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Rui Zhou

Publications and source records attributed to Rui Zhou.

At least 19 recordsLinked to original sources
arXiv2026

NMR evidence of pressure-induced structural transition and enhanced spin fluctuations up to 14~GPa in SrCu$_2$(BO$_3$)$_2$

The Shastry-Sutherland compound SrCu$_2$(BO$_3$)$_2$ has attracted considerable interest as a platform for exploring quantum phases and quantum phase transitions driven by magnetic frustration. The pressure-induced structural and magnetic phase transitions in SrCu$_2$(BO$_3$)$_2$, however, remain controversial. To address this issue, we performed high-pressure $^{11}$B nuclear magnetic resonance (NMR) measurements on SrCu$_2$(BO$_3$)$_2$ up to 14~GPa. The NMR spectra reveal two pressure-induced monoclinic phases. With pressure above 4~GPa and with temperature below 10~K, the rapid broadening of the NMR spectrum and the power-law behavior in the spin-lattice relaxation rate $1/T_1$ provide clear evidence for a gapless 3D antiferromagnetic (AFM) phase in the monoclinic phase. At an intermediate temperature range around 20~K, the emergence of the field-dependent NMR line splits resolves a two-dimensional, short-range ordered AFM phase; at temperature above 30~K, the sublinear power-law behavior of $1/T_1$ identifies an extended correlated paramagnetic regime.

cond-mat.str-el↗
arXiv2026

Task-Oriented Formation Decision via Reinforcement Learning: Herding an Attacking Swarm

Multi-robot systems can accomplish tasks that are difficult for a single robot by organizing into task-specific formations. Different from existing studies on multi-robot shape formation, we here study the task-oriented formation decision problem, with a focus on the herding task. This task is challenging due to the attackers' superior maneuverability and their unknown strategies. To address these challenges, we propose the following novel results. First, we encode the formation shape using a low-dimensional parameter vector. This parametric representation reformulates the formation decision as a parameter optimization problem, thereby resolving the limited flexibility of predefined shapes. By optimizing these formation parameters, the defenders' maneuverability disadvantage is mitigated through a formation shape that continuously adapts to task requirements. Second, we develop a reinforcement learning-based policy to regulate the formation parameters. Trained offline in simulations covering diverse attacking strategies, the learned policy can effectively handle adversarial unpredictability during online deployment. Comparative simulations against three baselines demonstrate that our method can successfully accomplish challenging herding tasks. Additional scalability simulations further verify its applicability to simulated scenarios involving dozens of robots. We also validate the practical feasibility of our method on a physical robotic platform with 3 attackers and 7 defenders.

cs.RO↗
arXiv2026

From Enumeration to Covering: Near-Optimal Densest P-Partite Subgraph Search over Large Heterogeneous Information Networks

Given a heterogeneous information network (HIN) and a query meta-path P of length i, the densest P-partite subgraph problem finds the subgraph, spanning the i typed layers of P, that maximizes a parameter-free density: the number of meta-path instances over the geometric mean of the layer sizes. It has applications across bibliographic, e-commerce, and biomedical networks. The state-of-the-art approximation linearizes the geometric-mean objective by fixing per-layer weights, but solves one subproblem for every feasible weight set, of which there are $O((n/i)^i)$, and on each achieves only a $1/i$ approximation. We show that neither the exhaustive enumeration nor the loose guarantee is necessary. First, we replace enumeration by covering: polylogarithmically many representative weight sets, localized further by a data-dependent bound, cover all feasible ones while losing only a tunable factor $1+\eta$ in density. Second, we cast each fixed-weight subproblem as a weighted supermodular densest-subgraph instance and solve it near-optimally, lifting the overall guarantee to $(1-\delta)/(1+\eta)$. To our knowledge, this is the first near-optimal density approximation beyond the bipartite ($i=2$) case, and it yields a PTAS for every fixed i. Algorithmically, our solver is an adaptive peeling scheme that never materializes the meta-path instances, whose number can exceed the graph size by orders of magnitude. An incumbent-driven reduction further discards representative weight sets before their subproblems are solved. Experiments on five real HINs show that our algorithms achieve substantial speedups over enumeration-based baselines and can further certify the near-optimality of the returned subgraph.

cs.DS↗
arXiv2026

SITA: Semantic Interest Tokens for Target-Aware Compression in Long-Sequence Recommendation

As user behavior histories continue to grow on modern Internet platforms, effectively modeling long behavior sequences has become crucial for predicting user interests in candidate items. Existing methods have evolved along two directions. One line dynamically retrieves target-relevant behaviors from long histories, enabling target-aware modeling but requiring target-dependent computation during inference. The other line compresses entire behavior sequences into compact user representations, achieving high efficiency and scalability but sacrificing target-specific adaptation due to target-independent encoding. The key challenge is therefore to enable target-aware modeling while preserving the efficiency and scalability of compressed user representations. To address this challenge, we propose \textbf{SITA}, a target-aware compression framework for long-sequence recommendation. SITA enables target-aware compression by organizing compressed interests into semantic structures through semantic identifiers learned via parallel semantic quantization. Conditioned on the semantic identifier of the target item, SITA adaptively aggregates the corresponding structured interests to construct the target-specific user representation. Extensive experiments on public datasets and a large-scale industrial dataset demonstrate that SITA consistently outperforms representative baselines while maintaining strong scalability, highlighting its strong potential for real-world recommender systems.

cs.IR↗
arXiv2026

Effects of Axion Interactions on Quark Stars in 4D Einstein-Gauss-Bonnet Gravity

We explore the properties of quark stars by combining the microscopic axion-extended Polyakov--Nambu--Jona-Lasinio model with the macroscopic framework of four-dimensional Einstein-Gauss-Bonnet (4D EGB) gravity. Our results show that the inclusion of axion-induced interactions stiffens the equation of state of quark matter, thereby increasing the sound speed and the maximum mass of quark stars. The inclusion of the 4D EGB correction effectively weakens gravitational compression and further modifies the stellar structure, allowing for larger radii and higher maximum masses while reducing the compactness and surface gravitational redshift. Notably, the combined effects yield mass-radius sequences that are more compatible with current observational constraints than those obtained under standard general relativity with conventional quark-matter equations of state. These findings suggest that the interplay between axion dynamics and 4D EGB gravity may provide a viable phenomenological framework for describing massive quark stars.

hep-ph↗
arXiv2026

Strain-Tuned Nodal Superconductivity in the Charge-Ordered Kagome Metal CsV$_3$Sb$_5$

The nature of the superconducting pairing symmetry in the kagome metal CsV$_3$Sb$_5$ and its relationship with the charge density wave (CDW) order are central unresolved issues. Here, we investigate the evolution of superconductivity in CsV$_3$Sb$_5$ under in-situ uniaxial pressure using $^{121}$Sb nuclear quadrupole resonance (NQR). We find that tensile strain significantly enhances the superconducting transition temperature, $T_{\rm c}$, while the CDW remains unchanged, demonstrating that superconductivity can be tuned independently of the bulk charge order. At a tensile strain of $\varepsilon$ = +0.90%, the nuclear spin-lattice relaxation rate reveals a remarkable double transition: an upper transition at $T_{\rm c1}$ = 3.6 K to a nodal gap state, and a lower one at $T_{\rm c2}$ = 3.0 K characterized by a nodeless gap. These results evidence degenerate superconducting states with different gap symmetry in the kagome metal at ambient pressure which split under strain. Our work demonstrates a high tunability of superconductivity by uniaxial pressure.

cond-mat.supr-con↗
arXiv2026

Coexistence and manipulation of multiple singularities in a reconfigurable non-Hermitian metasurface

Non-Hermitian frameworks extend conventional Hermitian physics, offering a powerful paradigm for describing open systems. Central to this field are various singularities within the complex parameter space, such as exceptional points (EPs) and scattering zeros, which dictate exotic physical behaviors. As research shifts from isolated singularities toward multi-singularity interactions, conventional planar metasurfaces remain constrained by limited tuning dimensions. Here, we propose a mirror-coupled design that maps a metasurface into a quasi-high-dimensional parameter space. By employing a metallic plane to generate image resonators, this scheme multiplies the system degrees of freedom without increasing the number of physical resonators. Its implementation on a reconfigurable platform integrated with PIN diodes yields the coexistence and manipulation of an EP and multiple reflection zeros. Through simulations and microwave experiments, we characterize the dynamic evolution of these singularities and exploit their synergistic effects for two distinct applications. First, for tunable absorption, multiple reflection zeros are spectrally coordinated to achieve a near-perfect absorption band exceeding $99.9\%$ across the X-band, thereby dynamically suppressing target scattering. Second, for enhanced sensing, a reflection zero couples with the EP to form a hybrid singularity. This hybrid state inherits the power-law sensitivity of the EP while substantially boosting robustness against fluctuations, resolving the conventional trade-off between sensitivity and stability and simplifying detection to direct peak tracking rather than complex multimode eigenvalue fitting. Our work provides a general methodology to circumvent parameter competition among non-Hermitian singularities, opening new avenues for multifunctional metadevices across the electromagnetic spectrum.

physics.optics↗
arXiv2026

Fora: From Weight-Space to Function-Space Protection in Capability-Preserving Fine-Tuning

Full fine-tuning adapts large language models to new tasks but can erode capabilities they already possess. Existing remedies protect through proxies such as parameter distances, importance penalties, output matching, or dominant singular directions of the weights, but none directly asks which activation directions the preserved capability relies on. We argue that a capability is characterized more faithfully by the activation subspace it induces than by the singular geometry of the weight matrix, and develop function-space protection, instantiated as FORA (Function-space Orthogonal Residual Adaptation). From label-free calibration inputs, FORA estimates, per layer, the principal directions $Q$ of the input-activation covariance and forms a right projector $P_Q = I - QQ^T$. Paired with a left projector $P_U$ from the weight SVD, the update is $\Delta W = P_U M P_Q + U_2 D_{\delta} V_2^T$: a high-capacity branch structurally barred from reading capability-relevant function directions, plus a narrow spectral channel for controlled plasticity. The construction extends to parameter-efficient adaptation via $M \to (\alpha/r) BA$. Across three settings on Qwen3-1.7B, including COGS and GSM8K learned while preserving translation and translation learned while preserving math, FORA consistently improves preservation over weight-space projection and standard regularization, with only a small new-task trade-off in the math-preservation setting. A controlled ablation isolating the projection source shows that the advantage comes not from projection itself, but from projecting onto capability-derived rather than weight-derived directions. Code is available at https://github.com/zrui239/FORA.

cs.LG↗
arXiv2026

Metamagnetism in UTe2: the roles of itinerancy and localization

The metamagnetic transition in UTe$_2$ plays a key role in stabilizing two enigmatic field-induced superconducting phases. One of these phases (SC2) is truncated by the transition, lying directly below it, while the other (SC3) sits predominantly above it and appears to be stabilized because of it. While numerous pulsed field studies have examined this transition, comparatively few steady field experiments have investigated it. Here we report a suite of measurements of metamgnetism in UTe$_2$, at ambient pressure by torque magnetometry and extraction magnetometry techniques, and of the magnetoconductance under pressure. Our steady field measurements resolve a complex sub-structure within the transition, with separate features that possess different temperature evolutions, pointing to distinct contributions from itinerant and localized moments. The itinerant contribution might relate to a possible spin-density wave state. We theoretically model the evolution of Kondo and RKKY interactions and propose that the SC2 state is stabilized under pressure due to the collapse of magnetic anisotropy, leading to an enhancement of longitudinal spin fluctuations along the hard $b$ axis, which are pair-forming in the $p$-wave channel.

cond-mat.str-el↗
arXiv2026

ESPO: Early-Stopping Proximal Policy Optimization

When a large language model under reinforcement learning commits a wrong reasoning step early in a trajectory, standard algorithms force it to keep generating until the maximum horizon, spending compute on tokens that never receive positive reward and polluting advantage estimates with post-failure noise. We propose ESPO (Early-Stopping Proximal Policy Optimization), which detects trajectory failure on-the-fly and terminates rollouts early. At each generation step, ESPO computes a surrogate regret using only the logits already computed during sampling, and terminates when the smoothed cumulative regret significantly exceeds its estimated values. Truncated trajectories are treated as absorbing failure states with a terminal reward, concentrating negative temporal-difference (TD) errors near the detected failure step without any additional reward model or human annotation. On DeepSeek-R1-Distill-Qwen-7B trained for mathematical reasoning, ESPO surpasses PPO on AIME~2024 (46.28% vs. 45.25%), AMC~2023 (85.83% vs. 82.94%), and MATH-500 (87.42% vs. 85.43%), while saving more than 20% rollout tokens cumulatively.

cs.LG↗
arXiv2026

How to augment cosmic shear measurements with radio polarimetry of galaxies?

The integral polarization of spiral galaxies in the radio band has been proposed as a new tracer of the intrinsic galaxy shape that augments lensing shear measurements. We revisit the method of shear estimation in this context. We introduce a new statistical model in which galaxy shape and polarization are Gaussian random variables with their covariance characterizing the quality of polarization-shape alignment. Applying the principle of likelihood maximization, we then analytically derive unbiased, minimal-variance estimators, which allow to simultaneously estimate gravitational shear, intrinsic shape alignment and line-of-sight polarization rotation, all at once and accurate to first order in these three effects. New to the literature, our estimators have the merits of being free of biases, robust in situations of few galaxies or poor polarization-shape alignment, allowing analytic reconstruction noise covariance, and minimizing uncertainties in power spectrum estimation, thus resolving conceptual issues of the existing estimation methods. This new analytic framework is generally applicable to future research that exploits the polarization-shape alignment effect of galaxies.

astro-ph.CO↗
arXiv2026

Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution

Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimization with high complexity. Motivated by the need for real-time scheduling, robust generalization, and low data dependency, this paper proposes a novel paradigm that leverages large language model (LLM)-guided evolutionary search (AlphaEvolve) to autonomously discover a closed-form power allocation solution for multi-target tracking. The approach encodes high-dimensional radar states into physically inspired features, then evolves a compact and interpretable scoring function, which is transformed to feasible power allocations via a deterministic constraint-satisfying transformation. Extensive experiments demonstrate that the discovered closed-form solution achieves near-optimal tracking accuracy (average relative performance loss of only $1.51\%$), reliable generalization across diverse scenarios and target counts, and over three orders of magnitude speedup compared to conventional iterative solvers. These results highlight the potential of LLM-guided symbolic search to revolutionize not only radar resource management but also broader classes of engineering optimization problems.

eess.SP↗
arXiv2026

$\rho$ mesons in finite magnetic field and finite temperature

The mass spectra of $\rho$ mesons ($\rho_{Q=\pm 1}^{s_z=0,\pm 1}$ and $\rho_{Q=0}^{s_z=0,\pm 1}$) at finite magnetic field and temperature are studied in frame of the two-flavor Nambu-Jona-Lasinio model. Fully considering the breaking of translational invariance induced by external magnetic field, the analytical form of $\rho$ meson propagators have been derived in the Ritus scheme and Schwinger scheme, which gives the same algebraic formula. When solving the pole equation of $\rho$ meson propagators, multiple solutions of the meson mass appear due to the dimension reduction of their constituent quarks in magnetic fields. At vanishing temperature, we focus on the $\rho$ meson masses $M_{\rho}$ corresponding to the lowest value solution of the pole equation. $M_{\rho^{-}_+}$, $M_{\rho^{0}_+}$ and $M_{\rho^{\pm}_0}$ increase with magnetic field. $M_{\rho^{+}_+}$ firstly decreases and then becomes saturated with increasing magnetic field. $M_{\rho^0_0}$ is not sensitive to magnetic field. These results are consistent with the available LQCD simulations. At finite temperature, we discuss the lowest four/five solutions of $\rho$ meson masses $M^{i=0,1,2,3,4}_{\rho}$. With fixed magnetic field, they decrease with temperature, and approach the mass sum of their constituent quarks at high temperature. The mass solution $M^{i}_{\rho}$ for different mesons $\rho_+^{0,\pm}$ and $\rho_0^{0,\pm}$ may become degenerate at finite magnetic field and temperature.

nucl-th↗
arXiv2026

Driving risk emerges from the required two-dimensional joint evasive acceleration

Most autonomous driving safety benchmarks use time-to-collision (TTC) to assess risk and guide safe behaviour. However, TTC-based methods treat risk as a one-dimensional closing problem, despite the inherently two-dimensional nature of collision avoidance, and therefore cannot faithfully capture risk or its evolution over time. Here, we report evasive acceleration (EA), a hyperparameter-free and physically interpretable two-dimensional paradigm for risk quantification. By evaluating all possible directions of collision avoidance, EA defines risk as the minimum magnitude of a constant relative acceleration vector required to alter the relative motion and make the interaction collision-free. Using interaction data from five open datasets and more than 600 real crashes, we derive percentile-based warning thresholds and show that EA provides the earliest statistically significant warning across all thresholds. Moreover, EA provides the best discrimination of eventual collision outcomes and improves information retention by 54.2-241.4% over all compared baselines. Adding EA to existing methods yields 17.5-95.5 times more information gain than adding existing methods to EA, indicating that EA captures much of the outcome-relevant information in existing methods while contributing substantial additional nonredundant information. Overall, EA better captures the structure of collision risk and provides a foundation for next-generation autonomous driving systems.

cs.RO↗
arXiv2026

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts

Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon termed Seeing but Not Thinking: models accurately perceive image content yet fail in subsequent reasoning, while correctly solving identical problems presented as pure text. Through systematic analysis, we first verify that cross-modal semantic sharing exists in MoE architectures, ruling out semantic alignment failure as the sole explanation. We then reveal that visual experts and domain experts exhibit layer-wise separation, with image inputs inducing significant routing divergence from text inputs in middle layers where domain experts concentrate. Based on these findings, we propose the Routing Distraction hypothesis: when processing visual inputs, the routing mechanism fails to adequately activate task-relevant reasoning experts. To validate this hypothesis, we design a routing-guided intervention method that enhances domain expert activation. Experiments on three multimodal MoE models across six benchmarks demonstrate consistent improvements, with gains of up to 3.17% on complex visual reasoning tasks. Our analysis further reveals that domain expert identification locates cognitive functions rather than sample-specific solutions, enabling effective transfer across tasks with different information structures.

cs.CV↗
arXiv2026

Cascade of Spin Liquids in a Bilayer Triangular-lattice Antiferromagnet Rb_2Co_2(SeO_3)_3

In frustrated Ising magnets, classical spin liquids (CSLs) with macroscopic ground-state degeneracy can survive against conventional magnetic order, as exemplified by systems on triangular, kagome and pyrochlore lattices at zero field. Here we report the discovery of a high-field route toward spin liquids in a bilayer triangular lattice antiferromagnet, Rb$_2$Co$_2$(SeO$_3$)$_3$. We demonstrate that a cascade of CSLs -- characterized by doubly degenerate one-up-one-down local spin configurations and a residual entropy of 1/2(1-M/M_s)Rln2 per mole -- emerges through field-controlled dilution of Ising dimers. Owing to the interplay of intra- and inter-layer interactions, these CSLs are further stabilized by lattice symmetry breaking at fractional magnetization plateaus. Such field-induced spin liquids can be understood as a consequence of generalized ice rules, analogous to those governing in pyrochlore antiferromagnets. In particular, the 5/6-plateau state is a candidate quantum spin liquid. Our results thereby establish a new pathway for exploring diverse spin liquid states across both classical and quantum regimes.

cond-mat.str-el↗
arXiv2026

DIET: Learning to Distill Dataset Continually for Recommender Systems

Modern deep recommender models are trained under a continual learning paradigm, relying on massive and continuously growing streaming behavioral logs. In large-scale platforms, retraining models on full historical data for architecture comparison or iteration is prohibitively expensive, severely slowing down model development. This challenge calls for data-efficient approaches that can faithfully approximate full-data training behavior without repeatedly processing the entire evolving data stream. We formulate this problem as \emph{streaming dataset distillation for recommender systems} and propose \textbf{DIET}, a unified framework that maintains a compact distilled dataset which evolves alongside streaming data while preserving training-critical signals. Unlike existing dataset distillation methods that construct a static distilled set, DIET models distilled data as an evolving training memory and updates it in a stage-wise manner to remain aligned with long-term training dynamics. DIET enables effective continual distillation through principled initialization from influential samples and selective updates guided by influence-aware memory addressing within a bi-level optimization framework. Experiments on large-scale recommendation benchmarks demonstrate that DIET compresses training data to as little as \textbf{1-2\%} of the original size while preserving performance trends consistent with full-data training, reducing model iteration cost by up to \textbf{60$\times$}. Moreover, the distilled datasets produced by DIET generalize well across different model architectures, highlighting streaming dataset distillation as a scalable and reusable data foundation for recommender system development.

cs.IR↗
arXiv2026

Memory Over Maps: 3D Object Localization Without Reconstruction

Target localization is a prerequisite for embodied tasks such as navigation and manipulation. Conventional approaches rely on constructing explicit 3D scene representations to enable target localization, such as point clouds, voxel grids, or scene graphs. While effective, these pipelines incur substantial mapping time, storage overhead, and scalability limitations. Recent advances in vision-language models suggest that rich semantic reasoning can be performed directly on 2D observations, raising a fundamental question: is a complete 3D scene reconstruction necessary for object localization? In this work, we revisit object localization and propose a map-free pipeline that stores only posed RGB-D keyframes as a lightweight visual memory--without constructing any global 3D representation of the scene. At query time, our method retrieves candidate views, re-ranks them with a vision-language model, and constructs a sparse, on-demand 3D estimate of the queried target through depth backprojection and multi-view fusion. Compared to reconstruction-based pipelines, this design drastically reduces preprocessing cost, enabling scene indexing that is over two orders of magnitude faster to build while using substantially less storage. We further validate the localized targets on downstream object-goal navigation tasks. Despite requiring no task-specific training, our approach achieves strong performance across multiple benchmarks, demonstrating that direct reasoning over image-based scene memory can effectively replace dense 3D reconstruction for object-centric robot navigation. Project page: https://ruizhou-cn.github.io/memory-over-maps/

cs.RO↗