arXiv subjects

Jie Zhu

Publications and source records attributed to Jie Zhu.

At least 73 records · Page 4Linked to original sources

arXiv2026

Frequency Security-Aware Production Scheduling of Utility-Scale Off-Grid Renewable P2H Systems Coordinating Heterogeneous Electrolyzers

Renewable power-to-hydrogen (ReP2H) enables large-scale renewable energy utilization and supports the decarbonization of hard-to-abate sectors, such as chemicals and maritime transport, via hydrogen-based renewable ammonia and methanol fuels. As a result, utility-scale ReP2H projects are expanding worldwide. However, off-grid ReP2H systems exhibit low inertia due to their converter-dominated nature, making frequency security a critical concern. Although recent studies show that electrolyzers can contribute to frequency regulation (FR), their support capability depends on operating states and loading levels, creating a trade-off between hydrogen output and frequency security. To address this challenge, this work develops a unified co-optimization framework for frequency security-aware production scheduling of utility-scale off-grid ReP2H systems coordinating heterogeneous electrolyzers. A system-level frequency response model is established to capture multi-stage FR from alkaline water electrolyzers (AWEs), proton exchange membrane electrolyzers (PEMELs), and other resources, including ammonia-fueled generators retrofitted in co-located chemical plants, battery energy storage, and wind turbines (WTs). Stage-wise transient frequency security constraints are derived, reformulated into tractable forms, and embedded into production scheduling, enabling coordinated on/off switching and load allocation across electrolyzers to maximize hydrogen output under uncertain renewable power input while enforcing frequency security constraints. Case studies based on real-world systems demonstrate that the proposed approach allows HPs to replace 55.52% and 96.85% of FR reserves from WTs and AFGs, respectively, while maintaining comparable hydrogen output. Year-long simulations show an average 28.96% increase in annual net profit resulting from reduced reliance on conventional reserves.

math.OC↗

arXiv2026

Plan and Budget: Effective and Efficient Test-Time Scaling on Reasoning Large Language Models

Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks, but their inference remains computationally inefficient. We observe a common failure mode in many prevalent LLMs, overthinking, where models generate verbose and tangential reasoning traces even for simple queries. Recent work has tried to mitigate this by enforcing fixed token budgets, however, this can lead to underthinking, especially on harder problems. Through empirical analysis, we identify that this inefficiency often stems from unclear problem-solving strategies. To formalize this, we develop a theoretical model, BAM (Budget Allocation Model), which models reasoning as a sequence of sub-questions with varying uncertainty, and introduce the E3 metric to capture the trade-off between correctness and computation efficiency. Building on theoretical results from BAM, we propose Plan-and-Budget, a model-agnostic, test-time framework that decomposes complex queries into sub-questions and allocates token budgets based on estimated complexity using adaptive scheduling. Plan-and-Budget improves reasoning efficiency across a range of tasks and models, achieving up to 70% accuracy gains, 39% token reduction, and 193.8% improvement in E3. Notably, it improves the efficiency of a smaller model (DS-Qwen-32B) to match the efficiency of a larger model (DS-LLaMA-70B), demonstrating Plan-and-Budget's ability to close performance gaps without retraining. Our code is available at https://github.com/junhongmit/P-and-B.

cs.LG↗

arXiv2026

Enhanced Hydrogen Electrolyzer with Integrated Energy Storage to Provide Grid-Forming Services for Off-Grid ReP2H Application

This article proposes an energy storage-enhanced hydrogen electrolyzer (ESEHE) to provide grid-forming (GFM) services for off-grid renewable power to hydrogen (ReP2H) systems. Unlike conventional ReP2H systems that use a centralized energy storage (ES) plant, the proposed topology directly connects batteries to the DC buses of electrolysis rectifiers. A tailored virtual synchronous machine (VSM) control framework enables the electrolyzer to autonomously provide real and reactive power support. A coordinated frequency-splitting energy extraction strategy is designed to exploit both the battery and the electrolysis stack's electrical double-layer (EDL) effect on different timescales, maximizing active power support while mitigating battery and stack degradation. An adaptive equalization control strategy is further developed to balance the battery state of charge (SOC) among multiple ESEHEs operating in parallel, which optimizes energy distribution and extends battery life. Real-time simulations on StarSim validate the proposed topology and control strategies. Techno-economic analysis shows that, compared with conventional off-grid ReP2H systems based on a centralized ES plant, the ESEHE improves overall energy efficiency by 0.23% and reduces the initial total converter investment cost by roughly 6%, mainly due to the elimination of bidirectional AC/DC conversion and its associated losses in the centralized ES plant.

math.OC↗

arXiv2026

TokenMixer-Large: Scaling Up Large Ranking Models in Industrial Recommenders

While scaling laws for recommendation models have gained significant traction, existing architectures such as Wukong, HiFormer and DHEN, often struggle with sub-optimal designs and hardware under-utilization, limiting their practical scalability. Our previous TokenMixer architecture (introduced in RankMixer paper) addressed effectiveness and efficiency by replacing self-attention with a ightweight token-mixing operator; however, it faced critical bottlenecks in deeper configurations, including sub-optimal residual paths, vanishing gradients, incomplete MoE sparsification and constrained scalability. In this paper, we propose TokenMixer-Large, a systematically evolved architecture designed for extreme-scale recommendation. By introducing a mixing-and-reverting operation, inter-layer residuals and the auxiliary loss, we ensure stable gradient propagation even as model depth increases. Furthermore, we incorporate a Sparse Per-token MoE to enable efficient parameter expansion. TokenMixer-Large successfully scales its parameters to 7-billion and 15-billion on online traffic and offline experiments, respectively. Currently deployed in multiple scenarios at ByteDance, TokenMixer-Large has achieved significant offline and online performance gains, delivering an increase of +1.66\% in orders and +2.98\% in per-capita preview payment GMV for e-commerce, improving ADSS by +2.0\% in advertising and achieving a +1.4\% revenue growth for live streaming.

cs.IR↗

arXiv2026

MDL: A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization

Industrial recommender systems increasingly adopt multi-scenario learning (MSL) and multi-task learning (MTL) to handle diverse user interactions and contexts, but existing approaches suffer from two critical drawbacks: (1) underutilization of large-scale model parameters due to limited interaction with complex feature modules, and (2) difficulty in jointly modeling scenario and task information in a unified framework. To address these challenges, we propose a unified \textbf{M}ulti-\textbf{D}istribution \textbf{L}earning (MDL) framework, inspired by the "prompting" paradigm in large language models (LLMs). MDL treats scenario and task information as specialized tokens rather than auxiliary inputs or gating signals. Specifically, we introduce a unified information tokenization module that transforms features, scenarios, and tasks into a unified tokenized format. To facilitate deep interaction, we design three synergistic mechanisms: (1) feature token self-attention for rich feature interactions, (2) domain-feature attention for scenario/task-adaptive feature activation, and (3) domain-fused aggregation for joint distribution prediction. By stacking these interactions, MDL enables scenario and task information to "prompt" and activate the model's vast parameter space in a bottom-up, layer-wise manner. Extensive experiments on real-world industrial datasets demonstrate that MDL significantly outperforms state-of-the-art MSL and MTL baselines. Online A/B testing on Douyin Search platform over one month yields +0.0626\% improvement in LT30 and -0.3267\% reduction in change query rate. MDL has been fully deployed in production, serving hundreds of millions of users daily.

cs.IR↗

arXiv2026

Mapping Nuclear Deformation with Differential Radial Flow in Heavy-Ion Collisions

In relativistic heavy-ion collisions, the radial flow of the fireball, usually characterized by transverse momentum spectra of final-state particles, encodes essential information about the hot and dense nuclear matter created in the collisions. However, the response of radial flow, including its $p_T$-differential structure $v_0(p_T)$ and longitudinal fluctuations $v_0(η)$, to intrinsic nuclear deformation remains unexplored. Using realistic $(3+1)$-dimensional viscous hydrodynamic calculations with Trento-3D initial conditions, we investigate how nuclear deformation affects the differential radial flow. We observe a clear, positive correlation between quadrupole deformation $β_2$ and radial flow: both magnitudes of $v_0$ and $v_0(p_T)$ are enhanced in central collisions when $β_2$ is increased. In contrast, the Pearson coefficient $ρ(n(p_T), [p_T])$ exhibits a universal step-like behavior across all collision systems and centralities. Further analysis of longitudinal decorrelation of radial flow reveals a rich structure: in central collisions, large $β_2$ tends to suppress the decorrelation, whereas hexadecapole deformation $β_4$ tends to enhance it. Such decorrelation effect increases toward peripheral collisions. Our results demonstrate that precise measurements of radial flow, spanning transverse momentum and longitudinal dependences, can provide powerful, complementary constraints on nuclear deformation in high-energy nucleus-nucleus collisions.

nucl-th↗

arXiv2026

Synthetic topological device for advancing elastic energy harvesting

High-efficiency energy harvesting of ultrasonic elastic waves are crucial for powering electric gadgets in many emerging technologies such as wearable devices, wireless sensing, and biomedical implants. Although topological phononic metamaterials have recently been demonstrated as a promising paradigm for confining and guiding elastic waves through robust bound states, achieving ultrahigh-Q topological resonance with enhanced energy conversion efficiency remains a challenge. In this work, we propose a synthetic-dimensional higher-order topological insulator by engineering the flexural bands of elastic metamaterials, featuring highly localized topological hinge states in the bulk bands. This topological hinge mode stems from the nonzero combination of the bulk polarization and the Chern number in the synthetic-dimensional band structure, thus giving rise to a strong elastic-to-electric energy conversion at the corner of the phononic plate. Through numerical simulations and experimental validations, straightforward evidence of the localized modes with robust protection and consequent abilities in activating the light-emitting diodes (LEDs) array have been demonstrated. Our findings open a new avenue for topological-physics-enabled ultrasonic devices and present promising prospects for applications in weak-signal detection and self-powered sensors.

physics.optics↗

arXiv2026

LocalScore: Local Density-Aware Similarity Scoring for Biometrics

Open-set biometrics faces challenges with probe subjects who may not be enrolled in the gallery, as traditional biometric systems struggle to detect these non-mated probes. Despite the growing prevalence of multi-sample galleries in real-world deployments, most existing methods collapse intra-subject variability into a single global representation, leading to suboptimal decision boundaries and poor open-set robustness. To address this issue, we propose LocalScore, a simple yet effective scoring algorithm that explicitly incorporates the local density of the gallery feature distribution using the k-th nearest neighbors. LocalScore is architecture-agnostic, loss-independent, and incurs negligible computational overhead, making it a plug-and-play solution for existing biometric systems. Extensive experiments across multiple modalities demonstrate that LocalScore consistently achieves substantial gains in open-set retrieval (FNIR@FPIR reduced from 53% to 40%) and verification (TAR@FAR improved from 51% to 74%). We further provide theoretical analysis and empirical validation explaining when and why the method achieves the most significant gains based on dataset characteristics.

cs.CV↗

arXiv2026

CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support Conversation

Emotional Support Conversation (ESC) plays a vital role in alleviating psychological stress and providing emotional value through dialogue. While recent studies have largely focused on data augmentation and synthetic corpus construction, they often overlook the deeper cognitive reasoning processes that underpin effective emotional support. To address this gap, we propose \textbf{CARE}, a novel framework that strengthens reasoning in ESC without relying on large-scale synthetic data. CARE leverages the original ESC training set to guide models in generating logically coherent and supportive responses, thereby explicitly enhancing cognitive reasoning. Building on this foundation, we further employ reinforcement learning to refine and reinforce the reasoning process. Experimental results demonstrate that CARE significantly improves both the logical soundness and supportive quality of responses, advancing the development of empathetic, cognitively robust, and human-like emotional support systems.

cs.CL↗

arXiv2026

Forge-and-Quench: Enhancing Image Generation for Higher Fidelity in Unified Multimodal Models

Integrating image generation and understanding into a single framework has become a pivotal goal in the multimodal domain. However, how understanding can effectively assist generation has not been fully explored. Unlike previous works that focus on leveraging reasoning abilities and world knowledge from understanding models, this paper introduces a novel perspective: leveraging understanding to enhance the fidelity and detail richness of generated images. To this end, we propose Forge-and-Quench, a new unified framework that puts this principle into practice. In the generation process of our framework, an MLLM first reasons over the entire conversational context, including text instructions, to produce an enhanced text instruction. This refined instruction is then mapped to a virtual visual representation, termed the Bridge Feature, via a novel Bridge Adapter. This feature acts as a crucial link, forging insights from the understanding model to quench and refine the generation process. It is subsequently injected into the T2I backbone as a visual guidance signal, alongside the enhanced text instruction that replaces the original input. To validate this paradigm, we conduct comprehensive studies on the design of the Bridge Feature and Bridge Adapter. Our framework demonstrates exceptional extensibility and flexibility, enabling efficient migration across different MLLM and T2I models with significant savings in training overhead, all without compromising the MLLM's inherent multimodal understanding capabilities. Experiments show that Forge-and-Quench significantly improves image fidelity and detail across multiple models, while also maintaining instruction-following accuracy and enhancing world knowledge application. Models and codes are available at https://github.com/YanbingZeng/Forge-and-Quench.

cs.CV↗

arXiv2026

On the Holistic Approach for Detecting Human Image Forgery

The rapid advancement of AI-generated content (AIGC) has escalated the threat of deepfakes, from facial manipulations to the synthesis of entire photorealistic human bodies. However, existing detection methods remain fragmented, specializing either in facial-region forgeries or full-body synthetic images, and consequently fail to generalize across the full spectrum of human image manipulations. We introduce HuForDet, a holistic framework for human image forgery detection, which features a dual-branch architecture comprising: (1) a face forgery detection branch that employs heterogeneous experts operating in both RGB and frequency domains, including an adaptive Laplacian-of-Gaussian (LoG) module designed to capture artifacts ranging from fine-grained blending boundaries to coarse-scale texture irregularities; and (2) a contextualized forgery detection branch that leverages a Multi-Modal Large Language Model (MLLM) to analyze full-body semantic consistency, enhanced with a confidence estimation mechanism that dynamically weights its contribution during feature fusion. We curate a human image forgery (HuFor) dataset that unifies existing face forgery data with a new corpus of full-body synthetic humans. Extensive experiments show that our HuForDet achieves state-of-the-art forgery detection performance and superior robustness across diverse human image forgeries.

cs.CV↗

arXiv2026

Exploring Lorentz Violation in Spacetime through Universal Finsler Geometry

Finsler geometry serves as a fundamental and natural extension of Riemannian geometry, providing a valuable framework for investigating Lorentz violation in spacetime. Previous studies have treated the Finsler structures associated with different particles as distinct entities. In this paper, we propose a novel hypothesis suggesting that the Finsler structure may represent an intrinsic property of the universe itself. Under this assumption, we derive a series of modified dispersion relations that have not been previously explored, and we analyze their implications. Our findings indicate that the scales of Lorentz violation for massive particles are proportional to their masses. Furthermore, we demonstrate that this hypothesis aligns well with existing phenomenological results regarding Lorentz violation observed in photons, neutrinos, and electrons.

gr-qc↗

arXiv2026

Photon Blockade Mediated by Two-Photon Absorption in an Optical Parametric Amplifier

Photon blockade (PB) is a quantum effect in strongly nonlinear systems where a single photon prevents the system from being excited to a higher level, generating anti-bunched light fields. It enables the generation of single-photon sources for quantum information processing. Conventional photon blockade (CPB) leverages strong nonlinear interactions to generate an anharmonic energy spectrum. Unconventional photon blockade (UPB) utilizes destructive quantum interference between excitation pathways. Recently, environmentally induced photon blockade (EPB) has emerged as a novel approach, exploiting two-photon absorption (TPA) to realize photon blockade. In this work, we combine UPB and EPB together, considering the TPA effect in the optical parametric amplifier (OPA), thereby achieving a more stable PB with stronger suppression of multi-photon states.

quant-ph↗

arXiv2025

Antarctic TianMu Staring Observation Project I: Overview and Implementation of the Prototype Telescope

Wide-field rapid sky surveys serve as critical observational methods for time-domain astronomical research. The Antarctic region, with several months of continuous dark nights annually, is an ideal site for time-domain astronomical observations. The Antarctic TianMu Staring Observation Project aims to deploy a fleet of small telescopes, adopting an array observation model to conduct time-domain optical observations in Antarctica, featuring wide-sky coverage, high-cadence sampling, long-period staring, and simultaneous multi-band measurements. Considering the severe challenges optical telescopes face in Antarctica, including extremely low temperatures, unattended operation, and limited power supply and network transmission, we have designed and developed the Antarctic TianMu prototype telescope based on drift-scan charge-coupled device technology. In October 2022, our prototype (with an aperture of 18 cm), named AT-Proto was transported to Zhongshan Station in Antarctica aboard China's 39th Antarctic Research Expedition. It has since operated stably and reliably in the frigid environment for over two years, demonstrating the significant advantages of this technology in polar astronomical observations. The experimental observation results of AT-Proto provide a solid foundation for the subsequent construction of a time-domain astronomy observation array in Antarctica.

astro-ph.IM↗

arXiv2025

Static Spherical Vacuum Solution to Bumblebee Gravity with Time-like VEVs

The static spherical vacuum solution in a bumblebee gravity model where the bumblebee field $B_μ$ has a one-component time-like vacuum expectation value $b_μ$ is studied. We show that in general curved space-time solutions are not allowed and only the Minkowski space-time exists. However, it is surprising that non-trivial solutions can be obtained so long as a unique condition for the vacuum expectation $b^2\equiv -b^μb_μ=2/κ$, where $κ=8πG$, is satisfied. We argue that naturally these solutions are not stable since quantum corrections would invalidate the likely numerical coincidence, unless there are some unknown \emph{fine-tuning} mechanisms preventing any deviation from this condition. Nevertheless, the naked singularities and the photon sphere of these novel but peculiar solutions are discussed, and we show that the extremal Reissner-Nordstr{ö}m solution is a limit of one of our solutions.

gr-qc↗

arXiv2025

Locus: Agentic Predicate Synthesis for Directed Fuzzing

Directed fuzzing aims to find program inputs that lead to specified target program states. It has broad applications, such as debugging system crashes, confirming reported bugs, and generating exploits for potential vulnerabilities. This task is inherently challenging because target states are often deeply nested in the program, while the search space manifested by numerous possible program inputs is prohibitively large. Existing approaches rely on branch distances or manually-specified constraints to guide the search; however, the branches alone are often insufficient to precisely characterize progress toward reaching the target states, while the manually specified constraints are often tailored for specific bug types and thus difficult to generalize to diverse target states and programs. We present Locus, a novel framework to improve the efficiency of directed fuzzing. Our key insight is to synthesize predicates to capture fuzzing progress as semantically meaningful intermediate states, serving as milestones towards reaching the target states. When used to instrument the program under fuzzing, they can reject executions unlikely to reach the target states, while providing additional coverage guidance. To automate this task and generalize to diverse programs, Locus features an agentic framework with program analysis tools to synthesize and iteratively refine the candidate predicates, while ensuring the predicates strictly relax the target states to prevent false rejections via symbolic execution. Our evaluation shows that Locus substantially improves the efficiency of eight state-of-the-art fuzzers in discovering real-world vulnerabilities, achieving an average speedup of 41.6x. So far, Locus has found nine previously unpatched bugs, with three already acknowledged with draft patches.

cs.CR↗

arXiv2025

Quantum-Plasmonic Dynamics Modeled via a Modified Langevin Noise Formalism: Numerical Studies of Single-Photon Emission and Two-Photon Interference

Recent studies have established and rigorously validated a modified Langevin noise formalism that enables first-principles quantization of electromagnetic fields in open and dissipative environments [1,2,3]. Building on this foundation, a fully quantum-mechanical multimode Jaynes-Cummings framework has been developed and verified, providing an accurate description of atom--field interactions in lossy and radiative systems [4]. In this work, we explore the potential of this formalism for nanophotonic applications by modeling representative quantum-plasmonic dynamics. In particular, we present detailed numerical examples for (i) two-photon interference mediated by a quantum plasmonic beam splitter, and (ii) non-Markovian dynamics of an atom located in plasmonic antennas and directional control of out-coupled single-photon fields. These results demonstrate that the proposed modeling approach can be directly used to guide the design and optimization of plasmonic single-photon sources and beam-splitting structures. Moreover, the framework is broadly applicable to the analysis of linear optical components and cavity quantum electrodynamics problems in open and dissipative photonic integrated circuits.

physics.optics↗

arXiv2025

(3+1)D event-by-event pre-equilibrium dynamics in heavy-ion collisions

So far a major source of uncertainty in the study of heavy-ion collisions arises from the early time dynamics which includes initial state and pre-equilibrium dynamics. The state-of-the-art framework, KoMPoST, employs non-equilibrium Green's functions to propagate the initial energy-momentum tensor to the hydrodynamic phase, yet currently only treats transverse plane dynamics under boost-invariant conditions. In this work, we extend KoMPoST to include non-boost-invariant responses to initial conditions, essential for accurately capturing the longitudinal structures observed in heavy-ion collisions. Non-boost-invariant fluctuations on top of a homogeneous background are evolved using (3+1)D response functions calculated in kinetic theory. To assess kinetic theory's transition towards hydrodynamic evolution, we systematically compare the out-of-equilibrium shear-stress tensor from KoMPoST-3D with estimates based on Navier-Stokes hydrodynamics. Subsequently, a comprehensive (3+1)D framework, McDIPPER+KoMPoST-3D+CLVisc+SMASH, is utilized to simulate the complete spacetime evolution of heavy-ion collisions. The sensitivity of key observables, including longitudinal structure of anisotropic flow, to variations in the hydrodynamic initialization time is thoroughly investigated.

hep-ph↗