Papers

Conflict-graph search can stagnate despite effective operators when interventions fail to address the conflicts blocking progress. A disruptive intervention may also require further search before its value emerges. We propose CIPHEUR, a large language model (LLM)-guided framework that connects conflict evidence to coordinated intervention and recovery through Structured Intervention Units (SIUs). The shared control interface supports offline synthesis of executable policies and online contextual decisions. Asynchronous online control sustains native search during inference, while all policies preserve the original objective and feasibility constraints. Across eight satellite-ground scheduling conflict views with five runs per view and a 900-second evaluation cap, CIPHEUR-online achieves the highest mean objective among 11 configurations and increases mean scheduled contact duration by 14.84% over the greedy baseline GWMIN. It exceeds the strongest external per-view mean in seven of eight views and outperforms CHILS in all 40 paired runs. Within the same runs, its normalized objective improvement from 360 to 900 seconds is 1.50 to 1.86 times that of the two programmatic controllers. A separately frozen, one-shot LLM-generated controller also improves the mean objective over CHILS without runtime model calls. Together, these results demonstrate how structural search control translates LLM design knowledge and contextual reasoning into improved solution quality and sustained search progress.

The increasing integration of variable renewable generation introduces weather-driven variability and uncertainty into power-system operations. System operators manage the resulting deviations between day-ahead forecasts and real-time net load by scheduling upward and downward flexibility reserves. These requirements have traditionally been set using simple deterministic rules. Newer data-driven methods tailor reserves to forecasts and operating conditions. However, their reliability may deteriorate when conditions depart from the historical data. We address this limitation with a sequential framework that updates day-ahead reserve requirements as operating outcomes become available. Within this framework, Adaptive Conformal Inference targets the frequency of insufficient reserve, whereas Rolling Risk Control targets expected uncovered deviation, the mean residual beyond the scheduled reserve. We prove finite-horizon bounds on realized miscoverage and capped uncovered-deviation loss. We test the framework on real ERCOT data in a chronological deployment simulation that respects operational timing: outcomes from one day first affect the reserve schedule two days later. Training and validation use 2022–2023 data; the uninterrupted test covers 2024–2025. Coverage calibration achieves approximately 90% coverage, with upward and downward miscoverage near 5%. Risk calibration brings expected uncovered deviations close to their budgets while using 5.3% less reserve than the uncalibrated requirements. The framework thus produces adaptive reserve requirements that provably control the selected reliability metrics over finite horizons without assuming stationarity or exchangeability.

As active power dispatch becomes increasingly coupled with reactive power support and voltage security in modern power systems, AC security regions (AC-SRs) under exact steady-state AC power flow (AC-PF) constraints provide valuable insights into admissible operating boundaries and security margins for security assessment. However, the nonconvexity of exact AC-PF constraints makes it difficult to achieve both accuracy and computational efficiency in AC-SR characterization. To this end, this paper proposes an AC-aware iterative exclusion refinement (AC-ITER) approach that refines a given convex-relaxed AC-SR toward the exact AC-SR by progressively constructing and removing convexlocal exclusion subregions around numerically identified insecure points. Boundary-point searches and dual-based local exclusion cuts provide a tractable approximation of relaxation-induced insecure portions. A statistical certification method based on uniform sampling is developed to support high-confidence assessment of residual-region security and guide termination, while an AC-PF security similarity-based acceleration (AC-SSA) approach is introduced to further accelerate the sampling-based search for insecure operating points. Numerical studies on academic test systems and real-world systems demonstrate the characterization accuracy of AC-ITER and the computational efficiency gained through AC-SSA.

A quickest change detection problem is studied where the decision-maker has access to multiple experiments, but can choose only one experiment at a time to detect the change. The true post-change distribution under each experiment is unknown, but known to belong to a family of distributions. It is assumed that each of these families of distributions has an element that is least favorable in a well-defined sense. An algorithm is proposed that is designed using the least favorable distributions. It is shown that the algorithm is first-order asymptotically optimal, as the rate of false alarms goes to zero. This algorithm minimizes the worst-case average detection delay over all post-change distributions within the assumed families, subject to a constraint on the average run length to false alarm and constraints on the average fraction of time each experiment is performed before change.

Automatic extraction and anatomical numbering (R01-R12, L01-L12) of all 24 rib centerlines in thoracic CT is essential for unfolded rib visualization, fracture detection, and surgical navigation, but remains challenging due to adjacent-bone confluence, calcified costal cartilage bridges, kyphotic pose variation, and partial visibility of terminal ribs. We present ribline, an open-source three-stage framework combining: (1) an automatically detected lung-apex and vertebral-midline coordinate frame with a kyphosis-aware azimuthal angle for point ordering along each rib; (2) a hybrid voxel detector fusing an anatomically anchored Semantic Context Forest (SCF) with a lightweight 3D U-Net; and (3) a global stage that decomposes the probability skeleton into candidate curves, assigns rib indices via a bilateral Viterbi dynamic program coupled across both hemithoraces, and refines curves via template recovery and normal-plane centering (RibSnake). On the Siemens Corporate Research 2 mm thoracic CT benchmark (47 training and 12 evaluation scans), our pipeline achieves a labeled Chamfer distance of 2.05 mm, labeled clDice of 0.886 (3 mm) and 0.945 (4 mm), 97.2% labeling accuracy (278/286 ribs), and zero false-positive curves on the evaluation split, and 3.64 mm (median 2.39 mm) with 91.8% labeling accuracy across all 59 annotated scans (1,402 ribs) in 5-fold cross-validation. Code: https://github.com/wq2012/ribline Pretrained Models: https://huggingface.co/collections/wq2012/ribline

The Zak-OTFS carrier waveform is a pulse in the delay-Doppler (DD) domain, and given the input-output (I/O) relation at one DD point in a frame, we can predict the I/O relation at all other points. While it is simple to acquire the effective wireless channel with Zak-OTFS, the cost of embracing inter-carrier interference is equalization complexity. The cost of linear minimum mean-square error (LMMSE) equalization grows cubically with the frame size. We describe how to use channel knowledge to reduce equalization complexity. For any pulse shaping filter, and for fractional delays and Dopplers, we show that the DD channel matrix is unitarily similar, through the discrete Zak transform, to a time-domain (TD) matrix that is a weighted sum of cyclic delay and Doppler operators, where the weights are the taps of the effective channel. We also obtain a dual frequency-domain (FD) form through an additional Fourier transform. The TD matrix is banded with a width set by the delay spread that is independent of the Doppler spread, whereas the FD matrix is banded with a width set only by the Doppler spread. We design a receiver that reads off the two widths from the channel estimate, selects the cheaper domain, and solves the banded system exactly, using a bordered Cholesky factorization that treats the quasi-periodic wrap-around without sacrificing degrees of freedom. Filters that decay slowly need a few preconditioned conjugate gradient steps. We show that the complexity of equalization becomes linear in the frame size. Simulations match direct DD-LMMSE error rates with three to four orders of magnitude fewer multiplications. A single receiver handles channels with very different delay and Doppler characteristics.

A printed tapered-slot coupling probe with a partially inserted 12-mm wire element is characterized for short-range microwave coupling through a brain-mimicking agar–gelatin–saline phantom and ex vivo brain. Simulated and measured free-space $S_{11}$ responses agree over a measured $-10$-dB band spanning 5.78 to 6.86~GHz, corresponding to a 1.08-GHz bandwidth and approximately 17.1~percent fractional bandwidth. In a controlled two-probe experiment, the phantom was introduced and removed while the probe arrangement and measurement conditions were retained. This comparison was repeated under the same retained arrangement, with the same general $S_{21}$ differential behavior observed across repetitions. Near 6.3~GHz, measured $S_{21}$ was approximately $-25$~dB with the phantom present and approximately $-40$~dB in air, corresponding to an $\approx 16~\mathrm dB$. Ex vivo goat brain measurements repeated more than five times showed reproducible behavior. Across 5.78–6.86~GHz, the ex vivo goat-brain $S_{21}$ response closely followed that of the phantom, with an approximately figure-derived maximum operating-band difference of about 1.5~dB. The observed increase in $|S_{21}|$ is interpreted as a property of the complete probe–medium–probe system, including loading, mutual coupling, interfaces, geometry, propagation, and material loss. The measurement does not uniquely identify a tissue-intrinsic mechanism.

Layer-wise post-training quantization methods let each layer compensate for the quantization error inherited from earlier layers. We ask under what assumptions such compensation guarantees a network output error no larger than without it. For linear networks of any depth, with the required calibration Gram matrices invertible, if the rounding error of each layer has zero conditional mean and a deterministic second-moment map common to both schemes, as under subtractive dither on an unbounded grid, then compensation with strengths in $[0,1]$ fixed in advance gives a mean-square output error no larger than no compensation, each scheme being followed along its own trajectory. The exact gain separates error removed directly from reduced excitation of later rounding noise. Outside this setting the comparison can fail. With rectified linear activations it fails for two independent reasons: noise changes activation states, and, even when no state changes, the correction moves error from samples on which a unit is inactive to samples on which it is active. With stable activations, commutators between the calibration projection and the activation patterns bound the excess. With a bounded codebook, even unique exact layer-wise minimizers with no overload can give a larger output error. For fixed rounding errors it already fails for two scalar linear layers, refuting a published universal guarantee. An exact residual identity gives the necessary and sufficient condition on a common layer input. All these comparisons hold the calibration input fixed. On two 8B models at 4 bits, fixed half-strength compensation beat uncompensated rounding in every model, text and draw tested, yet no rule for choosing the strength beat a layer-wise rule consistently, and lowering the full-network loss on the data used to choose did not reliably lower it on new text.

A reading departs from what a monitoring system expects for one of three reasons: the physical process changed, the instrument is failing, or the value was corrupted between the instrument and the software. The three call for different responses and look alike in a single sample. We state the three-cause problem, prove which pairs of causes one channel cannot separate, and propose a bank of online detectors - a Kalman tracker with CUSUM and EWMA charts on its innovation, a Hampel median with generalized-ESD confirmation, a robust noise statistic with ADWIN, exact-value checks and an optional two-channel complementary filter - combined by a deterministic, inspectable attribution rule. The detectors were merged into a public algorithm library. On 2,942 injected events of thirteen fault types the method detects 96.3% (95% interval 95.7-97.0) at 0.22 false alarms per hour and attributes 91.2% (90.6-91.8) of detected events correctly from one channel and 99.0% (98.6-99.4) with a second, rate channel; from one channel only 2 of 207 detected sensor drifts are attributed to the instrument, as the problem statement predicts. Injected into recorded industrial signals, the same catalogue is detected at 80.8% and attributed correctly at 84.5%, at 3.6 false alarms per hour. On the Skoltech Anomaly Benchmark the method detects 32 of 33 experiments with a median delay of 15 s, at 46 false alarms per hour against 11 for a rolling z-score.

This paper presents AutoSec-RAN, a specification-grounded large language model (LLM) framework for automated security triage of Open Radio Access Network (O-RAN) testing. Unlike approaches relying on static rule sets or privileged access to raw packet captures, AutoSec-RAN fine-tunes Mistral-7B-Instruct through two Low-Rank Adaptation (LoRA) stages over a curated corpus of 69 O-RAN, 3GPP, and adjacent standards documents, then selectively augments inference with retrieval-augmented generation (RAG) over the same specification index. At inference, the model receives only an investigator-style question and a scenario metadata block, simulating an automated analyst operating without raw packet evidence. AutoSec-RAN is validated on a purpose-built 5G/O-RAN testbed across labeled fronthaul distributed denial of service replay scenarios. A Gemma-4-31B judge LLM scores each candidate's answer on faithfulness, correctness, and actionability relative to a reference authored from full packet capture and log evidence. AutoSec-RAN achieves a mean judge overall score of 0.713 across all 313 evaluation items (0.755 restricted to the five supervised taxonomy classes), clearing the 0.70 reporting threshold, and a blended score of 0.778 (0.808 supervised-only) when cosine similarity over Beijing Academy of Artificial Intelligence (BAAI) General Embedding (BGE) is incorporated as a secondary semantic signal. The framework performs strongest on mitigation and threat-model reasoning (with a mean overall of 0.849 and 0.848, respectively) and identifies a measurable gap on the held-out attack-direction class (0.515), a controlled result that exposes a specific training data requirement. These results establish specification-grounded LoRA fine-tuning combined with RAG retrieval as a viable foundation for automated security validation of AI-native O-RAN.

Radio map estimation (RME), also known as spectrum cartography, aims to recover radio signal strength across multiple domains (e.g., spatial and spectral domains) from sparse measurements, naturally leading to a tensor completion problem. Recent advances show that deep learning-based generative models can provide data-driven structural constraints for RME, yielding substantial performance improvements over traditional model-based approaches. Diffusion models (DMs) represent the current state of the art in generative modeling and hold strong promise for advancing RME. However, existing diffusion-based RME methods often require extensive training data, necessitate retraining when scenarios or measurement formats change, and lack recoverability guarantees. This paper proposes a latent factor diffusion framework for spatio-spectral radio map tensor completion. By training two independent DMs on single-emitter's spatial loss field and spectral signature and embedding them into the factors of the radio map tensor, the proposed approach enables diffusion posterior sampling-based recovery with reduced training complexity as well as enhanced measurement format/sensing pattern flexibility. Recoverability and sample complexity guarantees under the proposed approach are also established. Numerical experiments validate the effectiveness of the proposed method, showing strong generalization across city environments unseen in training data.

Linking speakers across independently recorded rooms is difficult because microphone, room, and codec mismatch distort speaker embeddings. We study visitor linking without visitor enrollment or identity labels in simulated outpatient workflows. The proposed method combines per-device embedding centering, concurrency constraints, room-type-specific visit constraints, and a clustering threshold calibrated from concurrent visitors and identified staff voices. On simulated days built from VOiCES recordings, with companions, return visits, walk-ins, silent visits, and acoustic leakage, it improves B$^3$-F1 over the best label-free baseline for all 11 extractors, by 5.3-14.6 points (all 95% intervals exclude zero). ECAPA-TDNN reaches 86.6, compared with 76.4 for the best label-free baseline and 87.7 for the best oracle-informed baseline. Gains persist across the reported workflow perturbations, but depend on removing duplicated cross-room speech: without this step, the method wins for only 2 of 11 extractors. Single-visit patients also remain vulnerable to false merges. These results support structure-aware linking while identifying preprocessing and identity-safety limitations that must be addressed before clinical deployment.

We consider the problem of mono-to-stereo audio upmixing: generating a high-fidelity two-channel stereo signal from a single-channel recording. The task is fundamentally ill-posed i.e., many stereo signals are consistent with a given mono recording. An effective upmixer must therefore generate a realistic, musically coherent stereo image while preserving the underlying audio content. Previous approaches have relied on signal-processing driven decorrelation, ambience extraction and supervised learning, offering limited spatial control or requiring task-specific training. In response, we propose Aura (Audio Upmixing using Reward-tilted Flows), which reformulates upmixing as a generative inverse problem using a pretrained flow prior. To enable controllable spatial imaging, we design a reward-tilted posterior sampler whose reward penalizes divergences between generated and target distributions of inter-channel cues. Composing forward operators further enables joint restoration and upmixing without retraining or fine-tuning. Objective and subjective evaluations on real-world music recordings from MUSDB18-HQ demonstrate our method's effectiveness relative to several baselines.

With the rapid increase in interest towards large scale earth observing satellite constellations, the problem of properly commanding and utilizing these systems has become increasingly relevant. This work focuses on a complete distributed approach to this problem, incorporating an observation task allocation and scheduling algorithm in addition to a sensor data fusion algorithm for end-to-end target coverage and processing. Observation task allocation and scheduling is performed in this work through the combination of the bid warped consensus based bundle algorithm (BWCBBA) and a lightweight observation scheduler for bid scoring. This method produces a unique mapping of tasks to command agents when allowed to iterate sufficiently. The approach to distributed data fusion in this work occurs primarily through an ownership model in which one satellite takes ownership of a system track in the constellation. After local information is shared between the sensing nodes to create the system tracks, the dedicated fusion nodes assume ownership for further information fusion and track information is disseminated throughout the constellation for data association. This paper analyzes the performance of these two algorithms over a set of increasingly decentralized architectures to highlight the performance losses compared to more traditional centralized methods.

Heap-leaching hydraulics vary between regions of an ore bed, motivating fast prediction models that accommodate different material properties. This study develops a parameter-conditioned physics-guided machine-learning (PGML) surrogate and demonstrates its use in simulated saturation control. A dataset of 4096 numerical Richards-equation trajectories was divided into 2867 training, 614 validation and 615 evaluation cases. The network predicts hourly saturation profiles from the current profile, irrigation rate and four hydraulic parameters, and is trained with an approximate Gardner mass-balance penalty. A daily predictive controller compared 200 constant irrigation rates over a 24-hour horizon. Across the evaluation cases, the median full-cycle tracking deviation was 4.9% of a 0.70 saturation target, equal to that of a tuned proportional-integral (PI) controller, while the PGML controller reached the target without overshoot in the median and applied approximately 4% less irrigation. Moderate physics guidance reduced long-horizon prediction error by approximately 30% relative to data-only training and reduced irrigation-bound activity, but slightly worsened closed-loop tracking. For a matched prediction workload, the surrogate was approximately 70 times faster than the numerical solver, and batched evaluation cost under 2 ms per column for 5000 columns. The results support a reusable physics-guided surrogate for large-scale saturation control in simulation.

Among Navier–Stokes systems, the Rayleigh–Bénard layer, a fluid between two horizontal plates heated from below and cooled from above, is the benchmark of hydrodynamic instability that manifests itself as the unsteady convection phenomenon, which in turn can be interpreted as the onset of chaos: the Lorenz equations represent the Fourier–Galerkin approximation coefficients for the minimal three-mode truncation of it. Its instability has no mean flow behind it. The fluid is at rest, the velocity and the temperature are each stable alone, and buoyancy closes a positive loop between them. The linearization is self-adjoint, its unstable spectrum real, and the number of unstable modes grows without bound in the Rayleigh number; at $\Ra=10^6$ the layer has $85$ of them, with growth rates up to $2137$. We stabilize the conductive state at every Rayleigh and Prandtl number, with any prescribed decay rate, by explicit feedback of the full state to the tangential velocity, temperature, and normal velocity of the upper plate. The feedback laws are obtained by Fredholm backstepping of the coupled velocity–temperature system, with pole placement for the lowest modes.

Safety Filters (SF) are leveraged to design certifiably safe controllers, which ensures forward invariance on a defined safe set when the filter is feasible. Predictive Safety Filters (PSF) embed these constraints to a closed-loop model-based optimization method to improve constraint satisfaction, and reduce feasibility loss caused by myopic, one-step filtering. Related viability-based methods emphasize approximating a viability kernel which ensures admissible control for all future time, but requires significant computational expense. This work introduces a Jacobian-based kinematic safety filter leveraging an augmented state variable with task-space velocity and filters task-space acceleration to preserve the feasibility of a low-level filter while enforcing joint position and velocity. The proposed method introduces a nested Quadratic Program (QP) formulation, defines a conservative inner approximation of viability envelope and computes a viable planner online. The method is validated in a simplified 2R planar manipulator simulation environment and shows significant improvement compared to a baseline Control Barrier Function Quadratic Program (CBF-QP) based filter. The experiments highlights the improvements in terms of computation time compared to traditional PSF and Reference Governor approach while also showing significant reduction in feasibility violations. Lastly, a simplified demonstration on a simulated 6-DoF manipulator highlights the potential scalability of the method to higher degree-of-freedom setups.

Motion planning for automated vehicles in interactive traffic has to simultaneously ensure safety, respect traffic rules, and maintain comfortable progress under multi-modal behavior uncertainty. Imposing all these requirements as hard constraints reduces feasibility, whereas treating them all as costs allows safety-critical requirements to be traded against progress. We propose a hybrid hard–soft Signal Temporal Logic (STL) MPC framework that optimizes a single shared ego trajectory against every retained behavior hypothesis: minimum-distance and speed predicates are imposed as explicit hard constraints, while soft STL robustness terms shape clearance, comfort, and interaction quality within the resulting feasible driving corridor. This retains robustness to discrete interaction modes without branch-specific contingent ego policies. We further identify substantial symbolic graph-construction overhead and introduce reachability-certified relevance screening and vectorized symbolic construction to reduce computational cost while preserving the feasible set and objective. Closed-loop Monte Carlo evaluation over six highway and intersection scenarios shows the highest strict success rate among the compared planners with no observed collisions or rule violations, at roughly a quarter of the planning latency of the unoptimized formulation. An ablation study separates the two layers: the hard constraints define the safety envelope, while the soft terms improve clearance and motion quality within it.

We study deterministic bounded-noise state reconstruction for nonlinear systems when the measurement schedule drives the sampled-output map through a rank-deficient configuration. Local reconstruction is governed by the leading pairwise separation order and its corresponding higher-order modulus, which together determine the leading minimax noise-to-state law. For corank-one cubic singular schedules, including overdetermined sampled maps, we derive the exact physical $\ell_\infty$ cubic modulus through quotient geometry in $\mathbb{R}^q/\operatorname{Im}DF$ and show how the required finite jet is computed from trajectory sensitivities. We then prove a uniform signed timing crossover: a transversal timing perturbation has transition scale $\varepsilon=O(\delta^{2/3})$, linking the regular, cubic-singular, and fold regimes. A damped pendulum provides closed-form crossover coefficients and exact nonlinear minimax validation, while a four-state downward cart–pole verifies the rectangular theory. The results show that measurement timing can change not only reconstruction conditioning but the exponent governing bounded-noise state uncertainty.

Open Radio Access Networks (O-RAN) empower operators with choice and customization through their disaggregated and interoperable architecture. However, ensuring interoperability in O-RAN presents challenges due to complex testing environments and evolving specifications. This paper addresses these challenges by proposing the Generative AI and Graph Neural Network-based Open RAN Testing and Automation (GNG-OTA) framework. GNG-OTA utilizes state-of-the-art AI techniques including generative AI, natural language processing, and graph neural networks to enable accurate, reliable, efficient, and automatic O-RAN interoperability and performance testing.

Split Gibbs sampling (SGS) is a versatile tool to sample from complex posterior distributions, such as those arising in imaging inverse problems. However, existing SGS instances are limited to quadratic variable couplings, restricting their applicability to Gaussian likelihoods. This letter genearalizes SGS to non-Euclidean geometry by introducing a coupling kernel defined through a Jensen-Bregman divergence. This framework not only enables posterior sampling in these settings but also unifies several ad hoc sampling schemes from the literature under a principled theoretical foundation.

Generating a full song from lyrics and a style description requires long-range musical planning without sacrificing vocal and instrumental detail. Low-rate discrete semantic tokens make this tractable, but quantization constrains the information available to acoustic rendering, while separately optimized stages prevent downstream generation objectives from shaping the planner. We propose joint discrete and continuous modeling for high-fidelity song generation. A VQ tokenizer supplies tokens for autoregressive planning, and a separately trained SSL-derived variational encoder supplies continuous semantic targets. Conditioned on the completed token plan and differentiable readouts of the planner's predictive states, a full-sequence flow model generates semantic features for acoustic rendering. Its flow-matching gradients reach the language model through the readouts, without differentiating through token sampling; the standard pathway keeps continuous features out of autoregressive feedback. On 192 shared prompts, its 48D configuration improves SongEval from 4.152 to 4.269 and reduces micro-PER from 20.26% to 14.47% relative to a discrete baseline, while receiving substantially higher expert listening scores. We further analyze the system through downstream semantic probes and conditioned reconstruction.

Ordinal optimization selects designs that are good enough rather than optimal when evaluation is expensive and the budget is small. The alignment probability measures the chance that the selected set contains at least a prescribed number of truly good designs. We formulate quantum ordinal optimization as a class of problems that maximize this probability under a fixed oracle budget. We study exact, coherent pairwise comparisons with one selected design as a first instance, with the budget counted in comparison queries. For every classical comparison budget smaller than the number of designs, we prove that a knockout among uniformly sampled designs attains the optimum. The sample is drawn without replacement and contains one more design than the number of comparisons. For a uniformly random order, the selected design's rank has a polynomial probability law. At the same comparison budget, the maximum quantum degree is at most twice the classical maximum, a factor attained whenever there are more than twice as many designs as comparisons, so a quantum algorithm can realize rank laws that no classical algorithm with the same budget can. We determine the exact quantum optimum with one comparison for every number of designs and every good-enough set. For selecting the best of n designs, the optimal success probability with one quantum comparison is approximately 3.30/n for large n, compared with 2/n for one classical comparison. When the best design is sought, one quantum comparison gives a higher alignment probability than two classical comparisons whenever there are more than nine designs.

Music-understanding benchmarks should identify both the evidence available to a model and the operations a correct answer requires. We introduce HMU-Bench, a bilingual benchmark with notation and audio components. ABC Q&A contains 1,029 Chinese-English question pairs across six musical domains, labeled as extraction, single-step inference, or multi-step inference. Audio Q&A contains 460 question pairs on 92 recordings, spanning five scope-difficulty configurations. All music is Suno-generated, and questions and references are human-reviewed. Reference-based evaluation of seven omni models yields high notation scores for the strongest systems, but no model averages above 1.4 on the 0-4 audio scale. Notation and audio rankings show little correlation. A transposition case study also reveals octave errors missed by LLM grading. These findings motivate separate modality reporting and event-level checks for symbolic outputs. Data, annotations, evaluator prompts, and graded responses will be released at https://github.com/stepaudiollm/hmu-bench.

Trailer sway is a persistent contributor to severe crashes among light passenger vehicles towing trailers, yet it remains largely unaddressed by the autonomous vehicle systems now entering this new era of automation. Improper cargo placement rearward of the trailer axle reduces, and can invert, the tongue load transmitted through the hitch. The resulting loss of vertical load at the tow vehicle's rear axle diminishes the lateral tire force available there and degrades yaw stability of the coupled system, admitting a divergent lateral oscillation of the trailer known as trailer sway. Under adverse combinations of speed and loading, this oscillation can progress to jackknifing, in which the articulation angle diverges rapidly and directional control is lost. This work develops a linear state-space model of the yaw-plane dynamics of a light vehicle-trailer combination, parameterized to expose tongue load as an explicit stability-determining quantity. From this model a linear quadratic regulator is synthesized to suppress articulation-angle divergence. Because consumer trailers carry no sensors, the two trailer states the controller needs, trailer yaw rate and articulation angle, are estimated by a Kalman filter from measurements taken only on the tow vehicle. The controller is then implemented and evaluated in a MuJoCo multibody simulation across a range of longitudinal speeds and tongue-load conditions, including the negative-tongue-load regime. Results show a significant decrease in trailer oscillation when controlling a given vehicle's steering with an LQR using tow-vehicle IMU and LiDAR data. These findings indicate that LQR controlled anti-sway systems are a viable control method for future autonomous vehicles.

A quantum-dot array prepared in a timed Dicke state exhibits directional radiation asymmetry, which has often been interpreted as evidence of reciprocity breaking through quantum-state preparation. We show that this interpretation is incorrect: reciprocity concerns transmit-receive response, not necessarily pattern symmetry. Moreover, the first-order coherence of any single-excitation state is exactly equivalent to the power pattern of a classical phased array driven by conjugate weights, making the observed asymmetry classically reproducible. The genuine quantum signature instead appears in second-order correlations, where a single-excitation state violates the classical lower bound on photon coincidences. We therefore propose a quantum-antenna certificate combining nonzero directional asymmetry with photon antibunching at the beam peak. We further derive a tight asymmetry bound, establish the correct mirror operation for planar arrays, correct the collective decay rate with dipole-dipole coupling, and derive a saturation law for direction-finding precision. All theoretical results are validated through independent numerical implementations.

Fourier neural operators predict power-system transient trajectories with high accuracy. We extend this line of work by examining the choice of spatial spectral basis, studying both the graph Fourier transform derived from the grid's own coupling network and learnable bases trained from that starting point and from a Fourier control. Evaluated in one controlled pipeline across three protocols spanning unseen fault locations and shifted operating regimes, the learnable bases improve on a parameter- and budget-matched DFT variant by 7-14% in distribution, with per-case significance on both in-distribution protocols and no loss on the third; the gain comes from adaptation rather than from the coordinates alone. We further characterize the regime dependence of this gain, identifying its cause, locus, and continuity, and show that a resolution requires no new model: averaging the learned and frozen variants gives the best shift-robust predictor, while their disagreement provides a label-free alarm that flags every shifted case with no false alarms.

This article aims to design a distributed economic dispatch (ED) scheme based on marginal cost (MC) consensus for a battery energy storage system (BESS) network considering the utility grid switching. Due to the low control accuracy and slow convergence speed of previous schemes, some important changes have been made in this article. Firstly, the feedback coupling term is introduced into the closed-loop error to further improve the stability and convergence rate of the controller. Then, a proportional integral (PI) controller with a reset mechanism is implemented in the designed scheme, which improves the speed and control accuracy of MC consensus. The performance of a control system with a reset mechanism is well guaranteed by analyzing the eigenvalues of the corresponding matrix. Finally, some tests focus on UG switching, time phased pricing, load switching, a BESS/agent plug and play, and large-scale system applications to illustrate the effectiveness and progressiveness of the design scheme.

This paper investigates how bistatic delay information improves the observability and localization performance of 3D moving targets in distributed Wi-Fi sensing. Motivated by emerging IEEE 802.11bf WLAN sensing, we consider one transmitter, multiple distributed receivers, and a single target with unknown position and velocity. We compare Doppler-only sensing with delay-Doppler sensing, in which delay-capable receivers provide additional bistatic path-length information for target localization. We derive the Fisher information matrix (FIM) and the position equivalent FIM (EFIM) for both sensing formulations. The resulting observability analysis shows that Doppler-only localization generically requires six receivers, whereas delay-Doppler localization can become locally identifiable with three receivers under non-degenerate geometry. We further study mixed receiver architectures in which only a subset of receivers provides delay estimates. Simulations with Wi-Fi-like bandwidths confirm the transition and quantify the roles of bandwidth, Doppler noise, receiver geometry, and delay-capable receiver availability.

Movable antenna (MA) can enhance the physical layer security (PLS) performance of integrated sensing and communications (ISAC). However, most existing works assume either near-field (NF) or far-field (FF) propagation models for both communication users (CUs) and targets. This paper considers a more practical scenario where multiple CUs, an eavesdropper (Eve), and multiple targets are in a mixed near- and far-field environment due to the changes in the propagation field caused by antenna position updates in MA with a unified array response and continuous NF/FF weights. To evaluate the PLS performance of MA-aided ISAC in mixed-field environments, we formulate an optimization problem to maximize the sum secrecy rate (SSR) of CUs by jointly designing the transmit beamforming and antenna positions at the transmitter. The problem is highly non-convex, so a two-layer algorithm is proposed to iteratively solve the problem using the projected gradient ascent (PGA) method and the Lagrangian dual transform. Since the abrupt switch between the NF and the FF models during antenna movement can cause the objective function to oscillate during iteration, the unified array response is combined with the Barzilai-Borwein step, Armijo line search, and precise projection to ensure that the SSR does not decrease during the iteration and the objective function is differentiable on the Rayleigh boundary. Numerical results show that the MA-ISAC using the mixed-field model achieves almost identical PLS performance to that of using the full NF model in terms of SSR, and both outperform the FF model. Using the mismatched FF model during antenna position updates results in a loss of MA gain, potentially making MA inferior to the FA-based system using the correct model.