Tootfinder

Non-Hermitian edge burst of sound
Hong-Yu Zou, Bing-Bing Wang, Yong Ge, Ke-Qi Zhao, Yu-Qi Chen, Hong-Xiang Sun, Shou-Qi Yuan, Haoran Xue, Baile Zhang
https://arxiv.org/abs/2508.19255

Non-Hermitian edge burst of sound
Non-Hermitian band topology can give rise to phenomena with no counterparts in Hermitian systems. A well-known example is the non-Hermitian skin effect (NHSE), where Bloch eigenstates localize at a boundary, induced by a nontrivial spectrum winding number. In contrast, recent studies on lossy non-Hermitian lattices have uncovered an unexpected boundary-localized loss probability-a phenomenon that requires not only non-Hermitian band topology but also the closure of the imaginary (dissipative) g…

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization
Zhibo Xu, Jianhao Zhu, Jingwen Xu, Changze Lv, Zisu Huang, Xiaohua Wang, Muling Wu, Qi Qian, Xiaoqing Zheng, Xuanjing Huang
https://arxiv.org/abs/2508.18911

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization
Experimental results across various models and tasks demonstrate that our approach not only maintains strong model performance in federated learning settings but also achieves enhanced protection of model parameters compared to baseline methods.

🇺🇦 #NowPlaying on KEXP's #SoundsOfSurvivance
Elisapie:
🎵 Qimatsilunga
#Elisapie
https://elisapie.bandcamp.com/track/qimatsilunga-i-want-to-break-free
https://open.spotify.com/track/5c1azafxrJKFmuWo6dBiZG

Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Models
Yuexuan Xia, Benteng Ma, Jiang He, Zhiyong Wang, Qi Dou, Yong Xia
https://arxiv.org/abs/2508.18886

Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Models
Ensuring fairness across demographic groups in medical diagnosis is essential for equitable healthcare, particularly under distribution shifts caused by variations in imaging equipment and clinical practice. Vision-language models (VLMs) exhibit strong generalization, and text prompts encode identity attributes, enabling explicit identification and removal of sensitive directions. However, existing debiasing approaches typically address vision and text modalities independently, leaving residual…

MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
Zhenting Wang, Qi Chang, Hemani Patel, Shashank Biju, Cheng-En Wu, Quan Liu, Aolin Ding, Alireza Rezazadeh, Ankit Shah, Yujia Bao, Eugene Siow
https://arxiv.org/abs/2508.20453

MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
We introduce MCP-Bench, a benchmark for evaluating large language models (LLMs) on realistic, multi-step tasks that demand tool use, cross-tool coordination, precise parameter control, and planning/reasoning for solving tasks. Built on the Model Context Protocol (MCP), MCP-Bench connects LLMs to 28 representative live MCP servers spanning 250 tools across domains such as finance, traveling, scientific computing, and academic search. Unlike prior API-based benchmarks, each MCP server provides a …

PSRT: Accelerating LRM-based Guard Models via Prefilled Safe Reasoning Traces
Jiawei Zhao, Yuang Qi, Weiming Zhang, Nenghai Yu, Kejiang Chen
https://arxiv.org/abs/2509.21768 htt…

PSRT: Accelerating LRM-based Guard Models via Prefilled Safe Reasoning Traces
Large Reasoning Models (LRMs) have demonstrated remarkable performance on tasks such as mathematics and code generation. Motivated by these strengths, recent work has empirically demonstrated the effectiveness of LRMs as guard models in improving harmful query detection. However, LRMs typically generate long reasoning traces during inference, causing substantial computational overhead. In this paper, we introduce PSRT, a method that replaces the model's reasoning process with a Prefilled Safe R…

A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases
Qinlong Li, Pu Sun, Guanlin Zhu, Tianjiao Liang, Honggang QI
https://arxiv.org/abs/2507.19734

A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases
Prognostic evaluation in patients with colorectal liver metastases (CRLM) remains challenging due to suboptimal accuracy of conventional clinical models. This study developed and validated a robust machine learning model for predicting postoperative recurrence risk. Preliminary ensemble models achieved exceptionally high performance (AUC $>$ 0.98) but incorporated postoperative features, introducing data leakage risks. To enhance clinical applicability, we restricted input variables to preopera…

Deep Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction
Zhuoxing Wei, Qi Liu, Qingchen Xie
https://arxiv.org/abs/2508.20865 https://

Deep Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction
In Click-Through Rate (CTR) prediction, the long behavior sequence, comprising the user's long period of historical interactions with items has a vital influence on assessing the user's interest in the candidate item. Existing approaches strike efficiency and effectiveness through a two-stage paradigm: first retrieving hundreds of candidate-related items and then extracting interest intensity vector through target attention. However, we argue that the discrepancy in target attention's relevance…

A Dual One-way Doppler Cancellation Scheme for Enhanced Gravitational Redshift Tests
Cheng-Gang Qin, Tong Liu, Yang Li, Qi-Long Gong, Qin Li, Zhi-Yu Ma
https://arxiv.org/abs/2508.19886

A Dual One-way Doppler Cancellation Scheme for Enhanced Gravitational Redshift Tests
Gravitational redshift is an important prediction of General Relativity (GR). We propose a novel dual one-way frequency comparison scheme for gravitational redshift tests in satellite-ground clock experiments. Unlike conventional triple-link Doppler cancellation methods, our approach requires only two optical links, an uplink and a downlink, with Doppler data recorded independently at the satellite and ground station, respectively. Crucially, coherent combination of these measurements suppresse…

MDAR: A Multi-scene Dynamic Audio Reasoning Benchmark
Hui Li, Changhao Jiang, Hongyu Wang, Ming Zhang, Jiajun Sun, Zhixiong Yang, Yifei Cao, Shihan Dou, Xiaoran Fan, Baoyu Fan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
https://arxiv.org/abs/2509.22461

MDAR: A Multi-scene Dynamic Audio Reasoning Benchmark
The ability to reason from audio, including speech, paralinguistic cues, environmental sounds, and music, is essential for AI agents to interact effectively in real-world scenarios. Existing benchmarks mainly focus on static or single-scene settings and do not fully capture scenarios where multiple speakers, unfolding events, and heterogeneous audio sources interact. To address these challenges, we introduce MDAR, a benchmark for evaluating models on complex, multi-scene, and dynamically evolvi…

Analytical two-loop amplitudes of $e^{ } e^{-} \longrightarrow \boldsymbol{J} / \boldsymbol{\psi} \boldsymbol{\eta}_c$ at $B$ factories
Xiang Chen, Xin Guan, Chuan-Qi He, Yan-Qing Ma, Jian Wang, Da-Jiang Zhang
https://arxiv.org/abs/2508.20777

Analytical two-loop amplitudes of $e^{+} e^{-} \longrightarrow \boldsymbol{J} / \boldsymbolψ+\boldsymbolη_c$ at $B$ factories
In double charmonium production, a long-standing challenge is that the theoretical predictions are not consistent with the measurements at B factories. Within the NRQCD framework, the next-to-leading order (NLO) calculation has proved its power to cut down the discrepancy between theory and experiments. To further clarify this puzzle, we have performed the next-to-next-to-leading order (NNLO) calculation. The amplitude is obtained as an analytical asymptotic expansion in the ratio of the square…

Probe the missing baryons in cosmic filaments with Fast Radio Bursts
Jian-Feng Mo, Weishan Zhu, Qi-Rui Yang, Yi Zheng, Long-Long Feng
https://arxiv.org/abs/2508.19861 https://…

Probe the missing baryons in cosmic filaments with Fast Radio Bursts
Cosmic filaments are thought to host a substantial fraction of the missing baryons at redshifts $z<2$. In this study, we probe the baryonic content of these filaments using localized Fast Radio Bursts (FRBs). Filaments are identified from the galaxy distribution in the Dark Energy Spectroscopic Instrument (DESI) imaging surveys using the DisPerSe algorithm. We find tentative evidence ($\sim 3 σ$ significance) for a divergence in the relationship between the dispersion measure (DM) contributed …

Backward Harnack inequality and Hamilton estimates for heat type equations
Juanling Lu, Yuting Wu, Qi S. Zhang
https://arxiv.org/abs/2508.19680 https://arx…

Backward Harnack inequality and Hamilton estimates for heat type equations
Based on gradient estimates for the heat equation by Hamilton, we discover a backward in time Harnack inequality for positive solutions on compact manifolds without further restrictions such as boundedness or vanishing boundary value for solutions. Contrary to the usual Harnack inequality, it allows comparison of the values of a solution at two different space time points, in both directions of time. In view of the importance of the usual Harnack inequality, further application is expected.

Scallop Theorem for Swimming in Anisotropic Fluids
Mojtaba Rajabi, Enej Caf, Qi Xing Zhang, Stephen A. Crane, Miha Ravnik, Gareth P. Alexander, \v{Z}iga Kos, Kathleen J. Stebe
https://arxiv.org/abs/2509.22249

Scallop Theorem for Swimming in Anisotropic Fluids
In isotropic fluids like water, micrometer-scale swimmers have evolved swim strokes to translate despite their tiny size. As described by Purcell in his Scallop Theorem, reciprocal motions, like those performed by a scallop, cannot drive swimming when inertial effects are absent, as is typical at micrometer length scales. Thus, microswimmers have evolved complex structures that can perform non-reciprocal swim strokes or body displacements to generate motion. Microswimmer dynamics in structured …

Signed Higher-Order Interactions for Brain Disorder Diagnosis via Multi-Channel Transformers
Dengyi Zhao, Zhiheng Zhou, Guiying Yan, Dongxiao Yu, Xingqi Qi
https://arxiv.org/abs/2507.20205

Signed Higher-Order Interactions for Brain Disorder Diagnosis via Multi-Channel Transformers
Accurately characterizing higher-order interactions of brain regions and extracting interpretable organizational patterns from Functional Magnetic Resonance Imaging data is crucial for brain disease diagnosis. Current graph-based deep learning models primarily focus on pairwise or triadic patterns while neglecting signed higher-order interactions, limiting comprehensive understanding of brain-wide communication. We propose HOI-Brain, a novel computational framework leveraging signed higher-orde…

A Theory of $\theta$-Expectations
Qian Qi
https://arxiv.org/abs/2507.20353 https://arxiv.org/pdf/2507.20353

A Theory of $θ$-Expectations
The canonical theory of stochastic calculus under ambiguity, founded on sub-additivity, is insensitive to non-convex uncertainty structures, leading to an identifiability impasse. This paper develops a mathematical framework for an identifiable calculus sensitive to non-convex geometry. We introduce the $θ$-BSDE, a class of backward stochastic differential equations where the driver is determined by a pointwise maximization over a primitive, possibly non-convex, uncertainty set. The system's t…

Taking Language Embedded 3D Gaussian Splatting into the Wild
Yuze Wang, Yue Qi
https://arxiv.org/abs/2507.19830 https://arxiv.org/pdf/2507.19830

Taking Language Embedded 3D Gaussian Splatting into the Wild
Recent advances in leveraging large-scale Internet photo collections for 3D reconstruction have enabled immersive virtual exploration of landmarks and historic sites worldwide. However, little attention has been given to the immersive understanding of architectural styles and structural knowledge, which remains largely confined to browsing static text-image pairs. Therefore, can we draw inspiration from 3D in-the-wild reconstruction techniques and use unconstrained photo collections to create a…

Exact Solvability and Integrability Signatures in a Periodically Driven Infinite-Range Spin Chain: The Case of Floquet interval $\pi/2$
Harshit Sharma, Sashmita Rout, Avadhut V. Purohit, Udaysinh T. Bhosale
https://arxiv.org/abs/2509.19991

Exact Solvability and Integrability Signatures in a Periodically Driven Infinite-Range Spin Chain: The Case of Floquet interval $π/2$
We study the signatures of quantum integrability (QI) in a spin chain model, having infinite-range Ising interaction and subjected to a periodic pulse of an external magnetic field. We analyze the unitary operator, its eigensystem, the single-qubit reduced density matrix, and the entanglement dynamics for arbitrary initial state for any $N$. The QI in our model can be identified through key signatures such as the periodicity of entanglement dynamics and the time-evolved unitary operator, and hi…

50 mm $\times$ 50 mm Cesium Atomic Vapor Cell for Terahertz Imaging: Implementation and Application
Bin Zhang, Jun Wan, Tao Li, Xian-Zhe Li, Yu Wu, Qi-Rong Huang, Xin-Yu Yang, Wei Huang, Kai-Qing Zhang, Hai-Xiao Deng
https://arxiv.org/abs/2509.22098

50 mm $\times$ 50 mm Cesium Atomic Vapor Cell for Terahertz Imaging: Implementation and Application
Rydberg atomic sensors offer transformative potential for high-speed, high-sensitivity terahertz (THz) imaging. However, previous systems are hindered by restricted imaging areas, largely due to the compact dimension of atomic vapor cells and inefficient beam-shaping methodologies. We present a THz imaging system with a 50 mm $\times$ 50 mm area, enabled by a custom-engineered scaled-up atomic vapor cell and an optimized beam-shaping optical architecture. Experimental validation confirms that t…

CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks
Qi Chai, Zhang Zheng, Junlong Ren, Deheng Ye, Zichuan Lin, Hao Wang
https://arxiv.org/abs/2508.18797 …

CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks
Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy sequences of actions, single-agent approaches often face challenges related to inefficiency and limited fault tolerance. Despite these issues, research on multi-agent collaboration remains scarce. In …

Excitonic skin effect
Wenhui Xu, Qi Zhou
https://arxiv.org/abs/2508.20027 https://arxiv.org/pdf/2508.20027

Excitonic skin effect
We show that strong interactions combined with band-dependent imaginary vector potentials give rise to boundary localization of particle-hole pairs, which we term the excitonic skin effect. In a bilayer system with layer-specific gain/loss and an in-plane magnetic field, excitons experience a net imaginary vector potential, resulting in directional amplification of particle-hole pairs. Including nearest-neighbor interactions leads to a non-Hermitian bosonic Kitaev model, where the pairing effec…

Optimizing Particle Transport for Enhanced Confinement in Quasi-Isodynamic Stellarators
A. Ba\~n\'on Navarro, A. Di Siena, F. Jenko, A. Merlo, E. Laude
https://arxiv.org/abs/2507.21003

Optimizing Particle Transport for Enhanced Confinement in Quasi-Isodynamic Stellarators
Despite significant advances in reducing turbulent heat losses, modern quasi-isodynamic (QI) stellarators -- such as Stellaris -- continue to suffer from poor particle confinement, which fundamentally limits their overall performance. Using gyrokinetic simulations within the GENE--Tango framework, we identify suppressed inward thermodiffusion, caused by unfavorable magnetic geometry, as the primary cause. To overcome this limitation, we design a new configuration with a reduced mirror ratio, wh…

Robustness Assessment and Enhancement of Text Watermarking for Google's SynthID
Xia Han, Qi Li, Jianbing Ni, Mohammad Zulkernine
https://arxiv.org/abs/2508.20228 https://

Robustness Assessment and Enhancement of Text Watermarking for Google's SynthID
Recent advances in LLM watermarking methods such as SynthID-Text by Google DeepMind offer promising solutions for tracing the provenance of AI-generated text. However, our robustness assessment reveals that SynthID-Text is vulnerable to meaning-preserving attacks, such as paraphrasing, copy-paste modifications, and back-translation, which can significantly degrade watermark detectability. To address these limitations, we propose SynGuard, a hybrid framework that combines the semantic alignment …

Optimal $(\alpha,\beta)$-Dense Subgraph Search in Bipartite Graphs
Yalong Zhang, Rong-Hua Li, Qi Zhang, Guoren Wang
https://arxiv.org/abs/2508.18616 https://

Optimal $(α,β)$-Dense Subgraph Search in Bipartite Graphs
Dense subgraph search in bipartite graphs is a fundamental problem in graph analysis, with wide-ranging applications in fraud detection, recommendation systems, and social network analysis. The recently proposed $(α, β)$-dense subgraph model has demonstrated superior capability in capturing the intrinsic density structure of bipartite graphs compared to existing alternatives. However, despite its modeling advantages, the $(α, β)$-dense subgraph model lacks efficient support for query proces…

PSR J1838-0655: X-Ray Observations with NICER and NuSTAR
Xian-Ao Wang, Han-Long Peng, Jin-Tao Zheng, Shi-Qi Zhou, Wen-Tao Ye, Ming-Yu Ge, Xiang-Hua Li, Shi-Jie Zheng
https://arxiv.org/abs/2508.17678

PSR J1838-0655: X-Ray Observations with NICER and NuSTAR
We report on the timing and spectral properties of PSR J1838-0655 using joint observations from the Neutron Star Interior Composition Explorer (NICER) and the Nuclear Spectroscopic Telescope Array (NuSTAR). By disentangling the pulsar's emission from its surrounding wind nebula across joint Chandra, NuSTAR, and NICER observations, we find the pulsar's broad-band X-ray spectrum (1.3--79\,keV) is best-described by a broken power-law model. The model features photon indices of $Γ_1 = 1.19 \pm 0.0…

Federated Learning for Large Models in Medical Imaging: A Comprehensive Review
Mengyu Sun, Ziyuan Yang, Yongqiang Huang, Hui Yu, Yingyu Chen, Shuren Qi, Andrew Beng Jin Teoh, Yi Zhang
https://arxiv.org/abs/2508.20414

Federated Learning for Large Models in Medical Imaging: A Comprehensive Review
Artificial intelligence (AI) has demonstrated considerable potential in the realm of medical imaging. However, the development of high-performance AI models typically necessitates training on large-scale, centralized datasets. This approach is confronted with significant challenges due to strict patient privacy regulations and legal restrictions on data sharing and utilization. These limitations hinder the development of large-scale models in medical domains and impede continuous updates and tr…

Crosslisted article(s) found for q-fin.TR. https://arxiv.org/list/q-fin.TR/new
[1/1]:
- Empirical Analysis of the Model-Free Valuation Approach: Hedging Gaps, Conservatism, and Trading ...
Zixing Chen, Yihan Qi, Shanlan Que, Julian Sester, Xiao Zhang

SLAM-Free Visual Navigation with Hierarchical Vision-Language Perception and Coarse-to-Fine Semantic Topological Planning
Guoyang Zhao, Yudong Li, Weiqing Qi, Kai Zhang, Bonan Liu, Kai Chen, Haoang Li, Jun Ma
https://arxiv.org/abs/2509.20739

SLAM-Free Visual Navigation with Hierarchical Vision-Language Perception and Coarse-to-Fine Semantic Topological Planning
Conventional SLAM pipelines for legged robot navigation are fragile under rapid motion, calibration demands, and sensor drift, while offering limited semantic reasoning for task-driven exploration. To deal with these issues, we propose a vision-only, SLAM-free navigation framework that replaces dense geometry with semantic reasoning and lightweight topological representations. A hierarchical vision-language perception module fuses scene-level context with object-level cues for robust semantic i…

GSCache: Real-Time Radiance Caching for Volume Path Tracing using 3D Gaussian Splatting
David Bauer, Qi Wu, Hamid Gadirov, Kwan-Liu Ma
https://arxiv.org/abs/2507.19718 https://

GSCache: Real-Time Radiance Caching for Volume Path Tracing using 3D Gaussian Splatting
Real-time path tracing is rapidly becoming the standard for rendering in entertainment and professional applications. In scientific visualization, volume rendering plays a crucial role in helping researchers analyze and interpret complex 3D data. Recently, photorealistic rendering techniques have gained popularity in scientific visualization, yet they face significant challenges. One of the most prominent issues is slow rendering performance and high pixel variance caused by Monte Carlo integra…

Wavelength-scale noise-resistant on-chip spectrometer
Jianbo Yu, Hsuan Lo, Wenduo Chen, Changyan Zhu, Yujin Wu, Fakun Wang, Chongwu Wang, Congliao Yan, Cuong Dang, Bihan Wen, Hui Cao, Yidong Chong, Qi Jie Wang
https://arxiv.org/abs/2509.22286

Wavelength-scale noise-resistant on-chip spectrometer
Performant on-chip spectrometers are important for advancing sensing technologies, from environmental monitoring to biomedical diagnostics. As device footprints approach the scale of the operating wavelength, previously strategies, including those relying on multiple scattering in diffusive media, face fundamental accuracy constraints tied to limited optical path lengths. Here, we demonstrate a wavelength-scale, CMOS-compatible on-chip spectrometer that overcomes this challenge by exploiting in…

Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
Zekun Ni, Jonathan Weyn, Hang Zhang, Yanfei Xiang, Jiang Bian, Weixin Jin, Kit Thambiratnam, Qi Zhang, Haiyu Dong, Hongyu Sun
https://arxiv.org/abs/2508.18486

Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP). However, those models still rely on initial conditions from NWP, putting an upper limit on their forecast abilities. A few end-to-end systems have since been proposed, but they have yet to match the forecast skill of state…

MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs
Sixun Dong, Juhua Hu, Mian Zhang, Ming Yin, Yanjie Fu, Qi Qian
https://arxiv.org/abs/2508.18264 https://

MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs
Vision-Language Models (VLMs) demonstrate impressive performance in understanding visual content with language instruction by converting visual input to vision tokens. However, redundancy in vision tokens results in the degenerated inference efficiency of VLMs. While many algorithms have been proposed to reduce the number of vision tokens, most of them apply only unimodal information (i.e., vision/text) for pruning and ignore the inherent multimodal property of vision-language tasks. Moreover, …

Quantum Interference Supernodes, Thermoelectric Enhancement, and the Role of Dephasing
Justin P. Bergfield
https://arxiv.org/abs/2508.20352 https://arxiv.o…

Quantum Interference Supernodes, Thermoelectric Enhancement, and the Role of Dephasing
Quantum interference (QI) can strongly enhance thermoelectric response, with higher-order "supernodes" predicted to yield scalable gains in thermopower and efficiency. A central question, however, is whether such features are intrinsically more fragile to dephasing. Using $Büttiker$ voltage-temperature probes, we establish an order-selection rule: the effective near-node order is set by the lowest among coherent and probe-assisted channels. Supernodes are therefore fragile in an absolute sense…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- REArtGS: Reconstructing and Generating Articulated Objects via 3D Gaussian Splatting with Geometr...
Di Wu, Liu Liu, Zhou Linli, Anran Huang, Liangtu Song, Qiaojun Yu, Qi Wu, Cewu Lu

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
Chenghao Wu, Ruiyang Ren, Junjie Zhang, Ruirui Wang, Zhongrui Ma, Qi Ye, Wayne Xin Zhao
https://arxiv.org/abs/2508.18812

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
While modern recommender systems are instrumental in navigating information abundance, they remain fundamentally limited by static user modeling and reactive decision-making paradigms. Current large language model (LLM)-based agents inherit these shortcomings through their overreliance on heuristic pattern matching, yielding recommendations prone to shallow correlation bias, limited causal inference, and brittleness in sparse-data scenarios. We introduce STARec, a slow-thinking augmented agent …

Equinox: Holistic Fair Scheduling in Serving Large Language Models
Zhixiang Wei, James Yen, Jingyi Chen, Ziyang Zhang, Zhibai Huang, Chen Chen, Xingzi Yu, Yicheng Gu, Chenggang Wu, Yun Wang, Mingyuan Xia, Jie Wu, Hao Wang, Zhengwei Qi
https://arxiv.org/abs/2508.16646

Equinox: Holistic Fair Scheduling in Serving Large Language Models
We address the limitations of current LLM serving with a dual-counter framework separating user and operator perspectives. The User Fairness Counter measures quality of service via weighted tokens and latency; the Resource Fairness Counter measures operational efficiency through throughput and GPU utilization. Since these metrics are only available post-execution, creating a scheduling paradox, we introduce a deterministic Mixture of Prediction Experts (MoPE) framework to predict user-perceived…

The Rhythm of Tai Chi: Revitalizing Cultural Heritage in Virtual Reality through Interactive Visuals
Xianghan Wang
https://arxiv.org/abs/2508.16605 https://

The Rhythm of Tai Chi: Revitalizing Cultural Heritage in Virtual Reality through Interactive Visuals
The Rhythm of Tai Chi reinterprets the ancient Chinese martial art as a dynamic, interactive virtual reality (VR) experience. By leveraging computer vision and multimedia technologies, the project transforms Tai Chi's philosophy and movements into an immersive digital form. Real-time motion tracking captures user gestures, while visual feedback systems simulate the flow of Qi, enabling an intuitive and engaging practice environment. Beyond technological innovation, this work bridges traditional…

Shapley Features for Robust Signal Prediction in Tactile Internet
Mohammad Ali Vahedifar, Qi Zhang
https://arxiv.org/abs/2509.21032 https://arxiv.org/pdf/2…

Shapley Features for Robust Signal Prediction in Tactile Internet
The Tactile Internet (TI) requires ultra-low latency and reliable haptic signal transmission, yet packet loss and delay remain unresolved challenges. We present a novel prediction framework that integrates Gaussian Processes (GP) with a ResNet-based Neural Network, where GP acts as an oracle to recover signals lost or heavily delayed. To further optimize performance, we introduce Shapley Feature Values (SFV), a principled feature selection mechanism that isolates the most informative inputs for…

Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
Yongrui Chen, Junhao He, Linbo Fu, Shenyu Zhang, Rihui Jin, Xinbang Dai, Jiaqi Li, Dehai Min, Nan Hu, Yuxin Zhang, Guilin Qi, Yi Huang, Tongtong Wu
https://arxiv.org/abs/2508.17905

Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and knowledge graphs in a unified way. Existing USKR methods rely on task-specific strategies or bespoke representations, which hinder their ability to dismantle barriers between different SKR tasks, thereby constraining their overall performance in cross-task scenarios. In this paper, we introduce \textsc{Pandora}, a novel USKR framework that addresses …

Quantum-memory-assisted on-demand microwave-optical transduction
Hai-Tao Tu, Kai-Yu Liao, Si-Yuan Qiu, Xiao-Hong Liu, Yi-Qi Guo, Zheng-Qi Du, Yang Xu, Xin-Ding Zhang, Hui Yan, Shi-Liang Zhu
https://arxiv.org/abs/2509.18834

Enhancing DSMC simulations of rarefied gas mixtures using a fast-converging and asymptotic-preserving scheme
Liyan Luo, Jianan Zeng, Yanbin Zhang, Wei Li, Qi Li, Lei Wu
https://arxiv.org/abs/2508.17598

Enhancing DSMC simulations of rarefied gas mixtures using a fast-converging and asymptotic-preserving scheme
The numerical simulation of rarefied gas mixture dynamics with disparate masses using the direct simulation Monte Carlo (DSMC) method is slow, primarily because the time step is constrained by that of the lighter species, necessitating an enormous number of evolution steps to reach a steady state. Here, we address this issue by developing a general synthetic iterative scheme, in which the traditional DSMC simulation is intermittently enhanced using a macroscopic synthetic equation. Specifically…

Crosslisted article(s) found for q-fin.RM. https://arxiv.org/list/q-fin.RM/new
[1/1]:
- Empirical Analysis of the Model-Free Valuation Approach: Hedging Gaps, Conservatism, and Trading ...
Zixing Chen, Yihan Qi, Shanlan Que, Julian Sester, Xiao Zhang

Crosslisted article(s) found for q-fin.MF. https://arxiv.org/list/q-fin.MF/new
[1/1]:
- Empirical Analysis of the Model-Free Valuation Approach: Hedging Gaps, Conservatism, and Trading ...
Zixing Chen, Yihan Qi, Shanlan Que, Julian Sester, Xiao Zhang

Tri-Hybrid Beamforming for Radiation-Center Reconfigurable Antenna Array: Spectral Efficiency and Energy Efficiency
Yinchen Li, Chenhao Qi, Shiwen Mao, Octavia A. Dobre
https://arxiv.org/abs/2508.15924

Tri-Hybrid Beamforming for Radiation-Center Reconfigurable Antenna Array: Spectral Efficiency and Energy Efficiency
In this paper, we propose a tri-hybrid beamforming (THBF) architecture based on the radiation-center (RC) reconfigurable antenna array (RCRAA), including the digital beamforming, analog beamforming, and electromagnetic (EM) beamforming, where the EM beamformer design is modeled as RC selection. Aiming at spectral efficiency (SE) maximization subject to the hardware and power consumption constraints, we propose a tri-loop alternating optimization (TLAO) scheme for the THBF design, where the digi…

Triple top baryon $\Omega_{ttt}$
Zi-Qi Zhu, Chang Xiong, Yu-Jie Zhang
https://arxiv.org/abs/2508.19137 https://arxiv.org/pdf/2508.19137

Triple top baryon $Ω_{ttt}$
Recently, the toponium, a bound state of a top-antitop quark pair, was discovered by the CMS and ATLAS collaborations. This observation has sparked renewed interest in the study of heavy-quark bound states, particularly the hypothetical triple top baryon ($Ω_{ttt}$). We present a theoretical discussion on the mass, production, and decay of this exotic particle within the Standard Model. Using a variational method with an effective potential, we estimate the mass and binding energy of the $Ω_{…

Common Envelope Evolution of Ultralight Boson Clouds
Ao Guo, Qi-Yan Zhang, Huan Yang, Jun Zhang
https://arxiv.org/abs/2508.18738 https://arxiv.org/pdf/2508…

Common Envelope Evolution of Ultralight Boson Clouds
Ultralight bosons can be excited around spinning black holes via black hole superradiance. These boson clouds may play an important role in the orbital evolution of binary black holes. In this work, we investigate the formation and evolution of common envelopes of ultralight boson clouds in comparable mass-ratio black hole binaries. We describe the cloud evolution using gravitational molecular eigenstates and analyze the possible level transitions during orbital decay, as well as the impact on …

Every Subtlety Counts: Fine-grained Person Independence Micro-Action Recognition via Distributionally Robust Optimization
Feng-Qi Cui, Jinyang Huang, Anyang Tong, Ziyu Jia, Jie Zhang, Zhi Liu, Dan Guo, Jianwei Lu, Meng Wang
https://arxiv.org/abs/2509.21261

Every Subtlety Counts: Fine-grained Person Independence Micro-Action Recognition via Distributionally Robust Optimization
Micro-action Recognition is vital for psychological assessment and human-computer interaction. However, existing methods often fail in real-world scenarios because inter-person variability causes the same action to manifest differently, hindering robust generalization. To address this, we propose the Person Independence Universal Micro-action Recognition Framework, which integrates Distributionally Robust Optimization principles to learn person-agnostic representations. Our framework contains t…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- Explaining human cooperation through a dual mechanism of individual and social learning
Zhihao Hou, Zhikun She, Quanyi Liang, Qi Su, Daqing Li

Hilbert transforms on Coxeter groups and groups acting on buildings
Xiao-Qi Lu, Runlian Xia
https://arxiv.org/abs/2508.18176 https://arxiv.org/pdf/2508.181…

Hilbert transforms on Coxeter groups and groups acting on buildings
In this paper, we study Hilbert transforms and their boundedness on $L_p$-spaces associated with Coxeter groups and groups acting on buildings. We establish new models for Hilbert transforms on these groups through the geometric objects they act on, and we show that these Hilbert transforms satisfy a Cotlar identity which was developed in earlier work of Mei and Ricard, and that of González-Pérez, Parcet and the second named author, thus conclude the $L_p$-boundedness. We manage to solve the …

Log Calabi-Yau structure of algebaic varieties admitting a polarized endomorphism
Wentao Chang, De-Qi Zhang
https://arxiv.org/abs/2509.17927 https://arxiv.…

Log Calabi-Yau structure of algebaic varieties admitting a polarized endomorphism
Let $X$ be a normal projective variety admitting a polarized endomorphism $f$, i.e., $f^*H\sim qH$ for some ample divisor $H$ and integer $q>1$. Then Broustet and Gongyo proposed the conjecture that $X$ is of Calabi-Yau type (CY for short), i.e., $(X,Δ)$ is lc for some effective $\mathbb{Q}$-divisor $Δ$ and $K_X+Δ\sim_{\mathbb{Q}}0$. We prove the conjecture when $X$ is a Gorenstein terminal 3-fold, extending the result of Sheng Meng for smooth threefolds. We then study the singularity type a…

Hardwired-Neurons Language Processing Units as General-Purpose Cognitive Substrates
Yang Liu, Yi Chen, Yongwei Zhao, Yifan Hao, Zifu Zheng, Weihao Kong, Zhangmai Li, Dongchen Jiang, Ruiyang Xia, Zhihong Ma, Zisheng Liu, Zhaoyong Wan, Yunqi Lu, Ximing Liu, Hongrui Guo, Zhihao Yang, Zhe Wang, Tianrui Ma, Mo Zou, Rui Zhang, Ling Li, Xing Hu, Zidong Du, Zhiwei Xu, Qi Guo, Tianshi Chen, Yunji Chen

Hardwired-Neurons Language Processing Units as General-Purpose Cognitive Substrates
The rapid advancement of Large Language Models (LLMs) has established language as a core general-purpose cognitive substrate, driving the demand for specialized Language Processing Units (LPUs) tailored for LLM inference. To overcome the growing energy consumption of LLM inference systems, this paper proposes a Hardwired-Neurons Language Processing Unit (HNLPU), which physically hardwires LLM weight parameters into the computational fabric, achieving several orders of magnitude computational ef…

Fully Tensorized GPU-accelerated Multi-population Evolutionary Algorithm for Constrained Multiobjective Optimization Problems
Weixiong Huang, Rui Wang, Wenhua Li, Sheng Qi, Tianyu Luo, Delong Chen, Tao Zhang, Ling Wang
https://arxiv.org/abs/2509.19821

Fully Tensorized GPU-accelerated Multi-population Evolutionary Algorithm for Constrained Multiobjective Optimization Problems
Real world constrained multiobjective optimization problems (CMOPs) are prevalent and often come with stringent time-sensitive requirements. However, most contemporary constrained multiobjective evolutionary algorithms (CMOEAs) suffer from a number of drawbacks, including complex designs, low computational efficiency, and long convergence times, which are particularly pronounced when addressing time-sensitive CMOPs. Although research on accelerating evolutionary algorithms using GPU parallelism…

Low Power, Scalable Nanofabrication via Photon Upconversion
Qi Zhou, Hao-Chi Yen, Qizhen Lan, Arynn O. Gallegos, Manchen Hu, Kyle Frohna, Hannah Niese, Da Lin, Natalia Murrietta, Pournima Narayanan, Tracy H. Schloemer, Linda Pucurimay, Sebastian Fern\'andez, Michael Seitz, Daniel N. Congreve
https://arxiv.org/abs/2508.16668

Low Power, Scalable Nanofabrication via Photon Upconversion
Micro- and nanoscale fabrication, which enables precise construction of intricate three-dimensional structures, is of foundational importance for advancing innovation in plasmonics, nanophotonics, and biomedical applications. However, scaling fabrication to industrially relevant levels remains a significant challenge. We demonstrate that triplet-triplet annihilation upconversion (TTA-UC) offers a unique opportunity to increase fabrication speeds and scalability of micro- and nanoscale 3D struct…

Analysis of the Under-Sampling Effect for AI-based Dynamic Gravimeter
Wen-Zhang Wang, Xi Chen, Jin-ting Li, Dan-fang Zhang, Wei-hao Xu, Jia-yi Wei, Jia-qi Zhong, Biao Tang, Lin Zhou, Jin Wang, Ming-Sheng Zhan
https://arxiv.org/abs/2508.17765

Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Zhijian Zhou, Junyi An, Zongkai Liu, Yunfei Shi, Xuan Zhang, Fenglei Cao, Chao Qu, Yuan Qi
https://arxiv.org/abs/2508.16521

Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Generating physically realistic 3D molecular structures remains a core challenge in molecular generative modeling. While diffusion models equipped with equivariant neural networks have made progress in capturing molecular geometries, they often struggle to produce equilibrium structures that adhere to physical principles such as force field consistency. To bridge this gap, we propose Reinforcement Learning with Physical Feedback (RLPF), a novel framework that extends Denoising Diffusion Policy …

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, Zhaokai Wang, Zhe Chen, Hongjie Zhang, Ganlin Yang, Haomin Wang, Qi Wei, Jinhui Yin, Wenhao Li, Erfei Cui, Guanzhou Chen, Zichen Ding, Changyao Tian, Zhenyu Wu, Jingjing Xie, Zehao Li, Bowen Yang, Yuchen Duan, Xuehui Wang, Songze Li, Xiangyu Zhao, Haodong Duan, Nianche…

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g.…

RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou, Zhihua Han, Zhensong Yan, Mingjie Liu, Hechen Sun, Jiawei Liu, Yiwen Hu, Qi Wang, YiHan Xian, Wenjie Guo, Houyuan Xiang, Zhiyuan Zeng, Xiangrong Sheng, Bencheng Yan, Nan Hu, Yuheng Huang, Jinqing Lian, Ziru Xu, Yan Zhang, Ju Huang, Siran Yang, Huimin Yi, Jiamang Wang, Pengjie Wang, Han Zhu, Jian Wu, Dan Ou, Jian Xu, Haihong Tang, Yuning Jiang, Bo Zheng, Lin Qu

RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training framework based on the PyTorch ecosystem that meets the training needs of industrial-grade recommendation models that integrated with large models. 2.System Optimization To optimize the sparse component, offering superior efficiency over the TensorFlow-based recommendation models. The dense component, meanwhile, leverage…

InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
Zizhen Li, Chuanhao Li, Yibin Wang, Qi Chen, Diping Song, Yukang Feng, Jianwen Sun, Jiaxin Ai, Fanrui Zhang, Mingzhu Sun, Kaipeng Zhang
https://arxiv.org/abs/2508.16072

InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individualized reasoning styles that influence how people interpret and act in social contexts. Social deduction games (SDGs) provide a natural testbed for evaluating individualized reasoning styles, where different players may adopt diverse but contextually valid reasoning strategies under identical conditio…

Online Adaptation via Dual-Stage Alignment and Self-Supervision for Fast-Calibration Brain-Computer Interfaces
Sheng-Bin Duan, Jian-Long Hao, Tian-Yu Xiang, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Zeng-Guang Hou
https://arxiv.org/abs/2509.19403

Online Adaptation via Dual-Stage Alignment and Self-Supervision for Fast-Calibration Brain-Computer Interfaces
Individual differences in brain activity hinder the online application of electroencephalogram (EEG)-based brain computer interface (BCI) systems. To overcome this limitation, this study proposes an online adaptation algorithm for unseen subjects via dual-stage alignment and self-supervision. The alignment process begins by applying Euclidean alignment in the EEG data space and then updates batch normalization statistics in the representation space. Moreover, a self-supervised loss is designed …

Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction
Lei Hei, Tingjing Liao, Yingxin Pei, Yiyang Qi, Jiaqi Wang, Ruiting Li, Feiliang Ren
https://arxiv.org/abs/2509.21151

Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction
Relation extraction (RE) aims to identify semantic relations between entities in unstructured text. Although recent work extends traditional RE to multimodal scenarios, most approaches still adopt classification-based paradigms with fused multimodal features, representing relations as discrete labels. This paradigm has two significant limitations: (1) it overlooks structural constraints like entity types and positional cues, and (2) it lacks semantic expressiveness for fine-grained relation und…

Radiation of "breathing" vortex electron packets in magnetic field
G. V. Zmaga, G. K. Sizykh, D. V. Grosman, Qi Meng, Liping Zou, Pengming Zhang, D. V. Karlovets
https://arxiv.org/abs/2509.21195

Radiation of "breathing" vortex electron packets in magnetic field
When a vortex electron with an orbital angular momentum (OAM) enters a magnetic field, its quantum state is described with a nonstationary Laguerre-Gaussian (NSLG) state rather than with a stationary Landau state. A key feature of these NSLG states is oscillations of the electron wave packet's root-mean-square (r.m.s.) radius, similar to betatron oscillations. Classically, such an oscillating charge distribution is expected to emit photons. This raises a critical question: does this radiation c…

Certifiably Optimal Doppler Positioning using Opportunistic LEO Satellites
Baoshan Song, Weisong Wen, Qi Zhang, Bing Xu, Li-Ta Hsu
https://arxiv.org/abs/2509.17198 https://

Certifiably Optimal Doppler Positioning using Opportunistic LEO Satellites
To provide backup and augmentation to global navigation satellite system (GNSS), Doppler shift from Low Earth Orbit (LEO) satellites can be employed as signals of opportunity (SOP) for position, navigation and timing (PNT). Since the Doppler positioning problem is non-convex, local searching methods may produce two types of estimates: a global optimum without notice or a local optimum given an inexact initial estimate. As exact initialization is unavailable in some unknown environments, a guara…

NLO Corrections to Dimuonium Production in Photon-Photon Collision
Qi-Ming Feng, Si-Mao Guo, Qi-Wei Hu, Cong-Feng Qiao, Qi-Ming Qiu, Yu-Jie Tian, Bai-Rong Zhang, Hao Zhang, Xuan-Heng Zhang, Bo-Ting Zhou
https://arxiv.org/abs/2508.14424

NLO Corrections to Dimuonium Production in Photon-Photon Collision
Dimuonium ($μ^+ μ^-$) is one of the pure QED bound systems of leptons, together with positronium and ditauonium. The former had been observed in experiment in 1951, while the search for dimuonium and ditauonium ($τ^+ τ^-$) are still in vain. The ditauonium is thought to be hard to measure due to its short life time, whereas the dimuonium is very likely to be observed in current running experiments. We calculate in this work the para and ortho dimuonium production in photon-photon collision …

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Xuan Zhang, Zhijian Zhou, Weidi Xu, Yanting Miao, Chao Qu, Yuan Qi
https://arxiv.org/abs/2508.16524 https://

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network's output distribution to move closer to the symbolic constraints. While diffusion models have shown remarkable generative capability across various domains, we employ the powerful architecture to perform neuro-symbolic learning and solve logical puzzles. Our diffusion-based pipeline adopts a two-stage training strategy:…

Observation of Discrete Time Quasicrystal in Rydberg Atomic Gases
Dong-Yang Zhu, Zheng-Yuan Zhang, Qi-Feng Wang, Yu Ma, Tian-Yu Han, Chao Yu, Qiao-Qiao Fang, Shi-Yao Shao, Qing Li, Ya-Jun Wang, Jun Zhang, Han-Chao Chen, Xin Liu, Jia-Dou Nan, Yi-Ming Yin, Li-Hua Zhang, Guang-Can Guo, Bang Liu, Dong-Sheng Ding, Bao-Sen Shi
https://arxiv.org/…

Observation of Discrete Time Quasicrystal in Rydberg Atomic Gases
Discrete time quasicrystals (DTQC) constitute a class of non-equilibrium matter characterized by temporal order without strict periodicity, in contrast to conventional time crystals. Investigating these phenomena is essential for expanding our fundamental understanding of far-from-equilibrium quantum matter and spontaneous symmetry breaking beyond periodic regimes. Here, we experimentally observe a DTQC in a driven-dissipative ensemble of strongly interacting Rydberg atoms, displaying non-equil…

Probing the emission geometry of the X-ray pulsar 2S 1417$-$624 during a weak outburst with NICER, IXPE, and NuSTAR
Menglei Zhou, Pengju Wang, Honghui Liu, Lorenzo Ducci, Sergey S. Tsygankov, Qingchang Zhao, Juri Poutanen, Long Ji, Valery F. Suleimanov, Alexander A. Mushtukov, Qi Liu, Camille M. Diez, Luis Abalo Rodr\'iguez, Victoria Grinberg, Andrea Santangelo

Probing the emission geometry of the X-ray pulsar 2S 1417$-$624 during a weak outburst with NICER, IXPE, and NuSTAR
We report results from a multi-mission observational campaign of the transient X-ray pulsar 2S~1417$-$624 during its 2025 outburst, using data from NICER, IXPE, and NuSTAR. Phase-averaged and phase-resolved spectroscopy with NICER and NuSTAR reveal that a typical power-law model with a high-energy cut-off well describes the broadband spectra. Several spectral parameters, however, show clear and systematic modulations with pulse phase, indicating variations in the physical conditions of the emit…

Investigating red packet fraud in Android applications: Insights from user reviews
Yu Cheng, Xiaofang Qi, Yanhui Li
https://arxiv.org/abs/2508.16941 https://

Investigating red packet fraud in Android applications: Insights from user reviews
With the popularization of smartphones, red packets have been widely used in mobile apps. However, the issues of fraud associated with them have also become increasingly prominent. As reported in user reviews from mobile app markets, many users have complained about experiencing red packet fraud and being persistently troubled by fraudulent red packets. To uncover this phenomenon, we conduct the first investigation into an extensive collection of user reviews on apps with red packets. In this p…

Efficient Encoder-Free Pose Conditioning and Pose Control for Virtual Try-On
Qi Li, Shuwen Qiu, Julien Han, Xingzi Xu, Mehmet Saygin Seyfioglu, Kee Kiat Koo, Karim Bouyarmane
https://arxiv.org/abs/2509.20343

Efficient Encoder-Free Pose Conditioning and Pose Control for Virtual Try-On
As online shopping continues to grow, the demand for Virtual Try-On (VTON) technology has surged, allowing customers to visualize products on themselves by overlaying product images onto their own photos. An essential yet challenging condition for effective VTON is pose control, which ensures accurate alignment of products with the user's body while supporting diverse orientations for a more immersive experience. However, incorporating pose conditions into VTON models presents several challenge…

The quest for topped hadrons
Si-Qiang Luo, Qi Huang, Xiang Liu
https://arxiv.org/abs/2508.17646 https://arxiv.org/pdf/2508.17646

The quest for topped hadrons
Inspired by the recent discovery of an pseudoscalar enhancement structure near the $t\bar{t}$ threshold reported by the CMS and ATLAS collaborations, this work investigates the mass spectra of single topped hadrons-including both topped mesons and topped baryons-based on a relativistic potential model. Our results show that, using the same parameters obtained from the fit to mesons and baryons, the calculated mass of pseudoscalar topponium is in excellent agreement with the observed enhancement…

HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba
Yinuo Wang, Yuanyang Qi, Jinzhao Zhou, Gavin Tao
https://arxiv.org/abs/2509.18046 https://…

HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba
End-to-end reinforcement learning (RL) for humanoid locomotion is appealing for its compact perception-action mapping, yet practical policies often suffer from training instability, inefficient feature fusion, and high actuation cost. We present HuMam, a state-centric end-to-end RL framework that employs a single-layer Mamba encoder to fuse robot-centric states with oriented footstep targets and a continuous phase clock. The policy outputs joint position targets tracked by a low-level PD loop a…

DanceEditor: Towards Iterative Editable Music-driven Dance Generation with Open-Vocabulary Descriptions
Hengyuan Zhang, Zhe Li, Xingqun Qi, Mengze Li, Muyi Sun, Man Zhang, Sirui Han
https://arxiv.org/abs/2508.17342

DanceEditor: Towards Iterative Editable Music-driven Dance Generation with Open-Vocabulary Descriptions
Generating coherent and diverse human dances from music signals has gained tremendous progress in animating virtual avatars. While existing methods support direct dance synthesis, they fail to recognize that enabling users to edit dance movements is far more practical in real-world choreography scenarios. Moreover, the lack of high-quality dance datasets incorporating iterative editing also limits addressing this challenge. To achieve this goal, we first construct DanceRemix, a large-scale mult…

Reinforcement Learning on Pre-Training Data
Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Evander Yang, Kun Li, Haoyuan Wu, Jiajia Wu, Zihao Zheng, Chenchen Zhang, Kun Shi, Kyrierl Deng, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang Xu, Zheng Fang, Shuai Li, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bo Chao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Wayyt Wang, Bo Zhou, Di Wang

A Measurement Report Data-Driven Framework for Localized Statistical Channel Modeling
Xinyu Qin, Ye Xue, Qi Yan, Shutao Zhang, Bingsheng Peng, Tsung-Hui Chang
https://arxiv.org/abs/2509.19342

A Measurement Report Data-Driven Framework for Localized Statistical Channel Modeling
Localized statistical channel modeling (LSCM) is crucial for effective performance evaluation in digital twin-assisted network optimization. Solely relying on the multi-beam reference signal receiving power (RSRP), LSCM aims to model the localized statistical propagation environment by estimating the channel angular power spectrum (APS). However, existing methods rely heavily on drive test data with high collection costs and limited spatial coverage. In this paper, we propose a measurement repo…

Visual Autoregressive Modeling for Instruction-Guided Image Editing
Qingyang Mao, Qi Cai, Yehao Li, Yingwei Pan, Mingyue Cheng, Ting Yao, Qi Liu, Tao Mei
https://arxiv.org/abs/2508.15772

Visual Autoregressive Modeling for Instruction-Guided Image Editing
Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global denoising process inherently entangles the edited region with the entire image context, leading to unintended spurious modifications and compromised adherence to editing instructions. In contrast, autoregressive models offer a distinct paradigm by formulating image synthesis as a sequential process over discrete visual tokens. Their causal and compositional mech…

Piezomechanical scattering loss in electro-optics quantum transducers
Mai Zhang, Xin-Biao Xu, Ming Li, Jia-Qi Wang, Guang-Can Guo, Chang-Ling Zou
https://arxiv.org/abs/2509.19826

Piezomechanical scattering loss in electro-optics quantum transducers
Quantum transduction between microwave and optical photons is essential for building scalable quantum networks, with electro-optics conversion emerging as a promising approach. Recent experiments, however, observe significant quality factor degradation in superconducting microwave cavities when realizing the electro optics transducers. Here, we identify the piezomechanical scattering, where microwave photon loss through phonon radiation into substrate, as a universal dissipation mechanism in th…

Polarization-dependent chiral transport and chiral solitons in spin Kitaev models
Chenwei Lv, Thomas Bilitewski, Ana Maria Rey, Qi Zhou
https://arxiv.org/abs/2508.17084 https://…

Polarization-dependent chiral transport and chiral solitons in spin Kitaev models
Recent advances in synthetic quantum matter allow researchers to design quantum models inaccessible in traditional materials. Here, we propose protocols to engineer a new class of quantum spin models, which we call spin Kitaev models. The building blocks are basic spin-exchange interactions combined with locally selective Floquet pulses, a capability recently demonstrated in a range of experimental platforms. The resulting flip-flip and flop-flop terms lead to intriguing quantum transport dynam…

InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
Zizhen Li, Chuanhao Li, Yibin Wang, Qi Chen, Diping Song, Yukang Feng, Jianwen Sun, Jiaxin Ai, Fanrui Zhang, Mingzhu Sun, Kaipeng Zhang
https://arxiv.org/abs/2508.16072

InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individualized reasoning styles that influence how people interpret and act in social contexts. Social deduction games (SDGs) provide a natural testbed for evaluating individualized reasoning styles, where different players may adopt diverse but contextually valid reasoning strategies under identical conditio…

MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
Zilin Xiao, Qi Ma, Mengting Gu, Chun-cheng Jason Chen, Xintao Chen, Vicente Ordonez, Vijai Mohan
https://arxiv.org/abs/2509.18095

MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
Universal multimodal embedding models have achieved great success in capturing semantic relevance between queries and candidates. However, current methods either condense queries and candidates into a single vector, potentially limiting the expressiveness for fine-grained information, or produce too many vectors that are prohibitively expensive for multi-vector retrieval. In this work, we introduce MetaEmbed, a new framework for multimodal retrieval that rethinks how multimodal embeddings are c…

Improving Generalizability and Undetectability for Targeted Adversarial Attacks on Multimodal Pre-trained Models
Zhifang Zhang, Jiahan Zhang, Shengjie Zhou, Qi Wei, Shuo He, Feng Liu, Lei Feng
https://arxiv.org/abs/2509.19994

Improving Generalizability and Undetectability for Targeted Adversarial Attacks on Multimodal Pre-trained Models
Multimodal pre-trained models (e.g., ImageBind), which align distinct data modalities into a shared embedding space, have shown remarkable success across downstream tasks. However, their increasing adoption raises serious security concerns, especially regarding targeted adversarial attacks. In this paper, we show that existing targeted adversarial attacks on multimodal pre-trained models still have limitations in two aspects: generalizability and undetectability. Specifically, the crafted targe…

Spectrum of Light Hexaquark States in Triquark-antitriquark Configuration
Xuan-Heng Zhang, Sheng-Qi Zhang, Cong-Feng Qiao
https://arxiv.org/abs/2509.20711 https://

Spectrum of Light Hexaquark States in Triquark-antitriquark Configuration
To understand the nature of $X(2075)$ and $X(2085)$ observed by the BESIII collaboration in the $p\barΛ$ system, we systematically investigate the possibility that these states are compact hexaquark with triquark-antitriquark configurations for the first time. Within the framework of QCD sum rules, the mass spectrum and decay constants of such hexaquark states with quantum numbers $J^P=0^-, 0^+, 1^-, 1^+$ are studied. Consequently, six independent and nondegenerate hexaquark candidates are obt…

ArchGPT: Understanding the World's Architectures with Large Multimodal Models
Yuze Wang, Luo Yang, Junyi Wang, Yue Qi
https://arxiv.org/abs/2509.20858 https://

ArchGPT: Understanding the World's Architectures with Large Multimodal Models
Architecture embodies aesthetic, cultural, and historical values, standing as a tangible testament to human civilization. Researchers have long leveraged virtual reality (VR), mixed reality (MR), and augmented reality (AR) to enable immersive exploration and interpretation of architecture, enhancing accessibility, public understanding, and creative workflows around architecture in education, heritage preservation, and professional design practice. However, existing VR/MR/AR systems are often de…

A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers
Zhixiao Wu, Yao Lu, Jie Wen, Hao Sun, Qi Zhou, Guangming Lu
https://arxiv.org/abs/2509.19947

A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers
Poison-only Clean-label Backdoor Attacks aim to covertly inject attacker-desired behavior into DNNs by merely poisoning the dataset without changing the labels. To effectively implant a backdoor, multiple \textbf{triggers} are proposed for various attack requirements of Attack Success Rate (ASR) and stealthiness. Additionally, sample selection enhances clean-label backdoor attacks' ASR by meticulously selecting ``hard'' samples instead of random samples to poison. Current methods 1) usually han…

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Xuan Zhang, Zhijian Zhou, Weidi Xu, Yanting Miao, Chao Qu, Yuan Qi
https://arxiv.org/abs/2508.16524 https://

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning
Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network's output distribution to move closer to the symbolic constraints. While diffusion models have shown remarkable generative capability across various domains, we employ the powerful architecture to perform neuro-symbolic learning and solve logical puzzles. Our diffusion-based pipeline adopts a two-stage training strategy:…

Multiple-Noise-Resilient Nonadiabatic Geometric Quantum Control of Solid-State Spins in Diamond
Si-Qi Chen, Qi-Tao Duan, Chengxian Zhang, He Lu
https://arxiv.org/abs/2508.12221 …

Multiple-Noise-Resilient Nonadiabatic Geometric Quantum Control of Solid-State Spins in Diamond
Reliable and robust control lies at the core of implementing quantum information processing with diamond nitrogen-vacancy (NV) centers. However, control pulses inevitably introduce multiple errors, leading to decoherence and hindering scalable applications. Here, we experimentally report an experiment-friendly multiple-noise-resilient nonadiabatic geometric quantum gate~(MNR-NGQG) that can significantly improve conventional dynamical gate in both robustness and coherence. Notably, even when the…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- Challenges and Trends in Egocentric Vision: A Survey
Li, Qiu, Wang, Zhang, Qi, Han, Xiong, Li

TASO: Task-Aligned Sparse Optimization for Parameter-Efficient Model Adaptation
Daiye Miao, Yufang Liu, Jie Wang, Changzhi Sun, Yunke Zhang, Demei Yan, Shaokang Dong, Qi Zhang, Yuanbin Wu
https://arxiv.org/abs/2509.17688

TASO: Task-Aligned Sparse Optimization for Parameter-Efficient Model Adaptation
LoRA has become one of the most widely used parameter-efficient fine-tuning methods due to its simplicity and effectiveness. However, numerous studies have shown that LoRA often introduces substantial parameter redundancy, which not only increases the number of trainable parameters but also hinders the effectiveness of fine-tuning. Since identifying redundant parameters in LoRA is inherently difficult, how to eliminate them efficiently and accurately remains a challenging problem. In this paper…

Analysis of the hidden-charm pentaquark candidates in the $J/\psi \Lambda$ mass spectrum via the QCD sum rules
Zhi-Gang Wang, Qi Xin
https://arxiv.org/abs/2508.17373 https://

Analysis of the hidden-charm pentaquark candidates in the $J/ψΛ$ mass spectrum via the QCD sum rules
In this work, we distinguish the isospin for the first time and study the diquark-diquark-antiquark type $udsc\bar{c}$ pentaquark states with zero isospin via the QCD sum rules systematically. We distinguish contributions of the pentaquark states with negative parity from positive parity unambiguously and obtain clean QCD sum rules for the pentaquark states with negative parity. Then we adopt the modified energy scale formula to choose the optimal energy scales of the QCD spectral densities, an…

Code Driven Planning with Domain-Adaptive Critic
Zikang Tian, Shaohui Peng, Du Huang, Jiaming Guo, Ruizhi Chen, Rui Zhang, Xishan Zhang, Yuxuan Guo, Zidong Du, Qi Guo, Ling Li, Yewen Pu, Xing Hu, Yunji Chen
https://arxiv.org/abs/2509.19077

FlyTrap: Physical Distance-Pulling Attack Towards Camera-based Autonomous Target Tracking Systems
Shaoyuan Xie, Mohamad Habib Fakih, Junchi Lu, Fayzah Alshammari, Ningfei Wang, Takami Sato, Halima Bouzidi, Mohammad Abdullah Al Faruque, Qi Alfred Chen
https://arxiv.org/abs/2509.20362

FlyTrap: Physical Distance-Pulling Attack Towards Camera-based Autonomous Target Tracking Systems
Autonomous Target Tracking (ATT) systems, especially ATT drones, are widely used in applications such as surveillance, border control, and law enforcement, while also being misused in stalking and destructive actions. Thus, the security of ATT is highly critical for real-world applications. Under the scope, we present a new type of attack: distance-pulling attacks (DPA) and a systematic study of it, which exploits vulnerabilities in ATT systems to dangerously reduce tracking distances, leading …

LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Xixun Lin, Yucheng Ning, Jingwen Zhang, Yan Dong, Yilong Liu, Yongxuan Wu, Xiaohua Qi, Nan Sun, Yanmin Shang, Pengfei Cao, Lixin Zou, Xu Chen, Chuan Zhou, Jia Wu, Shirui Pan, Bin Wang, Yanan Cao, Kai Chen, Songlin Hu, Li Guo
https://arxiv.org/abs/250…

LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Driven by the rapid advancements of Large Language Models (LLMs), LLM-based agents have emerged as powerful intelligent systems capable of human-like cognition, reasoning, and interaction. These agents are increasingly being deployed across diverse real-world applications, including student education, scientific research, and financial analysis. However, despite their remarkable potential, LLM-based agents remain vulnerable to hallucination issues, which can result in erroneous task execution a…

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation
Jane Luo, Xin Zhang, Steven Liu, Jie Wu, Yiming Huang, Yangyu Huang, Chengyu Yin, Ying Xin, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Qi Chen, Scarlett Li, Mao Yang
https://arxiv.org/abs/2509.16198

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation
Large language models excel at function- and file-level code generation, yet generating complete repositories from scratch remains a fundamental challenge. This process demands coherent and reliable planning across proposal- and implementation-level stages, while natural language, due to its ambiguity and verbosity, is ill-suited for faithfully representing complex software structures. To address this, we introduce the Repository Planning Graph (RPG), a persistent representation that unifies pr…

Advances in Large Language Models for Medicine
Zhiyu Kan, Wensheng Gan, Zhenlian Qi, Philip S. Yu
https://arxiv.org/abs/2509.18690 https://arxiv.org/pdf/25…

TS-P$^2$CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification
Qi'ao Xu, Pengfei Wang, Bo Zhong, Tianwen Qian, Xiaoling Wang, Ye Wang, Hong Yu
https://arxiv.org/abs/2509.17802

TS-P$^2$CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification
Medical time series (MedTS) classification is pivotal for intelligent healthcare, yet its efficacy is severely limited by poor cross-subject generation due to the profound cross-individual heterogeneity. Despite advances in architectural innovations and transfer learning techniques, current methods remain constrained by modality-specific inductive biases that limit their ability to learn universally invariant representations. To overcome this, we propose TS-P$^2$CL, a novel plug-and-play framew…

From Restoration to Reconstruction: Rethinking 3D Gaussian Splatting for Underwater Scenes
Guoxi Huang, Haoran Wang, Zipeng Qi, Wenjun Lu, David Bull, Nantheera Anantrasirichai
https://arxiv.org/abs/2509.17789

From Restoration to Reconstruction: Rethinking 3D Gaussian Splatting for Underwater Scenes
Underwater image degradation poses significant challenges for 3D reconstruction, where simplified physical models often fail in complex scenes. We propose \textbf{R-Splatting}, a unified framework that bridges underwater image restoration (UIR) with 3D Gaussian Splatting (3DGS) to improve both rendering quality and geometric fidelity. Our method integrates multiple enhanced views produced by diverse UIR models into a single reconstruction pipeline. During inference, a lightweight illumination g…

Table2LaTeX-RL: High-Fidelity LaTeX Code Generation from Table Images via Reinforced Multimodal Language Models
Jun Ling, Yao Qi, Tao Huang, Shibo Zhou, Yanqin Huang, Jiang Yang, Ziqi Song, Ying Zhou, Yang Yang, Heng Tao Shen, Peng Wang
https://arxiv.org/abs/2509.17589

Table2LaTeX-RL: High-Fidelity LaTeX Code Generation from Table Images via Reinforced Multimodal Language Models
In this work, we address the task of table image to LaTeX code generation, with the goal of automating the reconstruction of high-quality, publication-ready tables from visual inputs. A central challenge of this task lies in accurately handling complex tables -- those with large sizes, deeply nested structures, and semantically rich or irregular cell content -- where existing methods often fail. We begin with a comprehensive analysis, identifying key challenges and highlighting the limitations …

UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning
Ye Liu, Zongyang Ma, Junfu Pu, Zhongang Qi, Yang Wu, Ying Shan, Chang Wen Chen
https://arxiv.org/abs/2509.18094

UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning
Recent advances in Large Multi-modal Models (LMMs) have demonstrated their remarkable success as general-purpose multi-modal assistants, with particular focuses on holistic image- and video-language understanding. Conversely, less attention has been given to scaling fine-grained pixel-level understanding capabilities, where the models are expected to realize pixel-level alignment between visual signals and language semantics. Some previous studies have applied LMMs to related tasks such as regi…

SPADE: A Large Language Model Framework for Soil Moisture Pattern Recognition and Anomaly Detection in Precision Agriculture
Yeonju Lee, Rui Qi Chen, Joseph Oboamah, Po Nien Su, Wei-zhen Liang, Yeyin Shi, Lu Gan, Yongsheng Chen, Xin Qiao, Jing Li
https://arxiv.org/abs/2509.18123

Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
Gang Cheng, Xin Gao, Li Hu, Siqi Hu, Mingyang Huang, Chaonan Ji, Ju Li, Dechao Meng, Jinwei Qi, Penchong Qiao, Zhen Shen, Yafei Song, Ke Sun, Linrui Tian, Feng Wang, Guangyuan Wang, Qi Wang, Zhongjian Wang, Jiayu Xiao, Sheng Xu, Bang Zhang, Peng Zhang, Xindi Zhang, Zhe Zhang, Jingren Zhou, Lian Zhuo

Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental i…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[10/11]:
- TS-P$^2$CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classif...
Qi'ao Xu, Pengfei Wang, Bo Zhong, Tianwen Qian, Xiaoling Wang, Ye Wang, Hong Yu

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/12]:
- LTA-thinker: Latent Thought-Augmented Training Framework for Large Language Models on Complex Rea...
Wang, Ji, Luo, Qi, Li, Wang, Han, Yang, Zhang, Ren