arXiv subjects

Xiao Yang

Publications and source records attributed to Xiao Yang.

At least 91 records · Page 5Linked to original sources

arXiv2025

Predictive-LoRA: A Proactive and Fragmentation-Aware Serverless Inference System for LLMs

The serverless computing paradigm offers compelling advantages for deploying Large Language Model (LLM) inference services, including elastic scaling and pay-per-use billing. However, serving multiple fine-tuned LLMs via Low-Rank Adaptation (LoRA) in serverless environments faces critical challenges: reactive adapter loading causes significant cold start latency, and frequent adapter swapping leads to severe GPU memory fragmentation. In this paper, we present Predictive-LoRA (P-LoRA), a proactive and fragmentation-aware serverless inference system for LoRA-based LLMs. P-LoRA introduces two key innovations: (1) a lightweight LSTM-based traffic predictor that forecasts adapter demand and proactively prefetches hot adapters from host memory to GPU, reducing cold start latency by up to 68%; and (2) a page-based adapter memory management mechanism inspired by operating system virtual memory, which keeps GPU memory utilization above 87% even under heterogeneous adapter ranks. We evaluate P-LoRA using production-like workloads derived from the Azure Functions trace. Experimental results demonstrate that P-LoRA achieves 1.52x higher throughput than S-LoRA while reducing the average Time-To-First-Token (TTFT) by 35% under high concurrency scenarios.

cs.DC↗

arXiv2025

SemCovert: Secure and Covert Video Transmission via Deep Semantic-Level Hiding

Video semantic communication, praised for its transmission efficiency, still faces critical challenges related to privacy leakage. Traditional security techniques like steganography and encryption are challenging to apply since they are not inherently robust against semantic-level transformations and abstractions. Moreover, the temporal continuity of video enables framewise statistical modeling over extended periods, which increases the risk of exposing distributional anomalies and reconstructing hidden content. To address these challenges, we propose SemCovert, a deep semantic-level hiding framework for secure and covert video transmission. SemCovert introduces a pair of co-designed models, namely the semantic hiding model and the secret semantic extractor, which are seamlessly integrated into the semantic communication pipeline. This design enables authorized receivers to reliably recover hidden information, while keeping it imperceptible to regular users. To further improve resistance to analysis, we introduce a randomized semantic hiding strategy, which breaks the determinism of embedding and introduces unpredictable distribution patterns. The experimental results demonstrate that SemCovert effectively mitigates potential eavesdropping and detection risks while reliably concealing secret videos during transmission. Meanwhile, video quality suffers only minor degradation, preserving transmission fidelity. These results confirm SemCovert's effectiveness in enabling secure and covert transmission without compromising semantic communication performance.

eess.IV↗

arXiv2025

Positivity and long-term behaviour of a diffusion model with measure-valued nonlocal reaction term

The behaviour is investigated of solutions to a diffusion equation on the real line with nonlocal and singular reaction term, i.e., given by a Dirac source or sink at the origin. It gives a simplified representation of for example a control system that senses concentration at a distance, but "intervenes" at the origin. Positivity of solutions (for positive initial conditions) cannot be guaranteed for all parameter settings in the model. We determine a parameter regime and conditions on the positive initial condition in terms of monotonicity and symmetry, that do allow us to conclude the positivity of the solution for all time. In addition, we provide conditions that ensure convergence of the system to a constant steady state (pointwise), outside the region of observation. Technically, we extensively use Laplace transform arguments to achieve these results.

math.AP↗

arXiv2025

Performance of USTC first batch resistive AC-LGAD sensor

In this paper, the design and characterization of AC-LGAD sensors at the University of Science and Technology of China is introduced. The sensors are characterized with an infrared laser Transient Current Technique (TCT) system for evaluating signal response characteristics and spatial resolution. The temporal resolution was quantified with electrons emitted by a Sr-90 radioactive source. The spatial resolution can reach 4 μm and a temporal resolution of 48 ps is achieved

physics.ins-det↗

arXiv2025

Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms

Cloud computing environments are increasingly vulnerable to security threats such as distributed denial-of-service (DDoS) attacks and SQL injection. Traditional security mechanisms, based on rule matching and feature recognition, struggle to adapt to evolving attack strategies. This paper proposes an adaptive security protection framework leveraging deep learning to construct a multi-layered defense architecture. The proposed system is evaluated in a real-world business environment, achieving a detection accuracy of 97.3%, an average response time of 18 ms, and an availability rate of 99.999%. Experimental results demonstrate that the proposed method significantly enhances detection accuracy, response efficiency, and resource utilization, offering a novel and effective approach to cloud computing security.

cs.CR↗

arXiv2025

Training Foundation Models on a Full-Stack AMD Platform: Compute, Networking, and System Design

We report on the first large-scale mixture-of-experts (MoE) pretraining study on pure AMD hardware, utilizing both MI300X GPUs and Pollara networking. We distill practical guidance for both systems and model design. On the systems side, we deliver a comprehensive cluster and networking characterization: microbenchmarks for all core collectives (all-reduce, reduce-scatter, all-gather, broadcast) across message sizes and GPU counts over Pollara. To our knowledge, this is the first at this scale. We further provide MI300X microbenchmarks on kernel sizing and memory bandwidth to inform model design. On the modeling side, we introduce and apply MI300X-aware transformer sizing rules for attention and MLP blocks and justify MoE widths that jointly optimize training throughput and inference latency. We describe our training stack in depth, including often-ignored utilities such as fault-tolerance and checkpoint-reshaping, as well as detailed information on our training recipe. We also provide a preview of our model architecture and base model - ZAYA1 (760M active, 8.3B total parameters MoE, available at https://huggingface.co/Zyphra/ZAYA1-base) - which will be further improved upon in forthcoming papers. ZAYA1-base achieves performance comparable to leading base models such as Qwen3-4B and Gemma3-12B at its scale and larger, and outperforms models including Llama-3-8B and OLMoE across reasoning, mathematics, and coding benchmarks. Together, these results demonstrate that the AMD hardware, network, and software stack are mature and optimized enough for competitive large-scale pretraining.

cs.CL↗

arXiv2025

WearVQA: A Visual Question Answering Benchmark for Wearables in Egocentric Authentic Real-world scenarios

We introduce WearVQA, the first benchmark specifically designed to evaluate the Visual Question Answering (VQA) capabilities of multi-model AI assistant on wearable devices like smart glasses. Unlike prior benchmarks that focus on high-quality, third-person imagery, WearVQA reflects the unique challenges of ego-centric interaction-where visual inputs may be occluded, poorly lit, unzoomed, or blurry, and questions are grounded in realistic wearable use cases. The benchmark comprises 2,520 carefully curated image-question-answer triplets, spanning 7 diverse image domains including both text-centric and general scenes, 10 cognitive task types ranging from basic recognition to various forms of reasoning, and 6 common wearables-specific image quality issues. All questions are designed to be answerable using only the visual input and common senses. WearVQA is paired with a rigorous LLM-as-a-judge evaluation framework with 96% labeling accuracy. Open-source and proprietary multi-model LLMs achieved a QA accuracy as low as 24-52% on WearVQA, with substantial drops on lower-quality images and reasoning-heavy tasks. These observations position WearVQA as a comprehensive and challenging benchmark for guiding technical advancement towards robust, real-world multi-model wearables AI systems.

cs.AI↗

arXiv2025

SkyMoE: A Vision-Language Foundation Model for Enhancing Geospatial Interpretation with Mixture of Experts

The emergence of large vision-language models (VLMs) has significantly enhanced the efficiency and flexibility of geospatial interpretation. However, general-purpose VLMs remain suboptimal for remote sensing (RS) tasks. Existing geospatial VLMs typically adopt a unified modeling strategy and struggle to differentiate between task types and interpretation granularities, limiting their ability to balance local detail perception and global contextual understanding. In this paper, we present SkyMoE, a Mixture-of-Experts (MoE) vision-language model tailored for multimodal, multi-task RS interpretation. SkyMoE employs an adaptive router that generates task- and granularity-aware routing instructions, enabling specialized large language model experts to handle diverse sub-tasks. To further promote expert decoupling and granularity sensitivity, we introduce a context-disentangled augmentation strategy that creates contrastive pairs between local and global features, guiding experts toward level-specific representation learning. We also construct MGRS-Bench, a comprehensive benchmark covering multiple RS interpretation tasks and granularity levels, to evaluate generalization in complex scenarios. Extensive experiments on 21 public datasets demonstrate that SkyMoE achieves state-of-the-art performance across tasks, validating its adaptability, scalability, and superior multi-granularity understanding in remote sensing.

cs.CV↗

arXiv2025

Multiwavelength Observations for a Double-decker Filament Channel in AR 13102

We present the observational evidence of the existence of a double-decker filament channel (FC) by using observations in extreme ultraviolet and Hα wavelengths. For both FCs, the east foot-point roots in the active region (AR), while the west one roots in the remote quiet region. The bottom FC (FC1) appears as intermittent filaments. Within the AR, the FC1 appears as an S-shaped filament (F1), which consisted of two J-shaped filaments (F1S/F1N for the south/north one). For the upper one (FC2), only the east part is filled with dark plasma and visible as a small filament (F2). Its east foot-point roots around the junction of F1S and F1N. Initially, due to the recurrent reconnections, F1N and F1S link to each other and form a new filament (F3) thread by thread. Meanwhile, the heated plasma, which appears as brightening features, flows from the east foot-point of F2 to the west, and becomes invisible about 1.1$\times$10^{5} km away. The failed eruption of F1S is triggered by the reconnection, which appears as the brightening threads changing their configuration from crossed to quasiparallel in between the F1S and F3, and is confined by the upper magnetic field. Associated with the eruption, the distant invisible plasma becomes visible as a brightening feature. It continuously flows to the remote foot-point, and becomes invisible before reaching it. The brightening plasma flow outlines the skeleton of FC2 gradually. The observations show the existence of a double-decker FC, as a magnetic structure, before they appear as a brightening/dark feature when fully filled with hot/cool plasma.

astro-ph.SR↗

arXiv2025

Feature Partitioning and Semantic Equalization for Intrinsic Robustness in Semantic Communication under Packet Loss

Semantic communication can improve transmission efficiency by focusing on task-relevant information. However, under packet-based communication protocols, any error typically results in the loss of an entire packet, making semantic communication particularly vulnerable to packet loss. Since high-dimensional semantic features must be partitioned into one-dimensional transmission units during packetization. A critical open question is how to partition semantic features to maximize robustness. To address this, we systematically investigate the performance of two mainstream architectures, Transformer and Convolutional neural networks (CNN), under various feature partitioning schemes. The results show that the Transformer architecture exhibits inherent robustness to packet loss when partitioned along the channel dimension. In contrast, the CNN-based baseline exhibits imbalanced channel utilization, causing severe degradation once dominant channels are lost. To enhance the CNN resilience, we propose a lightweight Semantic Equalization Mechanism (SEM) that balances channel contributions and prevents a few channels from dominating. SEM consists of two parallel approaches: a Dynamic Scale module that adaptively adjusts channel importance, and a Broadcast module that facilitates information interaction among channels. Experimental results demonstrate that CNN equipped with SEM achieve graceful degradation under packet loss (retaining about 85% of lossless PSNR at 40% packet loss), comparable to that of Transformer models. Our findings indicate that, under an appropriate partitioning strategy, maintaining a balanced semantic representation is a fundamental condition for achieving intrinsic robustness against packet loss. These insights may also extend to other modalities such as video and support practical semantic communication design.

eess.SY↗

arXiv2025

Overtourism to Equilibrium: A System Dynamics & Multi-Objective Model for Sustainable Destinations

Overtourism poses severe challenges to popular destinations worldwide, threatening natural environments and local communities. This paper develops a decision-making model integrating system dynamics with multi-objective evolutionary algorithms (NSGA-II) to balance economic returns, environmental protection, and social satisfaction. We collect multi-source data from 2008-2024 including visitor arrivals (up to 3.1M), government revenue/expenditure (up to $10.3M), glacier retreat (220-350 ft), CO2 emissions (77K-105K tons), and social satisfaction (0.29-0.48), and establish a dynamic system with four modules: tourist behavior, government finance, environmental evolution, and social well-being. We optimize three objectives via NSGA-II: cumulative net revenue, final environmental index, and final social satisfaction. Experiments on Juneau show optimal solutions yield net revenue up to $1.64B with environmental index 0.93 and social satisfaction 0.86. Extending to Iceland reveals Pareto fronts spanning revenues $150M-$200M, environment indices up to 0.92, and social satisfaction above 0.80. Sobol and Morris sensitivity analyses indicate carbon fees and price elasticity account for over 60% of environmental outcome variance, while capacity limits explain around 90% of net revenue variability. Scenario simulations demonstrate how capacity limits and dynamic pricing on crowded attractions, combined with marketing and infrastructure investment in lesser-known sites, mitigate congestion and enhance sustainability. This work contributes: (i) an integrated system-dynamics and NSGA-II framework for sustainable tourism management; (ii) demonstrated portability via case studies on Juneau and Iceland; and (iii) global sensitivity analysis highlighting influential policy levers for decision makers.

eess.SY↗

arXiv2025

Not Only Consistency: Enhance Test-Time Adaptation with Spatio-temporal Inconsistency for Remote Physiological Measurement

Remote physiological measurement (RPM) has emerged as a promising non-invasive method for monitoring physiological signals using the non-contact device. Although various domain adaptation and generalization methods were proposed to promote the adaptability of deep-based RPM models in unseen deployment environments, considerations in aspects such as privacy concerns and real-time adaptation restrict their application in real-world deployment. Thus, we aim to propose a novel fully Test-Time Adaptation (TTA) strategy tailored for RPM tasks in this work. Specifically, based on prior knowledge in physiology and our observations, we noticed not only there is spatio-temporal consistency in the frequency domain of BVP signals, but also that inconsistency in the time domain was significant. Given this, by leveraging both consistency and inconsistency priors, we introduce an innovative expert knowledge-based self-supervised \textbf{C}onsistency-\textbf{i}n\textbf{C}onsistency-\textbf{i}ntegration (\textbf{CiCi}) framework to enhances model adaptation during inference. Besides, our approach further incorporates a gradient dynamic control mechanism to mitigate potential conflicts between priors, ensuring stable adaptation across instances. Through extensive experiments on five diverse datasets under the TTA protocol, our method consistently outperforms existing techniques, presenting state-of-the-art performance in real-time self-supervised adaptation without accessing source data. The code will be released later.

cs.CV↗

arXiv2025

KG-DF: A Black-box Defense Framework against Jailbreak Attacks Based on Knowledge Graphs

With the widespread application of large language models (LLMs) in various fields, the security challenges they face have become increasingly prominent, especially the issue of jailbreak. These attacks induce the model to generate erroneous or uncontrolled outputs through crafted inputs, threatening the generality and security of the model. Although existing defense methods have shown some effectiveness, they often struggle to strike a balance between model generality and security. Excessive defense may limit the normal use of the model, while insufficient defense may lead to security vulnerabilities. In response to this problem, we propose a Knowledge Graph Defense Framework (KG-DF). Specifically, because of its structured knowledge representation and semantic association capabilities, Knowledge Graph(KG) can be searched by associating input content with safe knowledge in the knowledge base, thus identifying potentially harmful intentions and providing safe reasoning paths. However, traditional KG methods encounter significant challenges in keyword extraction, particularly when confronted with diverse and evolving attack strategies. To address this issue, we introduce an extensible semantic parsing module, whose core task is to transform the input query into a set of structured and secure concept representations, thereby enhancing the relevance of the matching process. Experimental results show that our framework enhances defense performance against various jailbreak attack methods, while also improving the response quality of the LLM in general QA scenarios by incorporating domain-general knowledge.

cs.CR↗

arXiv2025

CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmark

Wearable devices such as smart glasses are transforming the way people interact with their surroundings, enabling users to seek information regarding entities in their view. Multi-Modal Retrieval-Augmented Generation (MM-RAG) plays a key role in supporting such questions, yet there is still no comprehensive benchmark for this task, especially regarding wearables scenarios. To fill this gap, we present CRAG-MM -- a Comprehensive RAG benchmark for Multi-modal Multi-turn conversations. CRAG-MM contains a diverse set of 6.5K (image, question, answer) triplets and 2K visual-based multi-turn conversations across 13 domains, including 6.2K egocentric images designed to mimic captures from wearable devices. We carefully constructed the questions to reflect real-world scenarios and challenges, including five types of image-quality issues, six question types, varying entity popularity, differing information dynamism, and different conversation turns. We design three tasks: single-source augmentation, multi-source augmentation, and multi-turn conversations -- each paired with an associated retrieval corpus and APIs for both image-KG retrieval and webpage retrieval. Our evaluation shows that straightforward RAG approaches achieve only 32% and 43% truthfulness on CRAG-MM single- and multi-turn QA, respectively, whereas state-of-the-art industry solutions have similar quality (32%/45%), underscoring ample room for improvement. The benchmark has hosted KDD Cup 2025, attracting about 1K participants and 5K submissions, with winning solutions improving baseline performance by 28%, highlighting its early impact on advancing the field.

cs.CV↗

arXiv2025

FeatureFool: Zero-Query Fooling of Video Models via Feature Map

The vulnerability of deep neural networks (DNNs) has been preliminarily verified. Existing black-box adversarial attacks usually require multi-round interaction with the model and consume numerous queries, which is impractical in the real-world and hard to scale to recently emerged Video-LLMs. Moreover, no attack in the video domain directly leverages feature maps to shift the clean-video feature space. We therefore propose FeatureFool, a stealthy, video-domain, zero-query black-box attack that utilizes information extracted from a DNN to alter the feature space of clean videos. Unlike query-based methods that rely on iterative interaction, FeatureFool performs a zero-query attack by directly exploiting DNN-extracted information. This efficient approach is unprecedented in the video domain. Experiments show that FeatureFool achieves an attack success rate above 70\% against traditional video classifiers without any queries. Benefiting from the transferability of the feature map, it can also craft harmful content and bypass Video-LLM recognition. Additionally, adversarial videos generated by FeatureFool exhibit high quality in terms of SSIM, PSNR, and Temporal-Inconsistency, making the attack barely perceptible. This paper may contain violent or explicit content.

cs.CV↗

arXiv2025

GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modeling

The emergence of large language models (LLMs) enables the development of intelligent agents capable of engaging in complex and multi-turn dialogues. However, multi-agent collaboration faces critical safety challenges, such as hallucination amplification and error injection and propagation. This paper presents GUARDIAN, a unified method for detecting and mitigating multiple safety concerns in GUARDing Intelligent Agent collaboratioNs. By modeling the multi-agent collaboration process as a discrete-time temporal attributed graph, GUARDIAN explicitly captures the propagation dynamics of hallucinations and errors. The unsupervised encoder-decoder architecture incorporating an incremental training paradigm learns to reconstruct node attributes and graph structures from latent embeddings, enabling the identification of anomalous nodes and edges with unparalleled precision. Moreover, we introduce a graph abstraction mechanism based on the Information Bottleneck Theory, which compresses temporal interaction graphs while preserving essential patterns. Extensive experiments demonstrate GUARDIAN's effectiveness in safeguarding LLM multi-agent collaborations against diverse safety vulnerabilities, achieving state-of-the-art accuracy with efficient resource utilization. The code is available at https://github.com/JialongZhou666/GUARDIAN

cs.AI↗

arXiv2025

MTMD: A Multi-Task Multi-Domain Framework for Unified Ad Lightweight Ranking at Pinterest

The lightweight ad ranking layer, living after the retrieval stage and before the fine ranker, plays a critical role in the success of a cascaded ad recommendation system. Due to the fact that there are multiple optimization tasks depending on the ad domain, e.g., Click Through Rate (CTR) for click ads and Conversion Rate (CVR) for conversion ads, as well as multiple surfaces where an ad is served (home feed, search, or related item recommendation) with diverse ad products (shopping or standard ad); it is an essentially challenging problem in industry on how to do joint holistic optimization in the lightweight ranker, such that the overall platform's value, advertiser's value, and user's value are maximized. Deep Neural Network (DNN)-based multitask learning (MTL) can handle multiple goals naturally, with each prediction head mapping to a particular optimization goal. However, in practice, it is unclear how to unify data from different surfaces and ad products into a single model. It is critical to learn domain-specialized knowledge and explicitly transfer knowledge between domains to make MTL effective. We present a Multi-Task Multi-Domain (MTMD) architecture under the classic Two-Tower paradigm, with the following key contributions: 1) handle different prediction tasks, ad products, and ad serving surfaces in a unified framework; 2) propose a novel mixture-of-expert architecture to learn both specialized knowledge each domain and common knowledge shared between domains; 3) propose a domain adaption module to encourage knowledge transfer between experts; 4) constrain the modeling of different prediction tasks. MTMD improves the offline loss value by 12% to 36%, mapping to 2% online reduction in cost per click. We have deployed this single MTMD framework into production for Pinterest ad recommendation replacing 9 production models.

cs.IR↗

arXiv2025

R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science

Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alleviate some challenges, high-level machine learning engineering (MLE) tasks remain labor-intensive and iterative. We introduce R&D-Agent, a comprehensive, decoupled, and extensible framework that formalizes the MLE process. R&D-Agent defines the MLE workflow into two phases and six components, turning agent design for MLE from ad-hoc craftsmanship into a principled, testable process. Although several existing agents report promising gains on their chosen components, they can mostly be summarized as a partial optimization from our framework's simple baseline. Inspired by human experts, we designed efficient and effective agents within this framework that achieve state-of-the-art performance. Evaluated on MLE-Bench, the agent built on R&D-Agent ranks as the top-performing machine learning engineering agent, achieving 35.1% any medal rate, demonstrating the ability of the framework to speed up innovation and improve accuracy across a wide range of data science applications. We have open-sourced R&D-Agent on GitHub: https://github.com/microsoft/RD-Agent.

cs.AI↗