arXiv subjects

Chao Wu

Publications and source records attributed to Chao Wu.

At least 73 records · Page 4Linked to original sources

arXiv2025

Type III Valley Polarization and Anomalous Valley Hall Effect in Two-Dimensional Non-Janus and Janus Altermagnet Fe2WS2Se2

Exploiting the valley degree of freedom introduces a novel paradigm for advancing quantum information technology. Currently, the investigation on spontaneous valley polarization mainly focuses on two major types of systems. One type magnetic systems by breaking the time-reversal symmetry, the other is ferroelectric materials through breaking the inversion symmetry. Might there be additional scenarios? Here, we propose to realize spontaneous valley polarization by breaking the mirror symmetry in the altermagnets, named type III valley polarization. Through symmetry analysis and first-principles calculations, we confirm that this mechanism is feasible in Non-Janus Fe2WS2Se2. Monolayer Non-Janus and Janus Fe2WS2Se2 are stable Neel-type antiferromagnetic state with the direct band gap semiconductor. More interestingly, their magnetic anisotropy energy exhibits the rare biaxial anisotropy and a four-leaf clover shape in the xy plane, while the xz and yz planes show the common uniaxial anisotropy. This originated from the fourth-order single ion interactions. More importantly, the valley splitting is spontaneously generated in the Non-Janus Fe2WS2Se2 due to the Mxy symmetry breaking, without requiring the SOC effect. Both the Non-Janus and Janus Fe2WS2Se2 exhibit diverse valley polarization and anomalous valley Hall effect properties. In addition, the magnitude and direction of valley polarization can be effectively tuned by the biaxial strain and magnetic field. Our findings not only expand the realization system of spontaneous valley polarization, but also provide a theoretical basis for the high-density storage of valley degrees of freedom.

cond-mat.mtrl-sci↗

arXiv2025

Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction

As Large Language Models (LLMs) are widely applied in various domains, the safety of LLMs is increasingly attracting attention to avoid their powerful capabilities being misused. Existing jailbreak methods create a forced instruction-following scenario, or search adversarial prompts with prefix or suffix tokens to achieve a specific representation manually or automatically. However, they suffer from low efficiency and explicit jailbreak patterns, far from the real deployment of mass attacks to LLMs. In this paper, we point out that simply rewriting the original instruction can achieve a jailbreak, and we find that this rewriting approach is learnable and transferable. We propose the Rewrite to Jailbreak (R2J) approach, a transferable black-box jailbreak method to attack LLMs by iteratively exploring the weakness of the LLMs and automatically improving the attacking strategy. The jailbreak is more efficient and hard to identify since no additional features are introduced. Extensive experiments and analysis demonstrate the effectiveness of R2J, and we find that the jailbreak is also transferable to multiple datasets and various types of models with only a few queries. We hope our work motivates further investigation of LLM safety. The code can be found at https://github.com/ythuang02/R2J/.

cs.CL↗

arXiv2025

Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?

Large language Model (LLM) unlearning, i.e., selectively removing information from LLMs, is vital for responsible model deployment. Differently, LLM knowledge editing aims to modify LLM knowledge instead of removing it. Though editing and unlearning seem to be two distinct tasks, we find there is a tight connection between them. In this paper, we conceptualize unlearning as a special case of editing where information is modified to a refusal or "empty set" $\emptyset$ response, signifying its removal. This paper thus investigates if knowledge editing techniques are strong baselines for LLM unlearning. We evaluate state-of-the-art (SOTA) editing methods (e.g., ROME, MEMIT, GRACE, WISE, and AlphaEdit) against existing unlearning approaches on pretrained and finetuned knowledge. Results show certain editing methods, notably WISE and AlphaEdit, are effective unlearning baselines, especially for pretrained knowledge, and excel in generating human-aligned refusal answers. To better adapt editing methods for unlearning applications, we propose practical recipes including self-improvement and query merging. The former leverages the LLM's own in-context learning ability to craft a more human-aligned unlearning target, and the latter enables ROME and MEMIT to perform well in unlearning longer sample sequences. We advocate for the unlearning community to adopt SOTA editing methods as baselines and explore unlearning from an editing perspective for more holistic LLM memory control.

cs.LG↗

arXiv2025

FedGuCci: Making Local Models More Connected in Landscape for Federated Learning

Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion. The generalization of FL's global model has a large gap compared with centralized training, which is its bottleneck for broader applications. In this paper, we study and improve FL's generalization through a fundamental ``connectivity'' perspective, which means how the local models are connected in the parameter region and fused into a generalized global model. The term ``connectivity'' is derived from linear mode connectivity (LMC), studying the interpolated loss landscape of two different solutions (e.g., modes) of neural networks. Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL). Based on the findings, we propose FedGuCci(+), improving group connectivity for better generalization. It is shown that our methods can boost the generalization of FL under client heterogeneity across various tasks (4 CV datasets and 6 NLP datasets) and model architectures (e.g., ViTs and PLMs). The code is available here: \href{https://github.com/ZexiLee/fedgucci}{\faGithub~FedGuCci Codebase}.

cs.LG↗

arXiv2025

Leveraging Pretrained Diffusion Models for Zero-Shot Part Assembly

3D part assembly aims to understand part relationships and predict their 6-DoF poses to construct realistic 3D shapes, addressing the growing demand for autonomous assembly, which is crucial for robots. Existing methods mainly estimate the transformation of each part by training neural networks under supervision, which requires a substantial quantity of manually labeled data. However, the high cost of data collection and the immense variability of real-world shapes and parts make traditional methods impractical for large-scale applications. In this paper, we propose first a zero-shot part assembly method that utilizes pre-trained point cloud diffusion models as discriminators in the assembly process, guiding the manipulation of parts to form realistic shapes. Specifically, we theoretically demonstrate that utilizing a diffusion model for zero-shot part assembly can be transformed into an Iterative Closest Point (ICP) process. Then, we propose a novel pushing-away strategy to address the overlap parts, thereby further enhancing the robustness of the method. To verify our work, we conduct extensive experiments and quantitative comparisons to several strong baseline methods, demonstrating the effectiveness of the proposed approach, which even surpasses the supervised learning method. The code has been released on https://github.com/Ruiyuan-Zhang/Zero-Shot-Assembly.

cs.CV↗

arXiv2025

DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation

Compositional text-to-video generation, which requires synthesizing dynamic scenes with multiple interacting entities and precise spatial-temporal relationships, remains a critical challenge for diffusion-based models. Existing methods struggle with layout discontinuity, entity identity drift, and implausible interaction dynamics due to unconstrained cross-attention mechanisms and inadequate physics-aware reasoning. To address these limitations, we propose DyST-XL, a \textbf{training-free} framework that enhances off-the-shelf text-to-video models (e.g., CogVideoX-5B) through frame-aware control. DyST-XL integrates three key innovations: (1) A Dynamic Layout Planner that leverages large language models (LLMs) to parse input prompts into entity-attribute graphs and generates physics-aware keyframe layouts, with intermediate frames interpolated via trajectory optimization; (2) A Dual-Prompt Controlled Attention Mechanism that enforces localized text-video alignment through frame-aware attention masking, achieving precise control over individual entities; and (3) An Entity-Consistency Constraint strategy that propagates first-frame feature embeddings to subsequent frames during denoising, preserving object identity without manual annotation. Experiments demonstrate that DyST-XL excels in compositional text-to-video generation, significantly improving performance on complex prompts and bridging a crucial gap in training-free video synthesis. The code is released in https://github.com/XiaoBuL/DyST-XL.

cs.CV↗

arXiv2025

Valley Polarization and Anomalous Valley Hall Effect in Altermagnet Ti2Se2S with Multipiezo Properties

Recently, altermagnets demonstrate numerous newfangle physical phenomena due to their inherent antiferromagnetic coupling and spontaneous spin splitting, that are anticipated to enable innovative spintronic devices. However, the rare two-dimensional altermagnets have been reported, making it difficult to meet the requirements for high-performance spintronic devices on account of the growth big data. Here, we predict a stable monolayer Ti2Se2S with out-of-plane altermagnetic ground state and giant valley splitting. The electronic properties of altermagnet Ti2Se2S are highly dependent on the onsite electron correlation. Through symmetry analysis, we find that the valleys of X and Y points are protected by the mirror Mxy symmetry rather than the time-reversal symmetry. Therefore, the multipiezo effect, including piezovalley and piezomagnetism, can be induced by the uniaxial strain. The total valley splitting of monolayer Ti2Se2S can be as high as ~500 meV. Most interestingly, the direction of valley polarization can be effectively tuned by the uniaxial strain, based on this, we have defined logical "0", "+1", and "-1" states for data transmission and storage. In addition, we have designed a schematic diagram for observing the anomalous Hall effect in experimentally. Our findings have enriched the candidate materials of two-dimensional altermagnet for the ultra-fast and low power consumption device applications.

cond-mat.mtrl-sci↗

arXiv2025

Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge

Large Language Models (LLMs) stand out for their impressive performance in intricate language modeling tasks. However, their demanding computational and memory needs pose obstacles for broad use on edge devices. Quantization is then introduced to boost LLMs' on-device efficiency. Recent works show that 8-bit or lower weight quantization is feasible with minimal impact on end-to-end task performance, while the activation is still not quantized. On the other hand, mainstream commodity edge devices still struggle to execute these sub-8-bit quantized networks effectively. In this paper, we propose Agile-Quant, an activation-guided quantization framework for popular Large Language Models (LLMs), and implement an end-to-end accelerator on multiple edge devices for faster inference. Considering the hardware profiling and activation analysis, we first introduce a basic activation quantization strategy to balance the trade-off of task performance and real inference speed. Then we leverage the activation-aware token pruning technique to reduce the outliers and the adverse impact on attentivity. Ultimately, we utilize the SIMD-based 4-bit multiplier and our efficient TRIP matrix multiplication to implement the accelerator for LLMs on the edge. We apply our framework on different scales of LLMs including LLaMA, OPT, and BLOOM with 4-bit or 8-bit for the activation and 4-bit for the weight quantization. Experiments show that Agile-Quant achieves simultaneous quantization of model weights and activations while maintaining task performance comparable to existing weight-only quantization methods. Moreover, in the 8- and 4-bit scenario, Agile-Quant achieves an on-device speedup of up to 2.55x compared to its FP16 counterparts across multiple edge devices, marking a pioneering advancement in this domain. Code: https://github.com/shawnricecake/agile-quant

cs.LG↗

arXiv2025

Pricing Strategies for Different Accuracy Models from the Same Dataset Based on Generalized Hotelling's Law

We consider a scenario where a seller possesses a dataset $D$ and trains it into models of varying accuracies for sale in the market. Due to the reproducibility of data, the dataset can be reused to train models with different accuracies, and the training cost is independent of the sales volume. These two characteristics lead to fundamental differences between the data trading market and traditional trading markets. The introduction of different models into the market inevitably gives rise to competition. However, due to the varying accuracies of these models, traditional multi-oligopoly games are not applicable. We consider a generalized Hotelling's law, where the accuracy of the models is abstracted as distance. Buyers choose to purchase models based on a trade-off between accuracy and price, while sellers determine their pricing strategies based on the market's demand. We present two pricing strategies: static pricing strategy and dynamic pricing strategy, and we focus on the static pricing strategy. We propose static pricing mechanisms based on various market conditions and provide an example. Finally, we demonstrate that our pricing strategy remains robust in the context of incomplete information games.

cs.AI↗

arXiv2025

Singularity formation for the 1D model of EMHD

In this paper, we study the singularity formation phenomenon of the 1D model of Electron Magnetohydrodynamics (EMHD). we will construct a solution whose $C^3$-norm blows up in finite time. In the end, we will show that the solution is in $C^{\infty}(\mathbb{R}\backslash \{0\})\cap C^{3,s}(\mathbb{R})\cap H^3(\mathbb{R})$ and is not asymptotically self-similar.

math.AP↗

arXiv2025

Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalization

Generative artificial intelligence (GenAI) has made significant progress in understanding world knowledge and generating content from human languages across various modalities, like text-to-text large language models, text-to-image stable diffusion, and text-to-video Sora. While in this paper, we investigate the capability of GenAI for text-to-model generation, to see whether GenAI can comprehend hyper-level knowledge embedded within AI itself parameters. Specifically, we study a practical scenario termed train-once-for-all personalization, aiming to generate personalized models for diverse end-users and tasks using text prompts. Inspired by the recent emergence of neural network diffusion, we present Tina, a text-conditioned neural network diffusion for train-once-for-all personalization. Tina leverages a diffusion transformer model conditioned on task descriptions embedded using a CLIP model. Despite the astronomical number of potential personalized tasks (e.g., $1.73\times10^{13}$), by our design, Tina demonstrates remarkable in-distribution and out-of-distribution generalization even trained on small datasets ($\sim 1000$). We further verify whether and how \Tina understands world knowledge by analyzing its capabilities under zero-shot/few-shot image prompts, different numbers of personalized classes, prompts of natural language descriptions, and predicting unseen entities.

cs.LG↗

arXiv2025

CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model

Effectively handling the co-occurrence of non-IID data and long-tailed distributions remains a critical challenge in federated learning. While fine-tuning vision-language models (VLMs) like CLIP has shown to be promising in addressing non-IID data challenges, this approach leads to severe degradation of tail classes in federated long-tailed scenarios. Under the composite effects of strong non-IID data distribution and long-tailed class imbalances, VLM fine-tuning may even fail to yield any improvement. To address this issue, we propose Class-Aware Prompt Learning for Federated Long-tailed Learning (CAPT), a novel framework that leverages a pre-trained VLM to effectively handle both data heterogeneity and long-tailed distributions. CAPT introduces a dual-prompt mechanism that synergizes general and class-aware prompts, enabling the framework to capture global trends while preserving class-specific knowledge. To better aggregate and share knowledge across clients, we introduce a heterogeneity-aware client clustering strategy that groups clients based on their data distributions, enabling efficient collaboration and knowledge sharing. Extensive experiments on various long-tailed datasets with different levels of data heterogeneity demonstrate that CAPT significantly improves tail class performance without compromising overall accuracy, outperforming state-of-the-art methods in federated long-tailed learning scenarios.

cs.LG↗

arXiv2025

Multifield Induced Antiferromagnet Transformation into Altermagnet and Realized Anomalous Valley Hall Effect in Two-dimensional Materials

Altermagnetism, as a new category of collinear magnetism distinct from traditional ferromagnetism and antiferromagnetism, exhibits the spin splitting without net magnetization. Currently, researchers are focus on searching three-dimensional altermagnetism and exploring its novel physical properties. However, there is a lack of understanding of the physical origin of two-dimensional altermagnetic emergent behavior. Here, we propose an approach to realize the transition from Neel antiferromagnetism to altermagnetism in two-dimensional system using an electric field, Janus structure, and ferroelectric substrate. In monolayer VPSe3, we demonstrate that multiple-physical-fields cause the upper and lower Se atoms unequal to break PT symmetry, resulting in altermagnetic spin splitting. Noted that monolayer VPSe3 produces a spontaneous valley splitting of 2.91 meV at the conduction band minimum. The electric field can effectively tune the valley splitting magnitude, while the Janus structure not only changes the valley splitting magnitude, but also alters the direction. More interestingly, when the ferroelectric polarization of Al2S3 is upward, the direction of valley polarization is switched and the magnitude is almost unchanged. However, the valley splitting sigfinicantly increases under the downward. It is worth noting that the ferroelectric polarization can switch altermagnetic effect and realize anomalous valley Hall effect. Besides, we reveal the microscopic mechanism of valley splitting by an effective Hamiltonian. Our findings not only provide a method to designing altermagnet, but also enriches the valley physics.

cond-mat.mtrl-sci↗

arXiv2025

Coexisting Triferroic and Multiple Types of Valley Polarization by Structural Phase Transition in Two-Dimensional Materials

The multiferroic materials, which coexist magnetism, ferroelectric, and ferrovalley, have broad practical application prospects in promoting the miniaturization and integration of spintronic and valleytronic devices. However, it is rare that there are triferroic orders and multiple types of valley polarization in a real material. Here, we propose a mechanism to realize triferroic order coexistence and multiple types of valley polarization by structural phase transition in two-dimensional (2D) materials. The 1T and 2H phase OsBr2 monolayers exhibit non-magnetic semiconductor and ferromagnetic semiconductor with valley polarization up to 175.49 meV, respectively. Interestingly, the 1T phase OsBr2 bilayer shows the tri-state valley polarization due to lattice symmetry breaking, while the valley polarization of 2H phase bilayer originates from the combined effect of time-reversal symmetry breaking and spin-orbit coupling. Furthermore, the valley polarization and ferroelectric polarization of 1T phase AB stackings and 2H phase AA stackings can be manipulated via interlayer sliding. Importantly, we have verified that the 2H phase can be transformed to 1T phase by Li+ ion intercalation, while the 2H phase can occur the structural phase transition into the 1T phase by infrared laser induction. Our work provides a feasible strategy for manipulating valley polarization and a design idea for nano-devices with nonvolatile multiferroic properties.

cond-mat.mtrl-sci↗

arXiv2025

Advancing C-C Coupling of Electrocatalytic CO2 Reduction Reaction for C2+ Products

The production of multicarbon (C2+) products through electrocatalytic CO2 reduction reaction (CO2RR) is crucial to addressing global environmental challenges and advancing sustainable energy solutions. However, efficiently producing these high-value chemicals via C-C coupling reactions is a significant challenge. This requires catalysts with optimized surface configurations and electronic properties capable of breaking the scaling relations among various intermediates. In this report, we introduce the fundamentals of electrocatalytic CO2RR and the mechanism of C-C coupling. We examine the effects of catalytic surface interactions with key intermediates and reaction pathways, and discuss emerging strategies for enhancing C-C coupling reactions toward C2+ products. Despite varieties of these strategies, we summarize direct clues for the proper design of the catalyst for the electrocatalytic CO2RR towards C2+ products, aiming to provide valuable insights to broad readers in the field.

physics.chem-ph↗

arXiv2025

Massive Helicity-Chirality Spinor Formalism from Massless Amplitudes with On-shell Mass Insertion

We introduce a helicity-chirality spinor formalism to describe scattering amplitudes for particles of any masses and spins. The massive spin-spinors introduced by Arkani-hamed-Huang-Huang have been extended to the spin/helicity-transversality spinors, in which a new quantum number transversality, closely related to chirality, is introduced by extending the Poincare symmetry. The massive helicity-chirality amplitudes can be written by the large and small components of massless spinors $λ$ and $η$ following the $λ\sim \sqrt{E}, η\sim \mathbf{m}/\sqrt{E}$ expansion order by order, which formulate the power counting rules of a large energy effective theory. Diagrammatically the mass expansion in amplitudes originates from the on-shell mass insertion: the helicity flip and chirality flip, which completely determines the three-point massive amplitudes. From the chirality-helicity unification at the UV, any massive helicity-chirality amplitude can be one-to-one corresponded to massless helicity amplitudes with (without) additional Higgs insertion. This UV-IR correspondence explains the mass enhancement in the weak decay processes $π^+ \to μ^+ ν$ and $t \to W^+ b$, and isolates the correct UV of the three-point massive QED $F\bar{F}γ$ amplitudes in Arkani-hamed-Huang-Huang formalism. From massless-massive correspondence, the massless on-shell techniques can be utilized to construct higher-point massive amplitudes.

hep-ph↗

arXiv2025

Overview of EXL-50 Research Progress and Future Plan

XuanLong-50 (EXL-50) is the first medium-size spherical torus (ST) in China, with the toroidal field at major radius at 50 cm around 0.5T. CS-free and non-inductive current drive via electron cyclotron resonance heating (ECRH) was the main physics research issue for EXL-50. Discharges with plasma currents of 50 kA - 180 kA were routinely obtained in EXL-50, with the current flattop sustained for up to or beyond 2 s. The current drive effectiveness on EXL-50 was as high as 1 A/W for low-density discharges using 28GHz ECRH alone for heating power less than 200 kW. The plasma current reached Ip>80 kA for high-density (5*10e18m-2) discharges with 150 kW 28GHz ECRH. Higher performance discharge (Ip of about 120 kA and core density of about 1*10e19m-3) was achieved with 150 kW 50GHz ECRH. The plasma current in EXL-50 was mainly carried by the energetic electrons.Multi-fluid equilibrium model has been successfully applied to reconstruct the magnetic flux surface and the measured plasma parameters of the EXL-50 equilibrium. The physics mechanisms for the solenoid-free ECRH current drive and the energetic electrons has also been investigated. Preliminary experimental results show that 100 kW of lower hybrid current drive (LHCD) waves can drive 20 kA of plasma current. Several boron injection systems were installed and tested in EXL-50, including B2H6 gas puffing, boron powder injection, boron pellet injection. The research plan of EXL-50U, which is the upgrade machine of EXL-50, is also presented.

physics.plasm-ph↗

arXiv2024

Forced non-conformal relativistic fluid from the Chamblin-Reall gravity

The Chamblin-Reall gravity is a remarkable non-conformal platform for the fluid/gravity correspondence to achieve its maximum efficiency. When a probe scalar field that does not change the background metric is manually introduced into the action of the gravity side, an external scalar field will appear at the boundary, and the gradients of the external scalar field will act as a driving force exerting on the dual relativistic fluid. Thus the dynamics of the fluid will be affected in the way that the stress tensor is no longer conserved. We will use the fluid/gravity correspondence to derive the transport coefficients related to the external scalar field and the explicit expression of the driving force.

hep-th↗