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Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 199 records · Page 11Linked to original sources
arXiv2026

Efficient Implementation of a Single-Qutrit Gate Set via Coherent Control

Qutrits offer the potential for enhanced quantum computation by exploiting an enlarged Hilbert space. However, the synthesis of high-fidelity and fast qutrit gates, particularly for single qutrits, remains an ongoing challenge, as it involves overcoming intrinsic constraints in quantum platforms. Here, we develop a novel framework for the efficient implementation of a single-qutrit gate set via coherent control, leveraging SU(3) dynamics while obviating platform-specific constraints such as those arising from the selection rule. As a proof-of-principle demonstration, we realize 35-ns qutrit Hadamard and X gates using a superconducting transmon, achieving an average fidelity of 99.5\%, as verified by randomized benchmarking. We further demonstrate two paradigmatic quantum circuits, which can be naturally extended to scalable qudit algorithms for phase estimation and parity check. In addition, we propose an SU(3)-based decomposition strategy for an arbitrary single-qutrit gate and numerically demonstrate its substantial efficiency improvement over conventional SU(2)-based protocols. By addressing the challenge of efficiently implementing single-qutrit gates, our protocol paves the way for realizing high-performance qutrit processors in diverse quantum platforms.

quant-ph↗
arXiv2026

Searching for apparent baryon number violation in $Λ_c^+$ decays at the Super Tau-Charm Facility

Observation of baryon number violation (BNV) in laboratory experiments would constitute unambiguous evidence for physics beyond the Standard Model. We propose dedicated searches for \textit{apparent} BNV in charm-baryon decays, $Λ_c^+\to M^+ +$ missing energy ($M=π, K$) where the missing energy stems from a resonance. These channels have not been explored experimentally so far, despite the relatively clean environment potentially provided by near $Λ_c^+\overlineΛ_c^-$ threshold production at $e^+e^-$ colliders. Performing state-of-the-art Monte Carlo simulations for the proposed Super Tau-Charm Facility (STCF), we evaluate the signal efficiencies and derive projected model-independent sensitivities under the assumption of negligible background. We further interpret these sensitivities within two theoretical frameworks: a sterile-neutrino-extended low-energy effective field theory ($ν$LEFT) and R-parity-violating (RPV) supersymmetry. With an integrated luminosity of 1 ab$^{-1}$, STCF can probe new-physics scales of several TeV in the $ν$LEFT description and constrain the RPV model parameter $λ''_{212}/m^2_{\tilde{q}}$ down to about $0.1~\mathrm{TeV}^{-2}$. Our results demonstrate that STCF provides a highly competitive opportunity for probing BNV interactions in rare charm-baryon decays.

hep-ph↗
arXiv2026

Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion

Agentic search over large corpora relies on retriever-mediated interfaces (e.g., BM25 or ColBERT) for scalable candidate discovery. While effective at ranking relevant documents, these interfaces expose evidence only as ranked results or bounded document views, limiting agents' ability to reorganize material and verify constraints across documents. Direct Corpus Interaction (DCI) addresses this limitation by exposing shell-executable corpus operations for flexible search, filtering, comparison, and verification. However, full-corpus terminal commands become slow and unstable as the corpus grows, degrading performance and efficiency. We introduce DR-DCI, a retriever-steered DCI framework that treats retrieval as an agent-callable action for expanding a local workspace. Rather than operating directly over the full corpus, the agent dynamically pulls relevant documents into an evolving workspace and conducts DCI operations within it. This design combines retriever-level recall with DCI-style precision: retrieval keeps exploration scalable, while DCI preserves the local operations needed for effective evidence resolution. Experiments show that DR-DCI is both effective and efficient across scales. On Browsecomp-Plus, DR-DCI reaches 71.2\% accuracy, improving over raw DCI and ablated variants by up to 8.3 points while reducing tool usage, wall time, and estimated cost. With workspace-preserving context reset, accuracy further improves to 73.3\%. In corpus-scaling experiments, DR-DCI remains effective from 100K to 10M documents, whereas raw DCI becomes unstable and BM25 performs substantially worse. DR-DCI also scales to a 20M-scale file-per-document Wiki-18 QA setting, achieving an average score of 63.0 across six benchmarks and outperforming retrieval-based and trained search-agent baselines. Ablation analysis further shows that ranked previews and inter-document DCI are key to performance.

cs.AI↗
arXiv2026

Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning

Fall recovery is critical for autonomous legged locomotion. Existing methods have demonstrated that some legged robots, such as humanoids and quadrupeds, are capable of fall recovery from diverse postures by utilizing arms or coordinating multi-legs to generate support forces. Without arms or other legs to provide supportive assistance, a bipedal-wheeled robot must rely solely on the actuation of its legs, making recovery particularly difficult. To address this, we introduce FTSR (Force-guided Teacher-student framework with Stage-wise Rewards). The force-guided method constructs an external auxiliary force during simulation training that correlates directly with the robot's real-time height, explicitly formulating this force as an optimizable constraint. Through constrained reinforcement learning, the policy is guided toward reducing force dependency gradually and increasing the body height, developing internal recovery strategies despite having no arms for support. Height-progressive stage-Wise rewards progressively structure posture stabilization during recovery and transition to sustained locomotion, integrated with teacher-student architecture distilling privileged knowledge of force effects and recovery dynamics. After simulation training, the policy is deployed on a physical armless bipedal-wheeled robot and extensively evaluated. Experiments confirm robust and reliable fall recovery under diverse challenging conditions, demonstrating strong environmental adaptability and motion robustness, while maintaining full post-recovery motion capability. The framework also generalizes effectively to a high-DOF humanoid, confirming its practical generalizability. The project page is available at https://2350575870.github.io/force-guided.github.io/

cs.RO↗
arXiv2026

Large-scale semantic mapping of learner agency and autonomy reveals what measurement and generative AI research overlook

Learner agency and autonomy are foundational to personal development, yet a pervasive "jingle-jangle" fallacy (i.e. identical terms denoting different constructs, distinct terms denoting identical ones) has substantially hindered cumulative knowledge. Treating meaning as a phenomenon constituted through use in linguistic practice, we extracted 8,954 definitions and 2,700 scale items from over 14,000 publications, to investigate how researchers actually used learner agency and autonomy with a semantic analysis pipeline. The definitional landscape of two constructs resolves into three dimensions: regulation and control of learning (task), intrinsic motivation and internal decision-making (person), and social-relational action (sociocultural), thereby empirically quantifying the jingle-jangle fallacy. Existing scales, however, systematically underrepresent the sociocultural dimension. Critically, current generative AI research in education concentrates on learning regulation and control, narrowing the behavioral repertoire that AI-mediated learning environments are designed to cultivate. Beyond conceptual clarification, this work carries direct implications for conceptualization, measurement, and practice towards supporting the multidimensional learner agency and autonomy.

cs.AI↗
arXiv2026

Machine Learning Accelerated SSNEB for Efficient Minimum Energy Pathway Calculations

Metastable states and their minimum energy pathways (MEPs) are central to understanding transformations and phase stability in complex materials, yet mapping transition pathways between competing states remains computationally demanding and experimentally challenging. Here, we introduce a hybrid solid-state nudged elastic band (SSNEB) framework that integrates two pretrained machine learning models, EquiformerV2 (eqV2) and the equivariant Smooth Energy Network (eSEN), with DFT for energy, force, and stress evaluations. Applied to three solid-state systems, CsPbI$_3$, GaN, and TiO$_2$, our framework achieves up to a 7-fold speedup while converging to the same pathways predicted by first-principles calculations. Moreover, the hybrid SSNEB framework enables systematic benchmarking of existing ML models, providing both efficiency and reliability for predicting MEPs across various materials.

cond-mat.mtrl-sci↗
arXiv2026

Modular Anthropomorphic Hand Design via Multi-Parameter Finger Benchmarking and Selection

Designing anthropomorphic dexterous robotic hands remains challenging as the design space straddles morphology, actuation, and sensing properties, and performance metrics span both task-dependent and task-agnostic. Existing optimization methods are often unstructured or consider only a single performance metric, limiting systematic comparison and targeted refinement. While the design considerations of the entire hand are significant, the individual finger properties play a key role in dexterity. By developing a robotic hand platform where fingers can be modularly integrated into a full teleoperated hand, we propose that optimizing the fingers can significantly improve overall hand performance. This approach enables rapid screening of different finger-level prototypes through a number of quantitative benchmarks before their integration into the hand for task-level validation. Candidate finger designs (incorporating variations in joint, bone, skin, and sensor placement) are assessed using both mechanism-oriented and task-relevant metrics, which establish a quantitative link between component design and full hand embodiment. The framework is validated through the development of an anthropomorphic robotic hand with optimized fingers, demonstrating how these fingers enable performance improvements across tasks, including multi-object grasping and light bulb screwing.

cs.RO↗
arXiv2026

ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction

Autonomous high-fidelity object reconstruction is fundamental for creating digital assets and bridging the simulation-to-reality gap in robotics. We present ObjSplat, an active reconstruction framework that leverages Gaussian surfels as a unified representation to progressively reconstruct unknown objects with both photorealistic appearance and accurate geometry. Addressing the limitations of conventional opacity or depth-based cues, we introduce a geometry-aware viewpoint evaluation pipeline that explicitly models back-face visibility and occlusion-aware multi-view covisibility, reliably identifying under-reconstructed regions even on geometrically complex objects. Furthermore, to overcome the limitations of greedy planning strategies, ObjSplat employs a next-best-path (NBP) planner that performs multi-step lookahead on a dynamically constructed spatial graph. By jointly optimizing information gain and movement cost, this planner generates globally efficient trajectories. Extensive experiments in simulation and on real-world cultural artifacts demonstrate that ObjSplat produces physically consistent models within minutes, achieving superior reconstruction fidelity and surface completeness while significantly reducing scan time and path length compared to state-of-the-art approaches. Project page: https://li-yuetao.github.io/ObjSplat-page/ .

cs.RO↗
arXiv2026

IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation

In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynamic, multi-turn interleaved image-text dialogues is a crucial task for UMMs in real-world applications. However, existing benchmarks fail to evaluate this important task, as they are often limited to single-turn or static settings, and typically overlook exposure bias in multi-turn interactions. To bridge this gap, we propose IMUG-Bench, a comprehensive benchmark for multi-turn interleaved image-text dialogue of UMMs that jointly evaluates their understanding and generation capabilities. Our IMUG-Bench comprises three classes: Static Spatial, Temporal Causal, and Hybrid, covering 3,113 samples and 12,034 interaction turns. It also includes dynamic understanding questions, thereby supporting evaluation that better reflects real-world multi-turn interaction scenarios. Large-scale experiments on IMUG-Bench systematically evaluate mainstream open-source and closed-source UMMs, revealing their capability boundaries and failure modes, and uncovering pronounced exposure bias on the generation side in multi-turn interactions. We further explore several test-time scaling strategies, including Chain-of-Thought, Self-Verification, and Best-of-N Sampling, which effectively improve generation accuracy and mitigate exposure bias in generation tasks. These findings provide insights into enhancing the robustness and multi-turn interaction capability of future UMMs.

cs.AI↗
arXiv2026

Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory management. However, recent studies show that knowledge-extraction attacks can recover sensitive knowledge-base content through maliciously crafted queries, raising serious intellectual property and privacy concerns. While prior work has explored individual attack and defense techniques, the research landscape remains fragmented, spanning heterogeneous retrieval embeddings, diverse generation models, and evaluations based on non-standardized metrics and inconsistent datasets. To address this gap, we introduce the first systematic benchmark for knowledge-extraction attacks on RAG systems. Our benchmark covers broad attack/defense strategies, representative retrieval embedding models, open/closed-source generators, (non) graph-based indexing, all evaluated under a unified experimental framework with standardized protocols across multiple datasets spanning diverse languages. By consolidating the experimental landscape and enabling reproducible, comparable evaluation, this benchmark provides actionable insights and a practical foundation for developing privacy-preserving RAG systems in the face of emerging knowledge extraction threats.

cs.CR↗
arXiv2026

Hidden Density-Wave Instability in the Trimer Ruthenate Ba$_4$Ru$_3$O$_{10}$

We report a hidden density-wave instability in the trimer-based ruthenate Ba4Ru3O10, previously regarded as a pure antiferromagnet with a phase transition at TA=100 K. This transition is manifested in lattice parameters, transport, thermodynamics, and magnetic susceptibility, yet remains remarkably insensitive to magnetic fields up to at least 14 T, indicating an electronically driven reconstruction. At much lower temperatures T*= 20 K, charge transport becomes strongly nonlinear, exhibiting distinct depinning thresholds, negative differential resistance, pronounced current- and frequency-dependence, and slow collective dynamics in the Hertz range. While each feature is characteristic of density-wave transport, their simultaneous occurrence in an antiferromagnetic oxide is unprecedented. All nonlinear signatures vanish upon only 3% Ir substitution, which preserves the crystal structure and insulating state, ruling out Joule heating or extrinsic artifacts. The wide separation between the electronic reconstruction at TA and the emergence of nonlinear dynamics at T* identifies Ba4Ru3O10 as a rare correlated system hosting a strongly pinned collective electronic state intertwined with antiferromagnetism.

cond-mat.str-el↗
arXiv2026

Quantifying Non-Abelian Stability in Majorana Qubits through Rabi Beating Signatures

Evaluating the stability of Majorana qubits (MQs) is a central challenge for topological quantum computation. Here we propose a simple and experimentally accessible protocol to quantify MQ stability by coupling a quantum dot (QD) to an MQ, which induces Rabi oscillations in the QD charge occupation that can be directly detected using recently developed single-shot readout techniques. In realistic systems, deviations from ideal MQ behavior lead to a characteristic beating pattern in the Rabi dynamics. We show that the beating frequency scales linearly with these deviations while remaining independent of the base Rabi frequency, thereby providing a direct and quantitative measure of MQ stability. Importantly, the beating signature is robust against weak dissipation, and we further demonstrate that the effective model remains quantitatively accurate when benchmarked against a realistic minimal Kitaev chain. Our results establish a practical and scalable route for quantitatively characterizing Majorana qubit stability in current experimental platforms.

cond-mat.mes-hall↗
arXiv2026

Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving

Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and planning in this domain. However, large models demand substantial GPU memory and exhibit high inference latency, while conventional supervised fine-tuning (SFT) often struggles to bridge the capability gaps of small models. To address these limitations, we propose Drive-KD, a framework that decomposes autonomous driving into a "perception-reasoning-planning" triad and transfers these capabilities via knowledge distillation. We identify layer-specific attention as the distillation signal to construct capability-specific single-teacher models that outperform baselines. Moreover, we unify these single-teacher settings into a multi-teacher distillation framework and introduce asymmetric gradient projection to mitigate cross-capability gradient conflicts. Extensive evaluations validate the generalization of our method across diverse model families and scales. Experiments show that our distilled InternVL3-1B model, with ~42 times less GPU memory and ~11.4 times higher throughput, achieves better overall performance than the pretrained 78B model from the same family on DriveBench, and surpasses GPT-5.1 on the planning dimension, providing insights toward efficient autonomous driving VLMs.

cs.AI↗
arXiv2026

Rotating and non-linear magnetic-charged black hole with an anisotropic matter field

We present the solution of a non-linear magnetic-charged black hole with an anisotropic matter field and further extend it to obtain the corresponding rotating black hole solution using the modified Newman-Janis algorithm. The event horizon and ergosphere of the rotating black hole are studied in terms of the perspective of geometric properties, revealing that the rotating black hole can have up to three horizons. The first law of thermodynamics and the squared-mass formula for the rotating black hole are derived from a thermodynamic perspective, based on which we obtain the thermodynamic quantities and study the thermodynamic stability of the rotating black hole. Additionally, we calculate the Penrose process for the rotating black hole, indicating the influence of various black hole parameters on the maximal efficiency of the Penrose process.

gr-qc↗
arXiv2026

Structure-Guided Adaptive Propagation for Protein-Protein Interaction Site Prediction

Accurate prediction of protein-protein interaction sites (PPIS) is essential for understanding cellular processes, disease mechanisms, and therapeutic target discovery. Graph-based deep learning has advanced PPIS prediction by incorporating residue-level structural context. However, most graph-based models still rely on fixed propagation schemes that treat all residues similarly, despite the structural and functional heterogeneity of protein interfaces. Such propagation may limit the ability to adapt information diffusion to local geometric environments, making it difficult to distinguish true interaction sites from structurally similar non-interacting neighbors. We present SGAP-PPIS, a structure-guided adaptive propagation model for PPIS prediction. Rather than using a fixed propagation mechanism, SGAP-PPIS leverages multi-scale geometric states from an equivariant graph neural network to generate residue-wise propagation coefficients. This design allows each residue to adaptively balance local feature preservation and neighborhood diffusion according to its geometric microenvironment. Experimental results show that SGAP-PPIS achieves competitive performance among the state-of-the-art methods on Test\_60. Ablation studies show that geometry-conditioned adaptive propagation, scale-aligned geometric guidance, and multi-step propagation-state representation jointly drive these improvements.

cs.AI↗
arXiv2026

HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity

Respiratory viral infections pose a global health burden, yet the cellular immune mechanisms underlying protection and pathology remain unclear. Natural infection cohorts often lack pre-exposure baselines and time-controlled sampling, whereas inoculation and vaccination trials generate well-structured longitudinal transcriptomic data. However, these datasets are scattered across repositories and processed inconsistently, hindering integrative and AI-driven analyses. To address these challenges, we developed the Human Respiratory Viral Immunization LongitudinAl Gene Expression (HR-VILAGE-3K3M) repository: an AI-ready resource integrating bulk and single-cell transcriptomic profiles from 3,178 subjects across 66 studies. The dataset spans vaccination, inoculation, and mixed exposures, with samples from blood and nasal swabs collected from public repositories including GEO, ImmPort, and ArrayExpress. We curated and harmonized subject-level metadata, standardized outcome measures, and applied unified preprocessing with rigorous quality control. We further provide benchmark analyses illustrating its utility. This resource supports discovery of biomarkers, immune mechanisms, and methodological development. As one of the largest longitudinal transcriptomic resources for human respiratory viral immunization, HR-VILAGE-3K3M enables reproducible and scalable analyses to accelerate vaccine and antiviral research.

q-bio.GN↗
arXiv2026

Temporally-Aligned Evaluation for Audio-Driven Talking Head Generation

Audio-driven talking-head generation has advanced rapidly, yet existing evaluation protocols mainly rely on frame-wise metrics that assume strict temporal correspondence between generated and reference videos. This assumption does not match speech-driven facial motion, which naturally includes slight timing shifts, different speaking speeds, and stylistic variations. As a result, conventional metrics may treat harmless timing differences as quality errors, making it harder to fairly compare methods and understand their trade-offs. In this work, we argue that evaluation of dynamic generative models should be formulated as a sequence-alignment problem rather than independent frame comparison. We introduce a unified sequence-level reformulation that integrates Soft Dynamic Time Warping into established evaluation pipelines. By aligning feature trajectories while preserving temporal order, the proposed framework provides robustness to bounded temporal misalignments without altering the underlying perceptual, identity, or synchronization encoders. We show that frame-wise evaluation can be viewed as a special case under rigid alignment, while sequence-level alignment provides improved stability, lower sensitivity to timing differences, and clearer separation between modeling paradigms. Building on this principled formulation, we conduct a large-scale benchmark of 20 methods across seven datasets spanning canonical, in-the-wild, and style-diverse scenarios under standardized protocols. Extensive experiments show that temporally aligned metrics are more robust to timing differences, provide more consistent results across datasets, and better reveal systematic trade-offs between modeling paradigms, such as synchronization versus realism and expressiveness versus stability.

cs.GR↗
arXiv2026

UME: A Unified Meta-Generalization Framework for Cross-Domain ETA

Accurate Estimated Time of Arrival (ETA) prediction on checkout page is crucial in instant logistics for enhancing user satisfaction, optimizing dispatching, and controlling operational costs. In international on-demand delivery platforms, where ETA data originates from diverse countries or regions with different patterns, multi-domain modeling is of great importance and has been widely adopted. However, existing methods still face three critical challenges in real-world deployment. First, current multi-domain models struggle to generalize to completely unseen domains, failing to achieve zero-shot prediction during the initial cold-start phase. Second, cross-domain feature spaces are often assumed to be consistent, whereas new domains commonly suffer from structural missingness of offline (statistical) features due to the lack of historical data. Third, such feature missingness often compels industrial systems to model mature and cold-start domains separately, hindering knowledge transfer and increasing maintenance overhead. To address these challenges, we propose \textbf{UME}, a \textbf{U}nified \textbf{M}eta-generalization framework for \textbf{E}TA. Specifically, UME integrates a unified dual-branch architecture with a novel meta-learning mechanism that employs a hypernetwork-based meta learner. By leveraging domain-level knowledge and instance-level context, the meta learner empowers three meta modules to dynamically modulate feature gating, expert attention, and final prediction, capturing cross-domain correlations and facilitating intra-domain adaptation. A knowledge distillation strategy is further introduce to enhance performance. UME has now been deployed in Meituan-keeta delivery platform (the largest international food delivery platform in China). Extensive offline experiments and online A/B tests demonstrate that UME significantly outperforms existing baselines.

cs.LG↗