arXiv subjects

Jie Zhang

Publications and source records attributed to Jie Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2026

Sparse Incident-Cluster Learning for 12-hour Port Flood Pre-warning in Digital-Twin Analytics

Port flood digital twins require analytics that warn operators before disruption, but official warning incidents are often few and adjacent observations are temporally dependent. Row-level classification can therefore overstate performance by placing windows from the same event in both model-development and evaluation data. We formulate 12-hour port flood pre-warning as an incident-cluster learning problem and evaluate a digital-twin analytics module using eight-point water-level histories, prediction-time contextual covariates, and interpretable short-window dynamics. The protocol combines fold-specific sparse feature selection, warning-cluster grouping, negative-label controls, 100-repeat random top-k controls, and alert-episode evaluation. Liverpool is the primary four-cluster case study, with harmonised Humber/Hull-proxy and Wessex South data used for protocol-transfer checks. Across the Liverpool folds, the top-10 ElasticNet model achieves mean F2 = 0.696, compared with 0.633 without top-k truncation and 0.681 for full-feature weighted XGBoost. It is the strongest ElasticNet variant, remains competitive with the nonlinear reference using only ten predictors, and exceeds the repeat-level 95th percentile of broad and same-family random subsets. Contextual covariates provide a strong prediction-time anchor, complemented by physically interpretable local dynamics. Historical replay converts risk scores into alert episodes and measures alert duration and false-episode burden. The result is an offline-evaluated analytics and validation module designed for integration into a port digital twin.

cs.LG↗

arXiv2026

$X$-dispersionless solver for electromagnetic and axion fields in a cylindrical particle-in-cell code

We develop a quasi-cylindrical direction-splitting (QDS) dispersionless Maxwell solver for the open-source EPOCH particle-in-cell framework. The method preserves the exact axial phase velocity for every retained azimuthal mode by advecting transverse transport variables with the $cΔt=Δx$ shift. Building on the same transport viewpoint, we formulate an axion solver that advances the Klein--Gordon equation and the axion-regenerated electromagnetic fields within the QDS solver, so that axion--photon coupling is handled self-consistently. Benchmarks demonstrate that the cylindrical QDS solver reproduces the standard Yee wakefield structure while removing the axial group-velocity error and reducing computational cost by orders of magnitude. In the resonant axion generation test via two-color laser mixing, the QDS solver eliminates the spurious vacuum source term and maintains phase matching, whereas the Yee solver suffers from rapid dephasing due to numerical dispersion. The proposed algorithm therefore enables quasi-3D simulations of axion--photon physics in laser-plasma interaction with dispersion-free laser transport.

physics.plasm-ph↗

arXiv2026

Clean Engineering, Unstable Measurement: A Preregistered Reliability Failure of Black-Box LLM Observers on Shared Endpoints

Language-model judges now gate training data, score generations, and drive leaderboards. The judge is then a measurement instrument, resting on one rarely stated assumption: the same request, sent to the same model name, reads the same tomorrow. We audited that assumption in two preregistered campaigns with every threshold fixed in advance; neither got past validating its instrument. Across 52,988 audited request attempts, same-window repeat rankings agreed at Spearman 0.400 against a required 0.90, and byte-identical next-day replays agreed at 0.78 against a required 0.99, each time with the execution record at ceiling. Three mechanisms explain the gap: a label-to-meaning mapping that biased readouts as strongly as the signal; candidate gaps seven orders of magnitude below the instrument's own noise floor; and byte-identical inputs returning different rankings, a noise that exact-permutation readouts compound. Neither metric substitution nor sampling repaired it on the tested grid. Preregistered follow-ups bound the problem: waiting did not help on the days sampled (0.805 versus 0.800, replicated over five further days); switching providers did not help (four providers share the floor, medians 0.74 to 0.88, predicted by none of the metadata fields they expose); self-hosting on batch-invariant kernels helped only while the server was quiet; and on constructed errors with known gaps, the readout's separation tracks error type, not size. We distill the evidence into a three-level snapshot-identity ladder, eight design rules, and a reporting checklist; a pilot at roughly 2% of the study's call volume would have exposed both unreachable gates in advance. All results concern externally measured behaviour on shared serving infrastructure. On a shared endpoint, a model name is not a frozen instrument; a preregistered evaluation must measure its instrument before freezing any gate on it.

cs.AI↗

arXiv2026

Chirped-Pulse Forward Raman Amplification in Nonuniform Plasmas

Light amplification via Raman scattering in plasma has been severely constrained by stringent phase matching conditions and the need for plasma uniformity. To overcome these limitations, we propose a forward Raman amplification scheme that employs a positively chirped seed pulse co-propagating with a pump pulse in a nonuniform plasma with an upramp density profile. We demonstrate that the phase detuning induced by plasma nonuniformity can be dynamically compensated, enabling broadband amplification across the entire spectral bandwidth of the seed pulse. Concurrently, the chirped pulse duration undergoes continuous compression as a result of the spatially varying dispersion of the plasma. Our theoretical model, incorporating the detuning term and supported by particle-in-cell simulations, elucidates the compensation mechanism. It is shown that a chirped seed pulse with an initial bandwidth ~10% can be directly amplified by a factor of 1e7 to an intensity exceeding 1e17W/cm2 within a picosecond timescale in a steep density ramp. This scheme establishes a new foundation for advancing plasma-based light amplification toward practical applications.

physics.plasm-ph↗

arXiv2026

Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition

Wi-Fi-based human activity recognition (HAR) has become an important part of integrated sensing and communications, paving the way for a range of context-aware services. However, most existing Wi-Fi-based HAR systems rely on deep learning (DL) models that are computationally and memory intensive in both training and inference, which poses significant challenges for real-world deployment. Conventional training requires simultaneous updates of millions of parameters, leading to prohibitive memory consumption. In this paper, we propose a novel quantum-assisted memory-efficient training framework (Q-MET) designed to improve efficiency in both training and inference. Q-MET utilizes a hybrid quantum classical neural network to indirectly generate parameters for HAR models, significantly reducing the trainable parameter count compared to direct optimization. To further support the deployment on resource-constrained devices, we integrate structured pruning during the training phase. Experimental results demonstrate that Q-MET achieves a 90% to 95% reduction in trainable parameters compared with conventional backpropagation-based DL training while maintaining or even exceeding classical classification accuracy. Additionally, Q-MET supports lightweight inference through structured pruning, achieving 75% to 85% model sparsity with less than 2% loss in classification accuracy. To the best of our knowledge, this work represents the first quantum-assisted approach to simultaneously tackle memory inefficiencies in both the training and inference stages of HAR systems.

cs.LG↗

arXiv2026

A compact vapor-cell optical frequency reference with fractional frequency instability around $10^{-16}$

Compact optical frequency reference with high stability is essential for field applications such as navigation and geodesy, yet vapor cell systems have remained confined to fractional instabilities over $10^{-15}$. Here, we report a molecular iodine reference that reaches an instability of $7 \times 10^{-16}$ at 1000 s and operates at the $10^{-16}$ level from 200 to 2000 s, surpassing the best reported vapor cell standards by approximately a factor of three. This achievement is enabled by a monolithic, drift immune spectroscopic unit bonded to an ultra low expansion glass substrate with precision control of key parameters.The entire system occupies only 25 L.The system achieves $5 \times 10^{-15}$ instability at 1 s and reaches the $10^{-16}$ level over the 200 to 2000 s averaging-time range, representing the first medium term stability at the $10^{-16}$ level from a compact, field ready vapor-cell reference. Our work demonstrates that $10^{-16}$ instability can be engineered into portable systems, opening a path to high precision time-keeping beyond the laboratory.

physics.atom-ph↗

arXiv2026

Auditing Chinese Web-scale Corpora via Sampled BPE Token Statistics

Chinese web pollution has surfaced in LLMs, motivating audits of upstream Chinese corpora. However, auditing such corpora faces three challenges: (1) their web-scale size makes full scan costly; (2) prior analyses are often too coarse to expose token-level pollution; (3) Chinese web pollution is implicit and rapidly changing. We propose Sampled-BPE, a lightweight token-level auditing pipeline that sample a small subset and train BPE tokenizer to surface polluted tokens. Experiments show that Sampled-BPE preserves usable estimates while substantially reducing runtime and memory: a 148.4 $\times$ speedup and a 35.8 $\times$ memory reduction induce only 4.25% relative error for pollution categories. We apply the pipeline to 11 open Chinese corpora and 6 Chinese Common Crawl snapshots from 2021 to 2026. The audit reveals widespread but uneven pollution across open corpora, as well as highly polluted and temporally shifting Chinese web content. We further release a hierarchical Chinese web token dataset with 660k+ token records, each with web context, category, and explanation fields, organized as trees to support review and tracing of pollution.

cs.CL↗

arXiv2026

From Feature Interaction to Feature Transport - A Unified Block for Scalable Recommendation Models

Unified recommendation models aim to jointly model non-sequential multi-field features and sequential user behaviors, but existing interaction-centric designs mainly focus on mixing heterogeneous tokens within each layer. We argue that scalable unified recommendation also requires controlling how intent information is carried, filtered, and preserved across stacked blocks. Inspired by flow-based representation dynamics, we introduce feature transport, a view that treats deep unified recommendation as a discrete context-conditioned representation evolution process. We propose CRAFT, a Contextual Residual Adaptive Feature Transport block, which summarizes non-sequential features into a reliability-aware contextual field and uses it to generate residual displacement and memory-preserving signals for intent and sequence representations. In this way, non-sequential context acts as an active controller of representation evolution rather than a passive object of interaction. In the TAAC2026 advertising recommendation competition, CRAFT achieves a test AUC of 0.838090, surpassing the previous leaderboard-best score of 0.83798. Scaling experiments further show that CRAFT benefits from both depth and width expansion: stacking CRAFT to six blocks improves test AUC to 0.838148, while increasing the hidden dimension reaches 0.838106. These results demonstrate the effectiveness, scalability, and generalization potential of the feature transport paradigm. Source code: https://github.com/AshleyLuo001/CRAFT

cs.IR↗

arXiv2026

Checkerboard: Closed-Form and Data-Independent Trigger Design for Clean-Label Backdoor Attacks

Backdoor attacks threaten the deep-learning supply chain by poisoning a small fraction of the training data so that a model behaves normally on clean inputs but maps triggered inputs to an attacker-chosen class. Clean-label backdoor attacks are especially difficult to audit because poisoned examples preserve their semantic labels. Yet existing clean-label attacks often require surrogate model training, auxiliary data access, or iterative optimization. In this paper, we present \emph{Checkerboard}, a clean-label backdoor attack with closed-form, data-independent trigger design. We formulate trigger design through an input-space Fisher-separability objective and, under a ridge four-neighbor local-smoothness prior for natural images, obtain the pixel-wise checkerboard as a closed-form maximizer of the resulting design proxy without data access, model training, or optimization. Across four benchmark datasets, Checkerboard outperforms the evaluated norm-bounded clean-label attacks and achieves state-of-the-art performance under low global poisoning rates. For example, on CIFAR-10, under a trigger perturbation of $10/255$, poisoning 20 training samples achieves $95.72\%$ Attack Success Rate (ASR). On IN-100, a global poisoning rate of only $0.4\%$ yields over $83\%$ ASR without degrading clean accuracy. The proposed attack also remains effective against state-of-the-art backdoor defenses and shows resistance to adaptive defenses under simple modification.

cs.CR↗

arXiv2026

Beyond a Single Story: Meta-Reviewing Sparse and Incomplete User-generated Contents for Recommendation

Data sparsity remains a long-standing challenge in recommender systems, and it becomes more severe for methods relying on user-generated content (UGC) such as textual reviews, which capture fine-grained preferences but require more user efforts to produce. As a result, UGC exhibits (1) missing reviews, where interactions lack any review, and (2) incomplete reviews, where available reviews cover only a subset of relevant attributes. Existing approaches often overlook these UGC-specific issues, leading to degraded accuracy. Motivated by meta-review in academic peer review, we propose MOSAIC (Meta-review On Sparse And Incomplete user-generated Content), which constructs a meta-review for each target user by aggregating attribute-sentiment evidence from neighbor users' reviews. A multi-gate mixture-of-experts (MMoE) architecture jointly optimizes rating prediction and meta-review attribute-sentiment prediction, while an attention module personalizes the aggregated meta-review signals to each target user, yielding both refined rating predictions and attribute-level explanations. Experiments on four real-world datasets demonstrate that MOSAIC consistently outperforms state-of-the-art baselines in both recommendation accuracy and explanation quality, mitigating UGC sparsity and incompleteness while delivering consistent gains for users with limited interaction history.

cs.IR↗

arXiv2026

INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment

As large language models (LLMs) are deployed as autonomous agents, safety failures increasingly involve consequential actions. We study agentic misalignment, where agents take harmful actions under goal conflicts and pressures. Using chain-of-thought (CoT) monitoring, we find that harmful execution is often preceded by intent signals in reasoning. However, post-hoc CoT labels are too coarse to show how intent changes during generation. We introduce INTENT-AS-A-TOOL, an approach that adds intent-targeted tools to give the model a dedicated channel for expressing commitment to a target behavior. The probability of calling an intent tool provides a judge-free, fine-grained signal of the model's tendency to pursue that behavior. Our results show that INTENT-AS-A-TOOL complements CoT monitoring, expands post-hoc CoT labels into dense trajectories, and identifies critical steps for online intervention. These findings suggest that action preferences are useful for tracking agentic misalignment during reasoning. Our code and data are accessible: https://github.com/RebeccaZhang22/intent-as-a-tool.

cs.CL↗

arXiv2026

Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States

Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.g., malicious side-tasks hidden in external tool results. While many efforts have sought to address this threat, little is known about the internals of agentic LLMs when they are exposed to IPI attacks. For simplicity, we refer to this condition as IPI exposure. In this paper, we study IPI exposure from three perspectives. (1) Probing: Across eight models, including the 753B-parameter GLM-5.2 and the 2.8T-parameter Kimi-K3, simple linear probes trained on pre-generation hidden states can predict LLMs' IPI exposure. These probes achieve 0.90+ AUROC on unseen attacks, agent instructions, and task suites; they remain robustly predictive under adaptive attacks and in cross-lingual settings. (2) Defense: We reveal and diagnose a knowledge-action gap: post-trained LLMs encode signals predictive of IPI exposure, yet do not reliably bind these signals to safe agentic actions. We therefore introduce a probe-gated reasoning-based defense to bridge this gap at test time. On difficult AgentDojo settings, it substantially reduces attack success rate, e.g., from 34.6% to 0% on Qwen3.5-27B, and better preserves clean-task utility than the baselines. (3) Explanation: We introduce an analysis framework that identifies natural-language explanations strongly correlated with probe-captured signals. The resulting profiles differ across models: latent signals can align with either direct IPI-exposure sensing or indirect operational cues. Code is available at https://github.com/jianshuod/IPI-exposure-signal.

cs.CR↗

arXiv2026

A novel strategy for achieving a low-field lightweight permanent MRI magnet system with good magnetic field homogeneity and low eddy current

In low-field, lightweight, pole-pieceless permanent-magnet MRI systems built with sintered Nd-Fe-B or Sm-Co magnets, the rapid switching of gradient fields readily induces eddy currents in the sintered magnets, leading to image artifacts. To address this, we report for the first time a Sm-Fe-N permanent-magnet MRI system based on anisotropic Sm-Fe-N bonded magnets, whose high electrical resistivity reduces the eddy currents in the X, Y and Z directions to 0.093%, 0.172% and 2.38%, respectively, while a magnetic field inhomogeneity below 150 ppm is achieved at the boundary of a 220 mm diameter of spherical volume (DSV). Compared with sintered Nd-Fe-B and Sm-Co magnets, using Sm-Fe-N bonded magnets as the source of the static magnetic field not only suppresses eddy currents but also makes a closely tiled, densely packed magnetic-circuit layout feasible, providing a more uniform static magnetic field for the MRI system. Imaging results free of obvious geometric distortion and banding artifacts further indicate that the Sm-Fe-N magnet system delivers low eddy currents and high static magnetic field homogeneity.

physics.med-ph↗

arXiv2026

Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery

In laparoscopy, surgeon gaze tracks where the instruments will act; easing this demand through visual attention modeling requires dense labels of those interaction loci. These encode tacit knowledge: experts converge on consensus loci yet struggle to state the rules. Here we show that such labels can be recovered from completed actions in surgical videos, in which recorded instrument trajectories are converted into dense, continuous supervision. DiffeoAfford grounds tissue affordance by attaching instrument tips to the tissue and transporting them through deformation using diffeomorphism-constrained tracking, matching context-informed annotators' accuracy. Trained on these labels and never on gaze, a real-time model aligns with surgeon gaze more closely in space and time than does camera-assistant gaze. The framework also transfers across procedures: on hysterectomy videos, a separately trained predictor reaches 95.16% directional consistency with subsequent camera motion. In 12 paired cholecystectomies (24 procedures), the auto-framing application AffordView, which proactively centers predicted targets in view, lowered surgeon cognitive workload on converging subjective, physiological, and behavioral measures, including a reduced number of verbal instructions to the camera assistant. Deriving supervision from action rather than manual annotation offers a scalable route to anticipatory assistance.

cs.CV↗

arXiv2026

Current Status and Prospects of Neutron Detection and Neutron/Gamma Discrimination Technologies in Fusion Applications

Fusion research is progressing from physics-oriented experiments toward reactor-oriented engineering applications, placing increasing demands on the accuracy and reliability of neutron measurements. This review summarizes the current status and prospects of neutron detection and neutron/gamma discrimination technologies for fusion applications. The main sources, energy characteristics, and measurement requirements of fusion neutrons are first reviewed, with particular attention to 2.45 MeV D-D and 14.1 MeV D-T neutrons. Four principal neutron detection methods, including nuclear reaction, nuclear recoil, nuclear fission, and neutron activation, are discussed together with the operating characteristics and applicability of scintillation, gas, semiconductor, and other specialized detectors. The roles of neutron/gamma discrimination are then examined in plasma diagnostics, safe operation monitoring, radiation protection monitoring, and fusion reactor design. The review shows that different detection methods and detector types have distinct advantages and application boundaries, and no single detector provides an optimal solution for all fusion measurement tasks. Liquid scintillators and some organic crystals remain important for fast-neutron measurement and neutron/gamma discrimination, whereas gas and semiconductor detectors serve complementary roles in thermal-neutron monitoring, compact detection, radiation tolerance, and fast-neutron spectrometry. Future development is expected to focus on high-performance detectors, material damage assessment, intelligent real-time discrimination, and multi-detector coordination with system-level diagnostic integration.

physics.ins-det↗

arXiv2026

Axion generation and detection in laser-plasma wakefields

The axions are compelling candidates for cold dark matter, but their extremely weak interaction with photons makes laboratory searches challenging. We show that the quasi-static electromagnetic fields of a laser-plasma wakefield, which can exceed $10^{11}$\,V/m, enable axion generation without an external production magnet and enhance the conversion rate by two orders of magnitude over a conventional magnetic production region. Self-consistent particle-in-cell simulations reveal two complementary routes to detection. In the first route, axions are reconverted into photons within the wakefield and laser fields, eliminating the need for a separate regeneration magnet but requiring to suppress the intense laser-plasma background. The regenerated photons have polarization, harmonic-frequency, and Laguerre-Gaussian transverse-mode signatures that are largely absent from the driving fields, allowing successive filters to isolate the signal. In the second route, axions traverse a wall and undergo reconversion in a downstream magnet, providing a much lower background at the cost of requiring both the magnet and a seed pulse for coherent amplification. For axion masses below $0.1$\,meV, meter-scale wakefield guiding under our stated assumptions yields a projected coupling sensitivity down to $3.9\times10^{-12}\,\mathrm{GeV}^{-1}$, surpassing the projected constraint of next-generation laboratory searches. These results establish ultra-strong plasma wakefields as a magnet-free axion source with two experimentally distinct and complementary detection strategies.

physics.plasm-ph↗

arXiv2026

The Model's Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges

LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. However, processing these contexts alongside user queries creates an attack surface: adversarial inputs can induce models to disclose them. Prior probing studies suggest that leakage-related signals emerge in hidden states, yet the need to extract these states poses additional deployment challenges. In this paper, we explore whether this internal signal leaves a more accessible ``tell'' before decoding. We propose LeakGauge, which probes this response by appending a suffix that gauges leakage behavior and mapping its prefill token probabilities to an attack-risk score. While a direct gauge uses the initial tokens of confidential content, we find that a content-agnostic one that verbalizes leakage behavior yields more robust signals. Across 11 LLMs, including GLM-5.2 (753B) and Kimi-K3 (2.8T), LeakGauge reaches an AUROC range of 0.944--0.996 on unseen attacks. The signal remains stable when the content changes language or the attack shifts from verbatim to semantic disclosure. By activation-steering interventions, we further show that the risk score is sensitive to an internal leakage-related direction, relating the observable signal to the model's internal representation. In addition, LeakGauge enables an input detector with fewer than 0.5K extra parameters and added latency of 10.34 ms. Code: \href{https://github.com/yeasen-z/LeakGauge}.

cs.CR↗

arXiv2026

Task Specialization Fine-Tuning for Contextual Reinforcement Learning

Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While prior works often train from scratch and rely on either multi-task learning for a single policy or strategically training multiple policies, we advocate for a unified alternative: pretraining a single policy with good initial performance, followed by fine-tuning multiple policies for task specialization. This new paradigm, however, introduces unique challenges, such as heterogeneous marginal returns and sample inefficiency. This raises a critical research question: given a pretrained policy and a constrained budget, how much fine-tuning should each task region receive to enable sample-efficient CRL? To this end, we propose Task Specialization Fine-Tuning (TSFT), an online framework that predicts fine-tuning performance with a simple parametric model and exactly solves the resulting discrete budget allocation problem via integer linear programming. Extensive experiments across diverse decision domains, including combinatorial optimization, continuous control, and LLM fine-tuning, demonstrate that TSFT significantly outperforms baselines in task coverage and approaches oracle performance. Our work charts a new direction for model-based CRL, aligning with the modern pretrain-finetune era.

cs.LG↗