arXiv subjects

Kai Chang

Publications and source records attributed to Kai Chang.

At least 19 recordsLinked to original sources

arXiv2026

Rotational Doppler Effect and Helimagnon Frequency Comb in Sliding Magnetic Spirals

Collective spin motion can reshape magnon spectra by providing a dynamical magnetic background. Magnetic spirals offer a particularly clean setting, as they simultaneously host propagating helimagnons and a gapless Goldstone mode, i.e., the sliding of the spiral. Here, we show that once a helimagnon is excited and the spiral slides, the motion, which rotates the local spin frame, produces a thresholdless rotational Doppler shift of the helimagnon spectrum, with even winding for the linear mode and odd winding for its second harmonic. Stronger excitation generates one finite-momentum phason. Mixing of that phason with the helimagnon produces a helimagnon frequency comb only inside a finite driving window, spaced by the phason frequency fixed by the spiral length rather than by the sliding rate. These two scales are separable in a microwave experiment: the Doppler offset tracks the sliding velocity, whereas the comb spacing scales inversely with the length of the spiral.

cond-mat.mtrl-sci↗

arXiv2026

LingLanMedBench: A Unified Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine

Reliable evaluation of large language models in traditional Chinese medicine remains limited because existing benchmarks mainly focus on examination-style question answering or isolated generative tasks. We developed LingLanMedBench, a unified benchmark comprising 25,620 expert-verified items across 13 subtasks and five domains, covering knowledge, language understanding, information extraction, and diagnostic-therapeutic decision-making. LingLanMedBench standardizes task formats and evaluation metrics and further incorporates synonym-tolerant clinical-label matching, empirically derived hard subsets, and decision-recognition reformulations of open-ended clinical tasks. Fourteen open-source and proprietary models were evaluated under a standardized zero-shot protocol. Performance varied substantially across domains, model families, task formulations, and difficulty levels. The leading overall score decreased from 51.1 on the full benchmark to 31.9 on the hard subsets. Prescription performance also differed markedly between open-ended generation, with F1 scores of 32.7 and 20.3, and decision recognition, with accuracies of 90.6 and 56.8 on the full and hard subsets, respectively. These findings reveal a substantial gap between knowledge-oriented performance and clinically faithful reasoning. LingLanMedBench provides a standardized and reproducible framework for characterizing current capabilities, identifying systematic limitations, and tracking meaningful progress in large language models for traditional Chinese medicine.

cs.AI↗

arXiv2026

Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine

Large language models have advanced medical reasoning, but clinically grounded reasoning in traditional Chinese medicine (TCM) remains challenging because prescription decisions require integrating domain knowledge, syndrome differentiation, treatment principles, herbal prescription composition, and dosage determination. We develop a TCM language model family comprising models with 4 billion, 8 billion, and 14 billion parameters. The artificial intelligence contribution is a staged clinical alignment framework integrating large scale continued pretraining on TCM and clinical records, complementary reasoning supervision, and prescription-centered reinforcement learning (RL). We further formulate herbal prescription generation as a structured RL problem with prescription-level feedback, enabling direct optimization of clinical decision outputs beyond knowledge reproduction and generic instruction following. The engineering application is TCM clinical decision support, particularly herbal prescription recommendation. We evaluate the models on a public benchmark containing 25,620 expert-verified items across 13 TCM knowledge and clinical reasoning tasks and on a private retrospective dataset of 4,348 spleen and stomach disease prescription cases. The 14 billion parameter reinforcement aligned model achieves a prescription generation F1 of 37.0 on the public benchmark, compared with 32.3 for DeepSeek-R1 with 671 billion total parameters, and a herb-level F1 of 52.8 on the clinical dataset with improved dosage agreement. Reinforcement post-training consistently improves prescription recommendation across all scales, while physician-source analysis shows that expert heterogeneity affects reward stability. These findings demonstrate a scalable pathway from domain grounding and reasoning supervision to clinically oriented prescription decision support.

cs.CL↗

arXiv2026

Artificial intelligences and human scientists exhibit complementary strengths in theory building

We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social science such as theory formulation, predictions of novel empirical results, and theory revision in response to new evidence. The research domain was academic discourse regarding gender and race inequality. Our findings, comparing 25 LLMs with 13 senior researchers and 60 doctoral scholars, reveal that the AIs outperformed most humans individually on most of the present tasks, while human theories were more diverse and exhibited greater gains in predictive accuracy from aggregation. AI-generated theories were more extensively elaborated, involving additional theoretical paths and latent variables, and were rated as higher quality than human theories by independent raters blinded to source. However, this theoretical complexity was in part ornamental, in that it was not associated with more accurate predictions about empirical patterns in data; in contrast, human scientists achieved greater predictive efficiency with simpler theories. The AIs were significantly more likely than human scientists to revise their theories to incorporate new evidence; human scientists updated their beliefs in a selective way that is sensitive to prior prediction errors. We speculate that the superior processing capacity of artificial intelligences makes them especially well-suited to tasks requiring grappling with complexity, but that the greater diversity of human ideas is essential to wise crowds and collective creativity.

cs.AI↗

arXiv2026

Symmetry-enforced third-order nonlinear thermal Hall effects in altermagnets

Thermal Hall effect (THE) is a powerful probe of material properties, even in insulators. Here we investigate the thermal response of altermagnets by developing a theory on Berry curvature driven nonlinear THE from both electrons and magnons. We identify symmetry conditions under which the third-order THE dominates while linear and second-order contributions vanish. Notably, d-wave and i-wave altermagnets with out-of-plane Neel order satisfy these conditions, whereas in-plane order yields a dominant linear THE. Using KV2Se2O (a d-wave altermagnetic metal) and MnF2 (an altermagnetic insulator) as concrete examples, we show nonzero third-order nonlinear THE from electrons and magnons, respectively, both exhibiting a pi-periodic dependence on the direction of temperature gradient. These symmetry-guaranteed Berry curvature phenomena provide a diagnostic for altermagnetism in candidate materials and enable determination of the Neel vector orientation.

cond-mat.mtrl-sci↗

arXiv2026

DiagChain: A Diagnostic Benchmark for Evaluating LLM Agents on Evidence-Grounded Attack Chain Reconstruction

Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to infer ordered attacker actions. However, existing benchmarks mainly evaluate final outputs or aggregate accuracy, providing limited insight into how errors arise and propagate across intermediate reasoning stages. We present DiagChain, a diagnostic benchmark for evidence-grounded attack chain reconstruction that enables stage-wise evaluation of LLM agents. DiagChain includes MAIN-69, a suite of 69 scenarios spanning multiple operating systems, evidence noise levels, and chain lengths. It further introduces Evidence-Centric Retrieval-Augmented Generation (ECRAG), which couples evidence retrieval with an evolving structured representation of the reconstructed chain. Five complementary metrics are introduced to assess distinct stages of the reconstruction process and support systematic failure diagnosis. Based on evaluations using 6 LLMs, DiagChain reveals that even the strongest configuration succeeds on only 39.6% of the 849 reference steps in MAIN-69. Our analysis further shows that smaller models struggle with the more basic task of incorporating retrieved evidence into their outputs, whereas larger models can proceed to later steps, where correctly ordering that evidence becomes the main bottleneck. These results validate the importance of diagnostic evaluation beyond end-to-end accuracy and provide actionable insights for improving evidence-grounded cybersecurity agents.

cs.CR↗

arXiv2026

Fractional Chern Insulators Transition in Non-ideal Flat Bands of Twisted Mono-bilayer Graphene

Fractional Chern insulators (FCIs) in ideal flat bands with Chern number $C$ are commonly understood as color-entangled states constructed from $C$ copies of the lowest Landau level. In realistic moiré systems, however, the band geometry is generally non-ideal, and the mechanism that stabilizes such FCIs remains unclear. Using twisted monolayer-bilayer graphene as a platform, we find two FCIs separated by a topological transition that occurs in a regime signaled by a local geometric instability of the Bloch states. Below the transition, the target $C=2$ conduction band is geometrically stable, and the resulting fractional phase is naturally described by the Halperin-$(112)$ state. Above the transition, the system becomes geometrically unstable and enters a Laughlin-$1/3$ phase within the same target $C=2$ manifold, which persists even as standard quantum-geometry indicators degrade further. We attribute this Laughlin-$1/3$ phase to a hidden near-ideal $C=1$ component of the non-ideal $C=2$ Bloch states that becomes relevant under interactions, while its strongly non-ideal partner becomes irrelevant. We support this picture by applying a weak perpendicular magnetic field that acts as a ``color separator,'' directly visualizing the ideal subcomponent at the single-particle level. Together, these results clarify how non-ideal flat bands can stabilize FCIs, greatly expanding their parameter range and sharpening the role of quantum geometry in strongly correlated topological phases.

cond-mat.mes-hall↗

arXiv2026

LingShu: A Large-Scale Symptom-Centric Contextualized Knowledge Graph Bridging Traditional Chinese Medicine and Modern Biomedicine

Biomedical knowledge graphs (KGs) are pivotal for knowledge organization, yet traditional binary relations often struggle to represent the conditional nature of biomedical knowledge. Symptoms provide a shared phenotypic layer for linking Traditional Chinese Medicine (TCM), which relies on symptom patterns for syndrome differentiation and treatment selection, with modern biomedicine, which connects clinical manifestations to diseases and molecular mechanisms. We present LingShu, a large-scale symptom-centric contextualized knowledge graph designed to bridge TCM and modern biomedicine. The exported version of LingShu analyzed in this study comprises 17.33 million atom-level entity records and 39.47 million relation records, including 17.19 million semantic triples and 22.29 million contextualized quadruples. LingShu integrates multi-source data, including clinical electronic medical records, authoritative TCM texts, biomedical ontologies, and curated knowledge bases, through a pipeline combining natural language processing, terminology normalization, and human-in-the-loop verification. A key innovation of LingShu is its hybrid data model: it maintains 64 typed triple relation patterns to ensure broad connectivity, while incorporating 35 contextual quadruple relation patterns to capture conditional medical associations. This dual-structure approach explicitly encodes conditional knowledge, providing a granular representation of the contexts associated with medical relations. These contextualized relations cover syndrome-dependent herb efficacy, disease-contextualized drug effects, population-specific clinical associations, and mechanism-related therapeutic responses. Furthermore, we developed a web platform (http://www.tcmkg.com/) that integrates graph visualization, graph-based reasoning, and an evidence-grounded knowledge question-answering agent.

cs.CL↗

arXiv2026

Extreme Event Aware ($η$-) Learning

Quantifying and predicting rare and extreme events is challenging because such events are infrequent, severe, and expensive to simulate. Existing data-driven methods often require multiple extremes in the training data or sampling process, leading to accurate predictions in quiescent regimes but high epistemic uncertainty in extreme-event regions. To overcome this limitation, we introduce Extreme Event Aware ($η$-) Learning, which does not require extreme events in the available data. The method reduces uncertainty even in uncharted extreme regimes by enforcing during training the statistics of an observable indicative of extremeness, obtained from qualitative knowledge or unlabeled data. This statistical regularization results in models that fit observed data while remaining consistent with prescribed observable statistics, enabling the generation of unprecedented extreme events. Optimal-transport-based theoretical results offer rigorous justification and establish key optimality properties. Numerical experiments on prototype systems and real-world precipitation downscaling problems demonstrate the effectiveness of the $η$-learning framework.

stat.ML↗

arXiv2026

Imaging the Néel Vector in Two-Dimensional Antiferromagnets using Antisymmetric Compton Scattering

We demonstrate that antisymmetric Compton scattering can detect both the switching and the continuous rotation of the Néel vector in two-dimensional (2D) antiferromagnets. By probing magnetoelectric (ME) multipoles, which couple electric and magnetic dipoles, this approach overcomes the limitations of conventional techniques that rely on a finite net magnetization. Using a group-theoretical decomposition of the staggered moments in 2D MnPS$_3$ into irreducible representations, combined with first-principles calculations, we show that the antisymmetric Compton profile (ACP) is highly sensitive to the Néel vector orientation: it reverses sign under Néel vector reversal and exhibits distinct anisotropies under in-plane rotation. These results establish the ACP as a versatile probe of antiferromagnetic (AFM) order and magnetoelectric phenomena in van der Waals materials.

cond-mat.mtrl-sci↗

arXiv2026

Antiferromagnetic pseudospintronics without spin splitting

Antiferromagnets (AFMs) are promising for high-density spintronics due to their zero net magnetization, yet conventional AFM spintronics relies on spin splitting-a requirement that excludes many collinear AFMs with compensated spin sublattices. Here we exploit the sublattice degree of freedom in a honeycomb AFM with zero spin splitting. We uncover a coupling between spin and sublattice: the out-of-plane pseudospin polarization is spin-dependent, a mechanism we term partial pseudospin-spin coupling. This allows switching of the pseudospin polarization by reversing the Néel vector. Introducing an impurity into a specific sublattice induces Friedel oscillations with a sublattice-resolved amplitude ratio dictated solely by the pseudospin polarization, which is directly measurable by spin-polarized scanning tunneling microscopy. Furthermore, we demonstrate Néel-vector-controlled transmission and a large nonvolatile tunneling magnetoresistance in an all-in-one AFM junction, with pronounced resonant enhancement in gate-tunable two-dimensional devices. Our work establishes a new paradigm-AFM pseudospintronics-that utilizes the sublattice pseudospin in zero-spin-splitting AFMs, extending spintronics beyond the conventional spin-splitting paradigm.

cond-mat.mes-hall↗

arXiv2026

Selective stabilization of antiferromagnetic orders in FeTe films via local strain engineering

The parent compound FeTe hosts a complex magnetic landscape that is highly susceptible to lattice distortions. Although theoretical models have predicted a bicollinear to dimer antiferromagnetic (AFM) phase transition under tensile strain, its experimental realization and deterministic control has remained elusive owing to severe magnetic frustration. Here, combining high-resolution scanning tunneling microscopy (STM) and density functional theory (DFT) calculations, we demonstrate the selective stabilization of bicollinear and dimer AFM orders in few-layer FeTe films via local uniaxial strain engineering. By mapping the strain fields near dislocation areas in FeTe films and FeTe/FeSe heterostructures, we establish a direct correspondence between specific strain components and the resulting magnetic ground states. We find that uniaxial compression along the Fe-Fe next-nearest-neighbor direction stabilizes the bicollinear AFM order, with the stripe orientation aligning parallel to the compression axis. Crucially, we report the experimental realization of the long-range dimer AFM order, which emerges under anisotropic strain along the Fe-Fe nearest-neighbor direction. This phase manifests as a distinct $\sqrt{2} \times \sqrt{2}$ electronic reconstruction and shares a common Neel temperature with the bicollinear phase. Our findings reveal that anisotropic strain effectively lifts the magnetic degeneracy among competing states. This work provides a robust strategy for the manipulation of elusive magnetic orders and offers insights into the interplay between lattice, spin, and electronic degrees of freedom in iron-based superconductors.

cond-mat.str-el↗

arXiv2026

Tailoring pure valley-Zeeman spin-orbit coupling in WSe$_2$-encapsulated monolayer graphene

Engineering proximity effects in twisted van der Waals heterostructures offers a powerful platform for designing electronic properties. While theoretical predictions of quantum interference in transition metal dichalcogenide-encapsulated graphene can selectively control the spin-orbit coupling component, experimental realizations have remained elusive. Here, we report pure valley-Zeeman spin-orbit coupling in monolayer graphene, achieved by encapsulation between two parallel twisted WSe$_2$ monolayers. We observed a symmetry-enforced reordering of Landau levels, which is driven by the competition between the fixed valley-Zeeman energy and the magnetic-field-dependent cyclotron energy. This reordering is characterized by a transition from symmetry-broken states in the quantum Hall effect to a restored fourfold degeneracy with integer or half-integer quantum Hall sequences. We also demonstrate the ability to completely quench the proximity spin-orbit coupling by tuning the encapsulated geometry.

cond-mat.mes-hall↗

arXiv2026

Giant Nonlinear Photon-Drag Currents in Moiré Bilayers

The bulk photovoltaic effect provides a fundamental pathway for direct light-to-current conversion in quantum materials. However, these nonlinear currents are often strictly constrained or forbidden by crystal symmetries, hindering their exploration in a broader range of materials. While the nonlinear photon-drag effect leverages finite photon momentum to circumvent these constraints, its investigation has been largely confined to toy models, lacking a robust numerical framework for realistic materials. Here, we develop a unified microscopic theory of nonlinear photon-drag currents formulated within a geometric-loop framework, providing both a transparent quantum-geometric interpretation and numerical tractability. Applying this formalism to twisted bilayer graphene (TBG), we demonstrate that a finite, in-plane photon momentum can trigger massive nonlinear responses, rivaling the giant photovoltaic currents reported in typical 2D materials. These currents exhibit high tunability via photon wavevector, twist angle, and light polarization. Our work not only provides a generalized framework for momentum-dependent light-matter interactions but also establishes the nonlinear photon-drag effect as a potent mechanism for unlocking unprecedented optoelectronic functionalities beyond the limitations of the conventional bulk photovoltaic effect.

cond-mat.mes-hall↗

arXiv2026

Reversible tuning of magnetic order and intrinsic superconductivity in strained FeTe films via stoichiometry control

FeTe is a prototypical parent compound of iron-based superconductors. While bulk FeTe is non-superconducting with a long-range bicollinear antiferromagnetic order, superconductivity has been achieved in thin films. However, the approaches usually involve complex oxygen incorporation or interfacial effects, the microscopic mechanisms of which remain elusive. Here, we prepare high-purity, bare FeTe thin films on SrTiO3 and investigate their magnetic and superconducting states combining both microscopic and macroscopic characterizations. By reducing the interstitial Fe impurities, we successfully suppress the long-range antiferromagnetic order, enhance the quasiparticle coherence and induce superconductivity at ~10 K. Moreover, this process is readily reversible by tuning the Fe concentration. Our findings reveal that precise stoichiometric control is sufficient to induce intrinsic superconductivity in strained FeTe thin films. This work provides insights into the competition between magnetism and superconductivity in iron chalcogenides, and supplies methods for developing stable, high-purity superconducting FeTe films.

cond-mat.supr-con↗

arXiv2026

Ultrafast manipulation of magnetic skyrmions by microwave fields

We theoretically investigate the inertial dynamics of magnetic skyrmions driven by circularly polarized microwave-induced inverse Faraday effect (MIFE). By incorporating an inertial mass term into the Thiele equation and analytically deriving the microwave-induced magnetic fields and forces, we demonstrate fundamentally distinct dynamical regimes under continuous-wave (CW) versus pulsed excitation. Skyrmion inertia qualitatively transforms trajectories from smooth spirals to polygonal orbits under continuous driving, while enabling sustained post-pulse gyration that reveals the system's intrinsic relaxation dynamics. The handedness of the trajectory is determined by the topological charge and circularly polarized microwave (CPM) helicity: a left-circularly polarized (LCP) CPM attracts skyrmions toward the beam center, while a right-circularly polarized (RCP) CPM repels them. Systematic parameter analysis reveals how Gilbert damping, the intensity and frequency of CPM, and skyrmion mass control the transition between oscillatory and overdamped dynamical phases. Our work identifies inertia, topological charge, and CPM helicity as essential factors in ultrafast skyrmion manipulation and proposes a novel method for designing topological spin textures.

cond-mat.mtrl-sci↗

arXiv2026

Dissipation-Shaped Quantum Geometry in Nonlinear Transport

The theory of the intrinsic nonlinear Hall effect, a key probe of quantum geometry, is plagued by conflicting expressions for the conductivity that is independent of the dissipation strength (rate, $Γ^0$). We clarify the origin of this ambiguity by demonstrating that the "intrinsic" response is not universal, but is inextricably linked to the dissipation mechanism that establishes the non-equilibrium steady state (NESS). We establish a benchmark by solving the exact NESS density matrix for a generic Bloch system coupled to a featureless fermionic bath. Our exact $Γ^0$ conductivity decomposes into two parts: (i) a geometric contribution, $σ^{\text{geo}}$, whose form recovers the intraband quantum metric contribution ($\sim\partial_k g$), providing an exact derivation that clarifies inconsistencies in the literature, and (ii) a novel, purely kinetic contribution, $σ^{\text{kin}} \propto v^3 f^{(4)}_0$, which is absent when dissipation is modeled by white-noise disorder (e.g., a constant-$Γ$ Green's function model). The discrepancy in $σ^{\text{kin}}$ between these distinct physical mechanisms is a proof that the $Γ^0$ nonlinear conductivity is not a unique property of the Bloch Hamiltonian, but is contingent on the physical system-bath coupling.

cond-mat.mes-hall↗

arXiv2026

A ferroelectric junction transistor memory made from switchable van der Waals p-n heterojunctions

Van der Waals (vdW) p-n heterojunctions are important building blocks for advanced electronics and optoelectronics, in which high-quality heterojunctions essentially determine device performances or functionalities. Creating tunable depletion regions with substantially suppressed leakage currents presents huge challenges, but is crucial for heterojunction applications. Here, by using band-aligned p-type SnSe and n-type ferroelectric α-In2Se3 as a model, we report near-ideal multifunctional vdW p-n heterojunctions with small reverse leakage currents (0.1 pA) and a desired diode ideality factor (1.95). We realize ferroelectric-tuned band alignment with a giant barrier modulation of 900 meV. Based on such tunable heterojunctions, we propose and demonstrate a fundamental different memory device termed ferroelectric junction field-effect transistor memory, which shows large memory windows (1.8 V), ultrafast speed (100 ns), high operation temperature (393 K), and low cycle-to-cycle variation (2%). Additionally, the reliable synaptic characteristics of these memory devices promise low-power neuromorphic computing. Our work provides a new device platform with switchable memory heterojunctions, applicable to high performance brain-inspired electronics and optoelectronics.

cond-mat.mtrl-sci↗