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Shuai Xu

Publications and source records attributed to Shuai Xu.

At least 19 recordsLinked to original sources
arXiv2026

Determination of the $η$-$η^{\prime}$ Mixing Angle from $F_{η^{(\prime)}γ}(Q^2)$ within the Self-consistent Light-front Quark Model

We study the $η$-$η^{\prime}$ system in the self-consistent light-front quark model (LFQM) with the quark flavor mixing scheme, where the single mixing angle is constrained by the transition form factors (TFFs) $F_{η^{(\prime)}γ}(Q^2)$. A continuous combined $χ^2$ fit to the CLEO and BABAR data gives $θ=41.55^{+0.57}_{-0.60}\,{}^\circ$, in close agreement with the recent LHCb result $(41.6^{+1.0}_{-1.2})^\circ$. Using this mixing angle, we calculate the decay constants, light-cone distribution amplitudes (LCDAs), Gegenbauer moments, $ξ$-moments, transverse momentum moments, electromagnetic interaction radii and two-photon decay widths of the $η$ and $η^{\prime}$ mesons. The predicted $a_2^η$ and $\langleξ^2\rangle_η$ are consistent with the latest BABAR results, and the electromagnetic interaction radii also agree well with the A2 and BESIII measurements. These results demonstrate that TFF data strongly constrain the light-front description of the nonperturbative properties of the $η$-$η^{\prime}$ system. After the fit and LFQM-input uncertainties are propagated, the predicted widths are $0.428^{+0.038}_{-0.035}$ keV and $3.067^{+0.190}_{-0.180}$ keV for $η\toγγ$ and $η^{\prime}\toγγ$, respectively. The central values are lower than the corresponding experimental values, indicating that the real-photon point provides a more sensitive test of the absolute TFF normalization.

hep-ph↗
arXiv2026

Quantum computing for plasma physics in magnetic-confinement fusion

Plasma simulations for magnetic-confinement fusion (MCF) involve high-dimensional kinetic dynamics, strong nonlinear coupling, complex geometries, and multiscale spatiotemporal behavior, imposing severe computational bottlenecks. While quantum computing offers a potential alternative for accelerating plasma simulation problems, its practical advantage for fusion research has not yet been demonstrated. We review existing quantum computing studies across kinetic dynamics, wave interactions, equilibrium, and macroscopic stability, evaluating their hardware implementations and operational bottlenecks. Current studies remain largely restricted to linearized equations, reduced models, or isolated computational subroutines, leaving a substantial gap from realistic fusion simulations. We summarize key problem characteristics, including high dimensionality, structured dynamics, and measurement requirements, under which plasma applications are most likely to yield a quantum advantage. Guided by these considerations, we highlight potential approaches and present a roadmap toward practical quantum advantage of simulating plasma in MCF.

quant-ph↗
arXiv2026

TCMScience: An Autonomous Scientist for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) research connects classical texts, medicinal entities, molecular assays, computational predictions, and clinical studies. An autonomous research agent must preserve the inferential boundaries between these evidence types while coordinating analysis across heterogeneous resources. We introduce TCMScience, a scientific knowledge-discovery agent that separates language-model proposals from execution and release authority. Its architecture combines a trusted kernel, a scientific compiler, and claim-support contracts: plans are compiled into typed operations, execution is bounded by explicit permissions, and candidate findings are checked against source provenance, study design, and claim scope before release. The full capability registry contains 117 public-source connectors with 444 typed operations across 29 domains and 150 native tools across 12 domains; the TCM data hub catalogs 136 databases, 76 dataset specifications, and 84,294 formula-source records. These resources support model-independent workflows through browser and local execution interfaces. In a network-pharmacology analysis of Gegen Qinlian Tang, replacing the whole-proteome background with the assayed-protein background changed the number of significant pathways from 66 to zero; incorporating inactive screening records identified nine pathways, including drug-metabolism signals retained as mechanistic hypotheses. A governance ablation over 121 constructed erroneous outputs spanning 26 classes released 121 errors without governance, 57 with the domain layer, seven with the kernel, and zero with both layers. All 10 compliant controls were retained. These results establish complementary control of known failure modes and show how explicit evidence contracts change the conclusions an agent is permitted to report.

cs.LG↗
arXiv2026

Two-variable longitudinal distribution of the $Λ_b$ in a three-quark light-front model

We determine a normalized two-variable longitudinal distribution of the $Λ_b=(ud)b$ baryon from a covariant three-quark light-front wave function. The scalar $ud$ channel is selected with the good-component structure $Cγ_5γ^+$, and the two independent transverse momenta are integrated without introducing an elementary diquark. The resulting model-scale valence distribution, $Ψ_2^{\rm LF}(x_u,x_d)$, retains both the heavy--light and internal-light momentum scales of the parent wave function. It is symmetric under $x_u\leftrightarrow x_d$, is concentrated where both light-quark fractions are small, and separates the total light-pair momentum from its internal sharing. For the central parameter set we find $\langle x_u\rangle=\langle x_d\rangle=0.130$, $\langle x_b\rangle=0.739$, $\langleζ^2\rangle=0.153$, and $\langleζ^4\rangle=0.055$, where $ζ=(x_u-x_d)/(x_u+x_d)$. Parameter variations give $\langle x_u\rangle=0.130^{+0.007}_{-0.007}$, $\langle x_b\rangle=0.739^{+0.014}_{-0.014}$, $\langleζ^2\rangle=0.153^{+0.014}_{-0.016}$, and $\langleζ^4\rangle=0.055^{+0.009}_{-0.009}$. The heavy--light momentum partition is controlled mainly by $β_{b,ud}$ and $m_b$, whereas the internal $u$--$d$ sharing is most sensitive to $β_{ud}$. The result provides a compact finite-mass benchmark for three-quark longitudinal structure, with distinct observables for heavy--light momentum sharing and correlations inside the light pair.

hep-ph↗
arXiv2026

RSAJEV: Training-Free Abstaining Prototype Memory for Eliminating Silent Hallucination in JEV-Based Remote Sensing Classification

Scene classification of remote sensing imagery with multimodal large language models (MLLMs) is formulated as a single-forward-pass decision problem: candidate class names are lettered options in the prompt, a registered answer slot is conditioned on the prompt, and classification is read out by scoring letter tokens with the language-modeling head. We show that although this letter binding is highly effective within its training vocabulary (97.71% OA on UCM), it behaves erratically out of vocabulary: zero-adaptation accuracy across four cross-dataset directions fluctuates between 7.35% and 96.40%, and under out-of-vocabulary weights its confidence fails inversely on open-set evaluation, assigning higher confidence to unseen than to seen classes (AUROC 0.38), a failure we term silent hallucination. We retain the slot representation and replace the readout with RSAJEV, a training-free abstaining prototype memory that performs retrieval-based prototype matching: K reference images per class are cached as prototypes via their slot representations under the same prompt, and classification becomes nearest-neighbor retrieval over the prototypes, refined by a shrinkage-Mahalanobis geometric correction. This readout is mechanistically isomorphic to retrieval-augmented generation (RAG) but operates on discriminative decisions; it is the first retrieval-based readout for MLLM slot representations. The retrieval readout reaches 89.43% on the UCM cross-dataset gate with 16 references per class and zero gradient updates, and 96.90% on AID. Its ID-ness scores attain 0.91-0.99 AUROC on open-set evaluation over two datasets, exceeding symbolic confidence (0.38-0.96) in all four open-set settings, restoring a calibratable abstention channel and supporting class addition/removal at zero extra inference cost via offline rescoring of the cached matrix.

cs.CV↗
arXiv2026

TrustMed-RL: Long-Horizon Reinforcement Learning for Evidence-Grounded Clinical Diagnosis

Medical language models can produce correct diagnoses despite incomplete investigations and unsupported reasoning. To support long-horizon, evidence-grounded diagnosis, we introduce \textbf{TrustMed-RL}. Built from PubMed rare-disease cases and over 24,000 manually annotated image panels, it integrates interviews, examinations, testing, specialist consultation, and literature search through state-dependent actions. Our 8B vision--language policy, trained with clinically adapted GiGPO and coverage-adjusted diagnostic rewards, achieves 37.1\% diagnostic accuracy on 2,500 evaluation cases, outperforming all evaluated open-weight baselines and improving over supervised fine-tuning by 12.4 percentage points.. When success additionally requires acquiring at least 50\% of supporting test evidence, TrustMed-RL achieves 32.5\%, exceeding GPT-4o by 6.8 percentage points. Furthermore, it surpasses all evaluated baselines on MTMedDialog and multiple larger 27--32B models on AgentClinic. In physician review of 200 diagnostically accepted test-set trajectories, 83.0\% receive evidential-grounding scores of 4--5 out of 5. Physicians' assessments suggest that these diagnostic trajectories are trustworthy and aligned with human diagnostic reasoning.

cs.AI↗
arXiv2026

Semileptonic $\bar{B}^*_{c} \to (P, V) \ell^- \barν_{\ell}$ decays within the self-consistent light-front quark model

Motivated by the recent observation of the $B_c^*$ meson by the ATLAS Collaboration and its promising discovery potential at future high-luminosity colliders, we investigate the semileptonic decays $\bar{B}^*_{c} \to (P, V) \ell^- \barν_{\ell}$ ($P=D$, $B_{d,s}$, $η_c(1S,\,2S,\,3S)$; $V=D^*$, $B^*_{d,s}$, $ψ(1S,\,2S,\,3S)$) in the Standard Model. Using the relevant form factors obtained in the self-consistent covariant light-front quark model, we present theoretical predictions for several physical observables, including the branching fraction, the ratios of branching fractions $R_{(L)}$, the longitudinal polarization fraction of the daughter vector meson, the $τ$ lepton spin asymmetry and the forward-backward asymmetry. It is found that the branching fractions for the Cabibbo-favored $\bar{B}^*_{c}$ $\to$ $\bar{B}^*_{s} e^- \barν_e$ and $\bar{B}^*_{s} μ^- \barν_μ$ decays reach the order of ${\cal O}(10^{-6})$, which might be firstly observed at the future high-luminosity Large Hadron Collider.

hep-ph↗
arXiv2026

Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems

Efficiency is increasingly important for Large Language Model (LLM)-based multi-agent systems (MAS), as larger models and more agents introduce substantial execution costs. Recent methods aim to make MAS cheaper by pruning agents, removing communication edges, or searching for compact structures. However, we argue that existing evaluations may overestimate their true ability to improve MAS efficiency. Reported gains are often measured under method-specific prompts and starting topologies, making them difficult to attribute to the proposed structural changes. Moreover, many reported successes appear in non-MAS-demanding settings, where a single agent or a randomly pruned system can already preserve strong performance. To study these issues, we introduce a controlled and MAS-demanding diagnostic benchmark for representative MAS efficiency methods. We evaluate methods under a shared backbone model, agent registry, and runtime, across controlled variations in topology, scale, depth, and tool use. Our analysis shows that many reported gains are setup-dependent and may arise from structural collapse, disabled tool pathways, or starting systems where random pruning already preserves accuracy, rather than robust improvements in MAS efficiency.

cs.LG↗
arXiv2026

Photometric Distances for Metal-poor Giants and a Search for Hypervelocity Stars with LAMOST DR13 and Gaia DR3

Hypervelocity stars (HVSs) are stars with velocities high enough to escape the Milky Way, but their identification depends sensitively on distance estimates, particularly for distant giants. In this work, we search for metal-poor HVS candidates by combining LAMOST DR13 spectroscopy with Gaia DR3 astrometry. We calibrate a metallicity-dependent color--absolute-magnitude relation for normal metal-poor giants using a high-quality reference sample, and apply it to derive photometric distances for 41{,}331 stars. The relation reproduces the reference absolute magnitudes with a scatter of 0.24\,mag, corresponding to an intrinsic distance uncertainty of $\sim$9.7\%. Combining these distances with Gaia proper motions and LAMOST radial velocities, we identify 13 initially unbound candidates under the Galactic potential from \citet{McMillan2017}. Spectral inspection indicates that several are chromospherically active binaries or other non-standard systems for which a giant-star calibration is unreliable; removing these contaminants leaves nine metal-poor HVS candidates. Backward orbit integrations suggest that one candidate is most consistent with a disk origin, while three have trajectories suggestive of an association with the Sagittarius stream.

astro-ph.SR↗
arXiv2026

Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides

LLM-based agents are increasingly used as autonomous orchestrators in distributed and multi-agent systems, yet a control gap remains: no protocol-level mechanism allows human operators to inject binding directives that persist across planning cycles. Free-text overrides suffer from ambiguous interpretation due to lack of schema validation and from authority erosion as operator instructions lose influence within accumulating context. We propose Structured Authority Injection (SAI), a protocol that addresses these issues by routing operator corrections through typed, schema-validated Model Context Protocol (MCP) tool calls and maintaining a ranked authority context prepended to each LLM prompt. SAI defines three override classes (Constraint, Priority, and Strategy) with distinct persistence semantics and an explicit precedence order enforced via a directed authority channel inaccessible to the planning process. The method requires no model fine-tuning or access to model weights and applies to instruction-tuned LLMs supporting MCP tool use. Evaluation across nine LLMs and eight scenarios targeting agent failure modes shows reduced ambiguous directive interpretation and mitigated authority decay across planning iterations, with lower prompt overhead in the strongest models (up to 38% depending on model and scenario), though benefit varies substantially across models. SAI provides a general design pattern for agent systems requiring explicit and persistent human control.

cs.AI↗
arXiv2026

Graph-VQE: A CUDA-Q Multi-QPU Simulation Framework for Hamiltonian-Aware Protein-Folding VQE

The Variational Quantum Eigensolver (VQE) is essential for molecular simulation in drug discovery, but hardware noise and algorithmic limits restrict its precision. While the NVIDIA CUDA-Q platform mitigates some hardware issues via exact simulation, it lacks Qiskit support and restricts parallelization. To solve this, we introduce Graph-VQE, a novel framework that extends CUDA-Q with optimization-level parallelism. Graph-VQE leverages amino acid sequence structures by partitioning Hamiltonian interaction graphs into weakly coupled clusters using Louvain community detection. These clusters undergo restricted updates on the full-Hamiltonian objective, followed by a global refinement stage utilizing Hamiltonian batching. Furthermore, a custom Qiskit-CUDA-Q integration layer enables standard workflows with GPU acceleration. Evaluations on protein folding tasks prove that Graph-VQE outperforms baselines, achieving lower final energies. It delivers competitive RMSD and binding affinity compared to AlphaFold3 and IBM quantum processors while maintaining stable quality across multi-GPU environments, thereby providing a highly practical path toward high-fidelity biomolecular simulations.

cs.ET↗
arXiv2026

Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook

Multi-label image classification (MLIC), a fundamental task in computer vision, focuses on identifying multiple objects or concepts within an image, underpinning numerous read-world applications, such as autonomous driving, disease diagnosis, recommendation system, and mobile service robot. Over the past decade, deep learning paradigms based on convolutional neural networks, recurrent neural networks, and Transformers have significantly advanced this field, owing to their powerful capability in visual representation and relationship modeling. These advances have markedly improved the robustness, scalability, and generalization ability of MLIC models across diverse datasets and application domains. In this survey, we provide a comprehensive review of the deep learning-based literature on MLIC. Concretely, we first revisit the background, including problem definition, datasets, backbones and evaluation metrics. Next, we develop a plausible taxonomy for the deep learning-based MLIC approaches, organizing them into six groups: region-oriented methods, label-oriented methods, architecture-oriented methods, representation-oriented methods, learning-oriented methods, and data-oriented methods. Finally, we provide an insightful exposition of the underlying learning game in MLIC and its implications for other vision domains, and we empirically summarize the key challenges and research directions in MLIC while outlining promising avenues for future development. We believe this survey offers the research community a holistic and systematic perspective on MLIC, thereby facilitating subsequent exploration and innovation in this field and beyond.

cs.CV↗
arXiv2026

Study on the Cabibbo-favored ${\overline B}_{d,s}$ ${\to}$ $D_{d,s}^{(*)+}S^{-}$ weak decays in QCD factorization

Motivated by recent experimental progress and theoretical developments, we investigate the Cabibbo-favored $b\to c$ governed ${\overline B}_{d,s}$ ${\to}$ $D_{d,s}^{(*)+}S^{-}$~($S$$=$$K_0^*(1430)$, $a_0(1450)$) weak decays by considering the next-to-leading (NLO) contributions within QCDF framework. With the updated values of $B_{(s)}\to D_{(s)}^{(*)}$ transition form factors obtained from a covariant light-front quark model, branching ratios are estimated in two scenarios for scalar mesons. It is found that the branching ratios for ${\overline B}^0{\to}D^{+}{a_0^-}$ and ${\overline B}_{s}^0{\to}D_{s}^{+}{a_0^-}$ decays can reach up to the order of ${\cal O}(10^{-4})$ in scenario-2 by assuming that the scalar mesons are lowest-lying p-wave states, which deserve high-priority experimental searches and may be observed in the ongoing LHCb and SuperKEKB experiments.

hep-ph↗
arXiv2026

Light-cone Distribution Amplitudes of Vector Mesons within the Self-Consistent Light-front Quark Model

In this paper, we investigate the twist-2 and twist-3 light-cone distribution amplitudes (LCDAs) of vector mesons within the self-consistent light-front quark model, and the $n$th Gegenbauer moment $a_n(μ)$, $ξ$-moment $\langle ξ^n\rangle^{\parallel(\perp)}$ and transverse moment $\langle \mathbf{k}^n_\perp\rangle^{\parallel(\perp)}$ are also carried out. Adopting the parameter set fixed by confinements from the mesonic decay constants, we perform the numerical analysis and the results reveal several key insights: (i) The flavor symmetry breaking effects are more pronounced in the twist-3 LCDAs of vector mesons, which leads to the establishment of $a_1^\perp>a_1^\parallel$ and $\langle ξ^n\rangle^\perp>\langle ξ^n\rangle^\parallel$. This is consistent with previous findings in research for the LCDAs of pseudoscalar mesons. (ii) For vector mesons, the twist dependence decreases in the heavy quark limit which lead to $ϕ_{2}^\parallel(x)\simeqϕ_{3}^\perp(x)$. For pseudoscalar and vector mesons composed of the same quark constituents, their LCDAs with the same twist exhibit similarity and gradually converge as the increasing of quark mass, i.e., $ϕ_{2}^A(x)\simeqϕ_{2}^\parallel(x)$ and $ϕ_{3}^P(x)\simeqϕ_{3}^\perp(x)$ in $m_q\rightarrow\infty$. In the heavy-quark limit within the self-consistent LFQM framework, we find an approximate correlated spin-twist independence pattern, with $ϕ_2^A(x) \simeq ϕ_2^{\parallel}(x) \approxϕ_3^P(x) \simeq ϕ_3^{\perp}(x)$, resulting from the suppression of twist dependence and the approximate spin independence between pseudoscalar and vector mesons.

hep-ph↗
arXiv2026

InterQ: Communication-Aware Scheduling Across Modular QPUs with Classical and Quantum Links

As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect multiple QPUs. Different platforms realize this modularity through different communication models: superconducting systems rely on real-time classical links and dynamic-circuit coordination, trapped-ion systems use photonic interconnects for remote entanglement, and neutral-atom systems provide strong intra-core connectivity with proposed optical links for inter-core communication. This heterogeneity makes communication-aware scheduling essential for shared modular quantum cloud environments. We present InterQ, a communication-aware scheduler for modular QPU architectures with heterogeneous communication models. InterQ jointly considers qubit capacity, placement, parallel execution, and communication-driven dependencies across distributed subcircuits, while enabling adaptive circuit cutting to reduce makespan while balancing fidelity and communication overhead. The framework distinguishes classical-link execution, where measurement and feedforward impose synchronization constraints, from quantum-link execution, where entanglement distribution and state transfer determine coordination cost. Using a unified simulation framework to compare superconducting, trapped-ion, and neutral-atom modular systems, InterQ shows how communication models and scheduler-driven cutting decisions affect throughput, latency, and fidelity. Across evaluated workloads, InterQ exposes an architecture-dependent tradeoff: neutral-atom modular QPUs achieve the highest fidelity, superconducting systems minimize runtime, and trapped-ion systems provide a balanced intermediate profile across fidelity and makespan.

quant-ph↗
arXiv2026

QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting

The quantum computing community is increasingly positioning quantum processors as accelerators within classical HPC workflows, analogous to GPUs and TPUs. However, many real-world applications require scaling to hundreds or thousands of physical qubits to realize logical qubits via error correction. To reach these scales, hardware vendors employing diverse technologies -- such as trapped ions, photonics, neutral atoms, and superconducting circuits -- are moving beyond single, monolithic QPUs toward modular architectures connected via interconnects. For example, IonQ has proposed photonic links for scaling, while IBM has demonstrated a modular QPU architecture by classically linking two 127-qubit devices. Using dynamic circuits, Bell-pair-based teleportation, and circuit cutting, they have shown how to execute a large quantum circuit that cannot fit on a single QPU. As interest in quantum computing grows, cloud providers must ensure fair and efficient resource allocation for multiple users sharing such modular systems. Classical interconnection of QPUs introduces new scheduling challenges, particularly when multiple jobs execute in parallel. In this work, we develop a multi-programmable scheduler for modular quantum systems that jointly considers qubit mapping, parallel circuit execution, measurement synchronization across subcircuits, and teleportation operations between QPUs using dynamic circuits.

quant-ph↗
arXiv2026

Efficient Transpilation of OpenQASM 3.0 Dynamic Circuits to CUDA-Q: Performance and Expressiveness Advantages

Dynamic quantum circuits with mid-circuit measurement and classical feedforward are essential for near-term algorithms such as error mitigation, adaptive phase estimation, and Variational Quantum Eigensolvers (VQE), yet transpiling these programs across frameworks remains challenging due to inconsistent support for control flow and measurement semantics. We present a transpilation pipeline that converts OpenQASM 3.0 programs with classical control structures (conditionals and bounded loops) into optimized CUDA-Q C++ kernels, leveraging CUDA-Q's native mid-circuit measurement and host-language control flow to translate dynamic patterns without static circuit expansion. Our open-source framework is validated on comprehensive test suites derived from IBM Quantum's classical feedforward guide, including conditional reset, if-else branching, multi-bit predicates, and sequential feedforward, and on VQE-style parameterized circuits with runtime parameter optimization. Experiments show that the resulting CUDA-Q kernels reduce circuit depth by avoiding branch duplication, improve execution efficiency via low-latency classical feedback, and enhance code readability by directly mapping OpenQASM 3.0 control structures to C++ control flow, thereby bridging OpenQASM 3.0's portable circuit specification with CUDA-Q's performance-oriented execution model for NISQ-era applications requiring dynamic circuit capabilities.

quant-ph↗
arXiv2026

Enhanced electron injection for efficient proton acceleration and neutron production in femtosecond laser-driven nano-structured targets

Micro- or nano-structured targets are advantageous in enhancing and manipulating laser-proton acceleration, due to the increased absorption of laser energy and onset of direct laser acceleration for high-energy electrons. Here, we experimentally demonstrate that nano-wire-array printed on a flat substrate is an efficient nano-injector of relativistic electrons that leads to a significant boost of laser-driven proton acceleration and neutron production beyond normal geometry. By employing an ultra-intense (2*1021 W/cm2) femtosecond laser pulse to irradiate nano-wire-array targets, protons with cut-off energies of 62.8 MeV are generated, and notably, the energy conversion efficiency from laser to protons reaches up to 9% - 3.5 times higher than that of flat foils. After bombarding a beryllium converter, 1.1*1010 neutrons are produced. Full 3D particle-in-cell simulations have reproduced experimental results and reveal interference mechanisms between the nano-wires and substrate, leading to continuous pumping of electrons from the substrate and standing-wave enhanced re-injection from the wire tip. This efficient injection finally results in the large sheath field and thus high yield of energetic protons and neutrons. Dependence on the wire length and scaling with laser amplitude are further discussed. These results suggest that 3D-printed structures are promising in developing compact laser-driven high-flux proton and neutron sources for numerous applications.

physics.plasm-ph↗