligible impact on the team carrying ability. to the best of our knowledge, being among the first to jointly examine dynamic sampling, rotating leadership, and wireless protocol interactions in physicsrealistic multi robot cooperation, this work provides practical guidance for deploying coordinated robotic teams in real world settings where communications resources are limited.

Despite significant advancements in controlling wave energy converters, efficiently achieving real-time energy-maximising control remains a challenge. This paper addresses this obstacle by introducing Symphony, a novel non-optimisation-based (NOB) controller inspired by phase control techniques for wave energy devices. Symphony is a suboptimal control that inherits all the advantages of NOB controllers, such as simplicity of design, implementation, tuning, and use, while additionally providing hard-motion-constrained reference trajectories and close-to-optimal performance across the evaluated sea states, even in the presence of measurement noise and model uncertainty. Symphony is designed using a hierarchical structure: By relying on an implicit Gaussian differential equation that requires an excitation force estimate, an upper loop provides a close-to-optimal motion-constrained velocity trajectory and a feedforward control term. Then, a user-flexible lower loop tracks the desired velocity trajectory by combining feedback and feedforward terms. In this paper, Symphony performance is assessed in terms of power absorption. Statistically representative results are obtained using multiple sea-state realisations across varying peak wave periods, considering measurement noise and radiation model uncertainty. The results presented in this paper demonstrate the potential of Symphony, which achieves, in most of the evaluated scenarios, more than 90% of the power absorption obtained by a spectral (optimisation-based) control solution.

This paper studies hidden-target localization from range-bearing packets reported by a relay beacon whose global position and yaw are unknown. The vehicle knows its own trajectory but never directly senses the target; the relay packet contains only local-frame range and bearing to the vehicle and to the hidden target. Unlike bearing-only network localization, relative-frame localization, and target-enclosing control, the target is neither directly observed in the vehicle frame nor treated as a node in a relative-sensing graph. The main result characterizes the minimal motion that removes the resulting calibration ambiguity: one vehicle pose leaves a continuous yaw/translation/target gauge, whereas two distinct vehicle-relative observations from one unknown-pose relay constructively determine relay yaw (modulo 2 pi), relay position, and the anchored target in the noiseless case. A local rank corollary, a shared-target multi-beacon extension, and a trajectory-spread conditioning lemma connect relay self-calibration to finite-window excitation and native range-bearing estimation. In Monte Carlo evaluation the estimator recovers the hidden target with 5.5 mm RMSE, five times below the 30 mm per-packet range noise and thirteen times more accurate than a naive EKF baseline; it converges to the same accuracy from 2 m target offsets and 2.4 rad yaw errors, and Huber weighting preserves millimeter accuracy under 10% outlier corruption that drops the unprotected estimator to a 0.10 success rate. Trajectory spread predicts estimator quality: the two weakly excited trajectories carry condition numbers above 100 with success rates of 0.82 and 0.70, while every well-excited trajectory attains full success.

A reusable MATLAB signal-integrity (SI) framework is presented that converts compatible four-port S-parameter data, measured by VNA or obtained from electromagnetic simulation, into traceable link-level evidence rather than a single loss metric. Its contribution is the checked, traceable automation of established SI operations while keeping source data, model assumptions, analytical projections, and finite-record observations distinct. The framework is demonstrated on the four 1.25 Gbps differential routes (DP1-DP4) of the PPCB-1347-MuPix11 probe card using PTSL CST Microwave 3D-Solver-derived four-port S-parameters and a virtual time-domain solver. The automated pipeline preflights file structures, performs a power-normalized mixed-mode transformation, applies route-length-aware loss decomposition, constructs a causally loaded channel model, and propagates full PRBS-31 sequences into eye-diagram, conditional-BER, and 8b10b-coded-link analyses. At the 1.25 Gbps data rate (Nyquist 0.625 GHz), DP1-DP4 exhibit differential insertion loss (SDD21) from -0.350 to -0.300 dB, differential-to-common conversion from -31.038 to -28.236 dBc, and modeled FEB-input eye openings from 0.586 to 0.588 V. The comparison shows that path length alone is not an adequate SI ranking variable: DP3 has the lowest Nyquist insertion loss, DP1 the strongest differential-to-common isolation, and DP4 the largest modeled eye. All analytical BER values remain below the reporting floor and therefore do not support a BER ranking. By preserving the distinction between route-dependent waveform behavior, model projections, and finite-record observations, the framework provides an extensible basis for comparative high-speed-interconnect SI analysis from design review through calibrated VNA measurement interpretation.

Always-on direct yaw moment control (DYC) improves vehicle stability during critical maneuvers but can introduce unnecessary interventions under low-risk conditions. This paper proposes a Koopman risk-gated linear time-varying model predictive control (KRG-LTV-MPC) framework for low-intervention yaw stability assistance. Instead of replacing the physics-based execution model with a fully data-driven control predictor, this framework separates Koopman-based phase-risk preview from safety-critical execution. A Deep Koopman model predicts the nominal evolution of the sideslip-yaw rate phase risk to determine whether the constrained quadratic programming (QP) problem should be solved or skipped at each sampling time. When the gate is active, the LTV-MPC layer calculates the additional yaw moment; otherwise, the QP is skipped and the previously commanded moment is tapered to zero under a bounded-rate rule. Event-level shadow-mode evaluation shows that the Koopman predictor provides positive warning lead times of 0.19-0.28 s under low-friction and friction-transition conditions, whereas the LTV predictor gives delayed warnings. Under closed-loop low-friction conditions, KRG-LTV-MPC reduces the cumulative yaw-moment intervention by 43.9% relative to LTV-MPC and solves the QP for only 41.0% of the samples while maintaining vehicle stability within the phase plane. These results support the use of Koopman phase-risk information as an intelligent supervisory layer for low-intervention DYC.

This paper develops a life-cycle optimization model for the collector system (CS) of deep-sea co-located energy farms (CEFs), where co-located energy turbines (CETs) integrate wind, photovoltaic (PV), and tidal current resources across sea-area layers. The model captures multi-layer marine-space complementarity, wake effects, and output variability, while accommodating diverse dynamic submarine cable configurations. To improve computational efficiency, the adaptive piecewise linearization (A-PWL) method based on the outputs of CETs is proposed to transform the original mixed-integer quadratic programming (MIQP) problem into a mixed-integer linear programming (MILP) form to approximate quadratic operation costs and simplify absolute power flow modeling. Case studies demonstrate that incorporating multi-energy complementarity significantly enhances the economic performance of deep-sea CEFs. When external physical risks are negligible, the fully-suspended cable configuration proves more cost-effective than the lazy-wave design. The proposed linearization method achieves high accuracy while significantly reducing solution time. Overall, this work provides a practical and scalable framework for efficient CS planning in offshore renewable energy systems.

Continuous-time neural models are attractive for identifying nonlinear systems, but a small one-step error can grow rapidly when a learned vector field is rolled out under inputs that differ from those used for training. This paper develops an adaptive stability-constrained neural differential equation (AS-NDE) for systems with measured controls and unmatched, unknown perturbations. The nominal vector field and a state–input-dependent Riemannian metric are learned jointly. Positive definiteness is enforced by construction, while a sampled differential inequality penalizes violations of a prescribed contraction rate. An incremental input-to-state bound is derived: the distance between two trajectories decays exponentially up to gains determined by differences in their controls and disturbances. The statement explicitly accounts for the time derivative of an input-dependent metric, a term that is easily omitted in heuristic stability regularizers. We give a reproducible evaluation protocol for a forced Duffing oscillator and a permanent-magnet synchronous motor (PMSM) model. Because no measured data or executed training runs accompany this draft, all numerical curves and tables are clearly identified as illustrative synthetic placeholders; their PGFPlots coordinates are embedded in the source for direct replacement. The resulting manuscript is intended as a technically consistent starting point, not as evidence of empirical superiority before the prescribed experiments are run.

Advanced Metering Infrastructure (AMI) plays an important role in modern power systems by providing near real-time consumption data for operational planning and load estimation. Despite this, AMI communication networks are vulnerable to certain cyberattack types, such as denial-of-service (DoS) and time delay attacks (TDAs), which may compromise data availability. This paper evaluates the impact of these attack types on AMI communication using a Mininet-based simulation environment that implements a communication model inspired by the Distributed Network Protocol 3 (DNP3) for communication among smart meters, data concentrators, and utility systems. Different cyberattack scenarios are analyzed, including communication delay, packet loss, combined degradation rate (D_rate), and concentrator failure, to evaluate their effects on network performance and data availability. The results indicate increased latency, higher communication unavailability rates, and reduced data reliability under attack conditions. In addition, the study correlates how communication degradation can result in potential uncertainties in energy demand estimation and provides estimates of the associated financial impacts on energy procurement, using electricity pricing data from the Independent System Operator-New England (ISO-NE). The experimental results reveal that a 500 ms delay increased the average RTT (Round-trip time) from 0.22 ms to 69.5 ms, while a 50% packet loss scenario resulted in 87 timeout events and a 5.31% communication degradation rate. Under a regional-scale deployment of 10,000 smart meters, this degradation could cause an estimated economic impact of $1,009/day under normal market conditions and up to $5,097/day during high-price events.

Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sampled forecasts. To build such a tree, conventional methods focus on matching the underlying probability distribution—e.g., via Wasserstein-based scenario reduction—but improved distributional accuracy does not necessarily yield better control performance. We propose a control-oriented approach that learns scenario tree construction directly from its impact on downstream decisions. Fixing the tree topology, we formulate tree construction as a sequential assignment of sampled scenarios to leaves. This assignment is parameterized by an attention-based policy over the scenario set and trained using reinforcement learning, with closed-loop control profit as the objective. Training is stabilized by an asymmetric critic that leverages realized future trajectories. We evaluate the method on a risk-averse battery arbitrage problem. Across a range of forecast set sizes, the learned construction consistently achieves the highest profit, outperforming classical forward and backward reduction methods and certainty-equivalent (single-trajectory forecast) control. The learned policy also exhibits greater robustness on challenging instances, consistently demonstrating better tail-risk characteristics. Analysis of the resulting trees indicates that our method constructs compact, selectively branching structures that capture high-impact events while keeping most trajectories nearly deterministic. These findings highlight that the value of a scenario tree depends critically on the decisions it supports, and provide an effective framework to train scenario tree constructors merely based on the closed-loop control optimization signal.

The 4th "AI Olympics with RealAIGym" competition, to be held at IJCAI-ECAI 2026 in Bremen, challenges participants to develop a global control policy for swinging up and stabilizing an underactuated two-link system in its upright position. In contrast to previous editions, participants develop and evaluate their control strategies directly on remotely accessible CloudPendulum hardware, with limited interaction time and without prior knowledge of the system's model parameters. This paper presents an optimal-control-based approach employing real-time nonlinear model predictive control implemented using sequential quadratic programming. The results demonstrate that the proposed SQP-based MPC controller achieves reliable swing-up and stabilization performance, while maintaining robustness against disturbances.

Digital Twins (DTw) are emerging as transformative tools for High Voltage Direct Current (HVDC) transmission systems, enabling real-time monitoring, predictive maintenance, and operational optimization. However, the effectiveness of these virtual replicas fundamentally depends on seamless, reliable data exchange with their physical counterparts and any other support system. The current landscape of HVDC DTw implementations reveals a critical challenge: the absence of standardized data exchange protocols leads to vendor lock-in, interoperability issues between systems from different vendors. The contribution of this paper is to propose the standardization of Common Information Model (CIM) defined in IEC 61970 standard family, as the data exchange methodology in HVDC DTws, which is exemplified with the Cigre B4 example, as well as connected to the IEC 61850 standard for real time station control, via the harmonization guidelines from the IEC 62361-102 technical report.

Large-scale managed cloud databases leverage sophisticated load Packing and Migration (PAM) algorithms, which provide the efficiencies necessary for running these services at scale on cloud resources. Research into optimizing the resources and reliability of cloud databases at massive scales is limited by a lack of public NoSQL workloads. We address this in the context of Cosmos DB, Microsoft's flagship cloud-hosted NoSQL database. We first propose open-source NoSQL workloads from real Cosmos DB clusters, and analyze these traces to derive a novel reliability metric, Distressed Resource Volume (DRV), which captures the quality of service experienced by the end user. We then develop an open-source policy simulation framework, LoadStar, powered by a non-parametric statistical model of estimating the QoS of real traffic patterns. These form a reusable benchmark pipeline for validating policies for resource-centric NoSQL workloads. We then define a resource optimization problem for placing Cosmos DB replicas onto VM nodes, develop the Luna model for forecasting future load distributions, and the Orbit PAM algorithm that uses these forecasts to trigger and rebalance stressed replicas, to reduce tail-errors. Our experiments, validated using LoadStar for these workloads, demonstrate Orbit's benefits over the existing Cosmos DB policy and a worst-fit optimized baseline, with higher load delivered at lower error rates and up to $35\%$ reduction in resources. These have been deployed in production, with potential savings of $\$100M$s/yr while improving service reliability for millions of customers.

Large-scale renewable power-to-hydrogen (ReP2H) plants in cold regions suffer from prolonged startup and repeated thermal stress during frequent startup-shutdown operation. The situation becomes worse due to the lack of coordinated heat management among the alkaline electrolysis stacks, balance of plant (BoP), plant thermal utility system (PTUS), and plant building. This paper presents a plant-wide thermal topology and a hierarchical electricity-heat management framework to address the issues. Bidirectional thermal coupling between the stack cluster and PTUS enables preheating, thermal standby, and waste heat recovery, while minute-scale production scheduling is coordinated with second-scale thermal regulation. Case studies based on an 80 MW plant in Northern China show that the proposed framework eliminates cold startups in year round, increases hydrogen yield by 1.50, improves energy and exergy efficiencies by 0.99 and 4.33 percentage points, respectively, and reduces the levelized cost of hydrogen by 3.22%. It also reduces thermal fatigue damage and startup-shutdown-induced voltage degradation.

Intersections remain one of the most hazardous locations in urban road networks, where heterogeneous traffic participants and limited visibility frequently lead to severe traffic conflicts. In this paper, a vehicle-to-infrastructure-to-vehicle (V2I2V) cooperative system is proposed for improving road safety and traffic efficiency by using digital twins (DTs) deployed on roadside units (RSUs) to eliminate blind spots and centrally coordinate connected and automated vehicles (CAVs) in smart intersections. The proposed system integrates cloud-based global DTs for macroscopic guidance and RSU-based local DTs for real-time operations. Within this architecture, a hierarchical reinforcement learning (HRL) framework combines offline pre-training with online fine-tuning to achieve robust cooperative control. Experimental results show that the proposed system achieves substantial improvements in safety and efficiency in simulation experiments and real-world proof-of-concept (PoC) trials. In simulations, our system ensures high safety, efficiency, and smoothness under realistic communications and traffic constraints. In PoC trials, the RSU-centric control loop achieves a decision-making latency of approximately 42 ms and maintains a safe stopping distance of 8.5 m for pedestrians, while also shortening stop duration and overall traversal time. These results indicate that the proposed system provides robust and scalable performance at smart intersections.

This paper proposes a Ring Learning With Errors (Ring-LWE) based encrypted iterative learning control (ILC) framework for repetitive tracking tasks over networked control systems. The architecture integrates an encrypted dynamic feedback controller with an encrypted ILC computation. During each trial, the feedback controller is evaluated in the ciphertext domain, and the encrypted output trajectory is stored directly in the cloud. After each trial, the cloud evaluates the tracking error and performs the ILC computation from the stored ciphertexts, so that the plant side does not need to store the accumulated trial data. The proposed framework uses distinct packing parameters for ciphertext multiplication, allowing the cloud to handle both lower-dimensional output feedback control and higher-dimensional ILC computation without decryption. While error growth in Ring-LWE based encrypted control is generally suppressed by closed-loop stability, the marginally stable ILC iterations cause the injected errors to accumulate continuously. To address this challenge, a range-space decomposition is introduced in the encrypted ILC formulation to allow evaluation under unlimited updates. Numerical simulations show that the range-space decomposition suppresses encryption-induced perturbation, while ciphertext packing improves the computational efficiency of the encrypted ILC update.

This paper studies minimum-node observability and controllability of Leaky rectified linear unit (Leaky-ReLU) networks under degree constraints. The objective is to characterize how many state nodes must be measured or directly actuated to determine the initial state from a finite output sequence or to steer the network between arbitrary states within a finite horizon. For observability, a graph-theoretic analysis yields a class-wide upper bound on the minimum number of observation nodes. We construct a family of networks attaining this bound, thereby determining the exact worst-case minimum number of observation nodes. We also construct networks that are observable from a single node over a finite horizon, establishing the exact best-case value of one. By establishing an observability–controllability duality under the corresponding degree constraints, we obtain analogous exact best- and worst-case results for the minimum number of control nodes. A comparison with ReLU networks shows how replacing the zero negative slope with a nonzero slope changes the observation-node requirement. More generally, the observability arguments require only injectivity of the activation function, whereas the controllability results extend to bijective activation functions.

Previous works have shown that strong coupling among heterogeneous agents enforces practical synchronization, leading to collective behavior governed by emergent dynamics. However, it remains an open question whether emergent behavior persists in the presence of communication delays, as high-gain methods are typically sensitive to delays. To address this problem, we instead slow down the agent dynamics, reproducing the synchronization mechanism of strong coupling on a slow time scale. As a result, the emergence of collective behavior is guaranteed despite arbitrary constant communication delays. This, however, comes at a cost: delays induce a derivative-like term, thereby scaling down the vector field of the emergent dynamics. The amount of the scaling is explicitly characterized as a function of the coupling weights and the delay lengths. We further suggest that this delay-induced slowdown, which is of independent interest, may be a general feature of diffusive coupling under suitable assumptions.

What should a machine learning model learn when data is missing during training? We look at the learning process from a dynamical systems perspective, cast data missingness as a structured loss of actuation that limits controllability of the parameter error dynamics, and ultimately derive adaptation mechanisms with Lyapunov stability characteristics that throttle model updates in ways that preserve learning coherence under partial, intermittent observability. Under recurrent excitation, our analysis provides ISS-type residual-to-state bounds with respect to a bounded closed-loop mismatch between the loss residual and the preconditioned update geometry. We evaluate the efficacy of our directional observability-aware adaptive learning approach on multimodal contexts, reinforcing its premise in promoting learning coherence and stability even in pathologically sparse domains and problems.

Agentic AI extends energy management beyond fixed-form interaction by translating natural-language requests into coordinated scheduling actions. We present a hierarchical ReAct Energy Management System (EMS) in which one orchestrator coordinates specialist agent types for shiftable appliances, EV charging, and thermal control. Physical authorization is separated from language generation: a deterministic critic reconstructs each integrated day-ahead candidate and checks its schema, appliance cycles, device power, thermal comfort, and, when active, the whole power feeder limit. Across Qwen 3.5 checkpoints, single-appliance mixed-integer schedules were feasible in 83.3 percent of runs. Localized feedback produced no accepted coupled schedule, whereas a multi-step policy authorized 6/6 current coupled runs: 3/3 for 27B and 3/3 for 35B-A3B. The standard occupied-window policy permits pre-conditioning, enforces comfort from 09:00-18:00. Every accepted schedule passed an independent final replay. Feasible costs were 2522.499 JPY for 27B and 1592.697 JPY for 35B-A3B, which are slightly higher than the mathematical optimization optimum of 1343.380 JPY. These results establish a fail-closed workflow for agentic MIP and MILP energy scheduling under the declared physical model.

This work addresses nonlinear and nonstationary system identification using one-step-ahead prediction on the nonlinear autoregressive moving average benchmark with ten-step memory (NARMA-10). Baseline models, including autoregressive models with exogenous input (ARX), nonlinear ARX using a multilayer perceptron (NARX–MLP), and a single long short-term memory network (LSTM), are used to contextualize prediction performance. To improve robustness, multiple LSTM experts are combined through a convex mixture. A standard adaptive convex mixture updates the mixing weights using an error-driven rule with simplex projection, but this mechanism is reactive, hand-tuned, and not end-to-end learnable. The proposed method introduces a context-aware end-to-end mixture-of-experts (MoE) framework in which a differentiable softmax gating network learns context-aware mixing weights jointly with the expert parameters. On stationary NARMA-10, the proposed MoE gating approach achieves performance comparable to the adaptive convex mixture. Under regime-switching dynamics, a controlled frozen-experts ablation isolates the mixing-weight update mechanism and shows that the MoE gate significantly improves robustness, achieving approximately fivefold lower overall test error and about two-and-a-half-fold lower after-switch error.

In battery manufacturing, the low-dew-point air conditioning of dry rooms is among the largest energy consumers, and a physics-based digital twin offers insight for operating-point optimization beyond the installed monitoring points. Building one and calibrating it to field data each demand distinct expertise, which limits industrial uptake. We present a framework that generates a dynamic digital twin of an HVAC system from a declarative topology specification, concise enough to draft from a natural-language plant description, compiled against a purpose-built physical component library with wiring, solver, and telemetry synthesized automatically. The models carry equipment-level physics: the desiccant wheel couples heat and mass transfer through an interchangeable sorption-isotherm component, so an undisclosed commercial sorbent is calibrated as an effective isotherm rather than asserted as a material. For experimental validation we built an industrial-grade, ten-component low-dew-point system whose commercial desiccant-wheel unit holds a chamber near -40 {\deg}C frost point, and operated it in both dehumidification and bypass regimes. Generation reached a runnable model fifteen times faster than expert manual construction, and a single parameter set, fitted only to three closed-loop humidity nodes, predicts the bypass regime within 0.1 {\deg}C, the reactivation-heater power within 5%, and measured input-step responses. Identifiability analysis shows why this is prediction, not fitting: ordinary operating points constrain only one parameter combination, and the deep-dry equilibrium level of the recirculating loop supplies the missing signal. The framework shortens the path from plant description to measurement-validated twin; its criteria-based calibration is a step toward twins calibrated, not only constructed, automatically.

Flexible electronics (FE) based on indium gallium zinc oxide (IGZO) unipolar thin-film transistor (TFT) technologies enable lightweight, conformable systems for wearable sensing, biomedical monitoring, and human-machine interfaces. These applications rely on analog and mixed-signal (AMS) blocks that must remain accurate despite key challenges: increased device variability from low-cost unipolar fabrication; susceptibility to post-manufacturing failures due to the absence of rigid packaging; parametric faults that degrade analog accuracy without hard digital failures; and the high cost and in-field inaccessibility of specialized Automatic Test Equipment (ATE) for disposable FE systems. Conventional test approaches relying on analog-to-digital converters (ADCs) incur prohibitive area overhead in resource-constrained systems; critically, ADC accuracy is itself subject to process, voltage, and temperature (PVT) variations, leading to test invalidations. This work proposes a frequency-domain test signature circuit exploiting spectral energy from a voltage-controlled oscillator (VCO) to detect faults and parametric deviations in AMS circuits under test. The VCO converts fault-induced control-voltage deviations into frequency shifts processed through lightweight analog filters. RMS values of harmonic components serve as compact signatures with monotonic sensitivity to operating-point deviations while preserving ordering across PVT corners, ensuring robust operation without precision references or matched comparators. A ratiometric refinement suppresses post-calibration drift from temperature and supply variation by up to an order of magnitude. The signature is readable via two I/O pins, enabling in-field test without ATE. The circuit occupies 6390 $\mu$m$^2$ and consumes 16.7 $\mu$W, achieving 26$\times$ area reduction versus compact ADCs and nearly 1500$\times$ versus SAR implementations.

Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presented a probabilistic computer vision pipeline for runway-based pose estimation with fault detection inspired by Receiver Autonomous Integrity Monitoring. This work extends that framework by deriving protection levels, which provide probabilistic bounds on pose error that remain valid under undetected faults. We present an algorithm for computing protection levels for the nonlinear Perspective-$n$-Point problem applied to an aviation setting. The algorithm covers all six degrees of freedom of the aircraft pose (position and orientation) directly. We analyze the effect of measurement redundancy, pixel-level prediction uncertainty, and runway distance on the resulting protection levels. To make the results tangible, we demonstrate tradeoffs in the protection levels on an illustrative runway example.

We identify a base-parameter model of CloudPendulum cell 203 and track an offline swing-up reference with acados SQP-RTI NMPC at a target rate of 400 Hz. A 5 mNm passive-joint assist enabled development-stage swing-up and recovery. In organizer-run testing (16 trials of 300 s per configuration on cells 201–204), assisted pendubot and acrobot mean uptime scores were 80.19 s and 74.61 s. Acrobot scored zero on two cells, and seven trials ended on safety-limit exceptions. Because the assist is prohibited in evaluation, these results are diagnostic rather than qualification scores.

Operational Design Domain (ODD) specifications describe where an automated driving system (ADS) is permitted to operate, but they do not prescribe what the ADS must demonstrably do once deployed within that domain. This gap between operating condition specification and behavioral validation represents a critical unresolved challenge in ADS safety assurance. This paper presents a structured, standards-grounded taxonomy of 21 behavioral competencies organized across three operational domains-Highway (HWY), Urban (URB), and Hub (HUB)-derived systematically from the PEGASUS six-layer model-based ODD. Each behavior is decomposed along longitudinal and lateral control axes and characterized against a four-property framework: Safety (gap maintenance, conflict avoidance, kinematic stability), Compliance (legal rules and behavioral norms), Comfort (rider dynamics and trust), and Efficiency (mission completion and product-level metrics). We further demonstrate that the crossing of ODD layer parameterizations with behavioral competency specifications yields concrete scenario families suitable for systematic behavioral testing and SOTIF coverage evidence. The taxonomy is grounded in AVSC00008202111, SAE J3237, and SAE J3016, and is validated as an operational specification layer through its deployment in a rule-enforced trajectory optimization system. The Hub domain is identified as a structurally distinct, underspecified domain warranting dedicated research attention.