arXiv subjects

Wei Ren

Publications and source records attributed to Wei Ren.

At least 19 recordsLinked to original sources

arXiv2026

Phonon-Programmable Hidden Unconventional Magnetism in Two-Dimensional Spin-Degenerate Antiferromagnets

Spin-degenerate antiferromagnets can host hidden unconventional magnetism in their lattice degrees of freedom. We show that coherent phonons activate this magnetism by removing the spin-layer operations that enforce equilibrium band degeneracy without changing the collinear N\'eel order. The frequency and polarization of a pump electric field select a resonant $\Gamma$-point optical phonon and, within a doublet, its coordinate direction. This choice fixes the residual spin-layer symmetry. In monolayer MnPSe$_3$, an $A_{2u}$ mode produces $i$-wave splitting odd in the mass-weighted phonon coordinate $Q$, reversing sign under $Q\to-Q$, whereas two orthogonal directions of the same doubly degenerate $E_u$ doublet produce $d$- and $s$-wave splitting at a common resonance. Rotating the in-plane pump field $\bm E_\parallel$ therefore programs both the spin-splitting texture and the thermoelectric spin current, continuously tuning the response between transverse pure-spin and longitudinal spin-polarized currents. A complete classification of two-dimensional collinear spin layer groups identifies the $\Gamma$-point coordinates that remove the degeneracy-enforcing operations and activate such unconventional magnetism.

cond-mat.mtrl-sci↗

arXiv2026

Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays

Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances and inaccurate dynamic models can significantly compromise performance and reliability. To address this challenge, calibrated machine learning models, particularly Gaussian process (GP) regression, are extensively employed due to their interpretable performance quantification. As the interconnected communication of MASs facilitates cooperative learning, agents are able to enhance learning performance by exchanging local GP inferences with their neighbors and aggregating the received information via distributed GP strategies. However, variations in computational power and prediction tasks among agents inevitably lead to heterogeneous computational delays and differences in query points, which are often overlooked in existing aggregation methods. To overcome these limitations, this work proposes an asynchronous cooperative learning strategy that explicitly accounts for prediction accuracy, query point variations and delay effects. Additionally, a distributed control law based on an adjoint MAS is developed to ensure the desired control performance. Simulations on unmanned surface vehicles validate the effectiveness of the proposed approach, demonstrating substantial improvements in both learning and control performance compared to the state-of-the-art approaches.

cs.LG↗

arXiv2026

Transfer of Wakamatsu tilting modules along Frobenius extensions

Let $\iota: R\to A$ be a Frobenius extension and let $T$ be a Wakamatsu tilting left $R$-module. We give sufficient conditions for the induced module $A\otimes_R T$ to remain Wakamatsu tilting and establish an ascent--descent result for split centrally projective Frobenius extensions. We also characterize the tilting case by the vanishing of $\mathrm{Ext}_R^i(T,A\otimes_R T)$ for all $i>0$. Moreover, if $A\otimes_R T\in\mathrm{add}_R(T)$, then the natural map $S=\mathrm{End}_R(T)\to B=\mathrm{End}_A(A\otimes_R T)$ of endomorphism rings is a Frobenius extension and $T\otimes_S B\cong A\otimes_R T$ as $R$-$B$-bimodules. Applications to Brenner--Butler--Miyashita equivalences and some specific classes of Frobenius extensions are also discussed.

math.RT↗

arXiv2026

Geodesic strong convexity does not imply forward invariance under gradient flow on SO(3): a certified counterexample

Let $mathcal{C}=\overline{\mathcal{B}}_{\rho}(R_c)$ be a geodesic ball of radius $\rho<\pi/2$ in SO(3) with the bi-invariant metric, and let $f$ be geodesically strongly convex on $\mathcal{C}$ with an interior minimizer. It is tempting to expect the gradient flow $\dot R=R(-\nabla f)^\wedge$ to keep $\mathcal{C}$ forward invariant: the flow is attracted to an interior point, and strong convexity appears to leave no room for outward motion. We show this expectation is false by an explicit, fully certified construction with $\rho=0.3$: a cost, quadratic in the principal logarithmic chart with off-diagonal coupling $0.7$, whose geodesic Hessian satisfies $\Hess f\succeq\mu I_3$ on all of $\mathcal{C}$ with a machine-certified modulus $\mu\geq0.172$, rigorous ball arithmetic over exact rational inputs, yet whose descent velocity at a boundary point has the exact rational outward radial component $21/500$. A continuity corollary of the exact rate certifies that the flow exits the ball; numerical integration puts the peak excursion near $0.3143$ before convergence to the minimizer. The mechanism is elementary: strong convexity constrains the projection of the gradient onto the minimizer direction, not onto the inward radial direction. Code reproducing every certified constant and figure accompanies the note.

eess.SY↗

arXiv2026

Intracavity Dual-Resonance Stimulated Raman Spectroscopy with Cavity Ringdown Readout for Resolving Hydrogen Rotational Raman Transitions

Gas-phase rotational Raman lineshape metrology of hydrogen (H2) is challenging due to its weak Raman scattering cross-sections and intrinsically narrow linewidths. We report the first measurement of the complete Dicke-narrowing evolution of the H2 rotational Raman transitions S0(1) and S0(0), enabled by intracavity dual-resonance stimulated Raman spectroscopy with cavity ringdown readout and kHz-level spectral resolution. Our results establish a benchmark for gas-phase Raman lineshape measurements and provide stringent constraints for regime-based pressure-dependent linewidth models.

physics.optics↗

arXiv2026

Gorenstein dimensions and Hirsch length of groups

We study the relation between the Gorenstein homological and the Gorenstein cohomological dimension of groups. We prove that for every module of type $FP_\infty$ over any ring the Gorenstein projective and the Gorenstein flat dimensions are always equal to each other. In particular, we have an equality $\mathrm{Gcd}_kG=\mathrm{Ghd}_kG$ for every group $G$ of type $FP_\infty$ over a commutative coefficient ring $k$. For non-locally-finite groups in Kropholler's class ${\scriptstyle\mathbf{LH}}\mathfrak F$, we characterize the groups of Gorenstein (co)homological dimension one, in terms of actions on trees with finite stabilizers. The Hirsch length of a virtually soluble group $G$ determines its Gorenstein (co)homological dimension, even when $G$ has torsion: We show that $\mathrm{Ghd}_\mathbb{Z}G =h(G)$ and, if $G$ is countable, $h(G)\leq\mathrm{Gcd}_\mathbb{Z}G\leq h(G)+1$. Some consequences for elementary amenable groups of finite Hirsch length are also obtained. Finally, we investigate the Gorenstein dimension of modules over certain algebras of groups with torsion and discuss applications to classifying spaces for proper actions.

math.GR↗

arXiv2026

Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients

Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parameter models. Existing heterogeneous federated approaches attempt to bridge this gap through parameter-efficient tuning, model pruning, or knowledge distillation, yet each trades away a critical property, whether full-model memory reduction, architectural self-containedness, or representational fidelity, leaving the core tension unresolved. We propose FedSLM, a parameter-centric framework for federated fine-tuning with heterogeneous compressed clients. FedSLM uses SVD-based decomposition to produce self-contained client models, whose low-rank subspaces form nested manifolds that are structurally compatible for aggregation. It then applies a two-stage protocol that synchronizes lightweight adapters within compression groups and fuses full-rank reconstructions across groups via structural alignment. Finally, a weak-to-strong elicitation step with auxiliary confidence loss transfers the aggregated knowledge to the full-scale server, while an explicit bias--variance trade-off mitigates compression artifacts. We provide theoretical guarantees for adapter-level aggregation, subspace-alignment bounds for cross-group fusion, and a characterization of how the confidence loss mitigates weak-supervision noise. Experiments on natural language and vision--language benchmarks show that FedSLM outperforms existing federated baselines under both IID and non-IID partitions, while client models operate at roughly 50% of the GPU memory required by the full model.

cs.LG↗

arXiv2026

Switchable Altermagnetism via Spin-Induced Improper Polarization

Enabling reversible spin-splitting switching in stray-field-free altermagnets is promising for spintronic applications, but currently limited to a narrow class of polar materials. We propose a broader approach based on spin-induced improper polarization in nonpolar dual-sublattice magnets. We demonstrate this mechanism in DyFeO3, where the product of nonpolar Fe and Dy spin modes transforms as an induced polar mode. Density functional theory shows that the relative Dy--Fe spin alignment selects the polarization, while the Fe sublattice controls nonrelativistic spin splitting, thus enabling reversible switching. These results establish spin-induced improper polarization as a route to switchable altermagnetism in nonpolar bulk systems.

cond-mat.mtrl-sci↗

arXiv2026

WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management

Large language models (LLMs) are increasingly used as technical assistants, but their competence in solid waste management (SWM) remains difficult to assess because existing benchmarks emphasize general knowledge rather than professional decisions under engineering, environmental, and policy constraints. We introduce WuYuEval, a multi-level benchmark for evaluating LLMs in SWM across foundational knowledge, domain reasoning, and expert decision-making. After quality auditing, WuYuEval contains a Foundation Module with 4,590 closed-ended multiple-choice questions across six task types and eight domain categories, together with an Expert Module with 247 scenario-based open-ended questions involving multi-objective optimization, constraint trade-offs, and system design. For expert tasks, we combine anchor-calibrated LLM-as-a-Judge scoring with Elo-based pairwise comparison. Across 33 LLMs, performance varied widely. The leading model reached 94.64\% accuracy on the Foundation Module, but average accuracy still fell from 84.14\% on easy questions to 42.50\% on hard questions, with lower performance concentrated in calculation, experimental design, urban planning, and open-ended expert tasks. Reasoning-oriented Thinking modes improve most matched model pairs after auditing, but the gains depend on baseline capability and are not uniformly positive. These results suggest that visible deliberation helps only when it remains anchored to units, assumptions, and engineering constraints; otherwise, it may drift from decisive answer boundaries. WuYuEval therefore provides both an evaluation resource and an empirical basis for developing SWM-oriented foundation models with professional reasoning chains and explicit constraint control.

cs.CL↗

arXiv2026

Human-in-the-Loop Distributed Control of Grid-Interactive Buildings for Demand Response Participation

This paper proposes a human-in-the-loop distributed consensus control approach for demand-side management across multiple buildings. Specifically, a novel framework is introduced in which a human acts as the non-autonomous leader in consensus control of cooperative buildings participating in demand response programs. In this system, the facility manager in the facility building serves as the leader, determining the participation level of cooperative buildings in demand response events while simultaneously considering occupants' comfort. Cooperative buildings align their responses with the facility management building, despite lacking direct access to the facility manager's decisions, which presents a challenge for observer design. To address this, a nonlinear unknown input sliding-mode observer is proposed, tailored for leader-follower multi-agent systems (MASs). Furthermore, a human-in-the-loop leader-follower consensus protocol is introduced, enabling a framework to flexibly manage energy use and balance thermal comfort during demand response events. Simulation results validate the effectiveness of the proposed approach, demonstrating its ability to achieve consensus, maintain system performance, and enhance the adaptability of power grid operations under various demand response scenarios.

eess.SY↗

arXiv2026

CHMAS: A Coupled Hierarchical Framework for Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) systems face fundamental challenges in balancing global coordination with local execution across different temporal scales. This paper introduces the Coupled Hierarchical Multi-Agent System (CHMAS), a novel framework that decomposes multi-agent decision-making into centralized strategic planning and distributed tactical execution with bidirectional information flow. The strategic layer integrates all agents' states with an exclusive global environmental state to generate guidance actions every $T$ timesteps, while tactical agents execute distributed policies augmented by strategic guidance and local neighborhood observations. Unlike existing hierarchical approaches with unidirectional control, CHMAS establishes a feedback mechanism where accumulated tactical rewards influence strategic objectives through a coupling coefficient $\lambda$, ensuring strategic plans remain grounded in tactical feasibility. To address the non-stationarity inherent in hierarchical learning, we propose an asynchronous update protocol where strategic parameters update every $N_f$ tactical episodes, allowing tactical policies to converge to quasi-stationary points between strategic changes. We present both a general bi-level formulation capturing full system dynamics and a tractable additive approximation enabling rigorous analysis. Theoretical analysis proves that this asynchronous scheme achieves $\mathcal{O}(\log K/\sqrt{K})$ convergence for the strategic layer after $K$ strategic updates under standard assumptions. Experimental validation in a multi-agent foraging domain demonstrates successful learning of spatially partitioned exploration strategies, with both layers converging stably despite hierarchical coupling.

cs.MA↗

arXiv2026

Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces

We develop the Continuous Distributed Coupled Policy Gradient (CDCPG) algorithm for cooperative reinforcement learning in networked Markov decision processes with continuous state and action spaces. Each agent maintains a local actor over a bounded graph neighborhood, and a localized least-squares temporal-difference critic evaluates a truncated action-value function through a spectral random-feature representation of the local transition kernel. The analysis makes four contributions. First, the truncated action-value function is constructed as a conditional expectation over the neighborhood, yielding a well-posed localized Bellman theory that removes the continuation-kernel mismatch of naive truncation arguments. Second, we expose a dimensional obstruction to temporal-difference stability for normalized random features and prove an unconditional excitation bound that reduces stability to a symmetric persistence-of-excitation condition, monitorable through an online matrix-concentration certificate. Third, under exponential spatial decay of agent interactions, the excitation condition, and smoothness of the objective, CDCPG drives an averaged per-agent stationarity measure to within any excess $\epsilon$ of an explicitly characterized approximation floor using $\widetilde{\mathcal{O}}(\epsilon^{-2})$ shared-oracle samples, and the excess dependence matches the smooth nonconvex first-order rate; per-agent computation and communication are governed by the neighborhood size rather than the network size. Fourth, an adaptive-locality rule selects the radius that balances truncation and graph-decay residuals against the target accuracy. Experiments on a networked linear-quadratic benchmark corroborate the locality and feature-dimension predictions.

cs.MA↗

arXiv2026

Optimal Safety Control using High-Order Control Barrier Functions

This paper investigates the optimal safety control problem of nonlinear control systems by proposing novel high-order control barrier functions (HOCBFs). Different from zeroing HOCBFs, two novel HOCBFs are derived and the safety controllers are designed in an explicit way. Next, we implement vector Lyapunov function approach to propose a novel high-order control Lyapunov function (HOCLF) for the stabilization control problem. The relations between the proposed and existing HOCBFs are discussed. Afterwards, the compatibility of the proposed HOCLF and HOCBF is addressed to guarantee the stabilization and safety control objectives simultaneously, and thus the optimal controller is established. Finally, a numerical example from the navigation problem of quadrotors is presented to illustrate the efficacy of the derived results.

eess.SY↗

arXiv2026

How to Train a Real-World Silicon Concierge? Internalizing Complex Business Workflow to Only OneModel

Traditional industrial agents rely on modular pipelines, including Router, Retriever, Planner, Executor, Responder, Reviewer, and other components. These systems often fracture into a labyrinth of ad-hoc patches, leading to cascading errors and high latency. We propose OneModel, an applicable paradigm shift from external workflows to internalized knowledge representation. Unlike modular systems that slice fluid user intents into static steps, OneModel consolidates complex business logic and SOPs directly into the model parameters. Through Continual Pre-training (CPT) and logic-compilation SFT, we transform fragmented business rules into intuitive model reasoning within a unified attention space. Deployed in our global financial service system, OneModel effectively breaks the trade-off between latency, accuracy, and complexity. Online A/B testing demonstrates an end-to-end latency reduction of more than 50 percent, from 18.7 seconds to 8.0 seconds, while the Intelligent Resolution Rate (IRR) increases from 64.3 percent to 83.3 percent. The results show that OneModel can replace brittle engineering logic with internalized cognitive intuition, offering a scalable blueprint for transitioning industrial agents from complex, error-prone workflows to unified model architectures.

cs.CL↗

arXiv2026

Ferroelectric polarization controlled orbital Hall conductivity in a higher-order topological insulator: \textit{d1T}-phase monolayer MoS$_2$

The higher-order topological insulator is an extended concept of the conventional topological insulator, which obeys the generalization of the standard bulk-boundary correspondence. In our paper, we predict the monolayer \textit{d1T}-phase transition metal dichalcogenide MoS$_2$ to be a higher-order topological insulator, while also possessing intriguing ferroelectric characteristics. We explicitly demonstrate the nontrivial topological index and reveal the hallmark corner states with quantized fractional charge within the bulk band gap. Second, we show the existence of a nonzero orbital Hall conductivity plateau within the energy gap which is a signature to identify higher-order topology system. Additionally, we investigate the relationship between the ferroelectricity and the orbital Hall conductivity of \textit{d1T} MoS$_2$ and find that the direction of ferroelectric polarization can modulate the positive and negative values of the orbital Hall conductivity $\sigma_{\rm{OH}}^x$. Our findings provide the theory and material candidate for ferroelectricity tunable orbital Hall effect which is promising to realize the external electric field controllable orbitronics.

cond-mat.mtrl-sci↗

arXiv2026

Observation of flat-bottom U-shaped energy gap in high-Tc nickelate (La,Pr)3Ni2O7 thin films

The discovery of high transition temperature (high-Tc) superconductivity in Ruddlesden-Popper (R-P) bilayer nickelates under high pressure has stimulated extensive work to understand the underlying mechanism and search for superconductors with higher Tc. The recent realization of superconductivity in R-P bilayer nickelate thin films with onset Tc above 40 K at ambient-pressure enables the use of a wide array of powerful experimental tools to investigate the unconventional high-Tc superconductivity in bilayer nickelates. Here, using ultra-low temperature scanning tunneling microscopy/spectroscopy (STM/S) and electrical transport study, we report the first successful observation of an energy-symmetric, flat-bottom U-shaped gap with zero residual density of states around the Fermi level in the high-Tc nickelate (La,Pr)3Ni2O7 thin film grown on SrLaAlO4 substrate. Before and after STM/S studies, transport measurements on the same sample reveal consistent superconducting behaviors showing zero resistance, with an onset Tc above 40 K and zero resistance Tc above 20 K. The tunneling spectra exhibit highly unconventional temperature evolution, characterized by a rapid filling of the U-shaped energy gap to a V-shaped gap as the temperature increases. Furthermore, the U-shaped energy gap is reduced under a c-axis magnetic field of 14 T. The energy-symmetric U-shaped gap, taken together with its dependence on magnetic field and temperature, is consistent with the behavior of a superconducting gap, suggesting a nodeless gap function at ultra-low temperatures. Our findings shed new lights on the nature of high-Tc superconductivity and provide an encouraging and thought-provoking hint for a local superconductivity with Tc above liquid nitrogen boiling temperature in nickelate superconductors at ambient or zero pressure.

cond-mat.supr-con↗

arXiv2026

Reactive Planning based Control for Mobile Robots in Obstacle-Cluttered Environments

This paper addresses the motion control problem for mobile robots in obstacle-cluttered environments. The mobile robot has partial environment information only, and aims to move from an initial position to a target position without collisions. For this purpose, a reactive planning based control strategy (RPCS) is proposed. First, the initial and target positions are connected as a reference trajectory. Then, a reactive planning strategy (RPS) is developed to ensure the collision avoidance by modifying the reference trajectory locally based on the partial environment information. Next, an adaptive tracking control strategy (ATCS) is proposed to track the reference trajectory with potentially local modifications via the discretization techniques. Finally, the RPS and ATCS are combined to establish the RPCS, whose efficacy and advantages are illustrated by numerical examples.

cs.RO↗

arXiv2026

Probing the Chirality of Trigonal Selenium and Tellurium by Spin and Orbital Hall Effects

Chiral crystals exhibit enantiomer-dependent transport phenomena that generate pure spin or orbital currents, while the handedness sensitivity of spin and orbital Hall conductivities (SHC/OHC) remains insufficiently understood. Using first-principles calculations, we demonstrate that trigonal selenium and tellurium -- prototypical chiral semiconductors -- exhibit opposite signs of the SHC/OHC tensor elements $\sigma_{yx}^{S_y}$ and $\sigma_{yx}^{L_y}$ between their left- and right-handed enantiomers. This behavior originates from the mirror operation relating the two structures, described by space groups $P3_221$ (left-handed) and $P3_121$ (right-handed). Although both enantiomers share identical band structures and four nonzero SHC/OHC tensor components, $\sigma_{yx}^{S_y}$ and $\sigma_{yx}^{L_y}$ reverse sign due to the antisymmetric transformation of the spin/orbital Berry curvature under the $M_{xy}$ mirror operation. More generally, for mirror-related enantiomorphic structures, selected SHC/OHC tensor components can exhibit symmetry-governed sign reversal. For trigonal Se and Te, the calculated signs of these components can be directly correlated with the left- and right-handed structures under the chosen coordinate convention. These results clarify the symmetry origin of handedness-dependent SHC/OHC and suggest a possible route for correlating measurable SHC/OHC signals with structural handedness in specific chiral materials.

cond-mat.mtrl-sci↗