arXiv subjects

Qian Wang

Publications and source records attributed to Qian Wang.

At least 163 records · Page 9Linked to original sources

arXiv2025

Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference

Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more than doubling over the past decade to over 4.5 papers annually; (2) environmentally, with the carbon footprint of a single conference exceeding the daily emissions of its host city; (3) psychologically, with 71% of online community discourse reflecting negative sentiment and 35% referencing mental health concerns; and (4) logistically, with attendance at top conferences such as NeurIPS 2024 beginning to outpace venue capacity. These pressures point to a system that is misaligned with its core mission. In response, we propose the Community-Federated Conference (CFC) model, which separates peer review, presentation, and networking into globally coordinated but locally organized components, offering a more sustainable, inclusive, and resilient path forward for AI research.

cs.CY↗

arXiv2025

Hierarchical Frequency Tagging Probe (HFTP): A Unified Approach to Investigate Syntactic Structure Representations in Large Language Models and the Human Brain

Large Language Models (LLMs) demonstrate human-level or even superior language abilities, effectively modeling syntactic structures, yet the specific computational modules responsible remain unclear. A key question is whether LLM behavioral capabilities stem from mechanisms akin to those in the human brain. To address these questions, we introduce the Hierarchical Frequency Tagging Probe (HFTP), a tool that utilizes frequency-domain analysis to identify neuron-wise components of LLMs (e.g., individual Multilayer Perceptron (MLP) neurons) and cortical regions (via intracranial recordings) encoding syntactic structures. Our results show that models such as GPT-2, Gemma, Gemma 2, Llama 2, Llama 3.1, and GLM-4 process syntax in analogous layers, while the human brain relies on distinct cortical regions for different syntactic levels. Representational similarity analysis reveals a stronger alignment between LLM representations and the left hemisphere of the brain (dominant in language processing). Notably, upgraded models exhibit divergent trends: Gemma 2 shows greater brain similarity than Gemma, while Llama 3.1 shows less alignment with the brain compared to Llama 2. These findings offer new insights into the interpretability of LLM behavioral improvements, raising questions about whether these advancements are driven by human-like or non-human-like mechanisms, and establish HFTP as a valuable tool bridging computational linguistics and cognitive neuroscience. This project is available at https://github.com/LilTiger/HFTP.

cs.CL↗

arXiv2025

A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication

Deep generative models have demonstrated impressive performance in various computer vision applications, including image synthesis, video generation, and medical analysis. Despite their significant advancements, these models may be used for malicious purposes, such as misinformation, deception, and copyright violation. In this paper, we provide a systematic and timely review of research efforts on defenses against AI-generated visual media, covering detection, disruption, and authentication. We review existing methods and summarize the mainstream defense-related tasks within a unified passive and proactive framework. Moreover, we survey the derivative tasks concerning the trustworthiness of defenses, such as their robustness and fairness. For each defense strategy, we formulate its general pipeline and propose a multidimensional taxonomy applicable across defense tasks, based on methodological strategies. Additionally, we summarize the commonly used evaluation datasets, criteria, and metrics. Finally, by analyzing the reviewed studies, we provide insights into current research challenges and suggest possible directions for future research.

cs.CV↗

arXiv2025

Tensor-on-tensor Regression Neural Networks for Process Modeling with High-dimensional Data

Modern sensing and metrology systems now stream terabytes of heterogeneous, high-dimensional (HD) data profiles, images, and dense point clouds, whose natural representation is multi-way tensors. Understanding such data requires regression models that preserve tensor geometry, yet remain expressive enough to capture the pronounced nonlinear interactions that dominate many industrial and mechanical processes. Existing tensor-based regressors meet the first requirement but remain essentially linear. Conversely, conventional neural networks offer nonlinearity only after flattening, thereby discarding spatial structure and incurring prohibitive parameter counts. This paper introduces a Tensor-on-Tensor Regression Neural Network (TRNN) that unifies these two paradigms.

cs.LG↗

arXiv2025

A short review of the vector charmonium-like state $ψ(4230)$

We present a concise review of the vector charmonium state $ψ(4230)$, which was originally labelled as $Y(4260)$ in the literature. As one of the earliest candidates for a QCD exotic states, its interpretation has initiated various ideas about possible manifestations of non-perturbative mechanisms in the charmonium mass regime. In this short article we briefly review the experimental status of $ψ(4230)$ and discuss possible theoretical interpretations. We will focus on four broadly investigated scenarios, i.e. tetraquark, hybrid, hadro-quarkonium, and hadronic molecule, and highlight the key issues based on these approaches. Crucial experimental observables, e.g. mass position, lineshapes, di-lepton decay width $Γ_{ee}$, production rates in $B$ meson decays, dominant hadronic decay patterns, and the potential $1^{-+}$ and $0^{--}$ exotic partners, are assessed, which can provide crucial structure information for understanding this mysterious state.

hep-ph↗

arXiv2025

Towards Evaluting Fake Reasoning Bias in Language Models

Large Reasoning Models (LRMs), evolved from standard Large Language Models (LLMs), are increasingly utilized as automated judges because of their explicit reasoning processes. Yet we show that both LRMs and standard LLMs are vulnerable to Fake Reasoning Bias (FRB), where models favor the surface structure of reasoning even when the logic is flawed. To study this problem, we introduce THEATER, a comprehensive benchmark that systematically investigates FRB by manipulating reasoning structures to test whether language models are misled by superficial or fabricated cues. It covers two FRB types: (1) Simple Cues, minimal cues that resemble reasoning processes, and (2) Fake CoT, fabricated chains of thought that simulate multi-step reasoning. We evaluate 17 advanced LLMs and LRMs on both subjective DPO and factual datasets. Our results reveal four key findings: (1) Both LLMs and LRMs are vulnerable to FRB, but LLMs are generally more robust than LRMs. (2) Simple Cues are especially harmful, reducing accuracy by up to 15% on the most vulnerable datasets. (3) Subjective DPO tasks are the most vulnerable, with LRMs suffering sharper drops than LLMs. (4) Analysis of LRMs' thinking traces shows that Simple Cues hijack metacognitive confidence, while Fake CoT is absorbed as internal thought, creating a "more thinking, less robust" paradox in LRMs. Finally, prompt-based mitigation improves accuracy on factual tasks by up to 10%, but has little effect on subjective tasks, where self-reflection sometimes lowers LRM performance by 8%. These results highlight FRB as a persistent and unresolved challenge for language models.

cs.CY↗

arXiv2025

Optimal Particle-Conserved Linear Encoding for Practical Fermionic Simulation

Number-conserved subspace encoding reduces resources needed for quantum simulations, but scalable complexity trade-off bounds for $M$ modes and $N$ particles with $\mathcal{O}(N\log M)$ qubits have remained unknown. We study qubit-gate-measurement trade-offs through the lens of classical/quantum error correction complexity, and develop a framework of fermionic gate and measurement complexity based on encoder and decoder complexities appeared in error correction framework. We demonstrate optimal encoding with random classical parity check code and propose the Fermionic Expectation Decoder for scalable probability decoding in $\mathcal{O}(M^4)$ bases. The protocol is tested with variational quantum eigensolver on LiH in the STO-3G and 6-31G basis, and $\text{H}_2$ potential energy curve in the 6-311G* basis.

quant-ph↗

arXiv2025

AlignedGen: Aligning Style Across Generated Images

Despite their generative power, diffusion models struggle to maintain style consistency across images conditioned on the same style prompt, hindering their practical deployment in creative workflows. While several training-free methods attempt to solve this, they are constrained to the U-Net architecture, which not only leads to low-quality results and artifacts like object repetition but also renders them incompatible with superior Diffusion Transformer (DiT). To address these issues, we introduce AlignedGen, a novel training-free framework that enhances style consistency across images generated by DiT models. Our work first reveals a critical insight: naive attention sharing fails in DiT due to conflicting positional signals from improper position embeddings. We introduce Shifted Position Embedding (ShiftPE), an effective solution that resolves this conflict by allocating a non-overlapping set of positional indices to each image. Building on this foundation, we develop Advanced Attention Sharing (AAS), a suite of three techniques meticulously designed to fully unleash the potential of attention sharing within the DiT. Furthermore, to broaden the applicability of our method, we present an efficient query, key, and value feature extraction algorithm, enabling our method to seamlessly incorporate external images as style references. Extensive experimental results validate that our method effectively enhances style consistency across generated images while maintaining precise text-to-image alignment.

cs.CV↗

arXiv2025

Brain Connectivity Network Structure Learning For Brain Disorder Diagnosis

Recent studies in neuroscience highlight the significant potential of brain connectivity networks, which are commonly constructed from functional magnetic resonance imaging (fMRI) data for brain disorder diagnosis. Traditional brain connectivity networks are typically obtained using predefined methods that incorporate manually-set thresholds to estimate inter-regional relationships. However, such approaches often introduce redundant connections or overlook essential interactions, compromising the value of the constructed networks. Besides, the insufficiency of labeled data further increases the difficulty of learning generalized representations of intrinsic brain characteristics. To mitigate those issues, we propose a self-supervised framework to learn an optimal structure and representation for brain connectivity networks, focusing on individualized generation and optimization in an unsupervised manner. We firstly employ two existing whole-brain connectomes to adaptively construct their complementary brain network structure learner, and then introduce a multi-state graph-based encoder with a joint iterative learning strategy to simultaneously optimize both the generated network structure and its representation. By leveraging self-supervised pretraining on large-scale unlabeled brain connectivity data, our framework enables the brain connectivity network learner to generalize e ffectively to unseen disorders, while requiring only minimal finetuning of the encoder for adaptation to new diagnostic tasks. Extensive experiments on cross-dataset brain disorder diagnosis demonstrate that our method consistently outperforms state-of-the-art approaches, validating its effectiveness and generalizability. The code is publicly available at https://github.com/neochen1/BCNSL.

cs.GR↗

arXiv2025

Revisiting Transferable Adversarial Images: Systemization, Evaluation, and New Insights

Transferable adversarial images raise critical security concerns for computer vision systems in real-world, black-box attack scenarios. Although many transfer attacks have been proposed, existing research lacks a systematic and comprehensive evaluation. In this paper, we systemize transfer attacks into five categories around the general machine learning pipeline and provide the first comprehensive evaluation, with 23 representative attacks against 11 representative defenses, including the recent, transfer-oriented defense and the real-world Google Cloud Vision. In particular, we identify two main problems of existing evaluations: (1) for attack transferability, lack of intra-category analyses with fair hyperparameter settings, and (2) for attack stealthiness, lack of diverse measures. Our evaluation results validate that these problems have indeed caused misleading conclusions and missing points, and addressing them leads to new, \textit{consensus-challenging} insights, such as (1) an early attack, DI, even outperforms all similar follow-up ones, (2) the state-of-the-art (white-box) defense, DiffPure, is even vulnerable to (black-box) transfer attacks, and (3) even under the same $L_p$ constraint, different attacks yield dramatically different stealthiness results regarding diverse imperceptibility metrics, finer-grained measures, and a user study. We hope that our analyses will serve as guidance on properly evaluating transferable adversarial images and advance the design of attacks and defenses. Code is available at https://github.com/ZhengyuZhao/TransferAttackEval.

cs.CR↗

arXiv2025

Conceptual Design Report of Super Tau-Charm Facility: The Accelerator

Electron-positron colliders operating in the GeV region of center-of-mass energies or the Tau-Charm energy region, have been proven to enable competitive frontier research, due to its several unique features. With the progress of high energy physics in the last two decades, a new-generation Tau-Charm factory, Super Tau Charm Facility (STCF) has been actively promoting by the particle physics community in China. STCF holds great potential to address fundamental questions such as the essence of color confinement and the matter-antimatter asymmetry in the universe in the next decades. The main design goals of STCF are with a center-of-mass energy ranging from 2 to 7 GeV and a peak luminosity surpassing 5*10^34 cm^-2s^-1 that is optimized at a center-of-mass energy of 4 GeV, which is about 50 times that of the currently operating Tau-Charm factory - BEPCII. The STCF accelerator is composed of two main parts: a double-ring collider with the crab-waist collision scheme and an injector that provides top-up injections for both electron and positron beams. As a typical third-generation electron-positron circular collider, the STCF accelerator faces many challenges in both accelerator physics and technology. In this paper, the conceptual design of the STCF accelerator complex is presented, including the ongoing efforts and plans for technological R&D, as well as the required infrastructure. The STCF project aims to secure support from the Chinese central government for its construction during the 15th Five-Year Plan (2026-2030) in China.

physics.acc-ph↗

arXiv2025

Exploiting Timing Side-Channels in Quantum Circuits Simulation Via ML-Based Methods

As quantum computing advances, quantum circuit simulators serve as critical tools to bridge the current gap caused by limited quantum hardware availability. These simulators are typically deployed on cloud platforms, where users submit proprietary circuit designs for simulation. In this work, we demonstrate a novel timing side-channel attack targeting cloud-based quantum simulators. A co-located malicious process can observe fine-grained execution timing patterns to extract sensitive information about concurrently running quantum circuits. We systematically analyze simulator behavior using the QASMBench benchmark suite, profiling timing and memory characteristics across various circuit executions. Our experimental results show that timing profiles exhibit circuit-dependent patterns that can be effectively classified using pattern recognition techniques, enabling the adversary to infer circuit identities and compromise user confidentiality. We were able to achieve 88% to 99.9% identification rate of quantum circuits based on different datasets. This work highlights previously unexplored security risks in quantum simulation environments and calls for stronger isolation mechanisms to protect user workloads

cs.CR↗

arXiv2025

Igniting VLMs toward the Embodied Space

While foundation models show remarkable progress in language and vision, existing vision-language models (VLMs) still have limited spatial and embodiment understanding. Transferring VLMs to embodied domains reveals fundamental mismatches between modalities, pretraining distributions, and training objectives, leaving action comprehension and generation as a central bottleneck on the path to AGI. We introduce WALL-OSS, an end-to-end embodied foundation model that leverages large-scale multimodal pretraining to achieve (1) embodiment-aware vision-language understanding, (2) strong language-action association, and (3) robust manipulation capability. Our approach employs a tightly coupled architecture and multi-strategies training curriculum that enables Unified Cross-Level CoT-seamlessly unifying instruction reasoning, subgoal decomposition, and fine-grained action synthesis within a single differentiable framework. Our results show that WALL-OSS attains high success on complex long-horizon manipulations, demonstrates strong instruction-following capabilities, complex understanding and reasoning, and outperforms strong baselines, thereby providing a reliable and scalable path from VLMs to embodied foundation models.

cs.RO↗

arXiv2025

Simulation of radiation environment for the beam monitor of CEE experiment

The cooling storage ring external-target experiment is a large-scale nuclear physics experiment, which aims to study the physics of heavy-ion collisions at low temperatures and high baryon densities. A beam monitor (BM) is placed in the beam line to monitor the beam status and to improve the reconstruction resolution of the primary vertices. The radiation dose and particle fluence stemming from the beam interactions with gases and detector materials affect the performance of the sensors and electronics of BM. This paper uses FLUKA Monte Carlo code to simulate the radiation environment of BM detector. Radiation quantities including the total ionizing dose, 1 MeV neutron equivalent fluence, high-energy hadron flux, thermal neutron flux, and nuclear fragment flux are presented. Results of alternative simulation setups, including adding shielding layers inside the BM, are also investigated.

physics.ins-det↗

arXiv2025

Doubly charmed pentaquark states with strangeness $S=0, -1$

In this work, we have studied the mass spectra of doubly charmed pentaquark states with strangeness $S=0, -1$ by using the method of QCD sum rules. We use the parity projected sum rules to separate the contributions of negative and positive parities from the two-point correlation functions induced by the pentaquark interpolating currents. Our results predict the existence of some potential doubly charmed pentaquark bound states.

hep-ph↗

arXiv2025

Communicative Agents for Slideshow Storytelling Video Generation based on LLMs

With the rapid advancement of artificial intelligence (AI), the proliferation of AI-generated content (AIGC) tasks has significantly accelerated developments in text-to-video generation. As a result, the field of video production is undergoing a transformative shift. However, conventional text-to-video models are typically constrained by high computational costs. In this study, we propose Video-Generation-Team (VGTeam), a novel slide show video generation system designed to redefine the video creation pipeline through the integration of large language models (LLMs). VGTeam is composed of a suite of communicative agents, each responsible for a distinct aspect of video generation, such as scriptwriting, scene creation, and audio design. These agents operate collaboratively within a chat tower workflow, transforming user-provided textual prompts into coherent, slide-style narrative videos. By emulating the sequential stages of traditional video production, VGTeam achieves remarkable improvements in both efficiency and scalability, while substantially reducing computational overhead. On average, the system generates videos at a cost of only $0.103, with a successful generation rate of 98.4%. Importantly, this framework maintains a high degree of creative fidelity and customization. The implications of VGTeam are far-reaching. It democratizes video production by enabling broader access to high-quality content creation without the need for extensive resources. Furthermore, it highlights the transformative potential of language models in creative domains and positions VGTeam as a pioneering system for next-generation content creation.

cs.AI↗

arXiv2025

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution

Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks. However, they remain vulnerable to backdoor attacks, where models behave normally for standard queries but generate harmful responses or unintended output when specific triggers are activated. Existing backdoor defenses either lack comprehensiveness, focusing on narrow trigger settings, detection-only mechanisms, and limited domains, or fail to withstand advanced scenarios like model-editing-based, multi-trigger, and triggerless attacks. In this paper, we present LETHE, a novel method to eliminate backdoor behaviors from LLMs through knowledge dilution using both internal and external mechanisms. Internally, LETHE leverages a lightweight dataset to train a clean model, which is then merged with the backdoored model to neutralize malicious behaviors by diluting the backdoor impact within the model's parametric memory. Externally, LETHE incorporates benign and semantically relevant evidence into the prompt to distract LLM's attention from backdoor features. Experimental results on classification and generation domains across 5 widely used LLMs demonstrate that LETHE outperforms 8 state-of-the-art defense baselines against 8 backdoor attacks. LETHE reduces the attack success rate of advanced backdoor attacks by up to 98% while maintaining model utility. Furthermore, LETHE has proven to be cost-efficient and robust against adaptive backdoor attacks.

cs.CL↗

arXiv2025

ExpertWeave: Efficiently Serving Expert-Specialized Fine-Tuned Adapters at Scale

Expert-Specialized Fine-Tuning (ESFT) adapts Mixture-of-Experts (MoE) large language models to enhance their task-specific performance by selectively tuning the top-activated experts for the task. Serving these fine-tuned models at scale is challenging: deploying merged models in isolation is prohibitively resource-hungry, while existing multi-adapter serving systems with LoRA-style additive updates are incompatible with ESFT's expert-oriented paradigm. We present ExpertWeave, a system that serves multiple ESFT adapters concurrently over a single shared MoE base model, drastically reducing the memory footprint and improving resource utilization. To seamlessly integrate into existing inference pipelines for MoE models with non-intrusive modifications and minimal latency overhead, ExpertWeave introduces a virtual-memory-assisted expert weight manager that co-locates base-model and adapter experts without incurring memory overhead from fragmentation, and a fused kernel for batched rerouting to enable lightweight redirection of tokens to the appropriate experts at runtime. Our evaluations show that ExpertWeave can simultaneously serve multiple adapters of a 16B MoE model on a single accelerator where the baseline runs out of memory, or provides up to 94x more KV cache capacity and achieves up to 18% higher throughput while using comparable resources, all without compromising model accuracy. ExpertWeave maintains low overhead even when scaling to 20 adapters, with a 4-11% latency increase compared with serving the base model alone. Source code will be released soon.

cs.DC↗