arXiv subjects

Yimeng Gu

Publications and source records attributed to Yimeng Gu.

12 recordsLinked to original sources

arXiv2026

Kitaev interactions in the van der Waals antiferromagnet VBr3

Van der Waals materials hosting Kitaev interactions are promising platforms for exploring exotic quantum phenomena. Here, we report inelastic neutron scattering investigations of the van der Waals antiferromagnet VBr3, which forms a honeycomb lattice structure at room temperature and exhibits zigzag-type magnetic order below 26.5 K. Our observations reveal distinctive low-energy spin excitations arising from gamma, gamma', and M' points, each featuring a spin gap of around 2.5 meV. The overall spin excitation spectra can be effectively described by a spin Hamiltonian incorporating substantial nearest-neighbor Kitaev and biquadratic interactions, along with Heisenberg interactions. Our findings not only establish VBr3 as a new Kitaev magnet but also suggest that ligand engineering may provide a promising strategy to modulate Kitaev interactions, offering new opportunities for designing Kitaev materials with tailored quantum properties.

cond-mat.str-el↗

arXiv2025

CogniDir: Combating Cognitive Malicious Comments via Adaptive Distributional Learning for Robust Fake News Detection

The proliferation of Large Language Models (LLMs) has enabled a new class of psychologically grounded malicious comments, shifting fake news attacks from surface-level textual noise to deep cognitive and logical manipulation. This shift severely undermines existing detectors, which conventionally rely on static attack assumptions and fixed training distributions. To bridge this gap, we introduce CogniDir, an adaptive distributional learning framework that reformulates robust detection as a dynamic data mixture optimization problem for social media content safety. Grounded in cognitive psychology, we first formalize mechanism-specific cognitive adversarial paradigms to systematically expose deep-seated detector vulnerabilities. To address the vulnerability heterogeneity, CogniDir derives an information-theoretic score coupling empirical accuracy with probabilistic confidence, which is then mapped to adaptive sampling proportions through a Dirichlet-mean parameterization. This formulation enables smooth, feedback-driven reallocation of training exposure toward the most brittle attack mechanisms. Experimental results on three benchmarks demonstrate that CogniDir yields state-of-the-art robustness, improving F1 scores by up to 17.9% over competitive baselines under heterogeneous, AI-generated adversarial pressures.

cs.LG↗

arXiv2025

Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation

Large Language Models (LLMs) succeed in many natural language processing tasks. However, their tendency to hallucinate - generate plausible but inconsistent or factually incorrect text - can cause significant problems in certain tasks, including response generation in dialogue. To mitigate this issue, we propose two novel graph knowledge-augmented frameworks, Dialogue Response Generation via Textualised Graphs (TG-DRG) and Graph-Aware Dialogue Response Generation (GA-DRG), which combine reasoning-guided dialogue reformulation, dialogue sense knowledge selection, and graph-enhanced response generation to improve the factuality of dialogue responses. To evaluate the factuality of generated responses, we propose a dialogue fact score that addresses the limitations of existing fact-score methods in dialogue settings, providing a more reliable assessment of factual consistency. We evaluate our methods using different baselines on the OpendialKG and HybriDialogue datasets. Our methods noticeably improve factuality compared to other graph knowledge-augmentation baselines, including the state-of-the-art G-retriever, achieving improvements of 3.47% on OpendialKG and 3.12% on HybriDialogue in terms of dialogue fact score. The code will be released on GitHub.

cs.CL↗

arXiv2025

Multi-MLLM Knowledge Distillation for Out-of-Context News Detection

Multimodal out-of-context news is a type of misinformation in which the image is used outside of its original context. Many existing works have leveraged multimodal large language models (MLLMs) for detecting out-of-context news. However, observing the limited zero-shot performance of smaller MLLMs, they generally require label-rich fine-tuning and/or expensive API calls to GPT models to improve the performance, which is impractical in low-resource scenarios. In contrast, we aim to improve the performance of small MLLMs in a more label-efficient and cost-effective manner. To this end, we first prompt multiple teacher MLLMs to generate both label predictions and corresponding rationales, which collectively serve as the teachers' knowledge. We then introduce a two-stage knowledge distillation framework to transfer this knowledge to a student MLLM. In Stage 1, we apply LoRA fine-tuning to the student model using all training data. In Stage 2, we further fine-tune the student model using both LoRA fine-tuning and DPO on the data points where teachers' predictions conflict. This two-stage strategy reduces annotation costs and helps the student model uncover subtle patterns in more challenging cases. Experimental results demonstrate that our approach achieves state-of-the-art performance using less than 10% labeled data.

cs.CL↗

arXiv2024

Signatures of Kitaev interactions in the van der Waals ferromagnet VI3

Materials manifesting the Kitaev model, characterized by bond-dependent interactions on a honeycomb lattice, can host exotic phenomena like quantum spin liquid states and topological magnetic excitations. However, finding such materials remains a formidable challenge. Here, we report high-resolution inelastic neutron scattering measurements performed on VI3, a van der Waals ferromagnetic Mott insulator, covering a wide range of reciprocal space. Our measurements unveil highly anisotropic magnetic excitations in momentum space. Through a comprehensive comparative analysis of various models that incorporate diverse symmetry-allowed magnetic interactions, we find the observed excitations are well captured by a model with a large bond-dependent Kitaev interaction. These results not only help to understand the intriguing properties of VI3, such as the pronounced anomalous thermal Hall effects and strong pressure/structure dependence of magnetism, but also open a new avenue for exploring Kitaev physics.

cond-mat.str-el↗

arXiv2024

Lattice and magnetic structure in the van der Waals antiferromagnet VBr3

We report a comprehensive investigation of the lattice and magnetic structure in van der Waals antiferromagnet VBr3, characterized by a BiI3-type structure at room temperature. Neutron diffraction experiments were performed on both polycrystalline and single-crystalline VBr3 samples, revealing clear magnetic Bragg peaks emerging below the N\'eel temperature of TN = 26.5 K. These magnetic Bragg peaks can be indexed by k = (0, 0.5, 1) in hexagonal notation. Our refinement analysis suggests that the antiferromagnetic order in VBr3 manifests as a zigzag structure. Moreover, we observed peak splitting for nuclear Bragg peaks in the HK-plane below the structure transition temperature of Ts = 94 K, indicating the breaking of 3-fold symmetry within the ab-plane.

cond-mat.str-el↗

arXiv2024

Learning Domain-Invariant Features for Out-of-Context News Detection

Out-of-context news is a common type of misinformation on online media platforms. This involves posting a caption, alongside a mismatched news image. Existing out-of-context news detection models only consider the scenario where pre-labeled data is available for each domain, failing to address the out-of-context news detection on unlabeled domains (e.g. news topics or agencies). In this work, we therefore focus on domain adaptive out-of-context news detection. In order to effectively adapt the detection model to unlabeled news topics or agencies, we propose ConDA-TTA (Contrastive Domain Adaptation with Test-Time Adaptation) which applies contrastive learning and maximum mean discrepancy (MMD) to learn domain-invariant features. In addition, we leverage test-time target domain statistics to further assist domain adaptation. Experimental results show that our approach outperforms baselines in most domain adaptation settings on two public datasets, by as much as 2.93% in F1 and 2.08% in accuracy.

cs.CL↗

arXiv2023

Superconductivity in pressurized trilayer La$_4$Ni$_3$O$_{10-{\delta}}$ single crystals

The pursuit of discovering new high-temperature superconductors that diverge from the copper-based paradigm1-3 carries profound implications for elucidating mechanisms behind superconductivity and may also enable new applications4-8. Here, our investigation reveals that application of pressure effectively suppresses the spin and charge order in trilayer nickelate La4Ni3O10-{\delta} single crystals, leading to the emergence of superconductivity with a maximum critical temperature (Tc) of around 30 K at 69.0 GPa. The DC susceptibility measurements confirm a substantial diamagnetic response below Tc, indicating the presence of bulk superconductivity with a volume fraction exceeding 80%. In the normal state, we observe a "strange metal" behavior, characterized by a linear temperature-dependent resistance extending up to 300 K. Furthermore, the layer-dependent superconductivity observed hints at a unique interlayer coupling mechanism specific to nickelates, setting them apart from cuprates in this regard. Our findings provide crucial insights into the fundamental mechanisms underpinning superconductivity, while also introducing a new material platform to explore the intricate interplay between the spin/charge order, flat band structures, interlayer coupling, strange metal behavior and high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2023

Exchange renormalized crystal field excitation in a quantum Ising magnet KTmSe$_2$

Rare-earth delafossite compounds, ARCh$_2$ (A = alkali or monovalent ion, R = rare earth, Ch = chalcogen), have been proposed for a range of novel quantum phenomena. Particularly, the Tm series, ATmCh$_2$, featuring Tm ions on a triangular lattice, serves as a representative group of compounds to illustrate the interplay and competition between spin-orbit coupling, crystal fields, and exchange couplings in the presence of geometric frustration. Here we report the thermodynamic and inelastic neutron scattering studies on the newly discovered triangular-lattice magnet KTmSe$_2$. Both heat capacity and neutron diffraction reveal the absence of long-range magnetic order. Magnetic susceptibility shows strong Ising-like interactions with antiferromagnetic correlations. Furthermore, inelastic neutron scattering measurements reveal a branch of dispersive crystal field excitations. To analyze these observations, we employ both the transverse field Ising model and the full crystal field scheme, along with exchange interactions. Our results suggest a strong competition between spin exchange interactions and crystal field effects. This work is expected to offer a valuable framework for understanding low-temperature magnetism in KTmSe$_2$ and similar materials.

cond-mat.str-el↗

arXiv2022

Stripe order and spin dynamics in triangular-lattice antiferromagnet KErSe$_{2}$: A single-crystal study with a theoretical description

The rare-earth triangular-lattice chalcogenide is a great platform for exploring both spin liquids and novel magnetic orders with anisotropic spin interactions and magnetic frustrations. Here, we report the thermodynamic and neutron scattering measurements of rare-earth triangular-lattice chalcogenide KErSe$_{2}$, using single-crystal samples. Our experiments revealed a long-range stripe order below 0.2 K. Although the magnetic order was three-dimensional, magnetic excitations exhibited negligible modulation along the z direction, indicating very weak interlayer coupling. Furthermore, magnetic excitation developed a well-defined spin-wave dispersion with a gap of $\sim$0.03 meV at M points. Both the stripe order and spin-wave excitations could be quantitatively understood from the anisotropic spin interactions of the Er$^{3+}$ Kramers doublets.

cond-mat.str-el↗

arXiv2021

Neutron Scattering Studies of the Breathing Pyrochlore Antiferromagnet LiGaCr$_{4}$O$_{8}$

We report neutron scattering measurements of the spinel oxide LiGaCr$_{4}$O$_{8}$, in which magnetic ions Cr$^{3+}$ form a breathing pyrochlore lattice. Our experiments reveal the coexistence of a nearly dispersionless resonance mode and dispersive spin wave excitations in the magnetically ordered state, which can be quantitatively described by a quantum spin model of hexagonal loops and linear spin wave theory with the same set of exchange parameters, respectively. Comparison to other Cr spinel oxides reveals a linear relationship between the resonance energy and lattice constant across all these materials, which is in agreement with our hexagonal loop calculations. Our results suggest a unified picture for spin resonances in Cr spinel oxides.

cond-mat.str-el↗

arXiv2021

Magnetic order and its interplay with structure phase transition in van der Waals ferromagnet VI$_3$

Van der Waals magnet VI$_3$ demonstrates intriguing magnetic properties that render it great for use in various applications. However, its microscopic magnetic structure has not been determined yet. Here, we report neutron diffraction and susceptibility measurements in VI$_3$ that revealed a ferromagnetic order with the moment direction tilted from the $c$-axis by ~36° at 4 K. A spin reorientation accompanied by a structure distortion within the honeycomb plane is observed at a temperature of ~27 K, before the magnetic order completely disappears at $T_C$ = 50 K. The refined magnetic moment of ~1.3 $μ_B$ at 4 K is considerably lower than the fully ordered spin moment of 2 $μ_B$/ V$^{3+}$, suggesting the presence of a considerable orbital moment antiparallel to the spin moment and strong spin-orbit coupling in VI$_3$. This results in strong magnetoelastic interactions that make the magnetic properties of VI$_3$ easily tunable via strain and pressure.

cond-mat.str-el↗