arXiv subjects

Jianhui Wang

Publications and source records attributed to Jianhui Wang.

At least 19 recordsLinked to original sources

arXiv2026

Grid-Oriented Probabilistic Load Forecasting for AI Data Centers

Probabilistic load forecasts for AI data centers need to represent both uncertainty under changing workloads and temporal dependence to support grid operations. This paper develops an open, grid-oriented benchmark using measured GPU power from a 448-GPU AI cluster. Forecasts are evaluated through four stages: probabilistic prediction, online uncertainty calibration, temporal scenario generation, and horizon-wide reserve sizing. Following a change in cluster utilization, a quantile LightGBM forecast achieves only 63.4% coverage for a nominal 90% interval, while online regime-conditioned calibration increases coverage to 87.3%. Even with well-calibrated marginal forecasts, independent sampling produces unrealistic ramp distributions and excessive reserve procurement. Reconstructing temporal dependence from historical forecast errors reduces the discrepancy in ramp statistics and lowers realized reserve cost by 7.4-10.6% across three forecasting tasks. These findings show that marginal forecast quality alone is insufficient for operational decisions and motivate joint evaluation of calibration, temporal structure, and decision outcomes.

eess.SY↗

arXiv2026

Geometric Mode Steering of the Quantum Mpemba Effect

The slowest Liouvillian mode often bottlenecks the relaxation of an open quantum system to its steady state. Standard strategies circumvent this bottleneck by selecting special initial states or engineering the dissipator. Here we investigate this problem when the input and the subsequent dissipative generator are prescribed. We introduce a predissipative geometric steering protocol that reshapes a prescribed pure or mixed state before relaxation begins---coherent rotations interleaved with nonselective projective measurements---at a fixed Lindblad generator. By steering the state's Bloch direction along geodesic paths, the protocol suppresses its overlap with the slowest Liouvillian modes. The prepared state can then start farther from equilibrium yet relax faster, realizing the quantum Mpemba effect, as diagnosed by reduced slow-mode overlap and a larger initial distance to stationarity, with distance crossings for complex slow modes verified from the full dynamics. Our framework treats real and complex spectral gaps within a common mode-overlap description. We also quantify the state-side cost of this preparation through cumulative measurement-induced mixing and illustrate its tradeoff with fidelity for a mixed target. We demonstrate Mpemba acceleration in driven qubit and multiqubit systems and discuss the required operations in trapped-ion and superconducting platforms.

quant-ph↗

arXiv2026

A Defense-Aware Explainable AI Framework for Feature-Level Vulnerability Analysis in Optimal Power Flow

Optimal power flow (OPF) is a foundational analysis in power system operation, and inaccuracies in OPF decision-making can result in significant operational and security consequences. With the increasing adoption of machine-learning-based OPF surrogate models for real-time and large-scale applications, power system operation has become more susceptible to cyber threats, including data manipulation and false data injection attacks. Explainable artificial intelligence (XAI), particularly Shapley value-based approaches, has been employed to quantify the influence of input features on OPF outputs. Features exhibiting high influence are commonly interpreted as vulnerable. However, this interpretation neglects the role of attack detection mechanisms in the system, which typically monitor input-to-input relationships to detect anomalous behavior. Consequently, highly influential features may be readily detectable and may not represent the most vulnerable attack surfaces. To address this limitation, we propose a defense-aware explainable AI framework that explicitly incorporates attack detectability alongside operational impact. The framework integrates an OPF surrogate model representing input-to-output dependencies with a vanilla autoencoder representing input-to-input dependencies. Shapley-based explainability is applied to both models to construct a feature-level vulnerability metric. Empirical evaluation on IEEE test systems demonstrates that the proposed framework more accurately identifies security-relevant vulnerabilities than conventional OPF-surrogate-model-only explainability approaches.

cs.CR↗

arXiv2026

Coder as Editor: Code-driven Interpretable Molecular Editing

Molecular design is a central task in drug discovery that requires precise structural reasoning and domain knowledge. While large language models (LLMs) have shown promise in generating high-level editing intentions in natural language, they often struggle to faithfully execute these modifications, particularly when operating on non-intuitive representations like SMILES. We introduce MECo, a framework that bridges reasoning and execution by translating editing actions into executable code. MECo reformulates molecular modification for LLMs as a cascaded framework: generating human-interpretable editing intentions from a molecule and design objective, followed by translating those intentions into executable structural edits via code generation. Our approach achieves over 98% accuracy in reproducing held-out realistic edits derived from chemical reactions and target-specific compound pairs. On downstream benchmarks spanning physicochemical properties and target activities, MECo substantially improves consistency by 38-86 percentage points to 90%+ and achieves higher success rates over SMILES-based baselines while preserving structural similarity. By aligning intention with execution, MECo enables consistent, controllable and interpretable molecular design, laying the foundation for high-fidelity feedback loops and collaborative human-AI workflows in drug discovery.

cs.LG↗

arXiv2026

RAPAC-DP: Response-Aligned Pending-Action Compensation for Diffusion Policies under Delayed Execution

Cloud-side inference expands the computational resources available to imitation-learning policies, but communication and inference latencies create a mismatch between prediction and execution. We propose RAPAC-DP, a response-aligned pending-action compensation framework for diffusion- and flow-based action generators. By conditioning on actions committed during inference, RAPAC-DP generates a full-horizon action chunk aligned with the scheduled handover time. A lightweight pending-action encoder and LoRA adapters learn this compensation while keeping the pretrained base policy frozen. We construct delay-conditioned training samples from delay-free demonstrations, requiring neither an explicit dynamics model nor additional data collected under delayed execution. In simulation benchmarks at the largest evaluated delays, RAPAC-DP retains 81.4\% of its overall delay-free performance on Kinetix and achieves a mean success rate of 63.3\% across three RoboMimic tasks. Across 450 physical trials on a Franka Panda manipulator, RAPAC-DP increases the high-delay mean success rate from 28.9\% for RTC to 60.0\% across static and conveyor-based pick-and-place tasks.

cs.RO↗

arXiv2026

Identifiability-Guided Composite Load Modeling of AI Data Centers

The rapid growth of AI data centers introduces large, workload-dependent loads whose protection and reconnection behavior can affect power-system transient stability. Calibrating dynamic models of such loads is challenging because treating all associated elements as freely estimable may lead to weakly identified or physically confounded parameterizations. This paper proposes an identifiability-guided calibration framework for a composite load model with separate IT, cooling, and auxiliary branches. A nuisance-projected Fisher-information criterion characterizes the local identifiability of the cooling-motor composition, while a workload non-equivalence result shows that voltage-only recalibration cannot reproduce workload-dependent responses. Guided by these results, a grey-box estimator trained with branch-level supervision learns the workload-to-power relation and the cooling composition, while retaining a prescribed UPS protection law and an externally anchored reconnect-ramp duration. The learned workload relation is then distilled into a closed form for transient-stability simulation. Evaluation using 300,000 MIT Supercloud GPU power-utilization samples and 12,600 IEEE 39-bus fault scenarios yields an aggregate active-power $R^2$ of 0.948, compared with 0.856 for an LSTM baseline. Removing branch-level supervision preserves relatively high aggregate accuracy but substantially degrades cooling-component recovery. Under unseen deep voltage sags, the proposed model achieves a transient $R^2$ of 0.750, compared with 0.033 for the LSTM. Closed-loop simulations further show that cooling composition and protection behavior can affect retained-load states and voltage responses near protection thresholds.

eess.SY↗

arXiv2026

Local response theory of multiparameter sudden-quench quantum Otto engines

Multiparameter quantum Otto engines raise an optimization problem absent in single-parameter cycles: which small quench vector maximizes the quadratic approximation to the exact output work in a multidimensional control space? We develop a local response theory for sudden-quench Otto engines with quantum working media described by linearly controlled Hamiltonians. We show that the local quadratic work decomposes into a thermal-bias gain and a quadratic response cost, whose competition determines the quench vector that maximizes the local quadratic work at a prescribed quench amplitude. We illustrate the theory in a minimal two-level working medium, where radial and transverse quenches play distinct roles, and in a finite interacting XYZ spin chain, where the resulting quench vector is determined by the balance between thermal-bias and response cost. Our results provide a practical local criterion for designing small multiparameter quenches in sudden-quench quantum Otto engines.

quant-ph↗

arXiv2026

Autonomous Thermal Switching Outperforms the Frozen Optimum with Vanishing Correlations

Can autonomous thermal switching outperform the best frozen barrier allocation while keeping system--auxiliary correlations arbitrarily small? We establish this relaxation advantage at fixed lowering budget and visible equilibrium, preserving joint detailed balance. On finite reversible networks, optimal fast switching mixes profiles concentrating the budget on single edges. A three-state model gives a strict gain and its finite-rate threshold. Faster switching preserves the gain while correlations vanish throughout relaxation. Collective and continuous models suggest a colloidal test.

cond-mat.stat-mech↗

arXiv2026

Optimal phase control of a measurement-assisted quantum refrigerator

A tilted nonselective measurement can close the upper transition of a three-level quantum refrigerator, but it also generates coherence that feeds back on the populations and suppresses the cooling current. We formulate randomized open-loop control of the measurement azimuthal phase $α$, sampled independently at each event from a probability distribution $P(α)$, while keeping the event rate, polar angle, and single-event population-transfer probability fixed. Direct population transfer is phase independent, whereas the coherence source carries the factor $e^{-iα}$. Exact elimination of the stationary coherence gives $0\leq k_{\rm eff}[P]\leq A_m$, where $k_{\rm eff}[P]$ is the effective population-transfer rate between the upper levels and $A_m$ is the bare direct mixing rate. When the fixed measurement couples populations and coherence, the upper bound is attained if and only if $\langle e^{iα}\rangle_P=0$, where $\langle\cdot\rangle_P$ denotes an average over the phase distribution. The minimal realization uses two equally weighted phases separated by $π$: the population-to-coherence source and its return to the populations then vanish at the generator level, while the full direct mixing remains. Because the cold current increases strictly with $k_{\rm eff}$ in the refrigeration window, the same condition maximizes the stationary cooling power over the independent random-phase distributions considered here. For one representative parameter set, phase cycling increases the fixed-gap cooling power by about $32\%$ relative to a fixed-phase measurement, while leaving the ratio of extracted cold heat to supplied measurement energy unchanged.

quant-ph↗

arXiv2026

Aging-Aware Online Distributed Scheduling for Lifecycle Carbon Reduction in Geo-Distributed Data Centers

The rapid proliferation of data centers (DCs), driven by cloud computing and artificial intelligence (AI), has led to massive energy demand and carbon emissions, posing significant sustainability challenges. Carbon-aware optimization in geographically distributed data centers has been widely studied. Most existing approaches mainly focus on operational carbon emissions from server usage. However, existing literature often ignores workload-induced thermal stress, which accelerates nonlinear hardware degradation. This leads to more frequent server replacements and ultimately increases embodied carbon emissions. To address these limitations, we propose a comprehensive carbon life-cycle modeling framework for distributed data centers. Apart from operational carbon emissions, this work combines workload scheduling with a utilization-dependent exponential aging model to evaluate long-term carbon costs from server degradation. In order to solve the proposed optimization model in an online and privacy-preserving manner, an enhanced Lyapunov framework with time-varying queue shifting (TVQS) is first introduced to handle system uncertainties. Then, a zero-sum perturbation-based alternating direction method of multipliers (ZSP-ADMM) framework is developed to enable distributed coordination across geographically separated data centers while protecting locally exchanged workload information. Simulation results demonstrate that the proposed approach achieves up to 13.0% lower carbon emissions and 12.6% lower operational costs compared with benchmarks.

eess.SY↗

arXiv2026

Experimental Demonstration of a Measurement-Feedback Quantum Information Engine

Harnessing finite-time nonadiabatic transitions that are conventionally associated with quantum inner friction for useful work extraction remains an open experimental challenge. Here we address this issue by introducing and experimentally realizing an innovative measurement-feedback quantum information engine model in the trapped 40Ca+ ion system, in which the projective measurement replaces the hot reservoir as a nonthermal energy source and the feedback control conditionally steers the system through either unitary compression-expansion strokes or thermalization. We experimentally show that the repeated feedback cycle converges to a stable operating regime with a resolved energetic balance, and, by controlling the measurement angle and stroke duration, measurement-induced coherence and the finite-time nonadiabatic contribution can enhance work extraction and raise the efficiency above the corresponding Otto benchmark. The experimental results further show that, over finite ranges of stroke durations, the efficiency and intrinsic cycle power can increase simultaneously. Our experiment establishes a route toward information-to-work quantum engines that convert finite-time irreversibility into performance-enhancing resources.

quant-ph↗

arXiv2026

Measurement-Feedback Quantum Information Engine: Coherence-Transition Interference and Correlated Work Statistics

Measurement and feedback jointly prepare coherence and select finite-time dynamics in quantum information engines. Complementing a companion experimental realization, we develop a mechanism-resolved theory of the resulting work statistics and temporal correlations. The con?ditional work separates into population transfer and a phase-sensitive coherence-transition inter?ference term. Symmetric full counting statistics maps this interference to an equal-and-opposite half-quantum pair in the work quasiprobability, entering odd moments while leaving even moments fixed by endpoint mixing. An outcome-resolved tilted kernel then propagates these statistics through the correlated measurement record, yielding finite-cycle and fixed-time fluctuation corrections. The same memory reduces the reversible record-reset cost from the one-symbol entropy to the entropy rate. Our results link coherent work statistics, feedback memory, and information thermodynamics

quant-ph↗

arXiv2026

Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis

Multimodal large language models (LLMs) can combine topology, measurements, and incident text for grid diagnosis, yet answer accuracy does not establish that task-appropriate evidence was used. This letter proposes a general framework in order to conduct task-conditional faithfulness audit. It compares self-reported reliance, intervention-derived behavioral reliance, and preregistered engineering importance. The framework first registers task-specific evidence requirements and compares them with self-reported reliance and behavioral changes under controlled modality ablations. To resolve detected discrepancies, we design an evidence-gated correction and re-audit mechanism that regenerates failed responses under evidence constraints and independently re-ablates them to verify improved grounding without performance loss. Case studies evaluate three differently scaled LLMs on IEEE 39- and 118-bus scenarios. These results validate the framework ability to detect, diagnose, and correct task-conditional faithfulness failures.

cs.AI↗

arXiv2026

Twin Co-Adaptive Dialogue for Progressive Image Generation

Modern text-to-image generation systems have enabled the creation of remarkably realistic and high-quality visuals, yet they often falter when handling the inherent ambiguities in user prompts. In this work, we present Twin-Co, a framework that leverages synchronized, co-adaptive dialogue to progressively refine image generation. Instead of a static generation process, Twin-Co employs a dynamic, iterative workflow where an intelligent dialogue agent continuously interacts with the user. Initially, a base image is generated from the user's prompt. Then, through a series of synchronized dialogue exchanges, the system adapts and optimizes the image according to evolving user feedback. The co-adaptive process allows the system to progressively narrow down ambiguities and better align with user intent. Experiments demonstrate that Twin-Co not only enhances user experience by reducing trial-and-error iterations but also improves the quality of the generated images, streamlining creative process across various applications.

cs.CV↗

arXiv2025

Learning Protein-Ligand Binding in Hyperbolic Space

Protein-ligand binding prediction is central to virtual screening and affinity ranking, two fundamental tasks in drug discovery. While recent retrieval-based methods embed ligands and protein pockets into Euclidean space for similarity-based search, the geometry of Euclidean embeddings often fails to capture the hierarchical structure and fine-grained affinity variations intrinsic to molecular interactions. In this work, we propose HypSeek, a hyperbolic representation learning framework that embeds ligands, protein pockets, and sequences into Lorentz-model hyperbolic space. By leveraging the exponential geometry and negative curvature of hyperbolic space, HypSeek enables expressive, affinity-sensitive embeddings that can effectively model both global activity and subtle functional differences-particularly in challenging cases such as activity cliffs, where structurally similar ligands exhibit large affinity gaps. Our mode unifies virtual screening and affinity ranking in a single framework, introducing a protein-guided three-tower architecture to enhance representational structure. HypSeek improves early enrichment in virtual screening on DUD-E from 42.63 to 51.44 (+20.7%) and affinity ranking correlation on JACS from 0.5774 to 0.7239 (+25.4%), demonstrating the benefits of hyperbolic geometry across both tasks and highlighting its potential as a powerful inductive bias for protein-ligand modeling.

cs.LG↗

arXiv2025

AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation

Virtual screening (VS) is a critical component of modern drug discovery, yet most existing methods--whether physics-based or deep learning-based--are developed around holo protein structures with known ligand-bound pockets. Consequently, their performance degrades significantly on apo or predicted structures such as those from AlphaFold2, which are more representative of real-world early-stage drug discovery, where pocket information is often missing. In this paper, we introduce an alignment-and-aggregation framework to enable accurate virtual screening under structural uncertainty. Our method comprises two core components: (1) a tri-modal contrastive learning module that aligns representations of the ligand, the holo pocket, and cavities detected from structures, thereby enhancing robustness to pocket localization error; and (2) a cross-attention based adapter for dynamically aggregating candidate binding sites, enabling the model to learn from activity data even without precise pocket annotations. We evaluated our method on a newly curated benchmark of apo structures, where it significantly outperforms state-of-the-art methods in blind apo setting, improving the early enrichment factor (EF1%) from 11.75 to 37.19. Notably, it also maintains strong performance on holo structures. These results demonstrate the promise of our approach in advancing first-in-class drug discovery, particularly in scenarios lacking experimentally resolved protein-ligand complexes. Our implementation is publicly available at https://github.com/Wiley-Z/AANet.

cs.LG↗

arXiv2025

Topological Control of Transition Metal Networks for Reversible High-Capacity Li-rich Cathodes

Developing high-energy-density batteries is essential for advancing sustainable energy technologies. However, leading cathode materials such as Li-rich oxides, including Li$_2$MnO$_3$, suffer from capacity loss due to irreversible oxygen release and structural degradation, both consequences of the oxygen redox activity that also enables their high capacity. The atomic-scale mechanisms behind this degradation, and whether it can be made reversible, remain open questions. Here, using submicrosecond-scale molecular dynamics simulations with first-principles accuracy, we directly visualize the entire charge-discharge cycle of Li$_2$MnO$_3$, uncovering the full lifecycle of the O$_2$-filled nanovoids responsible for degradation and identifying the critical size limit for voids to remain fully repairable upon discharge. Our results reveal that the topology of the Mn cation network is the key factor governing void growth, coalescence, and reparability. Based on a structural topology-informed design principle, we computationally develop a novel Li$_2$MnO$_3$ structure featuring a Mn lattice with a Kagome-like pattern, demonstrating full electrochemical reversibility even under extreme 80% delithiation. Our work establishes a new paradigm for designing high-energy cathodes, shifting the focus from mitigating damage to engineering inherent stability through atomic-level topological control of transition metal network.

cond-mat.mtrl-sci↗

arXiv2025

MountainLion: A Multi-Modal LLM-Based Agent System for Interpretable and Adaptive Financial Trading

Cryptocurrency trading is a challenging task requiring the integration of heterogeneous data from multiple modalities. Traditional deep learning and reinforcement learning approaches typically demand large training datasets and encode diverse inputs into numerical representations, often at the cost of interpretability. Recent progress in large language model (LLM)-based agents has demonstrated the capacity to process multi-modal data and support complex investment decision-making. Building on these advances, we present \textbf{MountainLion}, a multi-modal, multi-agent system for financial trading that coordinates specialized LLM-based agents to interpret financial data and generate investment strategies. MountainLion processes textual news, candlestick charts, and trading signal charts to produce high-quality financial reports, while also enabling modification of reports and investment recommendations through data-driven user interaction and question answering. A central reflection module analyzes historical trading signals and outcomes to continuously refine decision processes, and the system is capable of real-time report analysis, summarization, and dynamic adjustment of investment strategies. Empirical results confirm that MountainLion systematically enriches technical price triggers with contextual macroeconomic and capital flow signals, providing a more interpretable, robust, and actionable investment framework that improves returns and strengthens investor confidence.

q-fin.TR↗