arXiv subjects

Xiang Liu

Publications and source records attributed to Xiang Liu.

At least 109 records · Page 6Linked to original sources

arXiv2026

Radiative decays of the $Ω(2012)$ as a hadronic molecule

We present a theoretical investigation of the radiative decay process $Ω(2012) \to γΩ$, where the $Ω(2012)$ resonance with spin-parity $J^P=\frac{3}{2}^-$, is treated as a dynamically generated state from $\bar{K}Ξ(1530)$ and $ηΩ$ in $s$-wave and $\bar{K}Ξ$ in $d$-wave. The radiative decay width of the $Ω(2012)$ is calculated using a triangular loop mechanism, where the $Ω(2012)$ couples to the $\bar{K} Ξ(1530)$ channel. Subsequently, the final state interactions between $Ξ(1530)$ and $\bar{K}$ transition to a photon and $Ω$ through the exchange of a $Ξ$ baryon. Our calculations yield a radiative decay width of $13.2 ^{+4.5}_{-3.9}$ KeV, with uncertainties arising from the model parameters. This result provides valuable insights into the nature of the $Ω(2012)$ resonance and its decay dynamics. It is expected that the calculations presented here could be verified by future experiments, which would open a new door for studying the still elusive nature of the $Ω(2012)$.

hep-ph↗

arXiv2026

Revealing dipion correlations for the observed substructure near the $π^+π^-$ mass threshold in $ψ(3686)\to J/ψπ^+π^-$

Based on the world's largest $ψ(3686)$ data sample, the BESIII Collaboration recently reported a substructure near the $π^+π^-$ mass threshold in the decay $ψ(3686) \to J/ψπ^+π^-$, challenging the established understanding of the dipion invariant mass spectrum. We propose that this substructure arises directly from dipion correlations. Using a chiral unitary approach, we successfully reproduce the observed anomaly, thereby providing strong evidence of dipion correlation in heavy quarkonium decays. This approach also allows us to predict the corresponding dipion correlation function.

hep-ph↗

arXiv2026

SciDataCopilot: An Agentic Data Preparation Framework for AGI-driven Scientific Discovery

The current landscape of AI for Science (AI4S) is predominantly anchored in large-scale textual corpora, where generative AI systems excel at hypothesis generation, literature search, and multi-modal reasoning. However, a critical bottleneck for accelerating closed-loop scientific discovery remains the utilization of raw experimental data. Characterized by extreme heterogeneity, high specificity, and deep domain expertise requirements, raw data possess neither direct semantic alignment with linguistic representations nor structural homogeneity suitable for a unified embedding space. The disconnect prevents the emerging class of Artificial General Intelligence for Science (AGI4S) from effectively interfacing with the physical reality of experimentation. In this work, we extend the text-centric AI-Ready concept to Scientific AI-Ready data paradigm, explicitly formalizing how scientific data is specified, structured, and composed within a computational workflow. To operationalize this idea, we propose SciDataCopilot, an autonomous agentic framework designed to handle data ingestion, scientific intent parsing, and multi-modal integration in a end-to-end manner. By positioning data readiness as a core operational primitive, the framework provides a principled foundation for reusable, transferable systems, enabling the transition toward experiment-driven scientific general intelligence. Extensive evaluations across three heterogeneous scientific domains show that SciDataCopilot improves efficiency, scalability, and consistency over manual pipelines, with up to 30$\times$ speedup in data preparation.

cs.DB↗

arXiv2026

From Literature to Lab: Closed-Loop Advancement of Perovskite Solar Cells via Domain Knowledge Guided LLM

Perovskite solar cells (PSCs) have been considered as a next-generation disruptive photovoltaic technology, yet their advancement is constrained by the complexity of perovskite recipe with high-dimensional material and process design space. Despite the impressive general reasoning of Large Language Models (LLMs), they struggle with two limitations for application in PSCs: an inability to align general semantics with the perovskite domain knowledge, and an inefficiency in navigating high-dimensional perovskite material and recipe design spaces. To address these limitations, we introduce a domain-knowledge-guided framework PVK-LLM, a specialized model to serve as an expert to bridge general semantics with perovskite domain knowledge. By integrating this domain knowledge into a hierarchical Bayesian Optimization workflow, our approach efficiently navigates the high-dimension design space on a solar cell simulator platform. The domain knowledge resolves cold-start problems while dynamically adapting to simulator feedback. Moreover, in an individual wet-lab experiment aimed at maximizing power conversion efficiency (PCE), our framework autonomously proposes a novel synergistic four-component recipe comprising specialized organic passivation recipe (3MTPAI, PDAI2, EDAI2, and PipDI) which has not been reported in existing literature. This AI-designed recipe effectively achieves a champion PCE value of over 26.0 %, approaching world records achieved through extensive expert trial-and-error. Our approach can effectively enable LLM comprehend the domain knowledge, which can efficiently navigate in a high-dimensional, capable to accelerate the advancement in real-world perovskite as well as other material science development.

cond-mat.mtrl-sci↗

arXiv2026

SPWOOD: Sparse Partial Weakly-Supervised Oriented Object Detection

A consistent trend throughout the research of oriented object detection has been the pursuit of maintaining comparable performance with fewer and weaker annotations. This is particularly crucial in the remote sensing domain, where the dense object distribution and a wide variety of categories contribute to prohibitively high costs. Based on the supervision level, existing oriented object detection algorithms can be broadly grouped into fully supervised, semi-supervised, and weakly supervised methods. Within the scope of this work, we further categorize them to include sparsely supervised and partially weakly-supervised methods. To address the challenges of large-scale labeling, we introduce the first Sparse Partial Weakly-Supervised Oriented Object Detection framework, designed to efficiently leverage only a few sparse weakly-labeled data and plenty of unlabeled data. Our framework incorporates three key innovations: (1) We design a Sparse-annotation-Orientation-and-Scale-aware Student (SOS-Student) model to separate unlabeled objects from the background in a sparsely-labeled setting, and learn orientation and scale information from orientation-agnostic or scale-agnostic weak annotations. (2) We construct a novel Multi-level Pseudo-label Filtering strategy that leverages the distribution of model predictions, which is informed by the model's multi-layer predictions. (3) We propose a unique sparse partitioning approach, ensuring equal treatment for each category. Extensive experiments on the DOTA and DIOR datasets show that our framework achieves a significant performance gain over traditional oriented object detection methods mentioned above, offering a highly cost-effective solution. Our code is publicly available at https://github.com/VisionXLab/SPWOOD.

cs.CV↗

arXiv2026

SONIC: Segmented Optimized Nexus for Information Compression in Key-Value Caching

The linear growth of Key-Value (KV) cache remains a bottleneck for multi-turn LLM deployment. Existing KV cache compression methods often fail to account for the structural properties of multi-turn dialogues, relying on heuristic eviction that risks losing critical context. We propose \textbf{SONIC}, a learning-based framework that compresses historical segments into compact and semantically rich \textbf{Nexus} tokens. By integrating dynamic budget training, SONIC allows flexible adaptation to varying memory constraints without retraining. Experiments show that at compression ratios of 80\% and 50\%, SONIC consistently outperforms baselines such as H2O and StreamingLLM on four diverse multi-turn benchmarks. Specifically, on the widely used MTBench101 benchmark, SONIC achieves an average score improvement of 35.55\% over state-of-the-art baselines, validating its effectiveness in sustaining coherent multi-turn dialogues. Furthermore, SONIC enhances deployment efficiency, accelerating the overall inference process by 50.1\% compared to full-context generation.

cs.CL↗

arXiv2026

Validation of a Software-Defined 100-Gb/s RDMA Streaming Architecture for Ultrafast Optoacoustic and Ultrasound Imaging

Optoacoustic (OA) imaging has emerged as a powerful investigation tool, with demonstrated applicability in oncology, neuroscience, and cardiovascular biology. However, its clinical translation is limited with the existing OA systems, which often rely on bulky and expensive acquisition hardware mainly optimized for pulse-echo ultrasound (US) imaging. Despite the fact that OA imaging has different requirements for receive bandwidths and timing synchronization with external laser sources, there is a strong need for unified OA-US imaging platforms, as pulse-echo US remains the standard tool for visualizing soft tissues. To address these challenges, we propose a new data acquisition architecture for ultrafast OA and US imaging that fully covers the requirements for large channel counts, wide bandwidth, and software-defined operation. LtL combines state-of-the-art wideband analog front-ends, a Zynq UltraScale+ MPSoC integrating FPGA fabric with an Application Processing Unit, and a 100 GbE Remote Direct Memory Access (RDMA) backend enabling raw-data streaming at up to 95.6 Gb/s. The architecture avoids local buffers followed by burst transfers, which commonly constrain sustainable frame rate and recording intervals, thus achieving true continuous and sustained streaming of raw data. We validate the core elements of the LtL architecture using a 16-channel demonstration system built from commercial evaluation boards. We further verify the signal chain for up to 256-channel scalability, confirming the wide bandwidth capabilities to support state-of-the-art data transmission speeds.

eess.SY↗

arXiv2026

Three-body resonances of $ααM$ clusters ($M=ϕ$, $J/ψ$, $η_c$) in $^{8}_{M}{\mathrm{Be}}$ nuclei

Motivated by the recently obtained HAL QCD potentials for the $N$-$ϕ$, $N$-$J/ψ$, and $N$-$η_c$ interactions, we investigate the structure of the exotic nuclei $^{8}_ϕ{\text{Be}}$, $^{8}_{J/ψ}{\text{Be}}$, and $^{8}_{η_c}{\text{Be}}$ as $α+α+M$ three-body systems ($M$ denotes the meson). The bound and resonant states are calculated consistently using the Gaussian expansion method, with resonances identified via the complex scaling method. For the $αϕ$ and $α$-charmonium interactions, a folding potential is constructed based on the HAL QCD potentials and fitted to a Woods-Saxon form. We find that the $ϕ$ meson exhibits a strong ``glue-like" effect, binding the $0^+_1$, $2^+_1$, and $4^+_1$ resonant states of $^8$Be into stable states and significantly reducing the $α$-$α$ distance. In contrast, the interactions of $J/ψ$ and $η_c$ with the nucleus are weaker, forming only shallow bound states with the $0^+_1$ state of $^8$Be and even increasing the $α$-$α$ separation. Notably, our analysis predicts weakly bound $α$-$J/ψ$ states in the $^4S_{3/2}$ and $^2S_{1/2}$ channels, a result not reported in prior studies, which suggests that $^{8}_{J/ψ}{\text{Be}}$ may not be a Borromean nucleus. The sensitivity of the $^{8}_{M}{\mathrm{Be}}(4^+_1)$ state-transitioning from bound to resonant depending on the $α$-particle radius-highlights the subtle dynamics at play. These results provide a systematic theoretical comparison of how different vector mesons modify nuclear clustering, offering critical predictions for future experimental searches of such exotic hadron-nucleus systems.

hep-ph↗

arXiv2025

Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression

The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms has created a pressing need for standardized and comprehensive evaluation tools, especially for compression task. Existing benchmarks often lack the specific metrics necessary to holistically assess the unique characteristics of different methods, such as rendering speed, rate distortion trade-offs memory efficiency, and geometric accuracy. To address this gap, we introduce Splatwizard, a unified benchmark toolkit designed specifically for benchmarking 3DGS compression models. Splatwizard provides an easy-to-use framework to implement new 3DGS compression model and utilize state-of-the-art techniques proposed by previous work. Besides, an integrated pipeline that automates the calculation of key performance indicators, including image-based quality metrics, chamfer distance of reconstruct mesh, rendering frame rates, and computational resource consumption is included in the framework as well. Code is available at https://github.com/splatwizard/splatwizard

cs.CV↗

arXiv2025

Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets

In the intensive care setting, sepsis continues to be a major contributor to patient illness and death; however, its timely detection is hindered by the complex, sparse, and heterogeneous nature of electronic health record (EHR) data. We propose Triplet-GCN, a single-branch graph convolutional model that represents each encounter as patient-feature-value triplets, constructs a bipartite EHR graph, and learns patient embeddings via a Graph Convolutional Network (GCN) followed by a lightweight multilayer perceptron (MLP). The pipeline applies type-specific preprocessing -- median imputation and standardization for numeric variables, effect coding for binary features, and mode imputation with low-dimensional embeddings for rare categorical attributes -- and initializes patient nodes with summary statistics, while retaining measurement values on edges to preserve "who measured what and by how much". In a retrospective, multi-center Chinese cohort (N = 648; 70/30 train-test split) drawn from three tertiary hospitals, Triplet-GCN consistently outperforms strong tabular baselines (KNN, SVM, XGBoost, Random Forest) across discrimination and balanced error metrics, yielding a more favorable sensitivity-specificity trade-off and improved overall utility for early warning. These findings indicate that encoding EHR as triplets and propagating information over a patient-feature graph produce more informative patient representations than feature-independent models, offering a simple, end-to-end blueprint for deployable sepsis risk stratification.

cs.LG↗

arXiv2025

Jet reorientation revealed by intermittent jet activity in radio galaxy 0954+556

Intermittent jet activity of AGNs is a common phenomenon, whereas significant jet reorientation during episodic jet activity in relatively young radio galaxies are rarely reported. The quasar 0954+556 at redshift of 0.903 is an intriguing source exhibiting an unusual radio jet structure with significantly different jet directions at kpc and pc scales. At kpc scales, images from the VLA exhibit a bright core, a linear jet extending 24 kpc to the northwest, and a discrete jet component 16 kpc to the northeast. At pc scales, images from the VLBA show a two-component structure with a projected separation of 360 pc in the north-south direction. The peculiar structure of 0954+556 might result from jet reorientation. Here, our aim was to investigate the possible mechanism via multiscale and multifrequency deep radio images. We performed VLA and VLBA observations of 0954+556. Together with some existing data in the NRAO data archive, we made multiple VLA images at 1.4-22 GHz and VLBA images at 1.7-43 GHz for various image analyses of the jet structure. We identified the location of the radio core at pc scales, detected the faint counter-jets at both pc and kpc scales for the first time, and revealed a diffuse emission region connecting pc and kpc scale forward jets. Our spectral index distribution and spectral aging analysis indicate that 0954+556 might undergo at least two episodes of jet activity during the current AGN phase. Moreover, pc scale polarization maps display a well-resolved spine-sheath polarization structure. It seems that the jet direction of 0954+556 changed significantly during intermittent jet activity. This may explain the different jet orientations and spectral ages observed from kpc to pc scales. The research provides a strong case that AGN jet direction might change rapidly on timescales of one million years.

astro-ph.HE↗

arXiv2025

Unified coupled-channel description for the five near-threshold structures $ψ(3770)$, $G(3900)$, $R(3760)$, $R(3780)$ and $R(3810)$ from $e^+e^-$ annihilation

The recent observation of multiple near-threshold structures in $e^+e^-$ annihilation-including $ψ(3770)$, $G(3900)$, $R(3760)$, $R(3780)$, and $R(3810)$-reveals limitations in existing models of charmonium and exotic hadrons. In this paper, we propose a unified coupled-channel description that simultaneously incorporates all five near-threshold structures using parameters constrained by hadron spectroscopy. Our model accurately reproduces the line shapes in both $D\bar{D}$ and nonopen-charm hadron (nOCH) channels, resolves the asymmetric profile of $ψ(3770)$, and produces the $G(3900)$ bump. We identify the $R(3780)$ as the dominant manifestation of $ψ(3770)$, attribute $R(3760)$ to $D\bar{D} \to \text{nOCH}$ rescattering and suggest that $R(3810)$ arises from coupling between the $ψ(1D)$ state and the $h_cππ$ channel. The significant non-$D\bar{D}$ decay of $ψ(3770)$ is explained by its near-threshold position. This analysis provides a coherent, unquenched framework centered on a charmonium $ψ(1D)$ core for near-threshold phenomena and offers a predictive approach extendable to bottomonium systems and future data from Belle II.

hep-ph↗

arXiv2025

Second-Order Optimality Conditions for Nonsmooth Constrained Optimization with Applications to Bilevel Programming

Second-order optimality conditions are essential for nonsmooth optimization, where both the objective and constraint functions are Lipschitz continuous and second-order directionally differentiable. This paper provides no-gap second-order necessary and sufficient optimality conditions for such problems without requiring convexity assumptions on the constraint set. We introduce the concept of second-order gph-regularity for constraint functions, which ensures the outer second-order regularity of the feasible region and enables the formulation of comprehensive optimality conditions through the parabolic curve approach. An important application of our results is bilevel optimization, where we derive second-order necessary and sufficient optimality conditions for bi-local optimal solutions, which are based on the local solutions of the lower-level problem. By leveraging the Mangasarian-Fromovitz constraint qualification (MFCQ), strong second-order sufficient condition (SSOSC) and constant rank constraint qualification (CRCQ) of lower-level problem, these second-order conditions are derived without requiring the uniqueness of the lower-level multipliers. In addition, if the linear independence constraint qualification (LICQ) holds, these conditions are expressed solely in terms of the second-order derivatives of the functions defining the bilevel problem, without relying on the second-order information from the solution mapping, which would introduce implicit complexities.

math.OC↗

arXiv2025

Beyond Single Embeddings: Capturing Diverse Targets with Multi-Query Retrieval

Most text retrievers generate \emph{one} query vector to retrieve relevant documents. Yet, the conditional distribution of relevant documents for the query may be multimodal, e.g., representing different interpretations of the query. We first quantify the limitations of existing retrievers. All retrievers we evaluate struggle more as the distance between target document embeddings grows. To address this limitation, we develop a new retriever architecture, \emph{A}utoregressive \emph{M}ulti-\emph{E}mbedding \emph{R}etriever (AMER). Our model autoregressively generates multiple query vectors, and all the predicted query vectors are used to retrieve documents from the corpus. We show that on the synthetic vectorized data, the proposed method could capture multiple target distributions perfectly, showing 4x better performance than single embedding model. We also fine-tune our model on real-world multi-answer retrieval datasets and evaluate in-domain. AMER presents 4 and 21\% relative gains over single-embedding baselines on two datasets we evaluate on. Furthermore, we consistently observe larger gains on the subset of dataset where the embeddings of the target documents are less similar to each other. We demonstrate the potential of using a multi-query vector retriever and open up a new direction for future work.

cs.CL↗

arXiv2025

Basis light-front quantization approach to $Λ$ and $Λ_c$ and their isospin triplet baryons

We obtain the masses, the electromagnetic properties, and the parton distribution functions (PDFs) of $Λ$, $Λ_c$, and their isospin triplet baryons, i.e, $Σ^0$, $Σ^+$, $Σ^-$ and $Σ_c^0$, $Σ_c^+$, $Σ_c^{++}$ from a light-front effective Hamiltonian in the leading Fock sector in the basis light-front quantization framework. The light-front wave functions of these baryons are given by the eigenstates of the effective Hamiltonian consisting of a three-dimensional confinement potential and a one-gluon exchange interaction with fixed coupling. The masses of these baryons in our approach are in the experimental range while isospin-dependent mass differences are too small. Meanwhile, the electromagnetic properties are in agreement with the available experimental data, the lattice QCD simulations, and the other theoretical calculations. We also present the gluon and the sea quark PDFs, which we generate dynamically from the QCD evolution of the valence quark distributions.

hep-ph↗

arXiv2025

Decoding the role of $ρ$ mesonic states for elucidating the $e^+e^-\to a_2(1320)π$ data and other reactions

Recently, the BESIII Collaboration observed a $ρ$-like structure $Y(2044)$ in $e^+e^-\to a_2(1320)π$, suggesting that $Y(2044)$ may be a candidate of vector meson $ρ(2D)$ by comparing resonance parameters. However, the theoretical prediction for the combined branching ratio $Γ_{e^+e^-}\mathcal{B}_{a_2(1320)π}$ for the pure $ρ(2D)$ state is about two orders of magnitude smaller than the experimental value. To resolve this discrepancy and decipher the nature of $Y(2044)$, this work propose an $S$-$D$ mixing scheme to reanalyze the cross section of $e^+e^-\to a_2(1320)π$, and find that the aforementioned branching ratio discrepancy can be resolved. Our results show that the $Y(2044)$ structure can be reproduced by introducing four theoretically predicted $S$-$D$ mixing $ρ$ meson states $ρ_{3S-2D}^{\prime}$, $ρ_{3S-2D}^{\prime\prime}$, $ρ_{4S-3D}^{\prime}$, and $ρ_{4S-3D}^{\prime\prime}$ as intermediate resonances, in which dominant contribution arises from $ρ_{3S-2D}^{\prime\prime}$ and their inference effect is also significant. Furthermore, we reanalyzed five additional isospin vector processes $e^+e^-\to ωπ^0$, $e^+e^-\to f_1(1285)π^+π^-$, $e^+e^-\to π^+π^-$, $e^+e^-\to ρη$, and $e^+e^-\to η^{\prime} π^+π^-$ based on the same $S$-$D$ mixing framework, and simultaneously reproduced their experimental cross section data. This work provides a unified framework to elucidate all observed $ρ$-like structures near 2 GeV in the $e^+e^-$ annihilation processes, and suggests that the $S$-$D$ mixing effect may be crucial for understanding the mass spectrum and decay behaviors of the higher $ρ$ meson states.

hep-ph↗

arXiv2025

Is the $3S$-$2D$ mixing strong for the charmonia $ψ(4040)$ and $ψ(4160)$?

In this work, we revisit the $3S$-$2D$ mixing scheme for the charmonia $ψ(4040)$ and $ψ(4160)$. We introduce a coupled-channel mechanism-distinct from the tensor-force contribution in potential models, which alone is insufficient to induce significant mixing-to describe the mixing between these states. Our analysis yields mixing angles of $θ_1=7^\circ$ and $θ_2=10^\circ$, inconsistent with the larger angle inferred from experimental data, such as the dilectronic widths of the $ψ(4040)$ and $ψ(4160)$. We discuss possible origins of this discrepancy and emphasize the need for future experiments to resolve it. Precise measurements of the resonance parameters and dilectronic decay widths, via both inclusive and exclusive processes, will be crucial in clarifying this issue.

hep-ph↗

arXiv2025

LyTimeT: Towards Robust and Interpretable State-Variable Discovery

Extracting the true dynamical variables of a system from high-dimensional video is challenging due to distracting visual factors such as background motion, occlusions, and texture changes. We propose LyTimeT, a two-phase framework for interpretable variable extraction that learns robust and stable latent representations of dynamical systems. In Phase 1, LyTimeT employs a spatio-temporal TimeSformer-based autoencoder that uses global attention to focus on dynamically relevant regions while suppressing nuisance variation, enabling distraction-robust latent state learning and accurate long-horizon video prediction. In Phase 2, we probe the learned latent space, select the most physically meaningful dimensions using linear correlation analysis, and refine the transition dynamics with a Lyapunov-based stability regularizer to enforce contraction and reduce error accumulation during roll-outs. Experiments on five synthetic benchmarks and four real-world dynamical systems, including chaotic phenomena, show that LyTimeT achieves mutual information and intrinsic dimension estimates closest to ground truth, remains invariant under background perturbations, and delivers the lowest analytical mean squared error among CNN-based (TIDE) and transformer-only baselines. Our results demonstrate that combining spatio-temporal attention with stability constraints yields predictive models that are not only accurate but also physically interpretable.

cs.CV↗