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Xiaoyu Zhang

Publications and source records attributed to Xiaoyu Zhang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Correlation-consistent Gaussian basis sets for copper solids from material-constrained atomic optimization

Correlation-consistent Gaussian basis sets are central to systematic molecular quantum chemistry, but their direct use in periodic solids is often limited by severe linear dependence from diffuse atomically optimized primitives. This problem is particularly acute for metallic and metal-containing systems, where reliable complete-basis-set (CBS) extrapolation is needed for correlated-wavefunction benchmarks. We introduce material-constrained atomic optimization (MCAO), a basis-set optimization framework that preserves the atomic and correlation-consistent character of Gaussian basis sets while penalizing large overlap-matrix condition numbers in representative solids. As a proof of concept, we generate Dunning-style MCAO-cc-pVXZ basis sets (X = D, T, Q) for Cu with all-electron, scalar-relativistic all-electron, effective core potential (ECP), and pseudopotential treatments. The resulting basis sets remain numerically stable for Cu solids and surfaces while reproducing molecular Cu dimer energetics and plane-wave reference properties of bulk Cu. CBS-extrapolated random-phase approximation calculations further enable a controlled assessment of pseudopotential, relativistic, and basis-set errors in bulk Cu and CO adsorption on Cu(111), providing scalar-relativistic all-electron Gaussian-basis benchmarks for the CO adsorption puzzle.

physics.chem-ph↗
arXiv2026

Rethinking Technology Stack Selection with AI Coding Proficiency

Large language models (LLMs) are now an integral part of software development workflows and are reshaping the whole process. However, existing technology selection methods mainly focus on the inherent attributes of technologies, overlooking whether the LLM can effectively leverage the chosen technology. Therefore, teams using LLM assistants risk choosing technologies that cannot be used effectively by LLMs, yielding high debugging effort and mounting technical debt. We foresee a practical question in the LLM era, is a technology ready for AI-assisted development? In this paper, we first propose the concept, AI coding proficiency, the degree to which LLMs can utilize a given technology to generate high-quality code snippets. We then conduct the first large-scale empirical study examining AI coding proficiency across 170 third-party libraries and six LLMs. Our findings reveal that libraries with similar functionalities can exhibit up to 84% differences in the quality score of LLM-generated code. These gaps can be translated into real engineering costs and steer developer choices toward a narrow set of technologies, threatening technological diversity in the ecosystem. We call on the community to integrate AI coding proficiency into technology selection frameworks and develop mitigation strategies, preserving competitive balance in AI-driven development.

cs.SE↗
arXiv2026

Two-Stage Robust Sparse Gradient Methods for Regression Under Heavy-Tailed Designs

We study high-dimensional sparse regression under simultaneous heavy-tailed covariates and noise. Heavy-tailed data affect sparse optimization in two different ways: extreme covariates can destabilize the gradient field during global localization, while heavy-tailed noise limits the final statistical accuracy during local refinement. Motivated by this two-phase structure, we propose two-stage RIGHT, a robust sparse first-order method based on coordinate-wise median-of-means (MoM) gradient estimation and delayed sample splitting. The MoM gradient estimator is computationally simple, compatible with hard-thresholded updates, and admits phase-adaptive concentration bounds whose rates depend on the current localization radius. Delayed splitting reuses data during global localization and reserves fresh batches for the shorter refinement stage, reducing the sample-splitting cost. The theoretical results reveal a decoupled rate structure: the design-tail index controls gradient stability and sample complexity, whereas the noise-tail index controls the final statistical rate. We also provide phase-wise lower-bound benchmarks showing that the design-driven localization barrier is intrinsic. Extensive simulation experiments and real data analysis showcase the efficacy of the proposed method over existing competitors.

stat.ME↗
arXiv2026

Bridging Short Videos and Live Streams: Reasoning-Guided Multimodal LLMs for Cross-Domain Representation Learning

As live streaming services grow, many platforms offer short videos and live streams to meet diverse needs. Short videos carry substantial traffic and rich behavior signals, whereas live streaming is a core conversion scenario with sparse behavior data, making cold start severe. Transferring user interests from short videos to live streaming recommendation can alleviate these issues. Meanwhile, short videos and live streams are complex multimodal items, and integrating multimodal signals improves recommendation performance. Although Multimodal Large Language Models (MLLMs) show strong multimodal understanding and reasoning, their application to cross-domain recommendation remains underexplored. To this end, we propose Reasoning-Guided Cross-Domain Representation Learning (RGCD-Rep), a reasoning-guided framework for cross-domain recommendation from short videos to live streams. RGCD-Rep introduces MLLM reasoning resource-efficiently and learns transferable item representations guided by behavioral collaboration via two-stage training. First, reasoning-aware distillation lets a frozen teacher MLLM generate structured cross-domain reasoning knowledge and distills it into a lightweight student MLLM. Second, transferability-guided cross-domain representation learning decomposes item representations into transferable and domain residual representations. The resulting representations are computed offline and integrated into downstream retrieval tasks, enabling low-cost industrial deployment. Extensive offline experiments demonstrate RGCD-Rep's superiority. After deployment in Kuaishou's live streaming recommendation system, A/B tests show significant gains across multiple core business metrics, confirming its effectiveness and practicality in real industrial scenarios. RGCD-Rep is fully deployed and serves over 400 million users daily.

cs.IR↗
arXiv2026

COLLAR: Cascaded Object-Level Latent Refinement for High-Fidelity Conditional Generation

Achieving high-fidelity object-level control in Diffusion Transformers remains a significant challenge despite the introduction of structural priors like depth and Canny maps. Current object-level conditional generation methods frequently suffer from visual artifacts and struggle to maintain precise control over objects within small localized regions. To address these limitations, we propose Cascaded Object-Level Latent Refinement (COLLAR), a training-free framework that progressively optimizes object-level features via the Field-of-View (FoV) expansion. First, we propose the Cross-Scale Semantic Alignment (CSSA) module to address spatial-semantic gaps by injecting object-level features into extended-FoV branches via attention mechanisms. To further optimize these features, the Cyclic Feature Injection (CFI) module introduces a reciprocal background feedback mechanism. It leverages a frequency-based adaptive strategy to selectively update the global backbone with context-aligned local information. Finally, the extended-FoV branch serves as a hub for feature optimization, ensuring that object-level features are integrated into the global generation process without compromising final image quality. Extensive experiments on the COCO-MIG and COCO-POS benchmarks demonstrate that our approach consistently outperforms state-of-the-art methods across semantic alignment, image quality, and spatial fidelity.

cs.CV↗
arXiv2026

Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting

Zero-shot time series forecasting aims to predict future values for previously unseen series, requiring models to generalize temporal dynamics beyond the training distribution. While recent foundation models achieve strong in-domain performance through large-scale pretraining, their effectiveness often relies on broad data coverage and implicit pattern memorization, which can limit generalization when data are scarce or source and target domains are disjoint. In this work, we propose FSA, a feature-to-strategy framework for controlled zero-shot univariate forecasting. Instead of directly modeling raw sequences in the observation space, FSA learns a structured mapping from an interpretable feature space to an autoregressive strategy space. This design introduces explicit inductive biases that disentangle global trends, periodic components, and local temporal dynamics, enabling the model to capture transferable time-series structure with fewer data assumptions. Empirical results show that, under identical pretraining data, training protocol, and comparable parameter budgets, FSA outperforms Transformer-based architectures in our controlled zero-shot setting.

cs.LG↗
arXiv2026

CATA: Continual Machine Unlearning via Conflict-Averse Task Arithmetic

Vision-language models (VLMs) have shown remarkable ability in aligning visual and textual representations, enabling a wide range of multimodal applications. However, their large-scale training data inevitably raises concerns about privacy, copyright, and undesirable content, creating a strong need for machine unlearning. While existing studies mainly focus on single-shot unlearning, practical VLM deployment often involves sequential removal requests over time, giving rise to continual machine unlearning. In this work, we make the first attempt to study continual unlearning for VLMs and identify three key challenges in this setting: effectiveness in removing target knowledge, fidelity in preserving retained model utility, and persistence in preventing knowledge re-emergence under sequential updates. To address these challenges, we propose CATA, a conflict-averse task arithmetic method that represents each forget request as an unlearning task vector. By maintaining historical task vectors and performing sign-aware conflict-averse aggregation, CATA suppresses conflicting update components that may weaken previous forgetting effects. Extensive experiments under both single-shot and continual settings show that CATA outperforms baselines in terms of forgetting effectiveness, model fidelity, and forgetting persistence.

cs.CV↗
arXiv2026

From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction

Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clinicians' measurement practices. Static prediction often fails clinically: models trained on observational care logs conflate disease biology with clinician behavior, particularly under treatment confounder feedback and irregular or informative observation. This Review focuses on intervention-aware disease trajectory modeling in clinical AI--methods estimating patient-specific longitudinal disease evolution and assessing trajectory changes under alternative treatments. We organize the field around six linked components: three decision tasks (factual forecasting, counterfactual estimation, policy evaluation) and three data-generating mechanisms (disease evolution, treatment assignment, observation process) that determine identifiability. We present the first unified framework bridging forecasting, counterfactual trajectories, and policy evaluation across discrete/continuous time, explicitly addressing treatment assignment, time-varying confounding, and observation bias. We synthesize key method families (multistate/joint models, temporal point-process, deep sequence architectures, longitudinal causal inference), map them to relevant components, and align evaluation with claim strength via overlap diagnostics, uncertainty quantification, off-policy robustness, and target-trial validation. This synthesis advances benchmark prediction to decision-grade clinical evidence, enabling treatment-sensitive individualized futures, pre-deployment policy stress-testing, and safer closed-loop learning health systems that adapt/abstain when evidence is insufficient.

cs.AI↗
arXiv2026

Magical Touch: Transforming Raw Capacitive Streams into Expressive Hand-Touchscreen Interaction

Modern touchscreens utilize capacitive sensing technology to enable precise and robust multi-touch interaction. However, the broader expressive potential of the human hand remains underutilized, since most existing methods directly filter out larger-area hand-screen contact. This paper introduces Magical Touch, an interaction method based on raw capacitive sensing data. By directly integrating raw touchscreen sensor data into the interaction loop, our method allows users to interact with the screen naturally and efficiently using arbitrary hand gestures on existing touchscreen devices. To demonstrate the feasibility and expressive capacity of this approach, we implement a physics-based interactive game featuring single-player, multiplayer collaborative, and pressure-sensitive modes. These scenarios showcase how digital objects can respond in real-time to both the geometry and contact intensity of the user's hand. Our results indicate that leveraging raw capacitive data can expand the design space of touchscreen interaction, offering an embodied and continuous interaction paradigm beyond existing fingertip-based approaches.

cs.HC↗
arXiv2026

Lattice vacancy migration barriers in Fe-Ni alloys, and why Ni atoms diffuse slowly: An ab initio study

The mobility of both Fe and Ni atoms in ferromagnetic Fe$_x$Ni$_{1-x}$ alloys ($0.4 \leq x \leq 0.6$) is investigated within the framework of ab initio electronic structure calculations, using the nudged elastic band (NEB) method to accurately quantify energetic barriers to lattice vacancy migration. Both the atomically disordered (A1) fcc phase, as well as the atomically ordered, tetragonal $\mathrm{L}1_0$ phase - which is under consideration as a material for a rare-earth-free 'gap' magnet for advanced engineering applications - are investigated. Across an ensemble of NEB calculations performed on supercell configurations spanning a range of compositions and containing disordered, partially ordered, and fully ordered structures, we find that Ni atoms are consistently significantly less mobile than Fe atoms. Crucially, we are able to interpret these findings in terms of the ferromagnetic alloy's underlying spin-polarised electronic structure. Specifically, we report a coupling between the size of local lattice distortions and the magnitude of the local electronic spin polarisation around vacancies. This causes Fe atoms to relax into lattice vacancies, while Ni atoms remain rigidly fixed to their original lattice positions. This effect plays a key role in determining the reduced mobility of Ni atoms compared to that of Fe atoms. These results shed atomic-scale insight into the longstanding experimental observation that Ni exhibits remarkably slow atomic diffusion in Fe-Ni alloys.

cond-mat.mtrl-sci↗
arXiv2026

InSpatio-WorldFM: An Open-Source Real-Time Generative Frame Model

We present InSpatio-WorldFM, an open-source real-time frame model for spatial intelligence. Unlike video-based world models that rely on sequential frame generation and incur substantial latency due to window-level processing, InSpatio-WorldFM adopts a frame-based paradigm that generates each frame independently, enabling low-latency real-time spatial inference. By enforcing multi-view spatial consistency through explicit 3D anchors and implicit spatial memory, the model preserves global scene geometry while maintaining fine-grained visual details across viewpoint changes. We further introduce a progressive three-stage training pipeline that transforms a pretrained image diffusion model into a controllable frame model and finally into a real-time generator through few-step distillation. Experimental results show that InSpatio-WorldFM achieves strong multi-view consistency while supporting interactive exploration on consumer-grade GPUs, providing an efficient alternative to traditional video-based world models for real-time world simulation.

cs.CV↗
arXiv2026

Stage Light is Sequence$^2$: Multi-Light Control via Imitation Learning

Music-inspired Automatic Stage Lighting Control (ASLC) has gained increasing attention in recent years due to the substantial time and financial costs associated with hiring and training professional lighting engineers. However, existing methods suffer from several notable limitations: the low interpretability of rule-based approaches, the restriction to single-primary-light control in music-to-color-space methods, and the limited transferability of music-to-controlling-parameter frameworks. To address these gaps, we propose SeqLight, a hierarchical deep learning framework that maps music to multi-light Hue-Saturation-Value (HSV) space. Our approach first customizes SkipBART, an end-to-end single primary light generation model, to predict the full light color distribution for each frame, followed by hybrid Imitation Learning (IL) techniques to derive an effective decomposition strategy that distributes the global color distribution among individual lights. Notably, the light decomposition module can be trained under varying venue-specific lighting configurations using only mixed light data and no professional demonstrations, thereby flexibly adapting across diverse venues. In this stage, we formulate the light decomposition task as a Goal-Conditioned Markov Decision Process (GCMDP), construct an expert demonstration set inspired by Hindsight Experience Replay (HER), and introduce a three-phase IL training pipeline, achieving strong generalization capability. To validate our IL solution for the proposed GCMDP, we conduct a series of quantitative analysis and human study. The code and trained models are provided at https://github.com/RS2002/SeqLight .

cs.MM↗
arXiv2026

Don't Trust Your Upstream: Exploiting LLM Multi-Agent System via Topology-Guided Adversarial Propagation

The digital world is witnessing the rapid rise of LLM-based multi-agent systems (MASs) and their powerful applications. However, their security remains insufficiently understood, as existing evaluations are largely limited to narrow attack settings and may substantially underestimate the real risks of MAS deployments. Inspired by the MAS inter-agent dependencies, where upstream outputs are reinterpreted and executed by downstream agents, we propose a topology-aware attack scheme that propagates adversarial contamination from exposed edge agents to high-privilege agents to induce malicious behaviors. By combining topology reconnaissance, contamination propagation modeling, and hierarchical payload encapsulation, our approach overcomes the key challenges of black-box attacks and makes such multi-hop compromise practical. Experiments show that our approach achieves success rates of 40\%--78\% on three widely-used MAS frameworks under five topologies, and 85\% on two real-world MAS applications across 20 representative scenarios. The results reveal fundamental vulnerabilities in MASs that have been overlooked by prior studies. Based on these findings, we propose a topology-trust mitigation that blocks 94.8\% of such composite attacks.

cs.CR↗
arXiv2026

False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models

Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs) remain largely unexplored. In this paper, we identify emoticon semantic confusion, a vulnerability where LLMs misinterpret ASCII-based emoticons to perform unintended and even destructive actions. To systematically study this phenomenon, we develop an automated data generation pipeline and construct a dataset containing 3,757 code-oriented test cases spanning 21 meta-scenarios, four programming languages, and varying contextual complexities. Our study on six LLMs reveals that emoticon semantic confusion is pervasive, with an average confusion ratio exceeding 38%. More critically, over 90% of confused responses yield 'silent failures', which are syntactically valid outputs but deviate from user intent, potentially leading to destructive security consequences. Furthermore, we observe that this vulnerability readily transfers to popular agent frameworks, while existing prompt-based mitigations remain largely ineffective. We call on the community to recognize this emerging vulnerability and develop effective mitigation methods to uphold the safety and reliability of the LLM system.

cs.CR↗
arXiv2026

A Unified Formulation for $\langle \hat{S}^2 \rangle $ in Two-Component TDDFT

Two-component linear-response time-dependent density functional theory (TDDFT) provides a unified framework that encompasses noncollinear excitations in noncollinear reference states, as well as both spin-conserving and spin-flip excitations in collinear reference states. In this work, we present a general formalism for evaluating the expectation value $\langle \hat{S}^2 \rangle$ of electronically excited states obtained within two-component TDDFT. We then derive and analyze specialized forms of the resulting equations for collinear reference determinants, for which the two-component formalism decomposes into conventional spin-conserving and spin-flip TDDFT. The resulting working equations are systematically compared with previously proposed theoretical approaches. On the basis of our analysis, $\langle \hat{S}^2 \rangle$ in the excited states is shown to arise from two distinct sources: (i) $\langle \hat{S}^2 \rangle_0$ in the reference state and (ii) additional $Δ\langle \hat{S}^2 \rangle$ introduced by the excitation process itself. Finally, we evaluate the expectation value $\langle \hat{S}^2 \rangle$ by performing two-component TDDFT calculations based on two-component DFT, unrestricted Kohn-Sham (UKS), and restricted open-shell Kohn-Sham (ROKS) reference states, respectively.

physics.chem-ph↗
arXiv2026

AgentSearchBench: A Benchmark for AI Agent Search in the Wild

The rapid growth of AI agent ecosystems is transforming how complex tasks are delegated and executed, creating a new challenge of identifying suitable agents for a given task. Unlike traditional tools, agent capabilities are often compositional and execution-dependent, making them difficult to assess from textual descriptions alone. However, existing research and benchmarks typically assume well-specified functionalities, controlled candidate pools, or only executable task queries, leaving realistic agent search scenarios insufficiently studied. We introduce AgentSearchBench, a large-scale benchmark for agent search in the wild, built from nearly 10,000 real-world agents across multiple providers. The benchmark formalizes agent search as retrieval and reranking problems under both executable task queries and high-level task descriptions, and evaluates relevance using execution-grounded performance signals. Experiments reveal a consistent gap between semantic similarity and actual agent performance, exposing the limitations of description-based retrieval and reranking methods. We further show that lightweight behavioral signals, including execution-aware probing, can substantially improve ranking quality, highlighting the importance of incorporating execution signals into agent discovery. Our code is available at https://github.com/Bingo-W/AgentSearchBench.

cs.AI↗
arXiv2026

Weaponizing the Commons: A Taxonomy and Detection Framework of Abuse on GitHub

GitHub plays a critical role in modern software supply chains, making its security an important research concern. Existing studies have primarily focused on CI/CD automation, collaboration patterns, and community management, while abuse behaviors on GitHub have received little systematic investigation. In this paper, we systematically review and summarize reported GitHub abuse behaviors and conduct an empirical analysis of publicly available abuse cases, curating a manually labeled dataset of 392 GitHub instances. Based on this investigation, we propose a comprehensive taxonomy that characterizes their diverse symptoms and root causes from a software security perspective. Building on this taxonomy, we develop a unified detection framework capable of identifying all abuse categories across repositories and user accounts. Evaluated on the constructed dataset, the proposed framework achieves high performance across all categories (e.g., F1-score exceeding 89%). Collectively, this work advances the understanding of GitHub abuse behaviors and lays the groundwork for large-scale, systematic analysis of the GitHub platform to strengthen software supply chain security.

cs.SE↗
arXiv2026

INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling

Building world models with spatial consistency and real-time interactivity remains a fundamental challenge in computer vision. Current video generation paradigms often struggle with a lack of spatial persistence and insufficient visual realism, making it difficult to support seamless navigation in complex environments. To address these challenges, we propose INSPATIO-WORLD, a novel real-time framework capable of recovering and generating high-fidelity, dynamic interactive scenes from a single reference video. At the core of our approach is a Spatiotemporal Autoregressive (STAR) architecture, which enables consistent and controllable scene evolution through two tightly coupled components: Implicit Spatiotemporal Cache aggregates reference and historical observations into a latent world representation, ensuring global consistency during long-horizon navigation; Explicit Spatial Constraint Module enforces geometric structure and translates user interactions into precise and physically plausible camera trajectories. Furthermore, we introduce Joint Distribution Matching Distillation (JDMD). By using real-world data distributions as a regularizing guide, JDMD effectively overcomes the fidelity degradation typically caused by over-reliance on synthetic data. Extensive experiments demonstrate that INSPATIO-WORLD significantly outperforms existing state-of-the-art (SOTA) models in spatial consistency and interaction precision, ranking first among real-time interactive methods on the WorldScore-Dynamic benchmark, and establishing a practical pipeline for navigating 4D environments reconstructed from monocular videos.

cs.CV↗