arXiv subjects

Yang Li

Publications and source records attributed to Yang Li.

At least 469 records · Page 26Linked to original sources

arXiv2025

Segregation-driven cross-slip mechanism of shockley partials in the gamma prime phase of CoNi-based superalloys

In general, the cross-slip of superdislocations (a/2<011>) from {111} planes to {001} planes has been frequently observed in superalloys, accompanied by the formation of an antiphase boundary (APB) and driven by thermal activation. However, no prior studies have evidenced the occurrence of Shockley partial dislocation (a/6<112>) cross-slip within the gamma prime phase of superalloys. In this work, we present a newly observed cross-slip phenomenon: the Shockley partial dislocations cross-slip from one {111} plane to another {111} conjugate plane, facilitated by the formation of a stair-rod dislocation in the ordered gamma prime phase of a CoNi-based superalloy. Compression tests were conducted at 850 degrees Celsius with a strain rate of 10^-4 s^-1. Defects such as stacking faults and dislocations, along with the associated chemical fluctuations, were characterized using high-resolution scanning transmission electron microscopy (HRSTEM) and energy-dispersive X-ray spectroscopy (EDS). Elemental segregation was found to reduce the activation energy required for cross-slip by decreasing the energies of stacking faults and dislocations. In addition to elemental segregation, local stress concentrations, arising from the combined effects of applied stress, shearing dislocations within the gamma prime phase, and dislocation pile-ups, also play a critical role in triggering cross-slip. The formation of sessile stair-rod dislocations via this newly identified Shockley partial cross-slip in the gamma prime phase is beneficial for enhancing the high-temperature deformation resistance of the alloy by increasing the critical resolved shear stress required for further plastic deformation.

cond-mat.mtrl-sci↗

arXiv2025

Electro-optic frequency comb-empowered precise measurement of the dynamic frequency of a laser

Frequency-modulated lasers (FMLs) are widely used in spectroscopy, biology, and LiDAR. The performance of these applications highly depends on the fast and precise tracking of the FMLs' absolute frequency, which remains a challenge. Here we demonstrate integrated lithium niobate electro-optic frequency combs with arbitrarily tunable repetition rates and a 29.45-nm bandwidth, enabling precise tracking of the absolute frequency of an FML with a chirp rate as high as $2\times10^{18}\,\mathrm{Hz/s}$, which is over three orders of magnitude above the state of the art. This method enables frequency-modulated continuous-wave ranging using an FML with severe mode hops, unlocking great potential for improving the ranging resolution and acquisition rate. Our method lays the foundation for FML-based high-precision measurements of frequency, distance, and time, leading to profound implications in fundamental science and engineering applications.

physics.optics↗

arXiv2025

Two-Timescale Dynamic Service Deployment and Task Scheduling with Spatiotemporal Collaboration in Mobile Edge Networks

Collaborative edge computing addresses the resource constraints of individual edge nodes by enabling resource sharing and task co-processing across multiple nodes. To fully leverage the advantages of collaborative edge computing, joint optimization of service deployment and task scheduling is necessary. Existing optimization methods insufficiently address the collaboration across spatial and temporal dimensions, which hinders their adaptability to the spatiotemporally varying nature of user demands and system states. This paper focuses on optimizing the expected task processing delay in edge networks. We propose a two-timescale online optimization framework to jointly determine: i) service deployment decisions at each large timescale; and ii) task scheduling decisions at each small timescale. Specifically, the convex optimization technique is used to solve the task scheduling problem, while a multi-agent deep reinforcement learning technique is employed for the service deployment problem. These two methods are combined for spatiotemporal co-optimization through a two-timescale alternating optimization approach. Compared to the baseline algorithms, the proposed scheme achieves better delay performance, while also exhibiting low running time and favorable convergence behavior.

cs.PF↗

arXiv2025

On optimal quantum LRCs from the Hermitian construction and $t$-designs

In a recent work, quantum locally recoverable codes (qLRCs) have been introduced for their potential application in large-scale quantum data storage and implication for quantum LDPC codes. This work focuses on the bounds and constructions of qLRCs derived from the Hermitian construction, which solves an open problem proposed by Luo $et~al.$ (IEEE Trans. Inf. Theory, 71 (3): 1794-1802, 2025). We present four bounds for qLRCs and give comparisons in terms of their asymptotic formulas. We construct several new infinite families of NMDS codes, with general and flexible dimensions, that support t-designs for $t\in \{2,3\}$, and apply them to obtain Hermitian dual-containing classical LRCs (cLRCs). As a result, we derive three explicit families of optimal qLRCs. Compared to the known qLRCs obtained by the CSS construction, our optimal qLRCs offer new and more flexible parameters. It is also worth noting that the constructed cLRCs themselves are interesting as they are optimal with respect to four distinct bounds for cLRCs.

cs.IT↗

arXiv2025

Uniqueness in the local Donaldson-Scaduto conjecture

The local Donaldson-Scaduto conjecture predicts the existence and uniqueness of a special Lagrangian pair of pants with three asymptotically cylindrical ends in the Calabi-Yau 3-fold $X \times \mathbb{R}^2$, where $X$ is an ALE hyperkähler 4-manifold of $A_2$-type. The existence of this special Lagrangian has previously been proved. In this paper, we prove a uniqueness theorem, showing that no other special Lagrangian pair of pants satisfies this conjecture.

math.DG↗

arXiv2025

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts

Description-based text-to-speech (TTS) models exhibit strong performance on in-domain text descriptions, i.e., those encountered during training. However, in real-world applications, the diverse range of user-generated descriptions inevitably introduces numerous out-of-domain inputs that challenge the text understanding capabilities of these systems. To address this issue, we propose MoE-TTS, a description-based TTS model designed to enhance the understanding of out-of-domain text descriptions. MoE-TTS employs a modality-based mixture-of-experts (MoE) approach to augment a pre-trained textual large language model (LLM) with a set of specialized weights adapted to the speech modality while maintaining the original LLM frozen during training. This approach allows MoE-TTS to effectively leverage the pre-trained knowledge and text understanding abilities of textual LLMs. Our experimental results indicate that: first, even the most advanced closed-source commercial products can be challenged by carefully designed out-of-domain description test sets; second, MoE-TTS achieves superior performance in generating speech that more accurately reflects the descriptions. We encourage readers to listen to the demos at https://welkinyang.github.io/MoE-TTS/.

eess.AS↗

arXiv2025

Ovis2.5 Technical Report

We present Ovis2.5, a successor to Ovis2 designed for native-resolution visual perception and strong multimodal reasoning. Ovis2.5 integrates a native-resolution vision transformer that processes images at their native, variable resolutions, avoiding the degradation from fixed-resolution tiling and preserving both fine detail and global layout -- crucial for visually dense content like complex charts. To strengthen reasoning, we train the model to move beyond linear chain-of-thought and perform reflection -- including self-checking and revision. This advanced capability is exposed as an optional "thinking mode" at inference time, allowing users to trade latency for enhanced accuracy on difficult inputs. The model is trained via a comprehensive five-phase curriculum that progressively builds its skills. The process begins with foundational visual and multimodal pretraining, advances through large-scale instruction tuning, and culminates in alignment and reasoning enhancement using DPO and GRPO. To scale these upgrades efficiently, we employ multimodal data packing and hybrid parallelism, yielding a significant end-to-end speedup. We release two open-source models: Ovis2.5-9B and Ovis2.5-2B. The latter continues the "small model, big performance" philosophy of Ovis2, making it ideal for resource-constrained, on-device scenarios. On the OpenCompass multimodal leaderboard, Ovis2.5-9B averages 78.3, marking a substantial improvement over its predecessor, Ovis2-8B, and achieving state-of-the-art results among open-source MLLMs in the sub-40B parameter range; Ovis2.5-2B scores 73.9, establishing SOTA for its size. Beyond aggregate scores, Ovis2.5 achieves leading results on STEM benchmarks, exhibits strong capabilities on grounding and video tasks, and achieves open-source SOTA at its scale for complex chart analysis.

cs.CV↗

arXiv2025

ByteGen: A Tokenizer-Free Generative Model for Orderbook Events in Byte Space

Generative modeling of high-frequency limit order book (LOB) dynamics is a critical yet unsolved challenge in quantitative finance, essential for robust market simulation and strategy backtesting. Existing approaches are often constrained by simplifying stochastic assumptions or, in the case of modern deep learning models like Transformers, rely on tokenization schemes that affect the high-precision, numerical nature of financial data through discretization and binning. To address these limitations, we introduce ByteGen, a novel generative model that operates directly on the raw byte streams of LOB events. Our approach treats the problem as an autoregressive next-byte prediction task, for which we design a compact and efficient 32-byte packed binary format to represent market messages without information loss. The core novelty of our work is the complete elimination of feature engineering and tokenization, enabling the model to learn market dynamics from its most fundamental representation. We achieve this by adapting the H-Net architecture, a hybrid Mamba-Transformer model that uses a dynamic chunking mechanism to discover the inherent structure of market messages without predefined rules. Our primary contributions are: 1) the first end-to-end, byte-level framework for LOB modeling; 2) an efficient packed data representation; and 3) a comprehensive evaluation on high-frequency data. Trained on over 34 million events from CME Bitcoin futures, ByteGen successfully reproduces key stylized facts of financial markets, generating realistic price distributions, heavy-tailed returns, and bursty event timing. Our findings demonstrate that learning directly from byte space is a promising and highly flexible paradigm for modeling complex financial systems, achieving competitive performance on standard market quality metrics without the biases of tokenization.

q-fin.CP↗

arXiv2025

Cross-LoRA: A Data-Free LoRA Transfer Framework across Heterogeneous LLMs

Traditional parameter-efficient fine-tuning (PEFT) methods such as LoRA are tightly coupled with the base model architecture, which constrains their applicability across heterogeneous pretrained large language models (LLMs). To address this limitation, we introduce Cross-LoRA, a data-free framework for transferring LoRA modules between diverse base models without requiring additional training data. Cross-LoRA consists of two key components: (a) LoRA-Align, which performs subspace alignment between source and target base models through rank-truncated singular value decomposition (SVD) and Frobenius-optimal linear transformation, ensuring compatibility under dimension mismatch; and (b) LoRA-Shift, which applies the aligned subspaces to project source LoRA weight updates into the target model parameter space. Both components are data-free, training-free, and enable lightweight adaptation on a commodity GPU in 20 minutes. Experiments on ARCs, OBOA and HellaSwag show that Cross-LoRA achieves relative gains of up to 5.26% over base models. Across other commonsense reasoning benchmarks, Cross-LoRA maintains performance comparable to that of directly trained LoRA adapters.

cs.LG↗

arXiv2025

Functional Connectivity Graph Neural Networks

Real-world networks often benefit from capturing both local and global interactions. Inspired by multi-modal analysis in brain imaging, where structural and functional connectivity offer complementary views of network organization, we propose a graph neural network framework that generalizes this approach to other domains. Our method introduces a functional connectivity block based on persistent graph homology to capture global topological features. Combined with structural information, this forms a multi-modal architecture called Functional Connectivity Graph Neural Networks. Experiments show consistent performance gains over existing methods, demonstrating the value of brain-inspired representations for graph-level classification across diverse networks.

cs.NE↗

arXiv2025

PAIRS: Parametric-Verified Adaptive Information Retrieval and Selection for Efficient RAG

Retrieval-Augmented Generation (RAG) has become a cornerstone technique for enhancing large language models (LLMs) with external knowledge. However, current RAG systems face two critical limitations: (1) they inefficiently retrieve information for every query, including simple questions that could be resolved using the LLM's parametric knowledge alone, and (2) they risk retrieving irrelevant documents when queries contain sparse information signals. To address these gaps, we introduce Parametric-verified Adaptive Information Retrieval and Selection (PAIRS), a training-free framework that integrates parametric and retrieved knowledge to adaptively determine whether to retrieve and how to select external information. Specifically, PAIRS employs a dual-path generation mechanism: First, the LLM produces both a direct answer and a context-augmented answer using self-generated pseudo-context. When these outputs converge, PAIRS bypasses external retrieval entirely, dramatically improving the RAG system's efficiency. For divergent cases, PAIRS activates a dual-path retrieval (DPR) process guided by both the original query and self-generated contextual signals, followed by an Adaptive Information Selection (AIS) module that filters documents through weighted similarity to both sources. This simple yet effective approach can not only enhance efficiency by eliminating unnecessary retrievals but also improve accuracy through contextually guided retrieval and adaptive information selection. Experimental results on six question-answering (QA) benchmarks show that PAIRS reduces retrieval costs by around 25% (triggering for only 75% of queries) while still improving accuracy-achieving +1.1% EM and +1.0% F1 over prior baselines on average.

cs.CL↗

arXiv2025

Properties and Decoding of Twisted GRS Codes and Their Extensions

Maximum distance separable (MDS) codes that are not equivalent to generalized Reed-Solomon (GRS) codes are called non-GRS MDS codes. Alongside near MDS (NMDS) codes, they are applicable in communication, cryptography, and storage systems. From theoretical perspective, it is particularly intriguing to investigate families of linear codes in which each element can be determined to be either a non-GRS MDS or an NMDS code. Two promising candidates for such families emerge from what is known as twisted GRS (TGRS) construction. These candidates are the $(+)$-TGRS codes and their extended versions, called $(+)$-extended TGRS (ETGRS) codes. Although many of their properties have been characterized, there are gaps to fill. Which among the codes are non-GRS MDS? Can we improve on their decoding by using their error-correcting pairs or deep holes? In this paper we solve these problems. The answer to the first problem leads us to two classes of non-GRS MDS Hermitian self-dual TGRS codes and a proof that there is no Galois self-dual ETGRS code. Addressing the second problem, we present an explicit decoding algorithm for ETGRS codes that outperforms existing decoding algorithms given some conditions. By considering the duals of TGRS codes which are MDS, we determine the covering radius and a class of deep holes of the recently constructed non-GRS MDS codes due to Han and Zhang.

cs.IT↗

arXiv2025

GUAVA: Generalizable Upper Body 3D Gaussian Avatar

Reconstructing a high-quality, animatable 3D human avatar with expressive facial and hand motions from a single image has gained significant attention due to its broad application potential. 3D human avatar reconstruction typically requires multi-view or monocular videos and training on individual IDs, which is both complex and time-consuming. Furthermore, limited by SMPLX's expressiveness, these methods often focus on body motion but struggle with facial expressions. To address these challenges, we first introduce an expressive human model (EHM) to enhance facial expression capabilities and develop an accurate tracking method. Based on this template model, we propose GUAVA, the first framework for fast animatable upper-body 3D Gaussian avatar reconstruction. We leverage inverse texture mapping and projection sampling techniques to infer Ubody (upper-body) Gaussians from a single image. The rendered images are refined through a neural refiner. Experimental results demonstrate that GUAVA significantly outperforms previous methods in rendering quality and offers significant speed improvements, with reconstruction times in the sub-second range (0.1s), and supports real-time animation and rendering.

cs.CV↗

arXiv2025

Meta CLIP 2: A Worldwide Scaling Recipe

Contrastive Language-Image Pretraining (CLIP) is a popular foundation model, supporting from zero-shot classification, retrieval to encoders for multimodal large language models (MLLMs). Although CLIP is successfully trained on billion-scale image-text pairs from the English world, scaling CLIP's training further to learning from the worldwide web data is still challenging: (1) no curation method is available to handle data points from non-English world; (2) the English performance from existing multilingual CLIP is worse than its English-only counterpart, i.e., "curse of multilinguality" that is common in LLMs. Here, we present Meta CLIP 2, the first recipe training CLIP from scratch on worldwide web-scale image-text pairs. To generalize our findings, we conduct rigorous ablations with minimal changes that are necessary to address the above challenges and present a recipe enabling mutual benefits from English and non-English world data. In zero-shot ImageNet classification, Meta CLIP 2 ViT-H/14 surpasses its English-only counterpart by 0.8% and mSigLIP by 0.7%, and surprisingly sets new state-of-the-art without system-level confounding factors (e.g., translation, bespoke architecture changes) on multilingual benchmarks, such as CVQA with 57.4%, Babel-ImageNet with 50.2% and XM3600 with 64.3% on image-to-text retrieval.

cs.CV↗

arXiv2025

FGeo-HyperGNet: Geometric Problem Solving Integrating FormalGeo Symbolic System and Hypergraph Neural Network

Geometric problem solving has always been a long-standing challenge in the fields of mathematical reasoning and artificial intelligence. We built a neural-symbolic system, called FGeo-HyperGNet, to automatically perform human-like geometric problem solving. The symbolic component is a formal system built on FormalGeo, which can automatically perform geometric relational reasoning and algebraic calculations and organize the solution into a hypergraph with conditions as hypernodes and theorems as hyperedges. The neural component, called HyperGNet, is a hypergraph neural network based on the attention mechanism, including an encoder to encode the structural and semantic information of the hypergraph and a theorem predictor to provide guidance in solving problems. The neural component predicts theorems according to the hypergraph, and the symbolic component applies theorems and updates the hypergraph, thus forming a predict-apply cycle to ultimately achieve readable and traceable automatic solving of geometric problems. Experiments demonstrate the effectiveness of this neural-symbolic architecture. We achieved state-of-the-art results with a TPA of 93.50% and a PSSR of 88.36% on the FormalGeo7K dataset. The code is available at https://github.com/BitSecret/HyperGNet.

cs.AI↗

arXiv2025

RecGPT Technical Report

Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, most current industrial systems remain heavily reliant on historical co-occurrence patterns and log-fitting objectives, i.e., optimizing for past user interactions without explicitly modeling user intent. This log-fitting approach often leads to overfitting to narrow historical preferences, failing to capture users' evolving and latent interests. As a result, it reinforces filter bubbles and long-tail phenomena, ultimately harming user experience and threatening the sustainability of the whole recommendation ecosystem. To address these challenges, we rethink the overall design paradigm of recommender systems and propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline. By integrating large language models (LLMs) into key stages of user interest mining, item retrieval, and explanation generation, RecGPT transforms log-fitting recommendation into an intent-centric process. To effectively align general-purpose LLMs to the above domain-specific recommendation tasks at scale, RecGPT incorporates a multi-stage training paradigm, which integrates reasoning-enhanced pre-alignment and self-training evolution, guided by a Human-LLM cooperative judge system. Currently, RecGPT has been fully deployed on the Taobao App. Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders: users benefit from increased content diversity and satisfaction, merchants and the platform gain greater exposure and conversions. These comprehensive improvement results across all stakeholders validates that LLM-driven, intent-centric design can foster a more sustainable and mutually beneficial recommendation ecosystem.

cs.IR↗

arXiv2025

FACTORY: A Challenging Human-Verified Prompt Set for Long-Form Factuality

Long-form factuality evaluation assesses the ability of models to generate accurate, comprehensive responses to short prompts. Existing benchmarks often lack human verification, leading to potential quality issues. To address this limitation, we introduce FACTORY, a large-scale, human-verified prompt set. Developed using a model-in-the-loop approach and refined by humans, FACTORY includes challenging prompts that are fact-seeking, answerable, and unambiguous. We conduct human evaluations on 6 state-of-the-art language models using FACTORY and existing datasets. Our results show that FACTORY is a challenging benchmark: approximately 40% of the claims made in the responses of SOTA models are not factual, compared to only 10% for other datasets. Our analysis identifies the strengths of FACTORY over prior benchmarks, emphasizing its reliability and the necessity for models to reason across long-tailed facts.

cs.CL↗

arXiv2025

Quantitative Benchmarking of Remote Excitation in Plasmonic Sensing with Enhanced Signal-to-Noise Ratio

Remote excitation using guided optical modes -- such as waveguides, fibers, or surface waves -- offers a promising alternative to direct optical excitation for surface-enhanced Raman scattering (SERS), particularly in applications requiring reduced heating, minimal invasiveness, and on-chip integration. However, despite its widespread use, systematic comparisons between remote and direct excitation remain limited. Here, we quantitatively benchmark both schemes by measuring power-dependent SERS responses from individual plasmonic nanogaps. We statistically analyze the maximum achievable SERS intensity before structural degradation, extract local temperatures, and evaluate signal-to-noise ratios (SNR). Our findings reveal that both remote and direct SERS share a common electric-field limit, despite exhibiting different levels of heating. This suggests that spectral evolution is primarily governed by the local electric field, which drives nanoscale atomic migration rather than excessive heating. Nonetheless, the lower heating associated with remote excitation enhances the Raman SNR by approximately 30%, improving measurement quality without compromising signal strength. This study establishes a quantitative framework for evaluating excitation strategies in plasmonic sensing, and challenges common assumptions about the role of heating in nanostructural stability under strong optical excitation.

physics.optics↗