arXiv subjects

Rongsheng Li

Publications and source records attributed to Rongsheng Li.

18 recordsLinked to original sources

arXiv2026

Ultrafast giant enhancement of second harmonic generation in a strongly correlated cobaltite

In quantum materials, nonlinear optical responses are highly sensitive to electronic structure and many-body interactions. Probing and manipulating such nonlinear processes is a complex and subtle endeavor, yet it offers deep insights into emerging physics and functionalities. Here, we report an anomalous ultrafast enhancement of second harmonic generation (SHG) in a strongly correlated cobaltite YbBaCo$_4$O$_7$. Above-bandgap femtosecond pumping increases SHG intensity by up to 60 % within 200 fs, with the enhancement persisting for tens of picoseconds. The enhancement is strongly anisotropic, with substantial amplification of in-plane susceptibility tensors, whereas the out-of-plane counterpart shows negligible change. We attribute these anomalies to ultrafast photodoping-induced modulation of the on-site Coulomb repulsion, which dynamically renormalizes the band structure and selectively amplifies specific $χ^{(2)}$ components. These nonlinear dynamics encode rich information about the orbital symmetries and energies of the states involved, opening new avenues for all-optical probing of electronic structure in strongly correlated materials.

cond-mat.str-el↗

arXiv2026

Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment

In dynamic environments, large language models need to keep adapting to new tasks, but continual learning often suffers from forgetting, limited transfer, and vulnerability to adversarial perturbations. To address this, we present AdvCL, which repurposes adversarial perturbations as a geometric control signal for stable continual adaptation. AdvCL combines three plug-in modules: Intra-Smooth promotes local smoothness via small adversarial perturbations; Proto-Clip uses similarity clipping to prevent excessive alignment to current task prototype; and Inter-Align applies directional alignment toward previous task prototype to reduce representational gaps. Experiments show consistent gains in both standard performance and robustness, with lower forgetting and stronger transfer. We further analyze key mechanisms by quantifying the sensitivity of Intra-Smooth to perturbation settings and the effect of Inter-Align on task similarity and geometric distance. In summary, the modules provide complementary gains when combined, and each can also be integrated individually into diverse CL paradigms, including replay, regularization, and dynamic architectures, thereby offering a geometric control mechanism for continual learning.

cs.LG↗

arXiv2026

The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck

Tool-using LLM agents must act on untrusted webpages, emails, files, and API outputs while issuing privileged tool calls. Existing defenses often mediate trust at the granularity of an entire tool invocation, forcing a brittle choice in mixed-trust workflows: allow external content to influence a call and risk hijacked destinations or commands, or quarantine the call and block benign retrieval-then-act behavior. The key observation behind this paper is that indirect prompt injection becomes dangerous not when untrusted content appears in context, but when it determines an authority-bearing argument. We present \textsc{PACT} (\emph{Provenance-Aware Capability Contracts}), a runtime monitor that assigns semantic roles to tool arguments, tracks value provenance across replanning steps, and checks whether each argument's origin satisfies its role-specific trust contract. Under oracle provenance, \textsc{PACT} achieves 100\% utility and 100\% security on mixed-trust diagnostic suites, while flat invocation-level monitors incur false positives or false negatives. In full AgentDojo deployments across five models, \textsc{PACT} reaches 100\% security on the three strongest models while recovering 38.1--46.4\% utility, 8--16 percentage points above CaMeL at the same security level. Ablations show that both semantic roles and cross-step provenance are necessary. \textsc{PACT} reframes agent security as authority binding, and isolates the remaining deployment bottleneck to provenance inference and contract synthesis.

cs.CR↗

arXiv2025

Mn4Al11: A Half-Semimetal Candidate with Anomalous Electronic Behaviors

Half-semimetals, characterized by their spin-polarized electronic states, hold significant promise for spintronic applications but remain scarce due to stringent electronic and magnetic criteria. Through a combination of transport measurements and optical spectroscopy, we investigated the intermetallic compound Mn4Al11, which features an exceptionally low carrier concentration and undergoes a magnetic phase transition near 68 K. Transport measurements reveal anomalies that deviate from typical metallic behavior at low temperatures. Optical spectroscopy indicates a small, nearly frequency-independent optical conductivity in the far-infrared region, with spectral weight decreasing as the temperature drops from 300 K to 50 K. These behaviors suggest a temperaturedependent carrier density and significant scattering of charge carriers. Combining experimental findings with calculated electronic band structures, we propose that Mn4Al11 is a novel half-semimetal candidate exhibiting a ferrimagnetic ground state.

cond-mat.str-el↗

arXiv2025

Field-induced spin continuum in twin-free Na$_3$Co$_2$SbO$_6$ revealed by magneto-THz spectroscopy

The honeycomb magnet Na$_3$Co$_2$SbO$_6$ recently emerged as a promising candidate for realizing Kitaev quantum spin liquid with relatively low levels of structural disorder. While the precise spin Hamiltonian remains controversial, the potential existence of a quantum spin liquid or other novel quantum magnetic phases continues to stimulate investigation. Here, we study the temperature and magnetic field-dependent spin excitations of Na$_3$Co$_2$SbO$_6$ on a twin-free single crystal using magneto-terahertz (THz) spectroscopy, focusing on magnetic anisotropy and field-induced unusual phases. We observe a low-energy continuum excitation above $T_N$ and a 0.5 THz (2 meV) spin wave excitation in magnetic order under zero field. Upon applying an in-plane magnetic field, the spin waves transform to a magnetic continuum over an intermediate field range, above which the system enters a spin-polarized state. Crucially, the spin excitation spectra reveal striking anisotropy between the $\textbf{a}$ and $\textbf{b}$ crystallographic axes, demanding description by a strongly anisotropic spin model. These findings establish Na$_3$Co$_2$SbO$_6$ as a model system for investigating field-tunable quantum magnetism and potential spin liquid behavior in highly anisotropic systems.

cond-mat.str-el↗

arXiv2025

TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection

Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate embedding-based approaches for text anomaly detection. TAD-Bench integrates multiple datasets spanning different domains, combining state-of-the-art embeddings from large language models with a variety of anomaly detection algorithms. Through extensive experiments, we analyze the interplay between embeddings and detection methods, uncovering their strengths, weaknesses, and applicability to different tasks. These findings offer new perspectives on building more robust, efficient, and generalizable anomaly detection systems for real-world applications.

cs.CL↗

arXiv2025

Curriculum-style Data Augmentation for LLM-based Metaphor Detection

Recently, utilizing large language models (LLMs) for metaphor detection has achieved promising results. However, these methods heavily rely on the capabilities of closed-source LLMs, which come with relatively high inference costs and latency. To address this, we propose a method for metaphor detection by fine-tuning open-source LLMs, effectively reducing inference costs and latency with a single inference step. Furthermore, metaphor detection suffers from a severe data scarcity problem, which hinders effective fine-tuning of LLMs. To tackle this, we introduce Curriculum-style Data Augmentation (CDA). Specifically, before fine-tuning, we evaluate the training data to identify correctly predicted instances for fine-tuning, while incorrectly predicted instances are used as seed data for data augmentation. This approach enables the model to quickly learn simpler knowledge and progressively acquire more complex knowledge, thereby improving performance incrementally. Experimental results demonstrate that our method achieves state-of-the-art performance across all baselines. Additionally, we provide detailed ablation studies to validate the effectiveness of CDA.

cs.CL↗

arXiv2025

Origin of the density wave instability in trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$ revealed by optical and ultrafast spectroscopy

In the intricate phase diagram of unconventional superconductors characterized by intertwined electronic orders and superconductivity, a key step in understanding the superconducting mechanism is to investigate the parent compounds from which superconductivity emerges through doping or pressure. In this study, we employed optical spectroscopy and ultrafast reflectivity measurements to examine the density wave instability in the trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$, which displays pressure-induced superconductivity up to 30 K. Our optical spectroscopy measurements reveal that La$_{4}$Ni$_{3}$O$_{10}$ behaves as a metal with a high plasma frequency. Upon cooling, we observed a distinct formation of a density wave energy gap in both optical conductivity and pump-probe measurements. The gap feature is more pronounced compared to the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$. Through a comparison of the experimentally determined plasma frequency with first-principles calculations, we classify La$_{4}$Ni$_{3}$O$_{10}$ as a moderately electron-correlated material, resembling the parent compound of iron-based superconductors but exhibiting weaker correlation than the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$. The enhanced gap feature and weaker electronic correlation in La$_{4}$Ni$_{3}$O$_{10}$ may explain its lower superconductivity transition temperature under high pressure. These findings significantly advance our comprehension of the density wave and superconductivity mechanisms in the trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$.

cond-mat.str-el↗

arXiv2024

Room-temperature non-volatile optical manipulation of polar order in a charge density wave

Utilizing ultrafast light-matter interaction to manipulate electronic states of quantum materials is an emerging area of research in condensed matter physics. It has significant implications for the development of future ultrafast electronic devices. However, the ability to induce long-lasting metastable electronic states in a fully reversible manner is a long-standing challenge.Here, by using ultrafast laser excitations, we demonstrate the capability to manipulate the electronic polar states in the charge-density-wavematerial EuTe4 in a non-volatile manner. The process is completely reversible and is achieved at room temperature with an all-optical approach. Each induced non-volatile state brings about modifications to the electrical resistance and second harmonic generation intensity. The results point to layer-specific phase inversion dynamics by which photoexcitation mediates the stacking polar order of the system. Our findings extend the scope of non-volatile all-optical control of electronic states to ambient conditions, and highlight a distinct role of layerdependent phase manipulation in quasi-two-dimensional systems with inherent sublayer stacking orders.

cond-mat.str-el↗

arXiv2024

A Survey on the Real Power of ChatGPT

ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT. A key challenge for the evaluation is that ChatGPT is still closed-source and traditional benchmark datasets may have been used by ChatGPT as the training data. In this paper, (i) we survey recent studies which uncover the real performance levels of ChatGPT in seven categories of NLP tasks, (ii) review the social implications and safety issues of ChatGPT, and (iii) emphasize key challenges and opportunities for its evaluation. We hope our survey can shed some light on its blackbox manner, so that researchers are not misleaded by its surface generation.

cs.CL↗

arXiv2024

Enhancing Metaphor Detection through Soft Labels and Target Word Prediction

Metaphors play a significant role in our everyday communication, yet detecting them presents a challenge. Traditional methods often struggle with improper application of language rules and a tendency to overlook data sparsity. To address these issues, we integrate knowledge distillation and prompt learning into metaphor detection. Our approach revolves around a tailored prompt learning framework specifically designed for metaphor detection. By strategically masking target words and providing relevant prompt data, we guide the model to accurately predict the contextual meanings of these words. This approach not only mitigates confusion stemming from the literal meanings of the words but also ensures effective application of language rules for metaphor detection. Furthermore, we've introduced a teacher model to generate valuable soft labels. These soft labels provide a similar effect to label smoothing and help prevent the model from becoming over confident and effectively addresses the challenge of data sparsity. Experimental results demonstrate that our model has achieved state-of-the-art performance, as evidenced by its remarkable results across various datasets.

cs.CL↗

arXiv2023

Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifically, although cross-modal MAE methods learn strong 3D representations via the auxiliary of other modal knowledge, they often suffer from heavy computational burdens and heavily rely on massive cross-modal data pairs that are often unavailable, which hinders their applications in practice. Instead, single-modal methods with solely point clouds as input are preferred in real applications due to their simplicity and efficiency. However, such methods easily suffer from limited 3D representations with global random mask input. To learn compact 3D representations, we propose a simple yet effective Point Feature Enhancement Masked Autoencoders (Point-FEMAE), which mainly consists of a global branch and a local branch to capture latent semantic features. Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct. Meanwhile, to further enhance features in the local branch, we propose a Local Enhancement Module with local patch convolution to perceive fine-grained local context at larger scales. Our method significantly improves the pre-training efficiency compared to cross-modal alternatives, and extensive downstream experiments underscore the state-of-the-art effectiveness, particularly outperforming our baseline (Point-MAE) by 5.16%, 5.00%, and 5.04% in three variants of ScanObjectNN, respectively. The code is available at https://github.com/zyh16143998882/AAAI24-PointFEMAE.

cs.CV↗

arXiv2023

Revealing the frequency-dependent oscillations in the nonlinear terahertz response induced by the Josephson current

Nonlinear responses of superconductors to intense terahertz radiation has been an active research frontier. Using terahertz pump-terahertz probe spectroscopy, we investigate the c-axis nonlinear optical response of a high-temperature superconducting cuprate. After excitation by a single-cycle terahertz pump pulse, the reflectivity of the probe pulse oscillates as the pump-probe delay is varied. Interestingly, the oscillatory central frequency scales linearly with the probe frequency, a fact widely overlooked in pump-probe experiments. By theoretically solving the nonlinear optical reflection problem on the interface, we show that our observation is well explained by the Josephson-type third-order nonlinear electrodynamics, together with the emission coefficient from inside the material into free space. The latter results in a strong enhancement of the emitted signal whose physical frequency is around the Josephson plasma edge. Our result offers a benchmark for and new insights into strong-field terahertz spectroscopy of related quantum materials.

cond-mat.supr-con↗

arXiv2023

Pump-induced terahertz conductivity response and peculiar bound state in Mn3Si2Te6

We report the significant enhancement on ultrafast terahertz optical conductivity and the unexpected formation of a polaronic-like state in semiconductor Mn3Si2Te6 at room temperature. With the absorption of pump photons, the low-frequency terahertz photoconductivity spectrum exhibits a significant rise, quickly forming a broad peak and subsequently shifting to higher energy. The short-lived nature of the broad peak, as well as the distribution of optical constants, strongly points towards a transient polaron mechanism. Our study not only provides profound insights into the remarkable photoelectric response of Mn3Si2Te6 but also highlights its significant potential for future photoelectric applications.

cond-mat.mtrl-sci↗

arXiv2023

Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection

Generalized Few-Shot Intent Detection (GFSID) is challenging and realistic because it needs to categorize both seen and novel intents simultaneously. Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents. To address the dilemma, we propose to convert the GFSID task into the class incremental learning paradigm. Specifically, we propose a two-stage learning framework, which sequentially learns the knowledge of different intents in various periods via prompt learning. And then we exploit prototypes for categorizing both seen and novel intents. Furthermore, to achieve the transfer knowledge of intents in different stages, for different scenarios we design two knowledge preservation methods which close to realistic applications. Extensive experiments and detailed analyses on two widely used datasets show that our framework based on the class incremental learning paradigm achieves promising performance.

cs.CL↗

arXiv2023

Retrieval-Augmented Meta Learning for Low-Resource Text Classification

Meta learning have achieved promising performance in low-resource text classification which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. However, due to the limited training data in the meta-learning scenario and the inherent properties of parameterized neural networks, poor generalization performance has become a pressing problem that needs to be addressed. To deal with this issue, we propose a meta-learning based method called Retrieval-Augmented Meta Learning(RAML). It not only uses parameterization for inference but also retrieves non-parametric knowledge from an external corpus to make inferences, which greatly alleviates the problem of poor generalization performance caused by the lack of diverse training data in meta-learning. This method differs from previous models that solely rely on parameters, as it explicitly emphasizes the importance of non-parametric knowledge, aiming to strike a balance between parameterized neural networks and non-parametric knowledge. The model is required to determine which knowledge to access and utilize during inference. Additionally, our multi-view passages fusion network module can effectively and efficiently integrate the retrieved information into low-resource classification task. The extensive experiments demonstrate that RAML significantly outperforms current SOTA low-resource text classification models.

cs.CL↗

arXiv2023

Optical spectroscopy and band structure calculations of structural phase transition in the Vanadium-based kagome metal ScV$_6$Sn$_6$

In condensed matter physics, materials with kagome lattice display a range of exotic quantum states, including charge density wave (CDW), superconductivity and magnetism. Recently, the intermetallic kagome metal ScV6Sn6 was discovered to undergo a first-order structural phase transition with the formation of a root3xroot3x3 CDW at around 92 K. The bulk electronic band properties are crucial to understanding the origin of the structural phase transition. Here, we conducted an optical spectroscopy study in combination with band structure calculations across the structural transition. Our findings showed abrupt changes in the optical reflectivity/conductivity spectra as a result of the structural transition, without any observable gap formation behavior. The optical measurements and band calculations actually reveal a sudden change of the band structure after transition. It is important to note that this phase transition is of the first-order type, which distinguishes it from conventional density-wave type condensations. Our results provide an insight into the origin of the structural phase transition in this new and unique kagome lattice.

cond-mat.str-el↗

arXiv2022

Strong nonlinear optical response and transient symmetry switching in Type-II Weyl semimetal $β$-WP2

The topological Weyl semimetals with peculiar band structure exhibit novel nonlinear optical enhancement phenomena even for light at optical wavelengths. While many intriguing nonlinear optical effects were constantly uncovered in type-I semimetals, few experimental works focused on basic nonlinear optical properties in type-II Weyl semimetals. Here we perform a fundamental static and time-resolved second harmonic generation (SHG) on the three dimensional Type-II Weyl semimetal candidate $β$-WP$_2$. Although $β$-WP$_2$ exhibits extremely high conductivity and an extraordinarily large mean free path, the second harmonic generation is unscreened by conduction electrons, we observed rather strong SHG response compared to non-topological polar metals and archetypal ferroelectric insulators. Additionally, our time-resolved SHG experiment traces ultrafast symmetry switching and reveals that polar metal $β$-WP$_2$ tends to form inversion symmetric metastable state after photo-excitation. Intense femtosecond laser pulse could optically drive symmetry switching and tune nonlinear optical response on ultrafast timescales although the interlayer coupling of $β$-WP$_2$ is very strong. Our work is illuminating for the polar metal nonlinear optics and potential ultrafast topological optoelectronic applications.

cond-mat.mtrl-sci↗