arXiv subjects

Qiang Zhang

Publications and source records attributed to Qiang Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Anomalous Hall Response Induced by Correlated Disorder in the Breathing Kagome Lattice Mn$_{3}$Sn

Macroscopic transport tensors are generally constrained by the average crystallographic and magnetic symmetries of a material. In the kagome antiferromagnetic Weyl semimetals Mn$_{3+\delta}X$ ($X=$~Sn or Ge), previous studies showed that the anomalous Hall conductivity $\sigma_{yx}$ is forbidden by the average \hexsg{} structure and coplanar inverse-triangular magnetic order. Here we report that nearly stoichiometric Mn$_3$Sn nevertheless exhibits a finite $\sigma_{yx}$ with large hysteresis, together with enhanced $\sigma_{zx}$ and $\sigma_{yz}$, in the inverse-triangular phase below $T_{\mathrm{N1}}\approx 440~\mathrm{K}$, whereas all AHE components vanish in the amplitude-modulated conical phase below $T_{\mathrm{N2}}\approx 280~\mathrm{K}$. Total scattering and magnetic pair distribution function analysis reveal correlated orthorhombic distortions and noncoplanar Mn moments. First-principles calculations show that this coupled lattice-spin distortion activates the average symmetry forbidden $\sigma_{yx}$ within the inverse-triangular phase. Its disappearance below $T_{\mathrm{N2}}$ indicates that the correlated disorder must cooperate with a long-range inverse-triangular antiferromagnetic order capable of supporting Berry curvature. Our results establish correlated disorder as an active symmetry-breaking degree of freedom that enables topological transport inaccessible from the Bragg-average structure alone.

cond-mat.str-el↗

arXiv2026

NVV-Locator: From Transcript Tags to Acoustic Boundaries for Fine-Grained Nonverbal Vocalization Grounding

Human speech includes nonverbal vocalizations (NVVs), such as laughter, sighs, breaths, and coughs, which convey affective and interactional information. Existing approaches typically represent NVVs as transcript-level tags, providing limited supervision for their waveform-time boundaries. We present NVV-Locator for fine-grained NVV temporal grounding. We first unify 26 NVV categories across public resources and construct large-scale timestamp-supervised training data through dual-LLM verification, transcript-guided forced alignment, and energy-based boundary refinement. We further introduce NVV-TimeBench, an expert-refined benchmark with 667 utterances and 1,094 events. NVV-Locator uses a non-autoregressive slot-filling architecture to jointly predict lexical timestamps, NVV categories, and event boundaries. On NVV-TimeBench, it achieves 71.0% Micro F1, 70.2% Macro F1, 80.4% Macro mIoU, and 59.6 ms Macro mMAE, outperforming the evaluated large audio model counterparts. Evaluation on an external corpus further demonstrates the cross-corpus generalization of NVV-Locator.

eess.AS↗

arXiv2026

ARISE-RL: Agentic Rubric-Grounded Iterative Self-Evolution with Reinforcement Learning

Training open-ended agents via reinforcement learning (RL) is hindered by the lack of verifiable gold answers and scalable rubrics. Moreover, even near the model's capability boundary, long-horizon open-ended agentic tasks often yield brittle and unstable rewards, resulting in weak or noisy rollout contrast that obscures fine-grained optimization signals for group-based policy learning. To address these challenges, we propose ARISE-RL, a novel full-cycle self-evolution framework that couples a task/rubric Generator and a reasoning Solver through rubric-mediated co-evolution. The Generator grounds tool-related rubric criteria in real tool observations and is rewarded for producing valid, intermediate-difficulty tasks aligned with the Solver's evolving capability boundary. The Solver, in turn, learns from fine-grained rubric satisfaction signals through multi-step reasoning and tool use. We further introduce Reward-Gated Self-Evolution Distillation (RG-SED), which selectively distills a memory-augmented variant of the same policy back into itself only when the memory yields empirical reward improvement, thereby reducing distribution mismatch and avoiding blind imitation of noisy guidance. Finally, to support rigorous evaluation, we present ECR-Bench, an expert-calibrated rubric benchmark suite covering single-tool deep research and multi-tool travel planning. Extensive experiments demonstrate that ARISE-RL consistently achieves robust and stable overall state-of-the-art performance across all evaluated benchmarks.

cs.AI↗

arXiv2026

Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall

Logit-based knowledge distillation (KD) is used to train smaller language models (LMs) via supervision from stronger teachers, but whether its benefits are consistent across training stages remains unclear. Through controlled experiments, we find that forward Kullback-Leibler (KL) distillation--the standard KD formulation--with post-trained teachers behaves fundamentally differently during mid-training, an intermediate phase of self-supervised learning on curated corpora. Surprisingly, while forward KD simultaneously improves reasoning and factual recall during pre-training relative to standard next-token prediction (NTP), it instead slows factual recall acquisition during mid-training despite continued reasoning gains. We trace this stage dependence to an asymmetry in teacher confidence across data domains and the student's evolving knowledge state: teachers are more confident on procedural than knowledge-intensive data, while students acquire low-entropy factual knowledge earlier in training. To mitigate this imbalance, we propose Switch Distillation, a simple mid-training objective that distills on tokens where the teacher is confident, using teacher predictive entropy as a lightweight routing signal, and otherwise falls back to cross-entropy. Switch Distillation consistently outperforms existing distillation objectives across teacher sizes. Relative to standard NTP, it achieves 1.61-1.71x the reasoning performance and 1.13-1.19x the knowledge and commonsense performance while preserving 96.7-96.8% of factual recall. Crucially, these benefits persist after post-training: Switch Distillation closes the factual recall gap while maintaining 1.25-1.32x and 1.13-1.20x gains in reasoning and knowledge and commonsense, respectively.

cs.CL↗

arXiv2026

SOLO: Stable Omni-terrain Long-Horizon Perceptive Humanoid Locomotion

Humans traverse complex terrain over long distances without losing balance, whereas perceptive humanoid policies become fragile as perception and control errors accumulate. We present SOLO, a unified framework addressing two compounding causes of this long-horizon fragility: dense terrain reconstruction smooths action-critical details, and pointwise imitation lacks temporal credit assignment. Its Query Reconstructor (QR) uses Fourier-encoded cell queries to retrieve spatially specific evidence from depth-proprioception tokens, preserving sharp terrain boundaries. Trajectory-Aware MSE (TA-MSE) Distillation adds next-state teacher-student disagreement to the PPO reward, enabling Generalized Advantage Estimation to propagate future disagreement penalties to preceding actions. In simulation, QR reduces height-map L1 error by factors of 3.3-4.0, while TA-MSE surpasses PPO and MSE+PPO in curriculum progression. On stress-test terrains, SOLO achieves 97.5% mean traversal success and 96% stepping-stone success, versus 75.0-75.6% and 0-3% for dense-reconstructor variants. Deployed zero-shot with only a chest-mounted depth camera and proprioception, SOLO completes a continuous 1.5-km outdoor route and an indoor mixed-terrain course. Project page: https://sunpihai-up.github.io/solo/

cs.RO↗

arXiv2026

Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classification

Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased classifiers on imbalanced data, sampling is one of the most popular data pre-processing techniques because of its classifier-agnostic nature. However, due to the complex temporal dependencies in original time-series data and the scarcity of minority-class samples, existing sampling methods, including interpolation-based oversampling methods and deep learning-based generative models, usually suffer from limited generalization and poor diversity when generating new time-series samples. This paper proposes a Frequency-domain representation-guided Multi-tree Genetic Programming-based oversampling approach (FreMGP) to imbalanced time-series classification, where each individual represents a set of synthetic samples for the minority class. A frequency-domain class-discriminative representation module based on contrastive learning is also developed, guiding the evolutionary search toward high-quality synthetic time-series samples. Experiments on imbalanced time-series datasets demonstrate that FreMGP outperforms existing oversampling methods and consistently improves the performance of different classifiers, including both general machine learning and deep learning models.

cs.LG↗

arXiv2026

Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models

Subsampling is effective in tackling computational challenges for massive data with rare events. Overly aggressive subsampling may adversely affect estimation efficiency, and optimal subsampling is essential to mitigate the information loss. However, existing optimal subsampling probabilities depend on data scales, and some scaling transformations may result in inefficient subsamples. This problem is more significant when there are inactive features, because their influence on the subsampling probabilities can be arbitrarily magnified by inappropriate scaling transformations. We tackle this challenge and introduce a scale-invariant optimal subsampling function in the context of sparse models, where inactive features are commonly assumed. Instead of focusing on estimating model parameters, we define an optimal subsampling function to minimize the prediction error, using adaptive lasso to outline the estimation procedure and study its theoretical guarantee. We first introduce the adaptive lasso estimator for rare-events data and establish its oracle properties, thereby validating the use of subsampling. Then we derive a scale-invariant optimal subsampling function that minimizes the prediction error of the inverse probability weighted (IPW) adaptive lasso. Finally, we present an estimator based on the maximum sampled conditional likelihood (MSCL) to further improve the estimation efficiency. We conduct numerical experiments using both simulated and real-world data sets to demonstrate the performance of the proposed methods.

stat.ML↗

arXiv2026

RoboStriker: Latent-Space Strategic Games for Autonomous Humanoid Boxing

Achieving human-level competitive intelligence and physical agility in humanoid robots remains a profound challenge, particularly in contact-rich and highly dynamic tasks such as boxing. While Multi-Agent Reinforcement Learning offers a principled framework for strategic interaction, its direct application to unstructured raw motor spaces inevitably leads to joint-level physical collapse, preventing the emergence of any viable combat tactics. To resolve this fundamental conflict between strategic exploration and physical feasibility, we formulate the humanoid combat task as a novel two-player latent-space zero-sum Markov game. Under standard regularity and approximate best-response assumptions, we show that the latent formulation induces an equivalent game over the decoder-reachable action manifold, providing an approximate-Nash interpretation of the resulting self-play dynamics. To instantiate this theoretical formulation, we propose RoboStriker, a hierarchical framework that decouples high-level reasoning from low-level execution. It first distills the tracking expertise of predefined boxing motions into a topologically bounded latent manifold. This structured latent foundation subsequently drives multi-agent co-evolution via Latent-Space Neural Fictitious Self-Play. Extensive experimental results demonstrate that gaming within this structured latent space substantially outperforms direct exploration. By constraining strategic exploration through a pretrained motion decoder, RoboStriker substantially reduces the catastrophic balance failures observed in raw action-space methods and achieves superior tactical performance in both competitive win rates and striking efficiency. Finally, we successfully deploy and validate our learned combat policies on real-world humanoid robots. Our code and video and supplementary materials are available at RoboStriker.

cs.RO↗

arXiv2026

HPSO: Particle Swarm Optimization with Hypergraph-Based Topology

Particle swarm optimization (PSO) has been widely applied to solve complex optimization problems from real-world applications due to its efficient exploration of large solution spaces and the ability to converge towards optimal solutions without requiring gradient information. Common swarm topologies in standard PSO and its variants, e.g., Ring and Star, can be regarded as graphs, where each edge connects only two particles. Such topology structures allow direct interactions only between connected particle pairs, and thus often fail to directly capture the higher-order social relationships that are necessary for navigating complex search landscapes. Therefore, this article proposes a novel PSO variant termed Hypergraph-assisted Particle Swarm Optimization (HPSO). In HPSO, the topology of the particles in a swarm is modeled by a hypergraph, in which hyperedges are used to connect multiple particles. This allows multiple particles within a hyperedge to interact directly. Furthermore, an adaptive hypergraph updating strategy is designed to periodically reconstruct the topology based on cumulative average particle displacement, thereby maintaining swarm diversity throughout the evolutionary process. In the experiments, the effectiveness of HPSO is verified on the IEEE CEC'17 benchmark suite, and the results demonstrate that HPSO achieves promising performance across various types of functions. Furthermore, the ablation experiment demonstrates that HPSO has excellent search capabilities.

cs.NE↗

arXiv2026

When Teachers Mislead: Spurious-Signal-Aware On-Policy Distillation

On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent selective OPD methods improve this process by prioritizing signals that are confident, informative, or learnable. However, the assumptions overlook a fundamental failure mode of language models: their token-level judgments can be driven by input-agnostic language priors, formatting conventions, or stereotyped reasoning templates rather than task-specific evidence. We refer to such optimization-relevant but weakly input-grounded supervision as spurious signals in OPD, which may produce large gradients while contributing little task-improving direction. To mitigate this issue, we propose SA-OPD, a Spurious-Signal-Aware On-Policy Distillation framework that identifies and filters misleading token-level supervision based on input-groundedness and optimization impact. SA-OPD introduces a lightweight input-groundedness proxy estimating whether a token-level distillation signal truly depends on the input. It then filters only tokens that simultaneously exhibit low input-groundedness and extreme distillation divergence, thereby removing high-impact spurious updates and achieving fine-grained OPD optimization. Extensive experiments on both large language model (LLM) and vision-language model (VLM) settings demonstrate that SA-OPD consistently outperforms Vanilla OPD and competitive selective methods. These results establish input-groundedness as a key dimension for OPD supervision selection and offer a simple, effective strategy for mitigating spurious updates.

cs.AI↗

arXiv2026

Local magnetic order in vacancy-disrupted spin ice Ho2TiO5

We investigate how local magnetic correlations evolve when the classical pyrochlore spin-ice Ho2Ti2O7 is transformed into the partially disordered stuffed compound Ho2TiO5. Neutron scattering measurements were analyzed using real-space magnetic pair distribution function, reciprocal-space reverse Monte Carlo, and half-polarized neutron powder diffraction methods to connect the average crystal structure with local magnetic correlations. Both compounds retain long-range $Fd-3m symmetry, but in Ho2TiO5 this average structure distorts locally through Ho-O, Ti-O and O-O bond-length disorder associated with partial Ho/Ti occupancy. Despite this disorder, half-polarized neutron powder diffraction shows that the Ho moments retain local <111> rangle Ising anisotropy and form spin-in/spin-out configurations on the tetrahedral network. In Ho2Ti2O7, real- and reciprocal-space analyses reveal a nearly ideal two-in/two-out spin-ice state at 0.3K, with 95% of tetrahedra satisfying the ice rule, followed by progressive thermal disordering on warming. In Ho2TiO5, most tetrahedra are magnetically incomplete because some tetrahedral vertices are occupied by nonmagnetic Ti rather than Ho; nevertheless, the dominant incomplete configurations are 2-in/1-out and 1-in/2-out, which are locally compatible with the ice rule if the missing Ho spin is restored. The single-ion <111> Ising anisotropy is therefore retained throughout, while the collective ice ordering is strongly disrupted: Ho2TiO5 is a vacancy-disrupted spin ice in which a local ice-rule tendency persists on a topologically incomplete magnetic network.

cond-mat.str-el↗

arXiv2026

Non-relativistic spin splitting in a triangular metal-excess magnet Fe$_{1+\delta}$Sb

Non-relativistic spin-splitting (NRSS) antiferromagnets have recently emerged as an important class of magnetic materials that combine compensated magnetism with momentum-dependent spin splitting, offering new opportunities for spintronic applications. Here, we investigate a NiAs-type Fe$_{1+\delta}$Sb series ($\delta = 0.17$-0.30) using neutron diffraction, pair distribution function, magnetometry and density functional theory calculations. Neutron diffraction establishes that Fe$_{1+\delta}$Sb adopts a $120^\circ$ coplanar compensated magnetic order with a non-zero propagation vector $\mathbf{k}=(1/3,\,1/3,\,0)$. Increasing interstitial Fe suppresses the ordered magnetic moment while inducing local symmetry lowering, as revealed by pair distribution function refinements. Density functional theory predicts momentum-dependent spin splitting, dominated by an out-of-plane spin polarization with an odd-parity f-wave-like symmetry, establishing the material as a non-collinear NRSS antiferromagnet. Motivated by the structural similarities between Fe$_{1+\delta}$Sb and a known altermagnet CrSb, we further investigate their solid solution and find that Cr substitution at intermediate concentrations gives rise to a ferromagnetic component and a cluster spin-glass behavior. These results establish Fe$_{1+\delta}$Sb as a new platform for non-collinear NRSS antiferromagnetism and demonstrate metal interstitial and substitution as effective parameters for tuning the magnetic order and properties.

cond-mat.mtrl-sci↗

arXiv2026

SciToolAgent-Evo: An Ontology-Aware Self-Evolving Agent for Open-World Scientific Tool Acquisition

Large language model (LLM) agents have been increasingly adopted in scientific research for organizing and invoking specialized computational tools. However, their reliance on predefined tool spaces with static semantics limits their applicability to open-world scientific workflows, where tool requirements, capabilities, and boundaries evolve dynamically. To this end, we propose SciToolAgent-Evo, an ontology-aware self-evolving agent for open-world scientific tool acquisition. Driven by an evolving memory of skills, experiences, and an ontologized tool graph, it distills generalizable knowledge from contrastive trajectories during accumulation, whereas during inference, it formulates active requests and utilizes a LinUCB-based bandit gate to dynamically balance exploration and exploitation. Once a novel tool is acquired, its scientific ontology is completed online for seamless integration into the known graph. Moreover, we introduce OpenSciToolBench, a benchmark containing 900 realistic tasks across four difficulty levels. Extensive evaluations show that SciToolAgent-Evo achieves state-of-the-art performance, validating its robustness and generalization.

cs.AI↗

arXiv2026

Commutator relators of one-relator groups do not force Hopficity, residual finiteness, or automaticity

Let $G=F/\langle\langle r\rangle\rangle$ be a one-relator group with the relator $r\in [F,F]$ or $r=[u,v] ~(u,v\in F)$, where $F$ is a finitely generated free group. Baumslag asked whether $G$ is Hopfian, residually finite or automatic. In the case of $r\in[F,F]$, a negative answer to the residual finiteness and automaticity has already been obtained by a result of Olshanskii. In this note, we construct a family of one-relator groups $$G_m=\left\langle a,t\ \middle|\ [t,a[a,t]^{-m}]\right\rangle,$$ whose relators are commutators, each of which has a Baumslag-Solitar subgroup as a retract. These groups provide negative answers to these three questions in both cases.

math.GR↗

arXiv2026

FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation

Vision-and-language navigation (VLN) in continuous environments requires an agent to ground instructions in egocentric observations while maintaining spatial understanding across long action sequences. Recent navigation foundation models have shown strong progress by scaling vision-language models, but they often learn navigation primarily as direct action generation, without explicitly modeling world states or predicting their future evolution. We introduce FutureNav, a VLM-based unified world-action modeling framework for vision-and-language navigation. Specifically, FutureNav jointly encodes text, visual, and spatial features and feeds them into the LLM, and optimizes four objectives for simultaneous world and action modeling: an action policy objective for navigation action prediction, inverse and forward dynamics objectives for modeling state transitions, and a future generation objective for predicting future spatial states. This unified architecture strengthens action prediction while explicitly modeling the world, without sacrificing inference speed. Extensive experiments show that, with only a 4B-scale backbone, FutureNav achieves state-of-the-art performance on multiple VLN benchmarks and substantially outperforms prior VLN methods, paving the way toward future world-action models for VLN. We will release the code and models to support future research.

cs.RO↗

arXiv2026

Survival of the metallic state in a single-hole multiband $p$-orbital molecular system

Strong correlations and ferromagnetic Hund's coupling lead to diverse electronic phenomena in transition-metal oxides that sensitively depend on the $d$-orbital electron filling. Fullerides, their $p$-electron counterparts, exhibit effective antiferromagnetic Hund's coupling in a different energy range. At half-filling ($n=3$, three electrons in triply degenerate orbitals), both $d-$ and $p$-electron systems are Mott insulators due to strong correlations and Hund's coupling. Away from half-filling, in single-electron/hole ($n=1,5$) $d$-orbital systems, Hund's coupling opposes the correlations, reducing the Mott gap and allowing survival of metallicity. Here we report a single-hole multiorbital correlated $p$-electron system, orthorhombic-structured Yb$_2$CsC$_{60}$ comprising pentavalent C$_{60}^{5-}$ anions, which also exhibits a robust metallic state with no Mott transition, just like in the metastable single-electron cubic-structured CsC$_{60}$. We assert that particle-hole symmetry holds well in ($n=1,5$) fullerides and that their $p$-electron-derived states are analogous to those in $d$-orbital solids, providing impetus for further study of these correlated systems.

cond-mat.str-el↗

arXiv2026

Value-order Decomposition for Generalist Anomaly Detection

Industrial anomaly detection suffers from limited data, making cross-domain generalization particularly challenging. Generalist Anomaly Detection (GAD) aims to train a unified model on a source domain that can effectively detect anomalies in unseen target domains. In the initial semantic feature space, strong entanglement between anomalies and object categories or defect types hinders effective generalization across domains. Recent works address this issue by projecting features into a residual space; however, such methods primarily increase cross-domain overlap for normal features, while anomalous features remain specific to object categories, defect types and data domains, leading to poor alignment and generalization. To address this limitation, we propose Value-order Decomposition (VOD), a simple yet effective technique that bridges \textbf{three types of generalization gaps} across object categories, defect types (including real and synthetic defects), and data domains. VOD disentangles and suppresses object-category-, defect-type-, and domain-specific information, promoting alignment within normal and abnormal samples while preserving their separability, thereby enabling robust generalization across the three gaps. Leveraging the strong alignment between real and synthetic defects within the same object, we perform anomaly detection using only normal and synthetic-abnormal reference, and effectively generalize to unseen real defect types. Experiments on diverse industrial and medical benchmarks demonstrate that our method, using a simple cut-and-paste anomaly simulation strategy, achieves strong generalization across the three gaps.

cs.CV↗

arXiv2026

GenHOI: Contact-Aware Humanoid-Object Interaction by Imitating Generated Videos without Task-Specific Training

Humanoid-Object Interaction (HOI) is a fundamental capability for humanoid robots, yet it remains challenging due to the tight coupling between dynamic balance and stable interaction with diverse objects. Existing methods often require time-consuming task-specific policy training or rely on rigid trajectory replay, which limits their ability to accommodate novel interaction scenarios. In this work, we present \textit{GenHOI}, a simple yet effective framework that enables humanoid robots to perform diverse object-interaction tasks in a zero-shot manner by directly imitating a single generated video, without task-specific training or physical demonstration data. GenHOI first reconstructs the robot-object scene in simulation and renders a first-frame image, which, together with the language command, conditions the synthesis of a task-oriented interaction video. The generated video is then analyzed to identify interaction-relevant contact events and estimate hand-object contact regions, which are encoded as object-centric geometric constraints that convert visual interaction cues into physically grounded optimization priors. Guided by these priors, the reference motion recovered from the video is refined and smoothed to resolve the scale ambiguity inherent in 2D video generation, while adapting a single reference trajectory to unseen robot-object relative poses. The optimized trajectory is finally executed by a closed-loop tracking controller. We validate the proposed framework in extensive simulation and real-world experiments across diverse object-interaction tasks, including box grasping, asymmetric bimanual chair carrying, table lifting from below, and cylindrical-object enveloping.

cs.RO↗