arXiv subjects

Hua Zhou

Publications and source records attributed to Hua Zhou.

At least 37 records · Page 2Linked to original sources

arXiv2026

ChartWalker: Benchmarking the Cross-Chart RAG Task with Hierarchical Knowledge Graphs

Cross-Chart Retrieval-Augmented Generation (RAG) is critical for complex multi-modal analytical tasks in scientific, business, and political domains. However, existing benchmarks either focus on tables, which are well-structured and textualized, or generate cross-chart questions by simply extracting key points, which often induces lexical overlap between queries and evidence and yields logically inconsistent reasoning chains. To address this, we introduce ChartWalker, a novel framework for constructing challenging cross-chart RAG tasks. ChartWalker features a hierarchical knowledge graph construction method tailored to charts, which organizes entities and relations by granularity to preserve analytical structure. We then propose a structure-aware sampling algorithm that synthesizes semantically coherent, multi-hop reasoning paths, enabling explicit control over query difficulty and granularity for QA generation. Built with this framework, we release ChartWalker-Bench, a comprehensive benchmark spanning diverse domains and cross-chart query types. Extensive evaluations across major RAG paradigms reveal significant performance gaps, underscoring the benchmark's difficulty and utility. Furthermore, we provide ChartWalker-Agent, an agentic baseline to facilitate analysis and inspire future system design.

cs.IR↗

arXiv2026

Persistent structural distortions and absent superconductivity in trilayer nickelate thin films

A new family of high-temperature superconductors was recently discovered in the $n=2,3$ Ruddlesden-Popper nickelates, where superconductivity emerges concomitant with suppression of parent density waves and structural octahedral rotations under hydrostatic pressure. Intriguingly, compressive strain mimics the structural effects of pressure in the $n=2$ phase, yielding ambient-pressure superconductivity. However, analogous strain-stabilized superconductivity has not been realized in the $n=3$. Here, we use atomically-precise synthesis, transport, picoscale electron microscopy, and synchrotron X-ray diffraction to probe $n=3$ La$_4$Ni$_3$O$_{10}$ thin films. Although compressive strain suppresses density wave order, we do not observe superconductivity even under the largest strain state. Importantly, we identify a structural distortion unique to strained $n=3$ thin films that may inhibit superconductivity: persistent, layer-inequivalent octahedral rotations around the $c$-axis. Our results highlight key differences between the $n=3$ and $n=2$ systems, suggesting that ambient-pressure superconductivity in the $n=3$ may require new methods beyond epitaxial strain engineering.

cond-mat.mtrl-sci↗

arXiv2026

RAFT: Data Refinement and Adaptive Distillation for Domain Fine-Tuning with Alleviated Forgetting

Domain-specific supervised fine-tuning (SFT) often improves in-domain performance at the cost of degrading a model's general capabilities. We view this degradation through two practical gaps in domain SFT: a supervision-compatibility gap, where domain targets differ in style and reasoning format from the original model's natural responses, and a trajectory-preservation gap, where teacher-forced SFT optimizes fixed target tokens without constraining the model's behavior on its own generated prefixes. This process fails to preserve the model's original behavior. We propose RAFT (Data Refinement and Adaptive Distillation for Domain Fine-Tuning with Alleviated Forgetting), a two-stage framework that addresses both factors. First, RAFT constructs model-compatible supervision through self-conditioned rewriting, semantic filtering, and answer fusion. Second, RAFT performs Answer-Conditioned On-Policy Distillation, where the original instruction-tuned model provides soft targets on student-generated trajectories while being conditioned on the fused answer as helpful context. We further introduce top-K temperature distillation and EMA-based adaptive loss balancing to stabilize the domain-general trade-off. Across three instruction-tuned backbones and five domains, RAFT improves average domain accuracy by 23.2% over standard SFT, while recovering part of the SFT-induced degradation on MS-Bench and IFEval, with relative improvements of 18.2% and 10.2%, respectively. These results show that coupling data refinement with trajectory-level preservation provides an effective recipe for domain fine-tuning with alleviated forgetting.

cs.LG↗

arXiv2026

User-Centric Clustering for uRLLC in Cell-Free RAN via Extreme Value Theory

Ultra-reliable low-latency communication (uRLLC) is a pivotal enabler for B5G/6G networks, yet it faces severe challenges from rare but critical extreme events, which are characterized by heavy tails in the delay distribution. While the cell-free radio access network (CF-RAN) architecture offers essential spatial diversity to combat these uncertainties, conventional user-centric clustering designs typically focus on average metrics, thereby inadequately addressing such tail behaviors. We propose a novel, tail-risk-aware, user-centric clustering framework operating within the finite blocklength (FBL) regime. Our approach employs extreme value theory (EVT), specifically the peaks-over-threshold (POT) model, to accurately quantify the probability of queue latency violations. This framework is applied to formulate an energy efficiency (EE) maximization problem under strict tail latency constraints. The problem is solved via an efficient online algorithm that integrates Lyapunov optimization with successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme, through its dynamic adaptation of cluster formation to mitigate tail risks, achieves a superior reliability-efficiency trade-off and leads to a significant suppression of extreme latency events.

cs.IT↗

arXiv2026

Deterministic fabrication of large-area, high-crystallinity oxide moire superlattices

Oxide twistronics extends moire engineering beyond van der Waals materials, offering a promising platform for accessing emergent interfacial phenomena arising from the strong coupling of lattice, charge, and orbital degrees of freedom in complex oxides. However, deterministic fabrication of high-crystallinity oxide moire superlattices over large lateral dimensions remains challenging due to the three-dimensional bonding network of oxides. Here, we demonstrate a scalable, generalized fabrication strategy that enables the formation of high-crystallinity oxide moire superlattices with clean, chemically bonded interfaces and precisely controlled twist angles down to nominal values of 0.1 degree, achieving sub-degree twist-angle accuracy across large contiguous lateral dimensions approaching the millimeter scale. Using NaNbO3 as a model system, we show that the resulting interlayer coupling drives pronounced structural reconstruction that modifies both the phase structure and ferroelectric domain configuration. Synchrotron-based X-ray 3D reciprocal space mapping reveals the emergence of a single-phase state in twisted bilayers, in contrast to the mixed-phase structure observed in single-layer membranes prior to twist assembly. The structural signatures are further consistent with gradual lattice rotation distributed along the thickness direction that may accommodate interfacial shear strain, distinct from reconstruction observed in van der Waals moire systems, which primarily occurs through in-plane stacking rearrangement. This collective lattice response is correlated with twist-dependent nanoscale electromechanical modulations observed by piezoresponse force microscopy. These results establish a scalable materials platform for oxide twistronics and open new pathways towards integrating twist-engineered complex oxides into practical, macroscale device architectures.

cond-mat.mtrl-sci↗

arXiv2026

Tactics for Improving Least Squares Estimation

This paper deals with tactics for fast computation in least squares regression in high dimensions. These tactics include: (a) the majorization-minimization (MM) principle, (b) smoothing by Moreau envelopes, and (c) the proximal distance principle for constrained estimation. In iteratively reweighted least squares, the MM principle can create a surrogate function that trades case weights for adjusted responses. Reduction to ordinary least squares then permits the reuse of the Gram matrix and its Cholesky decomposition across iterations. This tactic is pertinent to estimation in L2E regression and generalized linear models. For problems such as quantile regression, non-smooth terms of an objective function can be replaced by their Moreau envelope approximations and majorized by spherical quadratics. Finally, penalized regression with distance-to-set penalties also benefits from this perspective. Our numerical experiments validate the speed and utility of deweighting and Moreau envelope approximations. Julia software implementing these experiments is available on our web page.

stat.CO↗

arXiv2026

Learn then Decide: A Learning Approach for Designing Data Marketplaces

As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders' value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, ensuring truthful bidding while balancing revenue maximization with minimal price discrimination. On the theoretical side, we establish a statistical viewpoint that recasts revenue optimization as a valuation density estimation problem: we show that revenue regret can be controlled by uniform error in estimating the valuation density. MAPP achieves a regret of $O_p(n^{-1}(\log n)^2)$ when incorporating historical bid data, where $n$ is the number of bids in the current round. For sequential dataset sales over $T$ rounds, we propose an online MAPP mechanism that dynamically adjusts pricing across datasets with varying value distributions. Our approach achieves no-regret learning, with the average cumulative regret converging at a rate of $O_p(T^{-1/2}(\log T)^2)$. We validate the effectiveness of MAPP through simulations and real-world data from the FCC AWS-3 spectrum auction.

stat.ML↗

arXiv2026

Physics-Enforced Neural Ordinary Differential Equation for Chemical Kinetics Optimization in Reaction-Diffusion Systems

Calibrating chemical kinetics in a reaction-diffusion system is challenging because of complex dynamics governed by tightly coupled chemistry and transport, while experimental observations are often sparse and noisy. We propose a physics consistent diffusion-chemistry coupled neural ordinary differential equation (Diff-Chem Neural ODE) that embeds Arrhenius-structured reaction neurons into a fully differentiable streamline formulation and explicitly accounts for diffusion coupling. This design enables direct gradient-based analysis of kinetic parameters without sampling-based pretraining. We validate this method on burner-stabilized flat and stagnation reacting flows using mechanisms spanning different stiffness ranges. The proposed method reproduces species profiles with near-reference accuracy, whereas a pure chemistry Neural ODE that neglects diffusion coupling may misplace ignition and generate an incorrect thin reaction zone. Diff-Chem Neural ODE is more robust than pure chemistry Neural ODE and provides substantial speedups for gradient evaluation compared with fully discretized computations. In kinetics refinement, optimizing only a limited set of "primal" species reduces the loss by over 98% and simultaneously recovers unobserved variables, demonstrating physically consistent global control. Finally, tests with 1-20% noise in the objective show stable convergence without local overfitting, supporting its applicability under noisy measurements.

cs.CE↗

arXiv2026

Ambient-pressure 151-K superconductivity in HgBa2Ca2Cu3O8+δ via pressure quench

Superconductivity has been a vigorously researched topic since its discovery in 1911. Raising the superconducting transition temperature (Tc) has been the main driving force behind such long-sustained efforts due to its potential for impacting humanity and the fundamental knowledge gained from understanding this macroscopic coherent quantum state at high temperatures. The successful development of high-Tc superconductivity will make possible extraordinarily efficient generation, delivery, and utilization of energy, and could also enable the development of controlled fusion while impacting other burgeoning fields like quantum computation and quantum electronics. However, progress has been hindered by a longstanding plateau in the record ambient-pressure Tc, unchanged since 1993. Subsequent significant advancements in Tc have been achieved only under high pressures, preventing the realization of superconductivity's full potential. To directly address this challenge, we developed a pressure-quench protocol (PQP) to stabilize pressure-induced/-enhanced superconducting states at ambient pressure. Here we achieve a record ambient-pressure Tc of 151 K in the cuprate HgBa2Ca2Cu3O8+δ via PQP. The experimental results are further supported by synchrotron X-ray diffraction measurements and phonon and electronic structure calculations. This breakthrough opens new avenues for stabilizing and exploring ambient-pressure high-Tc superconducting states and other quantum states that have been previously only accessible under pressure, paving the way for deeper understanding and practical applications of high-Tc superconductivity and beyond.

cond-mat.supr-con↗

arXiv2026

The Evolution of Magnetism in a Thin Film Pyrochlore Ferromagnetic Insulator

The pyrochlore vanadates are compelling candidates for next-generation dissipationless devices. Lu2V2O7 and Y2V2O7 are ferromagnetic insulators (Tc ~ 70 K) that are believed to exhibit the magnon Hall effect and are expected to host topological magnons. Their completely dissipationless magnon edge states could be harnessed to realize low-power information transport in spintronic or magnonic devices. As a crucial step in the realization of devices, we synthesize the first thin films of pyrochlore Y2V2O7 on isostructural Y2Ti2O7 substrates and explore the evolution of their magnetic properties down to the ultrathin limit. All films are insulating ferromagnets with transition temperatures of up to the bulk value (Tc ~ 68 K) that decrease with thickness according to finite-size effects. Our films also exhibit a change in anisotropy from in-plane to out-of-plane easy axis coincident with the development of partial strain relaxation and nonzero magnetic hysteresis in an applied field. This evolution demonstrates the impact of strain on magnetic anisotropy and paves the way to tunable magnon topology.

cond-mat.mtrl-sci↗

arXiv2026

Three Dimensional Multiphysics Modelling of Helicon Wave Heating and Antenna Plasma Coupling for Boundary Density Control in Toroidal Fusion Plasmas

Active control of scrape off layer density is emerging as a critical requirement for improving ion cyclotron resonance heating and enabling high performance steady state operation in future magnetic confinement fusion devices. Helicon wave excitation offers a promising physics based approach to generating high density boundary plasmas with high ionization efficiency and low impurity release. In this work, we develop THEMIS code, a fully three dimensional (3D) multiphysics model of helicon wave propagation and power deposition in a toroidal fusion relevant configuration, employing a finite temperature thermal dielectric tensor. The code quantifies the relative contributions of Doppler shifted cyclotron damping, anomalous Doppler damping, collisional damping, and Landau damping, and demonstrates that slow wave propagation and electron Landau damping dominate the accessible heating regime in Helimak device. A comparative study of four planar antenna geometries under the present protruding window configuration shows that geometric cutoffs and SOL density gradients severely limit power penetration into the core accessible region. To address this constraint, we introduce a recessed window launch scheme that positions the dielectric window inside the vacuum vessel and perform systematic parameter scans of window position, antenna geometry, and installation orientation. From these analyses, we identify the key physics driven principles governing efficient helicon wave coupling: the importance of open circuit termination, maximized strap length and width, controlled inter turn spacing, and sufficient clearance from metallic walls to avoid near field suppression. Guided by these principles, we designed an optimized racetrack spiral antenna that increases coupling efficiency by more than an order of magnitude compared with conventional short circuited rectangular spiral antenna.

physics.plasm-ph↗

arXiv2026

Superconducting phase diagram of multi-layer square-planar nickelates

The discovery of superconductivity in square-planar nickelates has offered a rich materials platform to explore the origins of cuprate-like superconductivity. Experimental investigations however have largely been limited to the infinite-layer $R$NiO$_2$ ($R$=rare-earth) nickelates. Here, we construct a phase diagram of multi-layer square-planar Nd$_{n+1}$Ni$_n$O$_{2n+2}$ compounds and discover signatures of superconductivity for $n$ = 4 - 8. Upon decreasing the dimensionality $n$, the superconducting anisotropy evolves due to 4$f$ electron effects, and electronic structure characteristics approach cuprate-like behavior. Magnetic fluctuations persist from within the superconducting regime and into the over-doped, non-superconducting regime. Remarkably, the superconducting regime overlaps with that of chemically-doped infinite-layer nickelates, demonstrating underlying commonalities and distinct differences across varying structural realizations of square-planar nickelates. Our work establishes this layered template for creating new nickel-based superconductors.

cond-mat.supr-con↗

arXiv2026

Uncertainty-Aware Multimodal Learning via Conformal Shapley Intervals

Multimodal learning combines information from multiple data modalities to improve predictive performance. However, modalities often contribute unequally and in a data dependent way, making it unclear which data modalities are genuinely informative and to what extent their contributions can be trusted. Quantifying modality level importance together with uncertainty is therefore central to interpretable and reliable multimodal learning. We introduce conformal Shapley intervals, a framework that combines Shapley values with conformal inference to construct uncertainty-aware importance intervals for each modality. Building on these intervals, we propose a modality selection procedure with a provable optimality guarantee: conditional on the observed features, the selected subset of modalities achieves performance close to that of the optimal subset. We demonstrate the effectiveness of our approach on multiple datasets, showing that it provides meaningful uncertainty quantification and strong predictive performance while relying on only a small number of informative modalities.

stat.ML↗

arXiv2026

ALIGN: Aligned Delegation with Performance Guarantees for Multi-Agent LLM Reasoning

LLMs often underperform on complex reasoning tasks when relying on a single generation-and-selection pipeline. Inference-time ensemble methods can improve performance by sampling diverse reasoning paths or aggregating multiple candidate answers, but they typically treat candidates independently and provide no formal guarantees that ensembling improves reasoning quality. We propose a novel method, Aligned Delegation for Multi-Agent LLM Reasoning (ALIGN), which formulates LLM reasoning as an aligned delegation game. In ALIGN, a principal delegates a task to multiple agents that generate candidate solutions under designed incentives, and then selects among their outputs to produce a final answer. This formulation induces structured interaction among agents while preserving alignment between agent and principal objectives. We establish theoretical guarantees showing that, under a fair comparison with equal access to candidate solutions, ALIGN provably improves expected performance over single-agent generation. Our analysis accommodates correlated candidate answers and relaxes independence assumptions that are commonly used in prior work. Empirical results across a broad range of LLM reasoning benchmarks consistently demonstrate that ALIGN outperforms strong single-agent and ensemble baselines.

cs.LG↗

arXiv2025

Revealing the transient ionization dynamics and mode-coupling mechanisms of helicon discharge through a self-consistent multiphysics model

Helicon plasma sources play a central role in applications ranging from material treatment to space propulsion and fusion, yet the physical processes governing their ignition, transient ionization, and mode evolution remain incompletely understood. Here we develop a self-consistent, fully coupled multiphysics framework that integrates Maxwell equations, electron energy transport, drift-diffusion kinetics, and heavy-species chemistry to capture the complete spatiotemporal evolution of helicon discharges. The model reproduces experimental measurements across pressure, magnetic field, and frequency ranges, and reveals a previously unresolved transient ionization stage characterized by a rapid density rise within ~10-4 s, accompanied by a two-peak electron temperature structure that governs the formation of the dense plasma core. By tracking the RF power flow and field topology, we characterize the transient redistribution of RF energy during ignition. A short-lived phase of localized energy deposition accompanies the onset of ionization, followed by an evolution toward helicon-like field characteristics together with rapid density growth and profile restructuring. Systematic parametric scans further reveal the sensitivity of this mode-coupling process to gas pressure, magnetic field strength, and driving frequency. These results provide a unified picture of the ignition and mode-transition physics in helicon plasmas and establish a predictive tool for the design and optimization of RF plasma sources across space propulsion, manufacturing, and fusion technologies.

physics.plasm-ph↗

arXiv2025

Analysis of the Pion Electromagnetic Form Factor with Next-to-Next-to-Leading Order QCD Corrections

NNLO QCD corrections for the pion electromagnetic form factor at large momentum transfer have been recently performed in [Phys. Rev. Lett. 132, 201901 (2024); Phys. Rev. Lett. 134, 221901 (2025)], revealing that the NLO and NNLO contributions are positive and sizeable. Unfortunately, these predictions have been obtained using the conventional scale-setting method and thus they are plagued by large renormalization scale ambiguities. In this paper, we analyze the pion electromagnetic form factor at NNLO by applying the Principle of Maximum Conformality (PMC), which is introduced with the aim of resolving renormalization scheme and scale ambiguities. By applying the PMC, a more precise perturbative QCD (pQCD) prediction for the pion EMFF $Q^2F_π(Q^2)$ without conventional renormalization scale ambiguity can be achieved. This improved pQCD prediction is highly beneficial for the precise determination of the pion light-cone distribution amplitude. We then conduct a comprehensive comparison between theoretical predictions and experimental measurements of the pion EMFF $Q^2F_π(Q^2)$.

hep-ph↗

arXiv2025

Rethinking Supervised Fine-Tuning: Emphasizing Key Answer Tokens for Improved LLM Accuracy

With the rapid advancement of Large Language Models (LLMs), the Chain-of-Thought (CoT) component has become significant for complex reasoning tasks. However, in conventional Supervised Fine-Tuning (SFT), the model could allocate disproportionately more attention to CoT sequences with excessive length. This reduces focus on the much shorter but essential Key portion-the final answer, whose correctness directly determines task success and evaluation quality. To address this limitation, we propose SFTKey, a two-stage training scheme. In the first stage, conventional SFT is applied to ensure proper output format, while in the second stage, only the Key portion is fine-tuned to improve accuracy. Extensive experiments across multiple benchmarks and model families demonstrate that SFTKey achieves an average accuracy improvement exceeding 5\% over conventional SFT, while preserving the ability to generate correct formats. Overall, this study advances LLM fine-tuning by explicitly balancing CoT learning with additional optimization on answer-relevant tokens.

cs.CL↗

arXiv2025

Crossover Dynamics of Non-Fickian Ionic Diffusion in Solids

Ionic diffusion in solids is central to energy storage, electronics, and catalysis, yet its chemical origins are difficult to resolve because conventional diffusion models struggle with effects of confinement, crystallographic disorder, lattice distortions, and coupling to electronic or phononic carriers. These challenges are especially pronounced in battery materials, where ionic and electronic motion occur together, complicating interpretation of electrochemical measurements. Here we use tracer exchange as a direct, non-electrochemical probe to reveal distinct ion-transport regimes in the one-dimensional conductor olivine Li_xFePO4 (0 <= x <= 1). Lithium isotope exchange validates single-file diffusion governed by strong ion-ion correlations, where 1D confinement suppresses bypassing and preserves spatial order. Kinetic Monte Carlo simulations and chronoamperometry quantify both Faradaic and non-Faradaic surface exchange, identifying electron transport, rather than Li+ mobility, as the rate-limiting step for electrochemical reaction. In addition, Li-Na exchange exhibits apparent superdiffusion, with rates that increase with Na content. Simulations attribute this behavior to surface-exchange limitations and Na-induced lattice strain that enhances cross-channel Li+ hopping and drives a crossover from 1D to quasi-2D transport. Four-dimensional STEM, in situ synchrotron XRD, X-ray absorption spectroscopy, and Mossbauer spectroscopy confirm that lattice softening and concerted polaron motion contribute to the observed dynamics. These results establish tracer exchange as a powerful tool for probing coupled ion-electron transport and provide chemical insight into how lattice mechanics and multicomponent exchange shape ionic diffusion in solids.

cond-mat.mtrl-sci↗