arXiv subjects

Xue Liu

Publications and source records attributed to Xue Liu.

At least 127 records · Page 7Linked to original sources

arXiv2025

Generative Flow Networks for Personalized Multimedia Systems: A Case Study on Short Video Feeds

Multimedia systems underpin modern digital interactions, facilitating seamless integration and optimization of resources across diverse multimedia applications. To meet growing personalization demands, multimedia systems must efficiently manage competing resource needs, adaptive content, and user-specific data handling. This paper introduces Generative Flow Networks (GFlowNets, GFNs) as a brave new framework for enabling personalized multimedia systems. By integrating multi-candidate generative modeling with flow-based principles, GFlowNets offer a scalable and flexible solution for enhancing user-specific multimedia experiences. To illustrate the effectiveness of GFlowNets, we focus on short video feeds, a multimedia application characterized by high personalization demands and significant resource constraints, as a case study. Our proposed GFlowNet-based personalized feeds algorithm demonstrates superior performance compared to traditional rule-based and reinforcement learning methods across critical metrics, including video quality, resource utilization efficiency, and delivery cost. Moreover, we propose a unified GFlowNet-based framework generalizable to other multimedia systems, highlighting its adaptability and wide-ranging applicability. These findings underscore the potential of GFlowNets to advance personalized multimedia systems by addressing complex optimization challenges and supporting sophisticated multimedia application scenarios.

cs.MM↗

arXiv2025

Irregular sets for a class of skew product transformations

In this paper, we study the irregular set of any continuous observable for a class of skew product transformations, which is driven by a uniquely ergodic homeomorphism system $(Ω,\mathbb{P},θ)$ and satisfies Anosov and toplogical mixing on fibers property. We prove that if the irregular set of any continuous observable is nonempty on a fiber, then the irregular set must be nonempty, residual, and carry full fiber topological entropy on $\mathbb{P}$-a.e. fibers. The orbit-gluing technique provided by the fiber specification property are utilized.

math.DS↗

arXiv2025

Spin light-emitting devices in a 2D magnet

Emerging two-dimensional (2D) magnetic semiconductors represent transformative platforms to explore magneto-optics and opto-spintronic applications. Though 2D opto-spintronics has attracted tremendous research efforts in spin-dependent photodetectors and non-volatile memory components, the realization of one core application - spin-modulated light-emitting device (spin-LED) - remains elusive so far. Here we successfully realize prototype spin-LED integrated with a 2D semiconducting magnet CrSBr, demonstrating considerable electroluminescence (EL) down to bilayers. Intriguingly, the EL of the spin-LED is discovered to be directly manipulated by spin-flip and spin-canting transitions. Notably, spin-flip transitions enable unprecedented hysteretic behaviors of EL characteristics, while spin-canting transitions induce EL continuous modulation with robust anisotropy. This versatile manipulation is originated from the synergy of magnetic-order mediated excitonic transitions and spintronic transport. The prototype demonstration of spin-LED establishes an indispensable scheme of opto-spintronic devices leveraging 2D spin transitions and strong excitonic effects, presenting a critical step towards integrated 2D opto-spintronics.

physics.app-ph↗

arXiv2025

Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control

Large language model (LLM) has recently been considered a promising technique for many fields. This work explores LLM-based wireless network optimization via in-context learning. To showcase the potential of LLM technologies, we consider the base station (BS) power control as a case study, a fundamental but crucial technique that is widely investigated in wireless networks. Different from existing machine learning (ML) methods, our proposed in-context learning algorithm relies on LLM's inference capabilities. It avoids the complexity of tedious model training and hyper-parameter fine-tuning, which is a well-known bottleneck of many ML algorithms. Specifically, the proposed algorithm first describes the target task via formatted natural language, and then designs the in-context learning framework and demonstration examples. After that, it considers two cases, namely discrete-state and continuous-state problems, and proposes state-based and ranking-based methods to select appropriate examples for these two cases, respectively. Finally, the simulations demonstrate that the proposed algorithm can achieve comparable performance as conventional deep reinforcement learning (DRL) techniques without dedicated model training or fine-tuning. Such an efficient and low-complexity approach has great potential for future wireless network optimization.

eess.SY↗

arXiv2025

Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management

6G networks have become increasingly complicated due to novel network architecture and newly emerging signal processing and transmission techniques, leading to significant burdens to 6G network management. Large language models (LLMs) have recently been considered a promising technique to equip 6G networks with AI-native intelligence. Different from most existing studies that only consider a single LLM, this work involves a multi-LLM debate-based scheme for 6G network management, where multiple LLMs can collaboratively improve the initial solution sequentially. Considering the complex nature of 6G domain, we propose a novel hierarchical debate scheme: LLMs will first debate the sub-task decomposition, and then debate each subtask step-by-step. Such a hierarchical approach can significantly reduce the overall debate difficulty by sub-task decomposition, aligning well with the complex nature of 6G networks and ensuring the final solution qualities. In addition, to better evaluate the proposed technique, we have defined a novel dataset named 6GPlan, including 110 complex 6G network management tasks and 5000 keyword solutions. Finally, the experiments show that the proposed hierarchical debate can significantly improve performance compared to baseline techniques, e.g. more than 30% coverage rate and global recall rate improvement.

eess.SY↗

arXiv2025

Prompting Wireless Networks: Reinforced In-Context Learning for Power Control

To manage and optimize constantly evolving wireless networks, existing machine learning (ML)- based studies operate as black-box models, leading to increased computational costs during training and a lack of transparency in decision-making, which limits their practical applicability in wireless networks. Motivated by recent advancements in large language model (LLM)-enabled wireless networks, this paper proposes ProWin, a novel framework that leverages reinforced in-context learning to design task-specific demonstration Prompts for Wireless Network optimization, relying on the inference capabilities of LLMs without the need for dedicated model training or finetuning. The task-specific prompts are designed to incorporate natural language descriptions of the task description and formulation, enhancing interpretability and eliminating the need for specialized expertise in network optimization. We further propose a reinforced in-context learning scheme that incorporates a set of advisable examples into task-specific prompts, wherein informative examples capturing historical environment states and decisions are adaptively selected to guide current decision-making. Evaluations on a case study of base station power control showcases that the proposed ProWin outperforms reinforcement learning (RL)-based methods, highlighting the potential for next-generation future wireless network optimization.

eess.SP↗

arXiv2025

Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation

Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks. However, LLMs are still facing challenges when applied to domain-specific areas like telecommunications, which demands specialized expertise and adaptability to evolving standards. This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain. The framework leverages a KG to capture structured, domain-specific information about network protocols, standards, and other telecom-related entities, comprehensively representing their relationships. By integrating KG with RAG, LLMs can dynamically access and utilize the most relevant and up-to-date knowledge during response generation. This hybrid approach bridges the gap between structured knowledge representation and the generative capabilities of LLMs, significantly enhancing accuracy, adaptability, and domain-specific comprehension. Our results demonstrate the effectiveness of the KG-RAG framework in addressing complex technical queries with precision. The proposed KG-RAG model attained an accuracy of 88% for question answering tasks on a frequently used telecom-specific dataset, compared to 82% for the RAG-only and 48% for the LLM-only approaches.

cs.CL↗

arXiv2025

Understanding 6G through Language Models: A Case Study on LLM-aided Structured Entity Extraction in Telecom Domain

Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extraction plays a pivotal role in transforming fragmented telecom knowledge into well-structured formats, empowering diverse AI models to better understand network terminologies. This work proposes a novel language model-based information extraction technique, aiming to extract structured entities from the telecom context. The proposed telecom structured entity extraction (TeleSEE) technique applies a token-efficient representation method to predict entity types and attribute keys, aiming to save the number of output tokens and improve prediction accuracy. Meanwhile, TeleSEE involves a hierarchical parallel decoding method, improving the standard encoder-decoder architecture by integrating additional prompting and decoding strategies into entity extraction tasks. In addition, to better evaluate the performance of the proposed technique in the telecom domain, we further designed a dataset named 6GTech, including 2390 sentences and 23747 words from more than 100 6G-related technical publications. Finally, the experiment shows that the proposed TeleSEE method achieves higher accuracy than other baseline techniques, and also presents 5 to 9 times higher sample processing speed.

cs.CL↗

arXiv2025

Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

cs.AI↗

arXiv2025

A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?

As enthusiasm for scaling computation (data and parameters) in the pretraining era gradually diminished, test-time scaling (TTS), also referred to as ``test-time computing'' has emerged as a prominent research focus. Recent studies demonstrate that TTS can further elicit the problem-solving capabilities of large language models (LLMs), enabling significant breakthroughs not only in specialized reasoning tasks, such as mathematics and coding, but also in general tasks like open-ended Q&A. However, despite the explosion of recent efforts in this area, there remains an urgent need for a comprehensive survey offering a systemic understanding. To fill this gap, we propose a unified, multidimensional framework structured along four core dimensions of TTS research: what to scale, how to scale, where to scale, and how well to scale. Building upon this taxonomy, we conduct an extensive review of methods, application scenarios, and assessment aspects, and present an organized decomposition that highlights the unique functional roles of individual techniques within the broader TTS landscape. From this analysis, we distill the major developmental trajectories of TTS to date and offer hands-on guidelines for practical deployment. Furthermore, we identify several open challenges and offer insights into promising future directions, including further scaling, clarifying the functional essence of techniques, generalizing to more tasks, and more attributions. Our repository is available on https://github.com/testtimescaling/testtimescaling.github.io/

cs.CL↗

arXiv2025

What Makes Teamwork Work? A Multimodal Case Study on Emotions and Diagnostic Expertise in an Intelligent Tutoring System

Teamwork is pivotal in medical teamwork when professionals with diverse skills and emotional states collaborate to make critical decisions. This case study examines the interplay between emotions and professional skills in group decision-making during collaborative medical diagnosis within an Intelligent Tutoring System (ITS). By comparing verbal and physiological data between high-performing and low-performing teams of medical professionals working on a patient case within the ITS, alongside individuals' retrospective collaboration experiences, we employ multimodal data analysis to identify patterns in team emotional climate and their impact on diagnostic efficiency. Specifically, we investigate how emotion-driven dialogue and professional expertise influence both the information-seeking process and the final diagnostic decisions. Grounded in the socially shared regulation of learning framework and utilizing sentiment analysis, we found that social-motivational interactions are key drivers of a positive team emotional climate. Furthermore, through content analysis of dialogue and physiological signals to pinpoint emotional fluctuations, we identify episodes where knowledge exchange and skill acquisition are most likely to occur. Our findings offer valuable insights into optimizing group collaboration in medical contexts by harmonizing emotional dynamics with adaptive strategies for effective decision-making, ultimately enhancing diagnostic accuracy and teamwork effectiveness.

cs.HC↗

arXiv2025

Design Editing for Offline Model-based Optimization

Offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores. These tasks span various domains, such as robotics, material design, and protein and molecular engineering. A common approach involves training a surrogate model using existing designs and their corresponding scores, and then generating new designs through gradient-based updates with respect to the surrogate model. This method suffers from the out-of-distribution issue, where the surrogate model may erroneously predict high scores for unseen designs. To address this challenge, we introduce a novel method, Design Editing for Offline Model-based Optimization (DEMO), which leverages a diffusion prior to calibrate overly optimized designs. DEMO first generates pseudo design candidates by performing gradient ascent with respect to a surrogate model. While these pseudo design candidates contain information beyond the offline dataset, they might be invalid or have erroneously high predicted scores. Therefore, to address this challenge while utilizing the information provided by pseudo design candidates, we propose an editing process to refine these pseudo design candidates. We introduce noise to the pseudo design candidates and subsequently denoise them with a diffusion prior trained on the offline dataset, ensuring they align with the distribution of valid designs. Empirical evaluations on seven offline MBO tasks show that, with properly tuned hyperparameters, DEMOs score is competitive with the best previously reported scores in the literature.

cs.LG↗

arXiv2025

Opportunity-Cost-Driven Reward Mechanisms for Crowd-Sourced Computing Platforms

This paper introduces a game-theoretic model tailored for reward distribution on crowd-sourced computing platforms. It explores a repeated game framework where miners, as computation providers, decide their computation power contribution in each round, guided by the platform's designed reward distribution mechanism. The reward for each miner in every round is based on the platform's randomized task payments and the miners' computation transcripts. Specifically, it defines Opportunity-Cost-Driven Incentive Compatibility (OCD-IC) and Dynamic OCD-IC (DOCD-IC) for scenarios where strategic miners might allocate some computation power to more profitable activities, such as Bitcoin mining. The platform must also achieve Budget Balance (BB), aiming for a non-negative total income over the long term. This paper demonstrates that traditional Pay-Per-Share (PPS) reward schemes require assumptions about task demand and miners' opportunity costs to ensure OCD-IC and BB, yet they fail to satisfy DOCD-IC. The paper then introduces Pay-Per-Share with Subsidy (PPSS), a new reward mechanism that allows the platform to provide subsidies to miners, thus eliminating the need for assumptions on opportunity cost to achieve OCD-IC, DOCD-IC, and long-term BB.

cs.GT↗

arXiv2025

T-Graphormer: Using Transformers for Spatiotemporal Forecasting

Spatiotemporal data is ubiquitous, and forecasting it has important applications in many domains. However, its complex cross-component dependencies and non-linear temporal dynamics can be challenging for traditional techniques. Existing methods address this by learning the two dimensions separately. Here, we introduce Temporal Graphormer (T-Graphormer), a Transformer-based approach capable of modelling spatiotemporal correlations simultaneously. By adding temporal encodings in the Graphormer architecture, each node attends to all other tokens within the graph sequence, enabling the model to learn rich spacetime patterns with minimal predefined inductive biases. We show the effectiveness of T-Graphormer on real-world traffic prediction benchmark datasets. Compared to state-of-the-art methods, T-Graphormer reduces root mean squared error (RMSE) and mean absolute percentage error (MAPE) by up to 20% and 10%.

cs.LG↗

arXiv2025

TransDiffSBDD: Causality-Aware Multi-Modal Structure-Based Drug Design

Structure-based drug design (SBDD) is a critical task in drug discovery, requiring the generation of molecular information across two distinct modalities: discrete molecular graphs and continuous 3D coordinates. However, existing SBDD methods often overlook two key challenges: (1) the multi-modal nature of this task and (2) the causal relationship between these modalities, limiting their plausibility and performance. To address both challenges, we propose TransDiffSBDD, an integrated framework combining autoregressive transformers and diffusion models for SBDD. Specifically, the autoregressive transformer models discrete molecular information, while the diffusion model samples continuous distributions, effectively resolving the first challenge. To address the second challenge, we design a hybrid-modal sequence for protein-ligand complexes that explicitly respects the causality between modalities. Experiments on the CrossDocked2020 benchmark demonstrate that TransDiffSBDD outperforms existing baselines.

cs.CE↗

arXiv2025

CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization

The stock market, as a cornerstone of the financial markets, places forecasting stock price movements at the forefront of challenges in quantitative finance. Emerging learning-based approaches have made significant progress in capturing the intricate and ever-evolving data patterns of modern markets. With the rapid expansion of the stock market, it presents two characteristics, i.e., stock exogeneity and volatility heterogeneity, that heighten the complexity of price forecasting. Specifically, while stock exogeneity reflects the influence of external market factors on price movements, volatility heterogeneity showcases the varying difficulty in movement forecasting against price fluctuations. In this work, we introduce the framework of Cross-market Synergy with Pseudo-volatility Optimization (CSPO). Specifically, CSPO implements an effective deep neural architecture to leverage external futures knowledge. This enriches stock embeddings with cross-market insights and thus enhances the CSPO's predictive capability. Furthermore, CSPO incorporates pseudo-volatility to model stock-specific forecasting confidence, enabling a dynamic adaptation of its optimization process to improve accuracy and robustness. Our extensive experiments, encompassing industrial evaluation and public benchmarking, highlight CSPO's superior performance over existing methods and effectiveness of all proposed modules contained therein.

cs.LG↗

arXiv2025

Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning

Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large language models (LLMs) have attracted considerable interest from academia and telecom industry. This work considers a novel edge-cloud deployment of foundation models in 6G networks. Specifically, it aims to minimize the service delay of foundation models by radio resource allocation and task offloading, i.e., offloading diverse content generation tasks to proper LLMs at the network edge or cloud. In particular, we first introduce the communication system model, i.e., allocating radio resources and calculating link capacity to support generated content transmission, and then we present the LLM inference model to calculate the delay of content generation. After that, we propose a novel in-context learning method to optimize the task offloading decisions. It utilizes LLM's inference capabilities, and avoids the difficulty of dedicated model training or fine-tuning as in conventional machine learning algorithms. Finally, the simulations demonstrate that the proposed edge-cloud deployment and in-context learning task offloading method can achieve satisfactory generation service quality without dedicated model training or fine-tuning.

eess.SY↗

arXiv2025

Toward Resilient Airdrop Mechanisms: Empirical Measurement of Hunter Profits and Airdrop Game Theory Modeling

Airdrops issued by platforms are to distribute tokens, drive user adoption, and promote decentralized services. The distributions attract airdrop hunters (attackers), who exploit the system by employing Sybil attacks, i.e., using multiple identities to manipulate token allocations to meet eligibility criteria. While debates around airdrop hunting question the potential benefits to the ecosystem, exploitative behaviors like Sybil attacks clearly undermine the system's integrity, eroding trust and credibility. Despite the increasing prevalence of these tactics, a gap persists in the literature regarding systematic modeling of airdrop hunters' costs and returns, alongside the theoretical models capturing the interactions among all roles for airdrop mechanism design. Our study first conducts an empirical analysis of transaction data from the Hop Protocol and LayerZero, identifying prevalent attack patterns and estimating hunters' expected profits. Furthermore, we develop a game-theory model that simulates the interactions between attackers, organizers, and bounty hunters, proposing optimal incentive structures that enhance detection while minimizing organizational costs.

cs.GT↗