arXiv subjects

Ying Li

Publications and source records attributed to Ying Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

ES-VP : Energy-Shaped Dynamic Visual Prompting for Efficient Model Adaptation

Visual prompting (VP) has emerged as a parameter-efficient method for adapting pre-trained models to downstream tasks. However, existing approaches encounter a trade-off between flexibility and efficiency. Some methods apply a fixed prompt to all images, ignoring individual image characteristics, while others introduce auxiliary networks to generate diverse prompts. Although the latter can improve performance, it also significantly increases parameter usage and the potential for overfitting to specific datasets. Furthermore, the auxiliary networks, combined with inherent biases in pre-trained models, limit scalability and generalization. In this paper, we propose Energy-Shaped Visual Prompting (ES-VP), a novel approach that generates image-specific prompts using low-rank initialization and energy-guided dynamic adaptation, achieving superior performance with fewer parameters compared to single-prompt methods. ES-VP directly utilizes the pre-trained model for adaptive prompt generation, ensuring both parameter efficiency and improved generalization. Extensive experiments conducted on five architectures across fifteen datasets demonstrate that ES-VP consistently outperforms current state-of-the-art (SOTA) single and diverse VP methods. For instance, using the CLIP architecture across four datasets, ES-VP outperforms the SOTA method DAM-VP by an average of 2.6\% in accuracy while utilizing 590$\times$ fewer VP parameters, thereby establishing a new benchmark for efficient and generalizable model adaptation.

cs.CV↗

arXiv2026

Quadrupole White-light Sources in an X1.2 Flare Observed by ASO-S/LST/WST and SDO/HMI

We present observations of an X1.2 white-light flare on 2023 January 6, which exhibits a rare quadrupolar white-light source configuration. This event was observed by the White-light Solar Telescope (WST; 3600 Å) aboard the Advanced Space-based Solar Observatory and the Helioseismic and Magnetic Imager (HMI; 6173 Å) aboard the Solar Dynamics Observatory. Four flare-related footpoints (labeled as FP1--FP4) were nearly simultaneously identified in both WST 3600 Å and HMI 6173 Å continua, associated with a quadrupolar magnetic configuration and a failed filament eruption. The inner sources of FP1 and FP2 showed a similar enhancement of $\sim$65%/10% in the WST/HMI continuum, while the outer sources of FP3 and FP4 exhibited weaker responses. The inner footpoints had earlier responses in UV and EUV bands and were spatially coincident with the hard X-ray (HXR) footpoint sources. The two southern footpoints (FP2 and FP4) showed stronger HXR and white-light emissions than their northern counterparts (FP1 and FP3), with FP4 uniquely exhibiting a distinct HXR emission above 60 keV, in contrast to the absence of such an emission at FP3. Notably, faint WST 3600 Å enhancements at FP4 were observed during the gradual phase, temporally consistent with the fallback of filament material. This X1.2 flare presents a novel quadrupolar white-light structure, enriching our understanding of the generation and evolution of white-light flares.

astro-ph.SR↗

arXiv2026

When Stories Evolve: Benchmarking LLM Storytelling Across Agent Architectures in Open-Ended World Simulations

Large language models can write fluent stories, but open-ended storytelling requires more than local fluency. In evolving world simulations and AI-native games, models must preserve facts, relationships, causal dependencies, and character states as the world changes. We introduce WSE-bench, a process benchmark that separately evaluates sustained generation, canonical coherence, and meaningful development in dynamic LLM storytelling. Generation Coverage records the proportion of planned narrative steps produced; Consistency tracks when canon breaks; and Richness measures how meaningfully branching, player-shaped trajectories develop. Across frontier models, Consistency and Richness do not form a smooth trade-off: their empirical Pareto frontier is non-concave, with several non-dominated intermediate configurations that no positive linear weighting can select. Added structure can enrich trajectories, but it does not uniformly improve coherence and may shorten them. Model scale chiefly improves sustained generation, without producing reliable gains in canonical coherence or meaningful development. These results show that sustained generation, canonical coherence, and meaningful development are distinct and sometimes competing capacities. WSE-bench makes those dynamics visible by extending narrative evaluation from finished stories to the processes that create them.

cs.CL↗

arXiv2026

CEON: Circular Economy Ontology Network

Increasing the circularity of resource use in our society has been recognized as a path to sustainability, i.e., transitioning into a more circular economy. There are many different circular strategies to do so, such as reusing products and components, refurbishing and remanufacturing used products, or recycling left-over or used materials. To enable these strategies, it is necessary to share information at the infrastructure level and to communicate between industry sectors along the product life cycle. Enabling semantic interoperability in this information sharing and communication is therefore a key to increasing circularity. However, knowledge representation for the circular economy (CE) domain, which involves many relevant industry sectors related to product life cycles, remains challenging. To bridge this gap, we developed the Circular Economy Ontology Network (CEON) within the Onto-DESIDE project. This ontology network aims to fill gaps in CE by defining cross-sectorial concepts and to enable semantics-aware data documentation. We demonstrate CEON through cross-industry data documentation scenarios spanning construction, electronics, and textile sectors.

cs.AI↗

arXiv2026

Large CP violation in $Λ_b\rightarrow ΛD$ decays and extraction of the Cabibbo-Kobayashi-Maskawa angle $γ$

Motivated by the first observation of CP violation in $b$-baryon decays, the search for baryonic decays exhibiting large CP violation will be a primary focus in the coming years. We propose that significant CP-violating effects exist in the decay $Λ_b \to ΛD$, where $D$ denotes a CP eigenstate of the $D^0 - \bar{D}^0$ system. The predicted CP asymmetries for both the CP-even and CP-odd modes can reach magnitudes as large as $50\%$, making these decays promising targets for measurement at the LHCb experiment. Additionally, we predict for the first time several nonzero CP-violating observables associated with angular distribution parameters, providing valuable complementary information in the search for CP violation in baryon decays. Furthermore, we propose a novel strategy to extract the CKM angle $γ$ by combining data on angular distribution parameters and decay rates from the relevant channels. We emphasize that $Λ_b \rightarrow ΛD$ decays are among the most promising candidates for determining $γ$ in the baryon sector. Our findings may offer new insights for future theoretical and experimental investigations.

hep-ph↗

arXiv2026

Theoretical analysis towards accurate optomechanical detection of quantum gravity effects

Optomechanical systems offer a promising platform for observing dynamical signatures of quantum gravity through precision measurements of quantum harmonic oscillator dynamics. However, most existing analyses consider only the linear radiation-pressure interaction while neglecting higher-order optomechanical couplings and laser phase noise. These neglected contributions can be comparable in magnitude to the predicted quantum-gravity corrections and may therefore introduce spurious signals or mask the genuine physical effect. Here we reanalyze two experimentally realized platforms, a Fabry-Perot optomechanical system and a membrane-in-the-middle optomechanical system, by incorporating the complete nonlinear dynamics and realistic laser phase noise. Using measured device parameters, we derive revised protocols for generalized uncertainty principle tests and establish practical sensitivity bounds. Our results demonstrate that previous idealized estimates significantly overestimate the achievable resolution, underscoring the necessity of including higher-order interactions and implementing effective laser phase noise suppression in realistic assessments of optomechanical quantum gravity tests.

quant-ph↗

arXiv2026

Existence, non-degeneracy and local uniqueness of multi-peak solutions to the fractional Schrödinger equation with nearly critical exponent in $\mathbb{R}^N$

In this paper, we consider the following fractional Schrödinger equation \begin{equation*} \left\{ \begin{array}{lcl} (-Δ)^{s}u+V(x)u=u^{{p_s}-ε}\ \ \ &\hbox{in}\ \mathbb{R}^N,\\ u>0\ \ \ &\hbox{in}\ \mathbb{R}^N, \end{array} \right. \end{equation*} where $0 0$, $p_s=(N+2s)/(N-2s)$, $N>4s$ and $V(x)\in C^1(\mathbb{R}^N)\cap L^\infty (\mathbb{R}^N)$ is non-negative. We first use the Lyapunov-Schmidt reduction method to construct multi-peak solutions to the above equation provided that $V(x)$ possesses $k$ stable critical points. Then we prove the non-degeneracy and local uniqueness of the multi-peak solutions, for $\frac{1}{2}<s<1$, $N\geq 6s$, via the blow-up argument based on various local Pohozaev identities. Due to the nonlocal property of the fractional Laplacian, we need to make delicate analysis of the approximate solutions and establish the local Pohozaev identities for the corresponding harmonic extension instead of $u$. This approach not only requires to develop refined estimates for several integrals in the local Pohozaev identities, but also to apply Pohozaev identities through a markedly different way.

math.AP↗

arXiv2026

An Empirical Study of openPangu Quantization on Ascend NPUs

openPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs has not been systematically characterized. This paper conducts a controlled empirical study of openPangu 1B and 7B models on Huawei Ascend 910B1 NPUs. We evaluate representative weight-only and weight-activation post-training quantization methods, including RTN, GPTQ, AWQ, SmoothQuant, GPTAQ, BiLLM, and SliM-LLM, under a unified calibration and evaluation protocol. Across 18 evaluation tasks, we find that 8-bit weight-only quantization is effectively lossless for both models, while 4-bit quantization remains practical for the 7B model but is visibly more harmful for the 1B model on reasoning, math, and code tasks. Ultra-low precision remains challenging: most 2-bit and binary settings collapse to near-random behavior, and W4A4 SmoothQuant produces non-finite perplexity in our evaluation. These results provide an NPU-oriented accuracy map for selecting openPangu quantization settings and highlight the persistent difficulty of extreme low-bit compression.

cs.LG↗

arXiv2026

Classical Noise Inversion for Error-Propagatable Circuits with Minimal Overhead under General Gate-Dependent Noise

Quantum error mitigation (QEM) is critical for extracting reliable computations from noisy quantum processors, proving itself essential not only in the near term but also as a valuable supplement to fully fault-tolerant systems in the future. Despite the necessity, practical QEM deployment still confronts challenges, including the excessive cost of sampling quantum circuits and reliance on unrealistic assumptions such as gate-independent noise. In this paper, we propose Classical Noise Inversion (CNI), which shifts the QEM from sampling diverse quantum circuits to repeated single-circuit measurement, a drastic cost reduction given that the former incurs far heavier time overhead than the latter on realistic quantum hardware. We target general noise models and establish critical conditions for their classical tractability, under which CNI remains effective. For noise models that violate the condition, we propose partial CNI, which mitigates the classically tractable factor of noise via CNI and mitigates the other part via probabilistic error cancellation tailored to gate-dependent noise. To minimize the sampling overhead, we introduce noise compression, which groups noise components with equivalent effects on measurement outcomes, thereby attaining theoretically optimal error-mitigation overhead. The proposed protocols are particularly efficient for error-propagatable circuits, including adaptive universal quantum computing represented by Clifford+T fault-tolerant circuits, and generalized measurement represented by classical shadows. To demonstrate the practical merits of CNI, we integrate it with thrifty classical shadow, and use analysis and numerical simulations to show its advantages in both efficiency and accuracy over existing approaches.

quant-ph↗

arXiv2026

HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models

Although Multimodal Large Language Models have achieved strong performance across a wide range of vision-language tasks, they still suffer from hallucinations, where model outputs become inconsistent with the visual content, textual context, or commonsense knowledge. Existing studies primarily address this problem through coarse-grained detection. However, these approaches often provide insufficient diagnostic information for understanding hallucination types and supporting downstream hallucination mitigation. To bridge this gap, we propose fine-grained hallucination diagnosis for MLLMs, a new unified task that jointly performs hallucination detection, classification, and interpretable explanation generation. We develop an automated data generation pipeline and construct HalluScope-30K, a large-scale diagnostic dataset covering eight sources and five task categories. Based on this dataset, we design a multi-granular joint reward function and train two diagnosis models, HalluScope-4B and HalluScope-8B, which achieve state-of-the-art performance on both the MHALO benchmark and our fine-grained hallucination classification benchmark. Notably, detection and classification are mutually beneficial under joint optimization. Furthermore, diagnosis-driven feedback experiments show that the fine-grained diagnostic explanations produced by our model effectively guide target models to correct their hallucinations, with full diagnosis substantially outperforming all baselines on both Qwen3-VL-8B-Instruct and LLaVA-1.5-7B. Our code, data, and models are available at https://github.com/wkinglin/HalluScope.

cs.CV↗

arXiv2026

AST: Adaptive, Seamless, and Training-Free Precise Speech Editing

Text-based speech editing aims to modify specific segments while preserving speaker identity and acoustic context. Current approaches generally involve either expensive task-specific training or adapting pre-trained Text-to-Speech (TTS) models. However, both paradigms face challenges: task-specific methods often degrade fidelity in unedited regions, whereas TTS adaptations struggle with a trade-off between editing naturalness and temporal fidelity. To address these issues, we propose AST, an Adaptive, Seamless, and Training-free speech editing framework. Built upon pre-trained TTS, AST leverages Latent Recomposition to stitch preserved source segments with synthesized targets, guaranteeing fidelity in unedited regions. To break the quality-controllability trade-off, we introduce Adaptive Weak Fact Guidance (AWFG), which modulates a mel-space signal to ensure seamless boundary transitions without disrupting the generative manifold. Furthermore, to address evaluation gaps in temporal fidelity, we propose a new benchmark suite: the LibriSpeech-Edit dataset and a novel metric, Word-level Dynamic Time Warping (WDTW). Extensive experiments demonstrate that AST consistently outperforms existing task-specific and fine-tuned speech editing methods across content accuracy, perceptual quality, speaker preservation, and temporal fidelity. Remarkably, AST achieves state-of-the-art zero-shot speech editing performance without any task-specific training or paired editing data, validating the effectiveness of latent recomposition and AWFG in bridging the quality-controllability trade-off.

cs.SD↗

arXiv2026

BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition

Active feature acquisition (AFA) asks which unobserved feature to measure next for each test instance under a budget. Greedy rules are easy to train but can overlook context features whose value is realized only through later acquisitions, while reinforcement-learning and generative approaches introduce difficult optimization or conditional-density estimation. We introduce \method, a deployable, supervised alternative that learns a separate candidate-conditioned risk-to-go function for every remaining budget. Starting from the one-step terminal classification risk, the functions are fitted backward with Bellman targets; inference greedily minimizes the learned terminal risk using only observed values, the mask, candidate identity, and remaining budget. A controlled non-myopic benchmark shows the expected mechanism: at budgets two and three, \method improves accuracy over its one-step ablation by $4.84\pm2.17$ and $4.39\pm1.10$ percentage points (mean $\pm$ standard error over five seeds). On Fashion-MNIST with 20 candidate pixels, it improves accuracy at every nontrivial reported budget on average, including $10.20\pm0.74$ points at four acquisitions; its mean paired gain across budgets $\{2,4,8,12,16\}$ is $3.50\pm0.37$ points. A three-seed MiniBooNE study is mixed at small budgets but positive at 8 and 16 acquisitions, identifying a current boundary rather than supporting a universal claim. These results establish a reproducible mechanism-level case for direct Bellman risk regression and delimit the experiments still needed for state-of-the-art comparison.

cs.LG↗

arXiv2026

Modeling Unknown Nonlocal PDE Systems via Flow Map Learning

Nonlocal partial differential equations arise in many applications but are often difficult to model and learn because of the presence of nonlocal operators. We present a flow-map learning (FML) framework for modeling unknown nonlocal PDEs directly from solution data. Rather than learning or approximating the underlying nonlocal operators, the proposed approach learns the finite-time evolution operator in either modal or nodal space. Two complementary formulations are developed for spectral and grid-based solution representations. Numerical experiments on one- and two-dimensional fractional diffusion and wave equations demonstrate accurate and stable long-time prediction using only short observation windows. The proposed approach provides an effective data-driven framework for learning unknown nonlocal dynamics without explicit evaluation of nonlocal operators.

cs.LG↗

arXiv2026

Role-Break in Attention Heads: Understanding and Detecting Hallucinations in VLMs

Despite remarkable progress in vision-language generation, Vision-Language Models (VLMs) remain prone to hallucinations, producing content that is inconsistent with or unsupported by the input image. Existing works largely design detection or mitigation methods around one specific hallucination pattern, such as visual-textual imbalance, but real VLM hallucinations arise from a mixture of multiple patterns, so signals bound to a single pattern struggle to remain stable across models and tasks. Under a unified head-level view, we find that hallucination-induced changes manifest as localized deviations from each head's faithful contextual behavior, a phenomenon we term Role-Break. Detailed analysis reveals that these deviations are systematically organized across attention heads, contextual sources, and deviation directions, and that the resulting signal is linearly readable once head identity is preserved. Based on these findings, we build a lightweight linear detector on top of Role-Break that requires no fine-tuning of the VLM, whose feature dimension stays below 5,000 and reaches an average AUROC of 93.23 across six VLMs and four benchmarks. A small-scale intervention experiment further shows that the detected tokens can be directly acted upon in the discriminative setting.

cs.CV↗

arXiv2026

Qualitative properties of eigenfunctions in domains with small holes

In this paper we study qualitative properties of the eigenvalues and eigenfunctions of $-Δ$ with Dirichlet boundary condition in a smooth bounded domain $Ω$ with a small circular hole. In the literature, this is known as a "singular perturbation", in contrast with the "regular perturbation" case. Denoting by $Ω_ε:=Ω\setminus B(P,ε)$ where $B(P,ε)$ is the ball centered at $P$ and radius $ε$, for $P\inΩ$ and $ε$ small enough we investigate 1) quantitative estimates for the eigenfunctions of $-Δ$ in $Ω_ε$; 2) the simplicity of the eigenvalues of $-Δ$ in $Ω_ε$; 3) the behavior of nodal sets of the eigenfunctions of $-Δ$ in $Ω_ε$. A key ingredient in our analysis consists of pointwise estimates on the so-called $u$-capacitary potential firstly introduced in \cite{afhl}.

math.AP↗

arXiv2026

A Taxonomy of Performance Metrics for the Distributed Computing Continuum

Performance evaluation is essential for understanding, comparing, and improving computing systems, including Distributed Computing Continuum Systems (DCCS). In recent years, computational requirements have changed substantially with the growth of artificial intelligence and large-scale data-driven applications. These application tasks are increasingly distributed between resource-intensive data centers and resource-constrained edge environments. In this context, novel computing continuum architectures and algorithms are emerging, creating a need for transparent and consistent performance evaluation. However, existing evaluation practices often focus on isolated dimensions, such as computation, networking, energy efficiency, or application-level quality, and therefore provide only a partial view of cross-layer DCCS behavior. This paper presents a structured taxonomy of performance metrics for DCCS. The taxonomy organizes metrics into computing-level, network-level, and application/user-level categories, while also highlighting emerging dimensions such as sustainability, observability, adaptability, data locality, migration awareness, and continuum fragmentation. Further, we provide mathematical formulations and discuss their relevance to heterogeneous and dynamic continuum environments. We also summarize metric acquisition requirements in terms of acquisition scope, acquisition phase, and measurement method. These requirements help clarify whether a metric can be collected from a single node, multiple nodes, or the full system, and whether it is more suitable for operational monitoring or experimental evaluation.

cs.DC↗

arXiv2026

Altermagnetism from a Cu-Fe Lieb Lattice in FeSe/Cuprate Heterostructures

Realizing altermagnetism in high-$T_c$ cuprate-based systems would provide a direct route for studying spin-split electronic bands in the absence of net magnetization and investigate their interplay with unconventional superconductivity. Here, we propose that FeSe/cuprate heterostructures offer such a platform, where a 45$^\circ$ twist of Cu and Fe layers creates an effective CuFe$_2$ Lieb lattice in which Fe magnetic order and Cu-Fe hybridization through the ligands induces altermagnetic $d$-wave spin splitting. A minimal tight-binding model shows that this mechanism is generic. Furthermore, a substrate-induced inequivalence of the two Se sites in FeSe provides a second route in which altermagnetism originates in the Fe layer and is transferred to the cuprate layer by proximity. Density functional theory calculations for FeSe/Bi$_2$Sr$_2$CuO$_6$ heterostructures confirm the viability of both mechanisms and reveal ways to enhance the spin splitting. These results establish superconducting cuprate/transition metal chalcogenide heterostructures as a promising setting for engineering altermagnetism and studying its coupling to unconventional superconductivity.

cond-mat.str-el↗

arXiv2026

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning

Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery. Here, we present MEGA-CL, a foundation graph neural network framework for universal molecular ADMET prediction. MEGA-CL integrates self-supervised contrastive learning with a multi-head external attention mechanism and an enhanced message-passing architecture, enabling simultaneous modeling of local chemical substructures and global inter-graph relationships while mitigating over-smoothing effects commonly observed in deep graph networks. Across 13 benchmark datasets and 21 downstream ADMET tasks, MEGA-CL consistently outperforms state-of-the-art baseline models. In particular, the framework demonstrates robust performance on challenging regression tasks, including clearance (CL) and steady-state volume of distribution (VDss), while maintaining strong generalization ability in independent external validation. Clinically relevant predictive accuracy was achieved, with more than 75% of predictions falling within a 3-fold error range. In an external evaluation on 18 novel compounds derived from recently approved FDA drugs, over 50% of human liver microsome clearance (HLMC) predictions were within a 2-fold error range. To further assess its practical applicability, MEGA-CL was prospectively evaluated on three preclinical drug candidates using in vitro hepatic microsomal metabolism assays and CYP450 inhibition assays guided by model predictions. The predicted HLMC values for all candidates were within 2.5-fold of the experimentally measured values, and 73.3% of CYP450 inhibition endpoints (11/15) were correctly classified. These results demonstrate the potential of MEGA-CL as a generalizable framework for accelerating in silico ADMET evaluation and early-stage drug candidate optimization.

cs.LG↗