arXiv subjects

Yunhao Wang

Publications and source records attributed to Yunhao Wang.

At least 19 recordsLinked to original sources

arXiv2026

Decoupled domain-texture switching from magnetic easy axis in kagome ferromagnet EuTi3Bi4

Magnetic anisotropy defines the easy axis of a magnetic material and governs the spatial arrangement of its domains. To date, anisotropy engineering has focused on reorienting the easy axis or tuning the anisotropy energy, both of which demand substantial energy input. Here, we demonstrate that magnetic domain textures can be switched without reorienting the easy axis, as observed in a kagome ferromagnet EuTi3Bi4 crystal. Using low-temperature magnetic force microscopy, we observe that the preferred orientation of magnetic domains switches from the a-axis to the b-axis upon temperature variation, and that this switching can also be triggered by an out-of-plane magnetic-field reset. Magnetization measurements and density functional theory calculations confirm a robust c-axis easy magnetization, ruling out a conventional spin-reorientation transition. Instead, the texture switching is governed by the temperature dependence of the in-plane variation of the Magnetic anisotropy energy landscape, which arises from two competing interactions with different decay rates: single-ion anisotropy favors a-oriented spin components, while nearest-neighbor anisotropic exchange favors b-oriented ones. Furthermore, the critical switching temperature is substantially elevated in a mechanically exfoliated EuTi3Bi4 flake. Our findings establish that macroscopic magnetic textures can be effectively manipulated by tuning the competition between in-plane anisotropic interactions, without the energy cost of reorienting the easy axis.

cond-mat.mtrl-sci↗

arXiv2026

DocPO: Advancing Document Policy Optimization via Tailored Step-Aware Rewards

Reinforcement learning (RL) for document parsing often relies on reference-based rewards rooted in edit distance (e.g., tree edit distance), yet it remains hard to optimize in the high-accuracy regime because such rewards become weakly discriminative: near-correct outputs receive very similar scores, providing limited learning signal for hard cases. We propose Step-Aware Annealing (SAA), a plug-and-play reward sharpening mechanism that progressively increases reward curvature during training, amplifying subtle quality differences among high-scoring samples while preserving stability in early learning. Built on SAA, we introduce DocPO, a document policy optimization framework with element-specific, reference-based rewards anchored by edit-distance signals: normalized string edit distance (NED) for text, tree edit distance similarity (TEDS) for tables, and a hybrid Rubric+edit reward for formulas. Experiments on OmniDocBench and DocElemHard show that SAA consistently improves GRPO-style RL across document elements over non-annealed rewards, without requiring additional human supervision for reward construction.

cs.CV↗

arXiv2026

MORE: A Multilingual Document Parsing Benchmark and Evaluation

Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critical for unlocking global knowledge. However, existing benchmarks predominantly focus on high-resource languages like English and Chinese, creating an evaluation blind spot concerning model performance on other languages. While recent Vision-Language Models (VLMs) claim support for hundreds of languages, the lack of ground truth makes it impossible to empirically verify these capabilities. To bridge this gap, we introduce MORE, a large-scale benchmark designed for multilingual document parsing evaluation. MORE distinguishes itself through three key dimensions: (1) Unprecedented Scale: It covers 149 languages, making it the most linguistically diverse benchmark to date; (2) Structural Complexity: Unlike previous works, it extends evaluation beyond plain text to include structural elements such as code blocks, tables, and catalogs; and (3) Data Authenticity: All samples are curated from real-world documents via a model-assisted, human-refined annotation pipeline. We evaluate state-of-the-art models using MORE, establishing new performance baselines for long-tail languages and validating the benchmark's effectiveness in diagnosing model capabilities in realistic, diverse scenarios. The MORE dataset will be available at https://github.com/zimoqingfeng/MORE.

cs.CV↗

arXiv2026

Simultaneous nanoscale imaging of local conductivity and chemical potential in a quantum Hall isospin ferromagnet

Quantum Hall isospin ferromagnetism in multilayer graphene offers a versatile playground for exploring flat band correlated physics, driven by the intricate coupling of spin, valley, orbital, and layer degrees of freedom. However, a nanoscale probe capable of simultaneously mapping local conductivity and chemical potential in these exotic phases has yet to be realized. Here, we introduce scanning conductivity and chemical potential microscopy (SCCM), a technique integrating scanning microwave impedance microscopy and Kelvin probe force microscopy. We demonstrate SCCM by probing the quantum Hall states and many-body Landau level energy spectrum in bilayer graphene. Applied to marginally twisted double bilayer graphene, SCCM then reveals a cascade of quantum Hall isospin ferromagnetic states with unexpected re-emergence behaviors. Significantly, experimental many-body Landau level energy spectrum further uncovers the intricate connections of these complex phenomena to inter-subband Landau level crossings and Landau level single-particle wavefunctions. These insights enable the construction of a comprehensive quantum Hall phase diagram. Our results demonstrate SCCM's capability in decoding complex quantum phenomena, establishing it as a versatile nanoscale probe for electron correlation and topology.

cond-mat.mes-hall↗

arXiv2026

CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint

Latent-based diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free solution for intellectual property protection and digital forensics. However, these methods exhibit a critical vulnerability to the forgery attack, attackers can extract the watermark by inverting the watermarked image and re-generating it with an arbitrary prompt, thereby enabling false attribution on malicious content. In this paper, we propose the CSGuard, the first forgery-resistant watermarking schema that leverages compressed sensing to bind the watermarked image generation and verification to a secret matrix. This ensures that only users possessing the secret matrix can correctly embed or verify the image watermark, prevents the illegal users from forgery without compromising generation quality and watermark integrity. Experimental results demonstrate that CSGuard achieves strong forgery resistance, reduces the attack success rate from 100.0\% to 28.12\%, and achieve 100\% detection rate on benign watermarked images without compromising watermarking effectiveness.

cs.CV↗

arXiv2026

SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication

With the evolution of 6G, semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict bit-level correctness. However, existing transport mechanisms still rely on explicit port headers and bit-level validation, making them vulnerable to header corruption and the resulting packet loss. To address this issue, this paper proposes a Semantic Port-Aware Adaptive-Rate Transmission Protocol (SPAT) for semantic communication. The proposed framework jointly embeds source and destination port information into semantic representations, thereby reducing dependence on explicit port headers while enabling robust port-aware transmission. Furthermore, a differentiated semantic processing mechanism is developed for uplink and downlink scenarios, where port identification is introduced for uplink service recognition and destination-aware conditional gating is designed for downlink selective decoding. In addition, an adaptive-rate controller is incorporated to dynamically adjust the number of transmitted semantic channels according to channel conditions and feature importance, thereby improving both robustness and transmission efficiency. Experimental results on the AFHQ and ImageNet-10 datasets, together with real-world experimental measurements, demonstrate that SPAT consistently outperforms TCP, UDP, and SITP in reconstruction quality across different SNRs while maintaining low-latency transmission.

eess.SP↗

arXiv2026

Controllable highly oriented skyrmion track array in Fe3GaTe2

Magnetic skyrmions are emerging as promising candidates for next-generation information technologies, while the realization of scalable skyrmion lattices with tailored configurations is essential for advancing fundamental skyrmion physics and developing future applications. Here we achieved the controllable generation and regulation of a large-area, highly oriented skyrmion track array (STA) in ferromagnetic Fe3GaTe2 using a vector magnetic field manipulation technique. The orientation and ordering of STA, along with the types and density of skyrmions, are precisely controlled by modulating parameters during the manipulation. The critical roles of in-plane magnetic fields and Dzyaloshinskii-Moriya interaction in STA generation is further confirmed by micromagnetic simulation. Our findings develop a strategy for engineering large-area and highly-oriented skyrmion configurations, offering a new pathway for the future application of next-generation spintronic and information technologies.

cond-mat.mtrl-sci↗

arXiv2026

Spin-mediated hysteretic switching of unidirectional charge density waves by rotating magnetic fields

Charge density waves (CDWs) are a widespread collective electronic order in quantum materials, furnishing key insights into symmetry breaking and competing phases. However, their dynamic control with external fields remains a pivotal challenge. Here, we report deterministic and hysteretic switching of unidirectional CDW orientation via in-plane magnetic field rotation in magnetic kagome metal GdTi3Bi4. Atomically resolved spectroscopy shows two types of 3a0*1a0 CDW domains, Q1 and Q2 oriented 60 degree apart along two distinct crystallographic directions and separated by atomically sharp domain walls. Rotating the magnetic field drives reversible transitions between these CDW configurations, exhibiting a robust C2-symmetric phase diagram with pronounced hysteresis. This hysteretic switching is mediated by a field-dependent reorientation of underlying antiferromagnetic spins, revealing a tunable energy landscape with stable and metastable states and modulates the electronic charge order via spin-lattice coupling. Our findings not only demonstrate the switching of CDW configurations by in-plane magnetic field but also reveal the mechanism of coupling between CDW and magnetic fields, offering new insights into CDW manipulation and versatile platform for developing a spin-mediated multistate spin-charge coupling memory and programmable quantum devices.

cond-mat.str-el↗

arXiv2026

Tunable bifurcation of magnetic anisotropy and bi-oriented antiferromagnetic order in kagome metal GdTi3Bi4

The novel kagome family RTi3Bi4 (R: rare-earth) offers a unique platform for exploring distinctive physical phenomena such as anisotropy, spin density wave, and anomalous Hall effect. In particular, the magnetic frustration and behavior of magnetic anisotropy in antiferromagnetic (AFM) kagome materials are of great interest for the fundamental studies and hold promise for next-generation device applications. Here, we report a tunable bifurcation of magnetic anisotropic and bi-oriented AFM order observed in the quasi-1D kagome antiferromagnet GdTi3Bi4. The magnetic domain evolutions during two plateau transition processes are directly visualized, unveiling a pronounced in-plane anisotropy along the a-axis. Temperature-dependent characterization reveals a bifurcation transition of anisotropy at approximately 2 K, where the a-axis anisotropy splits into two special orientations, revealing a hidden bi-oriented in-plane AFM order deviating from the high-symmetry direction by 7 degree. More intriguingly, the characteristics of the bifurcated anisotropy are clearly illustrated through vector magnetic field modulation, revealing three distinct in-plane domain phases in the transverse magnetic field phase diagram. Our results not only provide valuable insights into the tunable bifurcation of magnetic anisotropic in GdTi3Bi4, but also pave a novel pathway for AFM spintronics development.

cond-mat.str-el↗

arXiv2026

WRAP++: Web discoveRy Amplified Pretraining

Synthetic data rephrasing has emerged as a powerful technique for enhancing knowledge acquisition during large language model (LLM) pretraining. However, existing approaches operate at the single-document level, rewriting individual web pages in isolation. This confines synthesized examples to intra-document knowledge, missing cross-document relationships and leaving facts with limited associative context. We propose WRAP++ (Web discoveRy Amplified Pretraining), which amplifies the associative context of factual knowledge by discovering cross-document relationships from web hyperlinks and synthesizing joint QA over each discovered document pair. Concretely, WRAP++ discovers high-confidence relational motifs including dual-links and co-mentions, and synthesizes QA that requires reasoning across both documents. This produces relational knowledge absent from either source document alone, creating diverse entry points to the same facts. Because the number of valid entity pairs grows combinatorially, this discovery-driven synthesis also amplifies data scale far beyond single-document rewriting. Instantiating WRAP++ on Wikipedia, we amplify ~8.4B tokens of raw text into 80B tokens of cross-document QA data. On SimpleQA, OLMo-based models at both 7B and 32B scales trained with WRAP++ substantially outperform single-document approaches and exhibit sustained scaling gains, underscoring the advantage of cross-document knowledge discovery and amplification.

cs.CL↗

arXiv2026

SIGMark: Scalable In-Generation Watermark with Blind Extraction for Video Diffusion

Artificial Intelligence Generated Content (AIGC), particularly video generation with diffusion models, has been advanced rapidly. Invisible watermarking is a key technology for protecting AI-generated videos and tracing harmful content, and thus plays a crucial role in AI safety. Beyond post-processing watermarks which inevitably degrade video quality, recent studies have proposed distortion-free in-generation watermarking for video diffusion models. However, existing in-generation approaches are non-blind: they require maintaining all the message-key pairs and performing template-based matching during extraction, which incurs prohibitive computational costs at scale. Moreover, when applied to modern video diffusion models with causal 3D Variational Autoencoders (VAEs), their robustness against temporal disturbance becomes extremely weak. To overcome these challenges, we propose SIGMark, a Scalable In-Generation watermarking framework with blind extraction for video diffusion. To achieve blind-extraction, we propose to generate watermarked initial noise using a Global set of Frame-wise PseudoRandom Coding keys (GF-PRC), reducing the cost of storing large-scale information while preserving noise distribution and diversity for distortion-free watermarking. To enhance robustness, we further design a Segment Group-Ordering module (SGO) tailored to causal 3D VAEs, ensuring robust watermark inversion during extraction under temporal disturbance. Comprehensive experiments on modern diffusion models show that SIGMark achieves very high bit-accuracy during extraction under both temporal and spatial disturbances with minimal overhead, demonstrating its scalability and robustness. Our project is available at https://jeremyzhao1998.github.io/SIGMark-release/.

cs.CV↗

arXiv2026

Activation-Space Anchored Access Control for Multi-Class Permission Reasoning in Large Language Models

Large language models (LLMs) are increasingly deployed over knowledge bases for efficient knowledge retrieval and question answering. However, LLMs can inadvertently answer beyond a user's permission scope, leaking sensitive content, thus making it difficult to deploy knowledge-base QA under fine-grained access control requirements. In this work, we identify a geometric regularity in intermediate activations: for the same query, representations induced by different permission scopes cluster distinctly and are readily separable. Building on this separability, we propose Activation-space Anchored Access Control (AAAC), a training-free framework for multi-class permission control. AAAC constructs an anchor bank, with one permission anchor per class, from a small offline sample set and requires no fine-tuning. At inference time, a multi-anchor steering mechanism redirects each query's activations toward the anchor-defined authorized region associated with the current user, thereby suppressing over-privileged generations by design. Finally, extensive experiments across three LLM families demonstrate that AAAC reduces permission violation rates by up to 86.5% and prompt-based attack success rates by 90.7%, while improving response usability with minor inference overhead compared to baselines.

cs.CL↗

arXiv2025

NOC4SC: A Bandwidth-Efficient Multi-User Semantic Communication Framework for Interference-Resilient Transmission

With the explosive growth of connected devices and emerging applications, current wireless networks are encountering unprecedented demands for massive user access, where the inter-user interference has become a critical challenge to maintaining high quality of service (QoS) in multi-user communication systems. To tackle this issue, we propose a bandwidth-efficient semantic communication paradigm termed Non-Orthogonal Codewords for Semantic Communication (NOC4SC), which enables simultaneous same-frequency transmission without spectrum spreading. By leveraging the Swin Transformer, the proposed NOC4SC framework enables each user to independently extract semantic features through a unified encoder-decoder architecture with shared network parameters across all users, which ensures that the user's data remains protected from unauthorized decoding. Furthermore, we introduce an adaptive NOC and SNR Modulation (NSM) block, which employs deep learning to dynamically regulate SNR and generate approximately orthogonal semantic features within distinct feature subspaces, thereby effectively mitigating inter-user interference. Extensive experiments demonstrate the proposed NOC4SC achieves comparable performance to the DeepJSCC-PNOMA and outperforms other multi-user SemCom baseline methods.

eess.IV↗

arXiv2025

SITP: A High-Reliability Semantic Information Transport Protocol Without Retransmission for Semantic Communication

With the evolution of 6G networks, modern communication systems are facing unprecedented demands for high reliability and low latency. However, conventional transport protocols are designed for bit-level reliability, failing to meet the semantic robustness requirements. To address this limitation, this paper proposes a novel Semantic Information Transport Protocol (SITP), which achieves TCP-level reliability and UDP level latency by verifying only packet headers while retaining potentially corrupted payloads for semantic decoding. Building upon SITP, a cross-layer analytical model is established to quantify packet-loss probability across the physical, data-link, network, transport, and application layers. The model provides a unified probabilistic formulation linking signal noise rate (SNR) and packet-loss rate, offering theoretical foundation into end-to-end semantic transmission. Furthermore, a cross-image feature interleaving mechanism is developed to mitigate consecutive burst losses by redistributing semantic features across multiple correlated images, thereby enhancing robustness in burst-fade channels. Extensive experiments show that SITP offers lower latency than TCP with comparable reliability at low SNRs, while matching UDP-level latency and delivering superior reconstruction quality. In addition, the proposed cross-image semantic interleaving mechanism further demonstrates its effectiveness in mitigating degradation caused by bursty packet losses.

eess.IV↗

arXiv2025

ACPO: Adaptive Curriculum Policy Optimization for Aligning Vision-Language Models in Complex Reasoning

Aligning large-scale vision-language models (VLMs) for complex reasoning via reinforcement learning is often hampered by the limitations of existing policy optimization algorithms, such as static training schedules and the rigid, uniform clipping mechanism in Proximal Policy Optimization (PPO). In this work, we introduce Adaptive Curriculum Policy Optimization (ACPO), a novel framework that addresses these challenges through a dual-component adaptive learning strategy. First, ACPO employs a dynamic curriculum that orchestrates a principled transition from a stable, near on-policy exploration phase to an efficient, off-policy exploitation phase by progressively increasing sample reuse. Second, we propose an Advantage-Aware Adaptive Clipping (AAAC) mechanism that replaces the fixed clipping hyperparameter with dynamic, sample-wise bounds modulated by the normalized advantage of each token. This allows for more granular and robust policy updates, enabling larger gradients for high-potential samples while safeguarding against destructive ones. We conduct extensive experiments on a suite of challenging multimodal reasoning benchmarks, including MathVista, LogicVista, and MMMU-Pro. Results demonstrate that ACPO consistently outperforms strong baselines such as DAPO and PAPO, achieving state-of-the-art performance, accelerated convergence, and superior training stability.

cs.AI↗

arXiv2025

Untraceable DeepFakes via Traceable Fingerprint Elimination

Recent advancements in DeepFakes attribution technologies have significantly enhanced forensic capabilities, enabling the extraction of traces left by generative models (GMs) in images, making DeepFakes traceable back to their source GMs. Meanwhile, several attacks have attempted to evade attribution models (AMs) for exploring their limitations, calling for more robust AMs. However, existing attacks fail to eliminate GMs' traces, thus can be mitigated by defensive measures. In this paper, we identify that untraceable DeepFakes can be achieved through a multiplicative attack, which can fundamentally eliminate GMs' traces, thereby evading AMs even enhanced with defensive measures. We design a universal and black-box attack method that trains an adversarial model solely using real data, applicable for various GMs and agnostic to AMs. Experimental results demonstrate the outstanding attack capability and universal applicability of our method, achieving an average attack success rate (ASR) of 97.08\% against 6 advanced AMs on DeepFakes generated by 9 GMs. Even in the presence of defensive mechanisms, our method maintains an ASR exceeding 72.39\%. Our work underscores the potential challenges posed by multiplicative attacks and highlights the need for more robust AMs.

cs.CR↗

arXiv2025

Invariant-based Robust Weights Watermark for Large Language Models

Watermarking technology has gained significant attention due to the increasing importance of intellectual property (IP) rights, particularly with the growing deployment of large language models (LLMs) on billions resource-constrained edge devices. To counter the potential threats of IP theft by malicious users, this paper introduces a robust watermarking scheme without retraining or fine-tuning for transformer models. The scheme generates a unique key for each user and derives a stable watermark value by solving linear constraints constructed from model invariants. Moreover, this technology utilizes noise mechanism to hide watermark locations in multi-user scenarios against collusion attack. This paper evaluates the approach on three popular models (Llama3, Phi3, Gemma), and the experimental results confirm the strong robustness across a range of attack methods (fine-tuning, pruning, quantization, permutation, scaling, reversible matrix and collusion attacks).

cs.CR↗

arXiv2025

Adaptive Deep Reasoning: Triggering Deep Thinking When Needed

Large language models (LLMs) have shown impressive capabilities in handling complex tasks through long-chain reasoning. However, the extensive reasoning steps involved can significantly increase computational costs, posing challenges for real-world deployment. Recent efforts have focused on optimizing reasoning efficiency by shortening the Chain-of-Thought (CoT) reasoning processes through various approaches, such as length-aware prompt engineering, supervised fine-tuning on CoT data with variable lengths, and reinforcement learning with length penalties. Although these methods effectively reduce reasoning length, they still necessitate an initial reasoning phase. More recent approaches have attempted to integrate long-chain and short-chain reasoning abilities into a single model, yet they still rely on manual control to toggle between short and long CoT. In this work, we propose a novel approach that autonomously switches between short and long reasoning chains based on problem complexity. Our method begins with supervised fine-tuning of the base model to equip both long-chain and short-chain reasoning abilities. We then employ reinforcement learning to further balance short and long CoT generation while maintaining accuracy through two key strategies: first, integrating reinforcement learning with a long-short adaptive group-wise reward strategy to assess prompt complexity and provide corresponding rewards; second, implementing a logit-based reasoning mode switching loss to optimize the model's initial token choice, thereby guiding the selection of the reasoning type. Evaluations on mathematical datasets demonstrate that our model can dynamically switch between long-chain and short-chain reasoning modes without substantially sacrificing performance. This advancement enhances the practicality of reasoning in large language models for real-world applications.

cs.CL↗