arXiv subjects

Jing Xu

Publications and source records attributed to Jing Xu.

At least 91 records · Page 5Linked to original sources

arXiv2025

Governable AI: Provable Safety Under Extreme Threat Models

As AI rapidly advances, the security risks posed by AI are becoming increasingly severe, especially in critical scenarios, including those posing existential risks. If AI becomes uncontrollable, manipulated, or actively evades safety mechanisms, it could trigger systemic disasters. Existing AI safety approaches-such as model enhancement, value alignment, and human intervention-suffer from fundamental, in-principle limitations when facing AI with extreme motivations and unlimited intelligence, and cannot guarantee security. To address this challenge, we propose a Governable AI (GAI) framework that shifts from traditional internal constraints to externally enforced structural compliance based on cryptographic mechanisms that are computationally infeasible to break, even for future AI, under the defined threat model and well-established cryptographic assumptions.The GAI framework is composed of a simple yet reliable, fully deterministic, powerful, flexible, and general-purpose rule enforcement module (REM); governance rules; and a governable secure super-platform (GSSP) that offers end-to-end protection against compromise or subversion by AI. The decoupling of the governance rules and the technical platform further enables a feasible and generalizable technical pathway for the safety governance of AI. REM enforces the bottom line defined by governance rules, while GSSP ensures non-bypassability, tamper-resistance, and unforgeability to eliminate all identified attack vectors. This paper also presents a rigorous formal proof of the security properties of this mechanism and demonstrates its effectiveness through a prototype implementation evaluated in representative high-stakes scenarios.

cs.AI↗

arXiv2025

Enhancing Code-switched Text-to-Speech Synthesis Capability in Large Language Models with only Monolingual Corpora

While Large Language Models (LLMs) have shown potential in speech generation and recognition, their applications are mainly confined to monolingual scenarios, with limited explorations in code-switched (CS) contexts. In this paper, we propose a Code-Switched Large Language Model (CS-LLM) to enhance the code-switched text-to-speech synthesis (CS TTS) capability in LLMs with only monolingual corpora. Specifically, we begin by enhancing the multilingual speech processing ability of LLMs through multilingual speech recognition and synthesis tasks. Then, we develop an effective code-switched (CS) data construction strategy that splits and concatenates words from different monolingual speech corpora to equip LLMs with improved CS TTS ability. Experiments show that our approach outperforms baselines in CS TTS in terms of naturalness, speaker consistency and similarity even with limited data. Additionally, the constructed CS data further improves multilingual speech synthesis and recognition.

eess.AS↗

arXiv2025

Seamless Language Expansion: Enhancing Multilingual Mastery in Self-Supervised Models

Self-supervised (SSL) models have shown great performance in various downstream tasks. However, they are typically developed for limited languages, and may encounter new languages in real-world. Developing a SSL model for each new language is costly. Thus, it is vital to figure out how to efficiently adapt existed SSL models to a new language without impairing its original abilities. We propose adaptation methods which integrate LoRA to existed SSL models to extend new language. We also develop preservation strategies which include data combination and re-clustering to retain abilities on existed languages. Applied to mHuBERT, we investigate their effectiveness on speech re-synthesis task. Experiments show that our adaptation methods enable mHuBERT to be applied to a new language (Mandarin) with MOS value increased about 1.6 and the relative value of WER reduced up to 61.72%. Also, our preservation strategies ensure that the performance on both existed and new languages remains intact.

cs.CL↗

arXiv2025

Distributed Shared Layered Storage Quantum Simulator: A novel quantum simulation system for efficient scaling and cost optimization

Quantum simulators are essential tools for developing and testing quantum algorithms. However, the high-frequency traversal characteristic of quantum simulators represents an unprecedented demand in the history of IT, and existing distributed technologies is unable to meet this requirement, resulting in a single-node bottleneck of quantum simulator. To overcome this limitation, this paper introduces a novel Distributed Shared Layered Storage Quantum Simulator (DSLSQS). By leveraging an innovative distributed architecture in which multiple computational nodes share data storage directly, together with De-TCP/IP networking technology, DSLSQS effectively eliminates East-West data flow in distributed systems. This approach mitigates the bottleneck of distributed quantum simulation clusters and enhances the scalability. Moreover, the system employs layered storage technology, which reduces usage of expensive high-performance memory and substantially lowers simulation costs. Furthermore, this paper systematically analyzes the performance and cost constraints of distributed quantum simulator cluster, identifying distributed networking as the primary performance bottleneck and highlighting that minimizing storage costs is crucial to reducing the total cost. Finally, experimental evaluations with a 27-qubit simulation confirm the successful implementation of layered storage within the quantum simulator. DSLSQS significantly enhances simulation efficiency, yielding a performance improvement of over 350% compared to existing distributed technologies. These results underscore the superior performance and scalability of the proposed architecture in managing complex quantum computing tasks. This paper provides crucial insights for the practical deployment of quantum computing and presents an effective framework for the development of distributed quantum simulation clusters.

cs.ET↗

arXiv2025

QVecOpt: An Efficient Storage and Computing Opti-mization Framework for Large-scale Quantum State Simulation

In response to the challenges in large-scale quantum state simulation on classical computing platforms, including memory limits, frequent disk I/O, and high computational complexity, this study builds upon a previously proposed hierarchical storage-based quantum simulation system and introduces an optimization framework, the Quantum Vector Optimization Framework (QVecOpt). QVecOpt integrates four strategies: amplitude pairing, cache optimization, block storage optimization, and parallel optimization. These collectively enhance state vector storage and computational scheduling. The amplitude pairing mechanism locates relevant amplitude pairs via bitwise XOR, reducing traversal complexity of single-qubit gates from $O(2^n)$ to $O(1)$. Cache optimization pre-allocates buffers and loads only required data, cutting disk I/O. Block storage optimization partitions the state vector for on-demand loading and local updates, reducing redundant access. Parallel optimization distributes the state vector across nodes for collaborative computation, achieving near-linear speedup. Complexity analysis shows that, compared with hierarchical storage simulation, the method reduces state vector traversals for single-qubit gates from $2^n$ to 1, removing the main bottleneck. It also lowers computational and I/O complexity from $O(2^n)$ to $O(2^n/C)$ and $O(2^n/B)$. In simulations of 16-29 qubits, efficiency improves nearly tenfold, breaking the memory bottleneck of existing tools and enabling high-bit quantum circuit simulations beyond traditional methods. This work provides an efficient, scalable solution for classical simulation of large-scale quantum computation with significant academic and practical value.

cs.ET↗

arXiv2025

Understanding Nonlinear Implicit Bias via Region Counts in Input Space

One explanation for the strong generalization ability of neural networks is implicit bias. Yet, the definition and mechanism of implicit bias in non-linear contexts remains little understood. In this work, we propose to characterize implicit bias by the count of connected regions in the input space with the same predicted label. Compared with parameter-dependent metrics (e.g., norm or normalized margin), region count can be better adapted to nonlinear, overparameterized models, because it is determined by the function mapping and is invariant to reparametrization. Empirically, we found that small region counts align with geometrically simple decision boundaries and correlate well with good generalization performance. We also observe that good hyper-parameter choices such as larger learning rates and smaller batch sizes can induce small region counts. We further establish the theoretical connections and explain how larger learning rate can induce small region counts in neural networks.

cs.LG↗

arXiv2025

Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)

Large Language Models (LLMs) are increasingly integrated with graph-structured data for tasks like node classification, a domain traditionally dominated by Graph Neural Networks (GNNs). While this integration leverages rich relational information to improve task performance, their robustness against adversarial attacks remains unexplored. We take the first step to explore the vulnerabilities of graph-aware LLMs by leveraging existing adversarial attack methods tailored for graph-based models, including those for poisoning (training-time attacks) and evasion (test-time attacks), on two representative models, LLAGA (Chen et al. 2024) and GRAPHPROMPTER (Liu et al. 2024). Additionally, we discover a new attack surface for LLAGA where an attacker can inject malicious nodes as placeholders into the node sequence template to severely degrade its performance. Our systematic analysis reveals that certain design choices in graph encoding can enhance attack success, with specific findings that: (1) the node sequence template in LLAGA increases its vulnerability; (2) the GNN encoder used in GRAPHPROMPTER demonstrates greater robustness; and (3) both approaches remain susceptible to imperceptible feature perturbation attacks. Finally, we propose an end-to-end defense framework GALGUARD, that combines an LLM-based feature correction module to mitigate feature-level perturbations and adapted GNN defenses to protect against structural attacks.

cs.CR↗

arXiv2025

ST-GDance: Long-Term and Collision-Free Group Choreography from Music

Group dance generation from music has broad applications in film, gaming, and animation production. However, it requires synchronizing multiple dancers while maintaining spatial coordination. As the number of dancers and sequence length increase, this task faces higher computational complexity and a greater risk of motion collisions. Existing methods often struggle to model dense spatial-temporal interactions, leading to scalability issues and multi-dancer collisions. To address these challenges, we propose ST-GDance, a novel framework that decouples spatial and temporal dependencies to optimize long-term and collision-free group choreography. We employ lightweight graph convolutions for distance-aware spatial modeling and accelerated sparse attention for efficient temporal modeling. This design significantly reduces computational costs while ensuring smooth and collision-free interactions. Experiments on the AIOZ-GDance dataset demonstrate that ST-GDance outperforms state-of-the-art baselines, particularly in generating long and coherent group dance sequences. Project page: https://yilliajing.github.io/ST-GDance-Website/.

cs.AI↗

arXiv2025

Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks

Graph-structured data is prevalent in many real-world applications, including social networks, financial systems, and molecular biology. Graph Neural Networks (GNNs) have become the de facto standard for learning from such data due to their strong representation capabilities. As GNNs are increasingly deployed in federated learning (FL) settings to preserve data locality and privacy, new privacy threats arise from the interaction between graph structures and decentralized training. In this paper, we present the first systematic study of cross-client membership inference attacks (CC-MIA) against node classification tasks of federated GNNs (FedGNNs), where a malicious client aims to infer which client owns the given data. Unlike prior centralized-focused work that focuses on whether a sample was included in training, our attack targets sample-to-client attribution, a finer-grained privacy risk unique to federated settings. We design a general attack framework that exploits FedGNNs' aggregation behaviors, gradient updates, and embedding proximity to link samples to their source clients across training rounds. We evaluate our attack across multiple graph datasets under realistic FL setups. Results show that our method achieves high performance on both membership inference and ownership identification. Our findings highlight a new privacy threat in federated graph learning-client identity leakage through structural and model-level cues, motivating the need for attribution-robust GNN design.

cs.LG↗

arXiv2025

NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database

Efficiently editing knowledge stored in large language models (LLMs) enables model updates without large-scale training. One possible solution is Locate-and-Edit (L\&E), allowing simultaneous modifications of a massive number of facts. However, such editing may compromise the general abilities of LLMs and even result in forgetting edited facts when scaling up to thousands of edits. In this paper, we model existing linear L\&E methods as querying a Key-Value (KV) database. From this perspective, we then propose NeuralDB, an editing framework that explicitly represents the edited facts as a neural KV database equipped with a non-linear gated retrieval module, % In particular, our gated module only operates when inference involves the edited facts, effectively preserving the general abilities of LLMs. Comprehensive experiments involving the editing of 10,000 facts were conducted on the ZsRE and CounterFacts datasets, using GPT2-XL, GPT-J (6B) and Llama-3 (8B). The results demonstrate that NeuralDB not only excels in editing efficacy, generalization, specificity, fluency, and consistency, but also preserves overall performance across six representative text understanding and generation tasks. Further experiments indicate that NeuralDB maintains its effectiveness even when scaled to 100,000 facts (\textbf{50x} more than in prior work).

cs.CL↗

arXiv2025

DexSim2Real$^{2}$: Building Explicit World Model for Precise Articulated Object Dexterous Manipulation

Articulated objects are ubiquitous in daily life. In this paper, we present DexSim2Real$^{2}$, a novel framework for goal-conditioned articulated object manipulation. The core of our framework is constructing an explicit world model of unseen articulated objects through active interactions, which enables sampling-based model predictive control to plan trajectories achieving different goals without requiring demonstrations or RL. It first predicts an interaction using an affordance network trained on self-supervised interaction data or videos of human manipulation. After executing the interactions on the real robot to move the object parts, we propose a novel modeling pipeline based on 3D AIGC to build a digital twin of the object in simulation from multiple frames of observations. For dexterous hands, we utilize eigengrasp to reduce the action dimension, enabling more efficient trajectory searching. Experiments validate the framework's effectiveness for precise manipulation using a suction gripper, a two-finger gripper and two dexterous hand. The generalizability of the explicit world model also enables advanced manipulation strategies like manipulating with tools.

cs.RO↗

arXiv2025

Self-Consistency Preference Optimization

Self-alignment, whereby models learn to improve themselves without human annotation, is a rapidly growing research area. However, existing techniques often fail to improve complex reasoning tasks due to the difficulty of assigning correct rewards. An orthogonal approach that is known to improve correctness is self-consistency, a method applied at inference time based on multiple sampling in order to find the most consistent answer. In this work, we extend the self-consistency concept to help train models. We thus introduce self-consistency preference optimization (ScPO), which iteratively trains consistent answers to be preferred over inconsistent ones on unsupervised new problems. We show ScPO leads to large improvements over conventional reward model training on reasoning tasks such as GSM8K and MATH, closing the gap with supervised training with gold answers or preferences, and that combining ScPO with standard supervised learning improves results even further. On ZebraLogic, ScPO finetunes Llama-3 8B to be superior to Llama-3 70B, Gemma-2 27B, and Claude-3 Haiku.

cs.CL↗

arXiv2025

Bridging Offline and Online Reinforcement Learning for LLMs

We investigate the effectiveness of reinforcement learning methods for finetuning large language models when transitioning from offline to semi-online to fully online regimes for both verifiable and non-verifiable tasks. Our experiments cover training on verifiable math as well as non-verifiable instruction following with a set of benchmark evaluations for both. Across these settings, we extensively compare online and semi-online Direct Preference Optimization and Group Reward Policy Optimization objectives, and surprisingly find similar performance and convergence between these variants, which all strongly outperform offline methods. We provide a detailed analysis of the training dynamics and hyperparameter selection strategies to achieve optimal results. Finally, we show that multi-tasking with verifiable and non-verifiable rewards jointly yields improved performance across both task types.

cs.CL↗

arXiv2025

On the metric of the jet bundle and similarity of Cowen-Douglas operators

The study of Cowen-Douglas operators not only involves traditional operator-theoretic tools but also concepts and results from complex geometry on holomorphic vector bundles. We make use of the ratio of the metric matrices first considered by Clark and Misra and a model theorem by Agler to describe the similarity of backward shift operators on analytic function spaces whose multiplier algebras are the space of bounded analytic functions. It is well-known that, in general, it becomes much more complicated to formulate a sufficient condition for similarity than a necessary one. We also give a sufficient condition for a Cowen-Douglas operator to be similar to the backward shift operator on the Dirichlet space with weights by introducing a condition on the jet bundle of a holomorphic vector bundle. Note that the multiplier algebras of these spaces do not coincide with the space of bounded, analytic functions as in other analytic functions spaces, requiring a different approach. The results by Müller on operator models related to Dirichlet shifts and by Kidane and Trent on the corona problem for the multiplier algebras of weighted Dirichlet spaces are indispensable tools in attaining this similarity result.

math.FA↗

arXiv2025

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs

Prompting has become a dominant paradigm for adapting large language models (LLMs). While discrete (textual) prompts are widely used for their interpretability, soft (parameter) prompts have recently gained traction in APIs. This is because they can encode information from more training samples while minimizing the user's token usage, leaving more space in the context window for task-specific input. However, soft prompts are tightly coupled to the LLM they are tuned on, limiting their generalization to other LLMs. This constraint is particularly problematic for efficiency and privacy: (1) tuning prompts on each LLM incurs high computational costs, especially as LLMs continue to grow in size. Additionally, (2) when the LLM is hosted externally, soft prompt tuning often requires sharing private data with the LLM provider. For instance, this is the case with the NVIDIA NeMo API. To address these issues, we propose POST (Privacy Of Soft prompt Transfer), a framework that enables private tuning of soft prompts on a small model and subsequently transfers these prompts to a larger LLM. POST uses knowledge distillation to derive a small model directly from the large LLM to improve prompt transferability, tunes the soft prompt locally, optionally with differential privacy guarantees, and transfers it back to the larger LLM using a small public dataset. Our experiments show that POST reduces computational costs, preserves privacy, and effectively transfers high-utility soft prompts.

cs.LG↗

arXiv2025

Video Individual Counting With Implicit One-to-Many Matching

Video Individual Counting (VIC) is a recently introduced task that aims to estimate pedestrian flux from a video. It extends conventional Video Crowd Counting (VCC) beyond the per-frame pedestrian count. In contrast to VCC that only learns to count repeated pedestrian patterns across frames, the key problem of VIC is how to identify co-existent pedestrians between frames, which turns out to be a correspondence problem. Existing VIC approaches, however, mainly follow a one-to-one (O2O) matching strategy where the same pedestrian must be exactly matched between frames, leading to sensitivity to appearance variations or missing detections. In this work, we show that the O2O matching could be relaxed to a one-to-many (O2M) matching problem, which better fits the problem nature of VIC and can leverage the social grouping behavior of walking pedestrians. We therefore introduce OMAN, a simple but effective VIC model with implicit One-to-Many mAtchiNg, featuring an implicit context generator and a one-to-many pairwise matcher. Experiments on the SenseCrowd and CroHD benchmarks show that OMAN achieves the state-of-the-art performance. Code is available at \href{https://github.com/tiny-smart/OMAN}{OMAN}.

cs.CV↗

arXiv2025

Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution

In recent years, the performance of lightweight Single-Image Super-Resolution (SISR) has been improved significantly with the application of Convolutional Neural Networks (CNNs) and Large Kernel Attention (LKA). However, existing information distillation modules for lightweight SISR struggle to map inputs into High-Dimensional Non-Linear (HDNL) feature spaces, limiting their representation learning. And their LKA modules possess restricted ability to capture the multi-shape multi-scale information for long-range dependencies while encountering a quadratic increase in the computational burden with increasing convolutional kernel size of its depth-wise convolutional layer. To address these issues, we firstly propose a Star Distillation Module (SDM) to enhance the discriminative representation learning via information distillation in the HDNL feature spaces. Besides, we present a Multi-shape Multi-scale Large Kernel Attention (MM-LKA) module to learn representative long-range dependencies while incurring low computational and memory footprints, leading to improving the performance of CNN-based self-attention significantly. Integrating SDM and MM-LKA, we develop a Residual Star Distillation Attention Module (RSDAM) and take it as the building block of the proposed efficient Star Distillation Attention Network (SDAN) which possesses high reconstruction efficiency to recover a higher-quality image from the corresponding low-resolution (LR) counterpart. When compared with other lightweight state-of-the-art SISR methods, extensive experiments show that our SDAN with low model complexity yields superior performance quantitatively and visually.

eess.IV↗

arXiv2025

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution

Existing diffusion-based video super-resolution (VSR) methods are susceptible to introducing complex degradations and noticeable artifacts into high-resolution videos due to their inherent randomness. In this paper, we propose a noise-robust real-world VSR framework by incorporating self-supervised learning and Mamba into pre-trained latent diffusion models. To ensure content consistency across adjacent frames, we enhance the diffusion model with a global spatio-temporal attention mechanism using the Video State-Space block with a 3D Selective Scan module, which reinforces coherence at an affordable computational cost. To further reduce artifacts in generated details, we introduce a self-supervised ControlNet that leverages HR features as guidance and employs contrastive learning to extract degradation-insensitive features from LR videos. Finally, a three-stage training strategy based on a mixture of HR-LR videos is proposed to stabilize VSR training. The proposed Self-supervised ControlNet with Spatio-Temporal Continuous Mamba based VSR algorithm achieves superior perceptual quality than state-of-the-arts on real-world VSR benchmark datasets, validating the effectiveness of the proposed model design and training strategies.

cs.CV↗