arXiv subjects

Na Wu

Publications and source records attributed to Na Wu.

7 recordsLinked to original sources

arXiv2026

Antiferromagnetism-altered plasmon dynamics

The interaction between plasmons and magnons is a long-sought phenomenon with implications for fundamental physics and spintronics applications. In three-dimensional systems, this coupling is suppressed by the large mismatch in energy scales, but two-dimensional (2D) plasmons with gapless dispersion can overlap with magnons over a broad spectral range. Despite numerous theoretical predictions, experimental observation of magnon-plasmon interaction has remained elusive. In this work, we study a first-of-its-kind hybrid plasmon-magnon platform based on 2D materials. By deploying scattering-type scanning near-field optical microscopy (s-SNOM) with terahertz radiation, we image propagating plasmon wavepackets at a graphene/NiPS$_3$ interface and track their dynamics across the antiferromagnetic transition of NiPS$_3$. We observe a clear renormalization of the plasmon-polariton dispersion concurrent with the onset of antiferromagnetic order. With complementary Raman scattering and nano-terahertz spectroscopy, we unveil spectral weight redistribution and dielectric screening changes, potentially associated with the multi-magnon continuum, as the underlying mechanism. These results provide solid evidence of coupling between plasmon and antiferromagnetic order, marking a cornerstone for a potential platform for hybrid magnon-plasmon interactions in 2D materials, opening avenues for coherent spin-plasmon devices and tunable terahertz spintronic components.

cond-mat.str-el↗

arXiv2026

EnterpriseRAG: Benchmarking LLM Instruction Adherence and Robustness under Non-Ideal Enterprise Retrieval

Enterprise RAG deployments face a critical reliability gap: while LLMs satisfy 80% of individual constraints, only 26.8% of responses meet all requirements simultaneously, revealing a 57-point orchestration gap. Existing benchmarks assume clean retrieval with simple queries, failing to capture production conditions where noisy documents and multi-dimensional constraints coexist. We introduce EnterpriseRAG, a benchmark of 983 expert-validated samples across six domains that systematically simulates three failure modes absent from prior work: retrieval noise, knowledge gaps, and factual conflicts, coupled with complex instructions. Evaluation of 13 state-of-the-art LLMs reveals a severe instruction adherence collapse, where high per-constraint satisfaction masks low holistic compliance. Critical findings expose deep barriers under knowledge gaps and factual conflicts, even with reasoning-enhanced inference, indicating production RAG requires explicit context-aware protocols and calibrated judgment. EnterpriseRAG provides a reproducible foundation for measuring and closing these gaps, directly informing deployment decisions for enterprise-scale RAG systems. We will release the benchmark and evaluation framework upon publication.

cs.AI↗

arXiv2026

JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data

We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe model toward a more comprehensive safety-by-design paradigm. JT-Safe-V2 emphasizes the joint optimization of general intelligence and safety-by-design through several key innovations: enriching pre-training data with contextual world knowledge, high-certainty pre-training procedures, and safety strengthening post-training mechanisms for enterprise-oriented agentic capabilities. Building on these safety-enhanced foundation models, we propose Safe-MoMA (Safe Mixture of Models and Agents), a framework that enables traceable and efficient inference through the orchestrated deployment of multiple models and agents. Extensive evaluations demonstrate that JT-Safe-V2 achieves state-of-the-art performance across both general intelligence and safety benchmarks. Moreover, Safe-MoMA reduces inference costs by more than 30\% compared to using the largest standalone model baseline while maintaining comparable performance. To facilitate future research on safety-by-design foundation models, we publicly release the post-trained JT-Safe-V2-35B model checkpoint.

cs.AI↗

arXiv2026

Coherent terahertz magnon-phonon three-wave mixing in a layered antiferromagnet

The coherent nonlinear dynamics between collective excitations, such as magnons and phonons, drive emergent phenomena in quantum materials, yet their direct observation remains a central challenge. Here, using double-terahertz-pump optical-probe spectroscopy, we report the direct observation of coherent magnon-phonon three-wave mixing in the layered antiferromagnetic insulator FePS$_{3}$. We resolve both second- and third-order nonlinear responses of antiferromagnetic magnons and identify a suite of nonlinear couplings in two-dimensional (2D) coherent spectra, including definitive sum- and difference-frequency generation between magnons and phonons. These results lay the groundwork for exploiting coherent nonlinearities to entangle magnetic and vibrational excitations, opening avenues for quantum control and hybrid quantum technologies in the terahertz regime.

cond-mat.mtrl-sci↗

arXiv2025

JT-Safe: Intrinsically Enhancing the Safety and Trustworthiness of LLMs

The hallucination and credibility concerns of large language models (LLMs) are global challenges that the industry is collectively addressing. Recently, a significant amount of advances have been made on post-training and inference techniques to mitigate these challenges. However, it is widely agreed that unsafe and hallucinations of LLMs intrinsically originate from pre-training, involving pre-training data and the next-token prediction learning mechanism. In this paper, we focus on enhancing pre-training data to improve the trustworthiness and safety of LLMs. Since the data is vast, it's almost impossible to entirely purge the data of factual errors, logical inconsistencies, or distributional biases. Moreover, the pre-training data lack grounding in real-world knowledge. Each piece of data is treated as a sequence of tokens rather than as a representation of a part of the world. To overcome these issues, we propose approaches to enhancing our pre-training data with its context in the world and increasing a substantial amount of data reflecting industrial scenarios. We argue that most source data are created by the authors for specific purposes in a certain spatial-temporal context. They have played a role in the real world. By incorporating related world context information, we aim to better anchor pre-training data within real-world scenarios, thereby reducing uncertainty in model training and enhancing the model's safety and trustworthiness. We refer to our Data with World Context as DWC. We continue pre-training an earlier checkpoint of JT-35B-Base with 1.5 trillion of DWC tokens. We introduce our post-training procedures to activate the potentials of DWC. Compared with the Qwen model of a similar scale, JT-Safe-35B achieves an average performance improvement of 1.79% on the Safety and Trustworthy evaluation benchmarks, while being pretrained with only 6.2 trillion tokens.

cs.CL↗

arXiv2025

Light-driven lattice metastability for enhanced superconductivity in FeSe/SrTiO3

Driven quantum materials with on demand properties controlled by external stimuli are critical for emergent quantum technology. In optically tunable superconducting heterostructures, the lattice responses at the buried interface may hold the key to the light susceptibility but is very challenging to detect. In this work, a nondestructive synchrotron-based X-ray scattering phase-retrieval technique is implemented in monolayer-FeSe/SrTiO3 heterostructures to capture the three-dimensional interfacial atomic displacements in-situ as the interface superconductivity is actively manipulated by light. It is found that the interlayer sliding between FeSe and SrTiO3 can drastically alter how the lattice responds to the light. In domains with selected stacking configurations, the interface transforms the very weak photoexcitation in SrTiO3 into significant Fe-atom displacements in FeSe and generate metastable interfacial structures that can lead to a persistent superconductivity enhancement. These findings demonstrate an effective strategy for achieving greatly amplified light-lattice coupling for efficient quantum phase manipulations at designed interfaces.

cond-mat.mtrl-sci↗

arXiv2013

Inhomogeneous dynamic nuclear polarization and suppression of electron-polarization decay in a quantum dot

We investigate the dynamic nuclear polarization process by frequently injecting polarized electron spins into a quantum dot. Due to the suppression of the direct dipolar and indirect electron-mediated nuclear spin interactions, by the frequently injected electron spins, the analytical predictions under the independent spin approximation agree well with quantum numerical simulations. Our results show that the acquired nuclear polarization is highly inhomogeneous, proportional to the square of the local electron-nuclear hyperfine interaction constant, if the injection frequency is high. Utilizing the inhomogeneously polarized nuclear spins as an initial state, we further show that the electron-polarization decay time can be extended 100 times even at a relatively low nuclear polarization (<20%), without much suppression of the fluctuation of the Overhauser field. Our results lay the foundation for future investigations of the effect of DNP in more complex spin systems, such as double quantum dots and nitrogen vacancy centers in diamonds.

cond-mat.mes-hall↗