arXiv subjects

Jiawei Chen

Publications and source records attributed to Jiawei Chen.

At least 145 records · Page 8Linked to original sources

arXiv2025

A numerical investigation of sweep effects on turbulent flow over iced wings

This study employs an improved delayed detached eddy simulation (AMD-IDDES) method to investigate the flow characteristics of an iced swept wing. The AMD-IDDES approach incorporates an anisotropic minimum-dissipation (AMD) model into the traditional IDDES framework to better account for local flow anisotropy and energy dissipation characteristics. The method is validated against the traditional IDDES approach, and the results show that AMD-IDDES more accurately captures the evolution of vortices and separated shear layers. Compared with an iced straight wing, a swept wing exhibits distinct aerodynamic behavior driven mainly by the sweep angle. The sweep angle induces strong spanwise flow, which reshapes the separation region and transforms the flow from two-dimensional to three-dimensional. This spanwise motion significantly alters vortex development and enhances the complexity of the unsteady flow. The shear layer separation, initiated by a Kelvin-Helmholtz instability, dominates the unsteady aerodynamic response. Although wingtip effects remain secondary, their interaction with the leading-edge vortex still contributes to lowering the dominant frequency. Overall, this study highlights that the aerodynamic forces of an iced swept wing are primarily governed by sweep-induced spanwise flow and associated shear layer dynamics, with minimal influence from the root and tip regions.

physics.flu-dyn↗

arXiv2025

Periodic orbits and gravitational waveforms in quantum-corrected black hole spacetimes

In this paper, we study the periodic orbits of massive particles around two quantum-corrected black holes proposed in effective quantum gravity, and explore the quantum gravity effect on both the particle orbits and the associated gravitational wave signals. First, we analyze the geodesic motion of the massive particle around the black holes. We then study two important types of bound orbits of the massive particles, the marginally bound orbit and the innermost stable circular orbit. We find that, for the first black hole, increasing the quantum parameter $ζ$ leads to larger orbital radii and reduced angular momenta for both orbits. In contrast, the second black hole shows $ζ$-independent orbital radii and angular momenta. By analyzing the effective potential, we determine the allowed range of the energy and the angular momentum for bound orbits, with $ζ$-dependence only for the first black hole. We further investigate periodic orbits with a fixed energy for both black holes, revealing that the parameter $ζ$ similarly affects the orbits, although its effect is negligible in the second black hole. Finally, we calculate the gravitational waves emitted by the periodic orbits. The results demonstrate that increasing $ζ$ leads to a significant phase delay for the first black hole, while only inducing a subtle phase advance for the second one. Therefore, we conclude that the first black hole can be distinguished from the Schwarzschild one through gravitational wave observations, whereas the second one cannot be effectively distinguished when the quantum correction is weak.

gr-qc↗

arXiv2025

Physics-informed mixture of experts network for interpretable battery degradation trajectory computation amid second-life complexities

Retired electric vehicle batteries offer immense potential to support low-carbon energy systems, but uncertainties in their degradation behavior and data inaccessibilities under second-life use pose major barriers to safe and scalable deployment. This work proposes a Physics-Informed Mixture of Experts (PIMOE) network that computes battery degradation trajectories using partial, field-accessible signals in a single cycle. PIMOE leverages an adaptive multi-degradation prediction module to classify degradation modes using expert weight synthesis underpinned by capacity-voltage and relaxation data, producing latent degradation trend embeddings. These are input to a use-dependent recurrent network for long-term trajectory prediction. Validated on 207 batteries across 77 use conditions and 67,902 cycles, PIMOE achieves an average mean absolute percentage (MAPE) errors of 0.88% with a 0.43 ms inference time. Compared to the state-of-the-art Informer and PatchTST, it reduces computational time and MAPE by 50%, respectively. Compatible with random state of charge region sampling, PIMOE supports 150-cycle forecasts with 1.50% average and 6.26% maximum MAPE, and operates effectively even with pruned 5MB training data. Broadly, PIMOE framework offers a deployable, history-free solution for battery degradation trajectory computation, redefining how second-life energy storage systems are assessed, optimized, and integrated into the sustainable energy landscape.

cs.LG↗

arXiv2025

Root Defence Strategies: Ensuring Safety of LLM at the Decoding Level

Large language models (LLMs) have demonstrated immense utility across various industries. However, as LLMs advance, the risk of harmful outputs increases due to incorrect or malicious instruction prompts. While current methods effectively address jailbreak risks, they share common limitations: 1) Judging harmful responses from the prefill-level lacks utilization of the model's decoding outputs, leading to relatively lower effectiveness and robustness. 2) Rejecting potentially harmful responses based on a single evaluation can significantly impair the model's helpfulness.This paper examines the LLMs' capability to recognize harmful outputs, revealing and quantifying their proficiency in assessing the danger of previous tokens. Motivated by pilot experiment results, we design a robust defense mechanism at the decoding level. Our novel decoder-oriented, step-by-step defense architecture corrects harmful queries directly rather than rejecting them outright. We introduce speculative decoding to enhance usability and facilitate deployment to boost secure decoding speed. Extensive experiments demonstrate that our approach improves model security without compromising reasoning speed. Notably, our method leverages the model's ability to discern hazardous information, maintaining its helpfulness compared to existing methods.

cs.CL↗

arXiv2025

Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution

Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective. Their theoretical connections and differences warrant in-depth exploration. This work conducts comprehensive analyses of these losses, yielding significant insights: 1) Common strengths -- both can be viewed as augmentations of traditional losses with Distributional Robust Optimization (DRO), enhancing robustness to distributional shifts; 2) Respective limitations -- stemming from their use of different distribution distance metrics in DRO optimization, SL exhibits high sensitivity to false negative instances, whereas CCL suffers from low data utilization. To address these limitations, this work proposes a new loss function, DrRL, which generalizes SL and CCL by leveraging Rényi-divergence in DRO optimization. DrRL incorporates the advantageous structures of both SL and CCL, and can be demonstrated to effectively mitigate their limitations. Extensive experiments have been conducted to validate the superiority of DrRL on both recommendation accuracy and robustness.

cs.IR↗

arXiv2025

GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies

The development of high-quality datasets is crucial for benchmarking and advancing research in Graphical User Interface (GUI) agents. Despite their importance, existing datasets are often constructed under idealized conditions, overlooking the diverse anomalies frequently encountered in real-world deployments. To address this limitation, we introduce GUI-Robust, a novel dataset designed for comprehensive GUI agent evaluation, explicitly incorporating seven common types of anomalies observed in everyday GUI interactions. Furthermore, we propose a semi-automated dataset construction paradigm that collects user action sequences from natural interactions via RPA tools and then generate corresponding step and task descriptions for these actions with the assistance of MLLMs. This paradigm significantly reduces annotation time cost by a factor of over 19 times. Finally, we assess state-of-the-art GUI agents using the GUI-Robust dataset, revealing their substantial performance degradation in abnormal scenarios. We anticipate that our work will highlight the importance of robustness in GUI agents and inspires more future research in this direction. The dataset and code are available at https://github.com/chessbean1/GUI-Robust..

cs.AI↗

arXiv2025

A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus in LLMs

The increasing prevalence of large language models (LLMs) is influencing global value systems. However, these models frequently exhibit a pronounced WEIRD (Western, Educated, Industrialized, Rich, Democratic) cultural bias due to lack of attention to minority values. This monocultural perspective may reinforce dominant values and marginalize diverse cultural viewpoints, posing challenges for the development of equitable and inclusive AI systems. In this work, we introduce a systematic framework designed to boost fair and robust cross-cultural consensus among LLMs. We model consensus as a Nash Equilibrium and employ a game-theoretic negotiation method based on Policy-Space Response Oracles (PSRO) to simulate an organized cross-cultural negotiation process. To evaluate this approach, we construct regional cultural agents using data transformed from the World Values Survey (WVS). Beyond the conventional model-level evaluation method, We further propose two quantitative metrics, Perplexity-based Acceptence and Values Self-Consistency, to assess consensus outcomes. Experimental results indicate that our approach generates consensus of higher quality while ensuring more balanced compromise compared to baselines. Overall, it mitigates WEIRD bias by guiding agents toward convergence through fair and gradual negotiation steps.

cs.AI↗

arXiv2025

LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation

Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy interactions, which significantly impairs recommendation performance. Accurately identifying such noisy interactions without additional information is particularly challenging due to the absence of explicit supervisory signals indicating noise. Large Language Models (LLMs), equipped with extensive open knowledge and semantic reasoning abilities, offer a promising avenue to bridge this information gap. However, employing LLMs for denoising in sequential recommendation presents notable challenges: 1) Direct application of pretrained LLMs may not be competent for the denoising task, frequently generating nonsensical responses; 2) Even after fine-tuning, the reliability of LLM outputs remains questionable, especially given the complexity of the denoising task and the inherent hallucinatory issue of LLMs. To tackle these challenges, we propose LLM4DSR, a tailored approach for denoising sequential recommendation using LLMs. We constructed a self-supervised fine-tuning task to activate LLMs' capabilities to identify noisy items and suggest replacements. Furthermore, we developed an uncertainty estimation module that ensures only high-confidence responses are utilized for sequence corrections. Remarkably, LLM4DSR is model-agnostic, allowing corrected sequences to be flexibly applied across various recommendation models. Extensive experiments validate the superiority of LLM4DSR over existing methods.

cs.IR↗

arXiv2025

MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender

Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS). Given the fundamental gap between the mechanism of LLMs and the requirement of RS, researchers have focused on fine-tuning LLMs with recommendation-specific data to enhance their performance. Language Modeling Loss (LML), originally designed for language generation tasks, is commonly adopted. However, we identify two critical limitations of LML: 1) it exhibits significant divergence from the recommendation objective; 2) it erroneously treats all fictitious item descriptions as negative samples, introducing misleading training signals. To address these limitations, we propose a novel Masked Softmax Loss (MSL) tailored for fine-tuning LLMs on recommendation. MSL improves LML by identifying and masking invalid tokens that could lead to fictitious item descriptions during loss computation. This strategy can effectively avoid the interference from erroneous negative signals and ensure well alignment with the recommendation objective supported by theoretical guarantees. During implementation, we identify a potential challenge related to gradient vanishing of MSL. To overcome this, we further introduce the temperature coefficient and propose an Adaptive Temperature Strategy (ATS) that adaptively adjusts the temperature without requiring extensive hyperparameter tuning. Extensive experiments conducted on four public datasets further validate the effectiveness of MSL, achieving an average improvement of 42.24% in NDCG@10. The code is available at https://github.com/WANGBohaO-jpg/MSL.

cs.IR↗

arXiv2025

OpenGT: A Comprehensive Benchmark For Graph Transformers

Graph Transformers (GTs) have recently demonstrated remarkable performance across diverse domains. By leveraging attention mechanisms, GTs are capable of modeling long-range dependencies and complex structural relationships beyond local neighborhoods. However, their applicable scenarios are still underexplored, this highlights the need to identify when and why they excel. Furthermore, unlike GNNs, which predominantly rely on message-passing mechanisms, GTs exhibit a diverse design space in areas such as positional encoding, attention mechanisms, and graph-specific adaptations. Yet, it remains unclear which of these design choices are truly effective and under what conditions. As a result, the community currently lacks a comprehensive benchmark and library to promote a deeper understanding and further development of GTs. To address this gap, this paper introduces OpenGT, a comprehensive benchmark for Graph Transformers. OpenGT enables fair comparisons and multidimensional analysis by establishing standardized experimental settings and incorporating a broad selection of state-of-the-art GNNs and GTs. Our benchmark evaluates GTs from multiple perspectives, encompassing diverse tasks and datasets with varying properties. Through extensive experiments, our benchmark has uncovered several critical insights, including the difficulty of transferring models across task levels, the limitations of local attention, the efficiency trade-offs in several models, the application scenarios of specific positional encodings, and the preprocessing overhead of some positional encodings. We aspire for this work to establish a foundation for future graph transformer research emphasizing fairness, reproducibility, and generalizability. We have developed an easy-to-use library OpenGT for training and evaluating existing GTs. The benchmark code is available at https://github.com/eaglelab-zju/OpenGT.

cs.LG↗

arXiv2025

Sounding that Object: Interactive Object-Aware Image to Audio Generation

Generating accurate sounds for complex audio-visual scenes is challenging, especially in the presence of multiple objects and sound sources. In this paper, we propose an {\em interactive object-aware audio generation} model that grounds sound generation in user-selected visual objects within images. Our method integrates object-centric learning into a conditional latent diffusion model, which learns to associate image regions with their corresponding sounds through multi-modal attention. At test time, our model employs image segmentation to allow users to interactively generate sounds at the {\em object} level. We theoretically validate that our attention mechanism functionally approximates test-time segmentation masks, ensuring the generated audio aligns with selected objects. Quantitative and qualitative evaluations show that our model outperforms baselines, achieving better alignment between objects and their associated sounds. Project page: https://tinglok.netlify.app/files/avobject/

cs.CV↗

arXiv2025

MLA-Trust: Benchmarking Trustworthiness of Multimodal LLM Agents in GUI Environments

The emergence of multimodal LLM-based agents (MLAs) has transformed interaction paradigms by seamlessly integrating vision, language, action and dynamic environments, enabling unprecedented autonomous capabilities across GUI applications ranging from web automation to mobile systems. However, MLAs introduce critical trustworthiness challenges that extend far beyond traditional language models' limitations, as they can directly modify digital states and trigger irreversible real-world consequences. Existing benchmarks inadequately tackle these unique challenges posed by MLAs' actionable outputs, long-horizon uncertainty and multimodal attack vectors. In this paper, we introduce MLA-Trust, the first comprehensive and unified framework that evaluates the MLA trustworthiness across four principled dimensions: truthfulness, controllability, safety and privacy. We utilize websites and mobile applications as realistic testbeds, designing 34 high-risk interactive tasks and curating rich evaluation datasets. Large-scale experiments involving 13 state-of-the-art agents reveal previously unexplored trustworthiness vulnerabilities unique to multimodal interactive scenarios. For instance, proprietary and open-source GUI-interacting MLAs pose more severe trustworthiness risks than static MLLMs, particularly in high-stakes domains; the transition from static MLLMs into interactive MLAs considerably compromises trustworthiness, enabling harmful content generation in multi-step interactions that standalone MLLMs would typically prevent; multi-step execution, while enhancing the adaptability of MLAs, involves latent nonlinear risk accumulation across successive interactions, circumventing existing safeguards and resulting in unpredictable derived risks. Moreover, we present an extensible toolbox to facilitate continuous evaluation of MLA trustworthiness across diverse interactive environments.

cs.AI↗

arXiv2025

Conditional Image Synthesis with Diffusion Models: A Survey

Conditional image synthesis based on user-specified requirements is a key component in creating complex visual content. In recent years, diffusion-based generative modeling has become a highly effective way for conditional image synthesis, leading to exponential growth in the literature. However, the complexity of diffusion-based modeling, the wide range of image synthesis tasks, and the diversity of conditioning mechanisms present significant challenges for researchers to keep up with rapid developments and to understand the core concepts on this topic. In this survey, we categorize existing works based on how conditions are integrated into the two fundamental components of diffusion-based modeling, $\textit{i.e.}$, the denoising network and the sampling process. We specifically highlight the underlying principles, advantages, and potential challenges of various conditioning approaches during the training, re-purposing, and specialization stages to construct a desired denoising network. We also summarize six mainstream conditioning mechanisms in the sampling process. All discussions are centered around popular applications. Finally, we pinpoint several critical yet still unsolved problems and suggest some possible solutions for future research. Our reviewed works are itemized at https://github.com/zju-pi/Awesome-Conditional-Diffusion-Models.

cs.CV↗

arXiv2025

MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation

Neural audio codecs have made significant strides in efficiently mapping raw audio waveforms into discrete token representations, which are foundational for contemporary audio generative models. However, most existing codecs are optimized primarily for reconstruction quality, often at the expense of the downstream modelability of the encoded tokens. Motivated by the need to overcome this bottleneck, we introduce $\textbf{MagiCodec}$, a novel single-layer, streaming Transformer-based audio codec. MagiCodec is designed with a multistage training pipeline that incorporates Gaussian noise injection and latent regularization, explicitly targeting the enhancement of semantic expressiveness in the generated codes while preserving high reconstruction fidelity. We analytically derive the effect of noise injection in the frequency domain, demonstrating its efficacy in attenuating high-frequency components and fostering robust tokenization. Extensive experimental evaluations show that MagiCodec surpasses state-of-the-art codecs in both reconstruction quality and downstream tasks. Notably, the tokens produced by MagiCodec exhibit Zipf-like distributions, as observed in natural languages, thereby improving compatibility with language-model-based generative architectures. The code and pre-trained models are available at https://github.com/Ereboas/MagiCodec.

cs.SD↗

arXiv2025

Heterogeneous Graph Backdoor Attack

Heterogeneous Graph Neural Networks (HGNNs) excel in modeling complex, multi-typed relationships across diverse domains, yet their vulnerability to backdoor attacks remains unexplored. To address this gap, we conduct the first investigation into the susceptibility of HGNNs to existing graph backdoor attacks, revealing three critical issues: (1) high attack budget required for effective backdoor injection, (2) inefficient and unreliable backdoor activation, and (3) inaccurate attack effectiveness evaluation. To tackle these issues, we propose the Heterogeneous Graph Backdoor Attack (HGBA), the first backdoor attack specifically designed for HGNNs, introducing a novel relation-based trigger mechanism that establishes specific connections between a strategically selected trigger node and poisoned nodes via the backdoor metapath. HGBA achieves efficient and stealthy backdoor injection with minimal structural modifications and supports easy backdoor activation through two flexible strategies: Self-Node Attack and Indiscriminate Attack. Additionally, we improve the ASR measurement protocol, enabling a more accurate assessment of attack effectiveness. Extensive experiments demonstrate that HGBA far surpasses multiple state-of-the-art graph backdoor attacks in black-box settings, efficiently attacking HGNNs with low attack budgets. Ablation studies show that the strength of HBGA benefits from our trigger node selection method and backdoor metapath selection strategy. In addition, HGBA shows superior robustness against node feature perturbations and multiple types of existing graph backdoor defense mechanisms. Finally, extension experiments demonstrate that the relation-based trigger mechanism can effectively extend to tasks in homogeneous graph scenarios, thereby posing severe threats to broader security-critical domains.

cs.CR↗

arXiv2025

Bridging the Gap: Self-Optimized Fine-Tuning for LLM-based Recommender Systems

Recent years have witnessed extensive exploration of Large Language Models (LLMs) on the field of Recommender Systems (RS). There are currently two commonly used strategies to enable LLMs to have recommendation capabilities: 1) The "Guidance-Only" strategy uses in-context learning to exploit and amplify the inherent semantic understanding and item recommendation capabilities of LLMs; 2) The "Tuning-Only" strategy uses supervised fine-tuning (SFT) to fine-tune LLMs with the aim of fitting them to real recommendation data. However, neither of these strategies can effectively bridge the gap between the knowledge space of LLMs and recommendation, and their performance do not meet our expectations. To better enable LLMs to learn recommendation knowledge, we combine the advantages of the above two strategies and proposed a novel "Guidance+Tuning" method called Self-Optimized Fine-Tuning (SOFT), which adopts the idea of curriculum learning. It first employs self-distillation to construct an auxiliary easy-to-learn but meaningful dataset from a fine-tuned LLM. Then it further utilizes a self-adaptive curriculum scheduler to enable LLMs to gradually learn from simpler data (self-distilled data) to more challenging data (real RS data). Extensive experiments demonstrate that SOFT significantly enhances the recommendation accuracy (37.59\% on average) of LLM-based methods. The code is available via https://anonymous.4open.science/r/Self-Optimized-Fine-Tuning-264E

cs.IR↗

arXiv2025

Shadows and optical appearance of quantum-corrected black holes illuminated by static thin accretions

Recently, two new quantum-corrected black hole models satisfying covariance have been proposed within the framework of effective quantum gravity. In this paper, we study how the quantum parameter $ζ$ affects the optical properties of two quantum-corrected black hole models. We first analyze the photon sphere, critical impact parameter, and innermost stable circular orbit as $ζ$ varies, and constrain $ζ$ using Event Horizon Telescope data. Additionally, by employing the ray-tracing method to study photon trajectories near the two quantum-corrected black holes, we find that $ζ$ can reduce the range of impact parameters corresponding to the photon ring and lensed ring. We then examine the optical appearance of these black holes with thin accretion disks, showing $ζ$ significantly brightens the first model's image but has little effect on the second. Meanwhile, we demonstrate the contributions of the transfer functions to the observed intensity of direct and lensed ring in the observer's field of view, which has rarely been separately illustrated in previous studies. Finally, we study the optical appearance of both quantum-corrected black holes under a static spherical accretion model, with results consistent with the above. Therefore, we conclude that the second quantum-corrected black hole is almost indistinguishable from the Schwarzschild black hole, while the first quantum-corrected black hole can be distinguished from the Schwarzschild black hole through its optical appearance.

gr-qc↗

arXiv2025

MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation

Diffusion models have shown excellent performance in text-to-image generation. Nevertheless, existing methods often suffer from performance bottlenecks when handling complex prompts that involve multiple objects, characteristics, and relations. Therefore, we propose a Multi-agent Collaboration-based Compositional Diffusion (MCCD) for text-to-image generation for complex scenes. Specifically, we design a multi-agent collaboration-based scene parsing module that generates an agent system comprising multiple agents with distinct tasks, utilizing MLLMs to extract various scene elements effectively. In addition, Hierarchical Compositional diffusion utilizes a Gaussian mask and filtering to refine bounding box regions and enhance objects through region enhancement, resulting in the accurate and high-fidelity generation of complex scenes. Comprehensive experiments demonstrate that our MCCD significantly improves the performance of the baseline models in a training-free manner, providing a substantial advantage in complex scene generation.

cs.CV↗