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Gang Li

Publications and source records attributed to Gang Li.

At least 109 records · Page 6Linked to original sources
arXiv2026

Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection

Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Unsupervised Anomaly Detection (UAD). However, existing UCAD approaches that rely solely on visual information are insufficient to capture the manifold of normality in complex scenes, thereby impeding further gains in anomaly detection accuracy. To overcome this limitation, we propose an unsupervised continual anomaly detection framework grounded in multimodal prompting. Specifically, we introduce a Continual Multimodal Prompt Memory Bank (CMPMB) that progressively distills and retains prototypical normal patterns from both visual and textual domains across consecutive tasks, yielding a richer representation of normality. Furthermore, we devise a Defect-Semantic-Guided Adaptive Fusion Mechanism (DSG-AFM) that integrates an Adaptive Normalization Module (ANM) with a Dynamic Fusion Strategy (DFS) to jointly enhance detection accuracy and adversarial robustness. Benchmark experiments on MVTec AD and VisA datasets show that our approach achieves state-of-the-art (SOTA) performance on image-level AUROC and pixel-level AUPR metrics.

cs.CV↗
arXiv2026

Site-selective renormalization and competing magnetic instabilities in paramagnet Y$_{3}$Cu$_{2}$Sb$_{3}$O$_{14}$

Quantum spin liquids (QSLs) are exotic phases of matter characterized by long-range entanglement and the absence of magnetic order even at zero temperature. Here, we present a comprehensive theoretical study of the frustrated magnet Y$_3$Cu$_2$Sb$_3$O$_{14}$ to elucidate its electronic and magnetic properties. We uncover completely opposite crystal-field splittings of the two inequivalent Cu sites owing to their fundamentally distinct oxygen coordination - trigonal distorted octahedral CuO$_6$ and axially compressed CuO$_8$. This inversion places the unpaired hole in the $d_{z^2}$ orbital at the Cu-2 site, while Cu-1 maintains conventional $d_{x^2-y^2}/d_{xy}$ character, which results in a selective band-renormalization of orbitals from the two Cu ions. We further find multiple magnetic instabilities competing with nearly equal strength in this system: the spin susceptibility lacks dominant peaks, and the leading eigenvalues approach unity simultaneously across all wavevectors with increasing interactions. This competitive interplay, originating from the distinct local environments and geometric frustration on the triangular lattice, agrees well with the absence of long-range magnetic order in experiment. Our results support Y$_3$Cu$_2$Sb$_3$O$_{14}$ as a promising QSL candidate where the unique combination of disparate crystal-field environments, strong correlations, and competing exchange interactions conspire to stabilize an exotic quantum ground state.

cond-mat.str-el↗
arXiv2026

Observation of Resonance of Kagome Flat Band Doublet

The interplay between local and itinerant electrons underpins many correlated and topological quantum states. Kagome lattices provide an ideal platform by hosting both flat (localized states) and dispersive bands (itinerant states), yet direct spectroscopic evidence of their dynamical coupling has remained elusive. Here we report the long-sought flat band resonance in the quasi-two-dimensional kagome bilayer material CsCr6Sb6. Using angle-resolved photoemission spectroscopy, transport measurements, and combined density functional theory and dynamical mean-field theory, we identify coexisting flat band doublets and dispersive bands near the Fermi energy. Upon cooling, the flat and dispersive bands exhibit a pronounced enhancement of spectral weight and hybridization, directly evidencing flat band resonance. Crucially, this emergence coincides with the onset of short-range antiferromagnetic correlations, contrasting sharply with conventional Kondo lattice behavior. Our findings demonstrate not only the long-sought flat band resonance in kagome materials, but also its unconventional correlation with magnetism.

cond-mat.str-el↗
arXiv2026

Exploring the $t\bar{t}$ threshold at an electron-positron collider

Future electron-positron colliders offer a unique opportunity for high-precision measurements of the top-quark mass, width, strong coupling constant, and top-quark Yukawa coupling via a scan of the $t\bar{t}$ threshold. We present the first prospect study of the simultaneous determination of these parameters, incorporating the latest reference detector design for the Circular Electron-Positron Collider (CEPC). We find that the precision of the top-quark mass measurement can reach a few MeV excluding the theoretical uncertainty on the cross-section, which is nearly two orders of magnitude better than the high-luminosity LHC (HL-LHC) projections. The current theoretical uncertainty of the cross-section calculation is the limiting factor.

hep-ph↗
arXiv2026

Beyond the EPICS: comprehensive Python IOC development with QueueIOC

Background and Purpose: Architectural deficiencies in EPICS lead to inefficiency in the development and application of EPICS IOCs. An unintrusive solution is replacing EPICS IOCs with more maintainable and flexible Python IOCs, only reusing the CA protocol of EPICS. While there are libraries like caproto and PCASPy that help to create Python IOCs, they still feel insufficient for more complex requirements. Methods: Noticing caput, caget and camonitor are just specialised combinations of requests/replies and notifications in client-server communication, by combining barebone caproto and event loops like those in server-like programs, the QueueIOC framework for Python IOCs is created, which has the potential to systematically reduce the development and maintenance cost of IOCs. Results: Examples based on QueueIOC are first given for workalikes of StreamDevice and asyn; also given are examples for "sequencer" applications, like those based on seq, include monochromators, motor anti-bumping and motor multiplexing. A QueueIOC-based framework for detector integration is presented in an accompanying paper. Also reported is a simple but expressive architecture for GUIs, as well as software to use with the ~/iocBoot convention which addresses some issues we find with a similar solution based on procServ.

physics.ins-det↗
arXiv2026

Tau-induced atrophy drives functional connectivity disruption in Alzheimer's disease

Alzheimer's disease involves progressive tau accumulation and spread, leading to regional brain atrophy and disruption of large-scale functional networks. While tau propagation and tissue degeneration have been widely modeled, how atrophy dynamics translate into functional connectivity (FC) degradation remains unclear. Here, we develop a multiphysics framework integrating anisotropic tau reaction-diffusion, finite-deformation biomechanics, and network modeling to link tau-driven atrophy with FC changes. Model fidelity is evaluated by quantitatively comparing simulated atrophy patterns with imaging-derived measurements. Using longitudinal structural and functional MRI, we identify an approximately linear relationship between regional atrophy rates and FC change. We then construct an atrophy-informed structural network degradation matrix from model-predicted region-specific atrophy rates and embed it into a neural oscillation model to predict FC disruption. Our results show that (i) the coupled reaction-diffusion-biomechanical model reproduces observed regional atrophy, (ii) regional atrophy rates parsimoniously predict longitudinal FC changes, and (iii) the atrophy-informed degradation matrix captures the direction and relative magnitude of regional FC disruption. By converting tau-driven atrophy into predictive FC trajectories, the proposed framework offers a clinically interpretable avenue for forecasting disease progression and informing trial design.

physics.med-ph↗
arXiv2026

A Decision Analysis Framework for High-fidelity and Low-fidelity Systems with Applications in Manufacturing Processes

Optimizing complex manufacturing processes often involves a trade-off between data accuracy and acquisition cost. High-fidelity data are accurate but limited, while low-fidelity data are abundant but often biased. Balancing these two sources is critical for efficient manufacturing optimization. To address this challenge, we develop a decision analysis framework based on multi-fidelity Gaussian process (GP) modeling based on the Kennedy-O'Hagan (KOH) framework. We propose a systematic Bayesian calibration approach using multi-fidelity GPs that explicitly quantifies the model discrepancy, and an algorithm that combines posterior sampling of calibration parameters with predictive sampling to characterize the distribution of optimal input settings and their associated uncertainty. These components are integrated into a five-stage practical workflow for the optimization of manufacturing processes. Through an illustrative example and two real-world applications in composite cure cycle optimization and injection molding process control, we demonstrate how the framework integrates information from both high-fidelity and low-fidelity data sources to support decision-making under parameter uncertainty.

stat.ME↗
arXiv2026

PolySkill: Learning Generalizable Skills Through Polymorphic Abstraction

Large language models (LLMs) are moving beyond static uses and are now powering agents that learn continually during their interaction with external environments. For example, agents can learn reusable skills while navigating web pages or toggling new tools. However, existing methods for skill learning often create skills that are over-specialized to a single website and fail to generalize. We introduce PolySkill, a new framework that enables agents to learn generalizable and compositional skills. The core idea, inspired by polymorphism in software engineering, is to decouple a skill's abstract goal (what it accomplishes) and its concrete implementation (how it is executed). Experiments show that our method (1) improves skill reuse by 1.7x on seen websites and (2) boosts success rates by up to 9.4% on Mind2Web and 13.9% on unseen websites, while reducing steps by over 20%. (3) In self-exploration settings without specified tasks, our framework improves the quality of proposed tasks and enables agents to learn generalizable skills that work across different sites. By enabling the agent to identify and refine its own goals, the PolySkill enhances the agent's ability to learn a better curriculum, leading to the acquisition of more generalizable skills compared to baseline methods. This work provides a practical path toward building agents capable of continual learning in adaptive environments. Our findings show that separating a skill's goal from its execution is a crucial step toward developing autonomous agents that can learn and generalize across the open web continuously. Our code can be found in https://github.com/simonucl/PolySkill.

cs.CL↗
arXiv2026

DRPO: Efficient Reasoning via Decoupled Reward Policy Optimization

Recent large reasoning models (LRMs) driven by reinforcement learning algorithms (e.g., GRPO) have achieved remarkable performance on challenging reasoning tasks. However, these models suffer from overthinking, generating unnecessarily long and redundant reasoning even for simple questions, which substantially increases computational cost and response latency. While existing methods incorporate length rewards to GRPO to promote concise reasoning, they incur significant performance degradation. We identify the root cause: when rewards for correct but long rollouts are penalized, GRPO's group-relative advantage function can assign them negative advantages, actively discouraging valid reasoning. To overcome this, we propose Decoupled Reward Policy Optimization (DRPO), a novel framework that decouples the length-based learning signal of correct rollouts from incorrect ones. DRPO ensures that reward signals for correct rollouts are normalized solely within the positive group, shielding them from interference by negative samples. The DRPO's objective is grounded in integrating an optimized positive data distribution, which maximizes length-based rewards under a KL regularization, into a discriminative objective. We derive a closed-form solution for this distribution, enabling efficient computation of the objective and its gradients using only on-policy data and importance weighting. Of independent interest, this formulation is general and can incorporate other preference rewards of positive data beyond length. Experiments on mathematical reasoning tasks demonstrate DRPO's significant superiority over six efficient reasoning baselines. Notably, with a 1.5B model, our method achieves 77\% length reduction with only 1.1\% performance loss on simple questions like GSM8k dataset, while the follow-up baseline sacrifices 4.3\% for 68\% length reduction.

cs.AI↗
arXiv2026

Tidal Synchronization of Binaries in Pleiades

Tidal interactions in close binaries play a key role in the long-term rotational and orbital evolution. The distributions of circularization across open clusters (OCs) place strong observational constraints on tidal dissipation in binaries. However, direct observational constraints on synchronization among binaries in OCs remain limited. For the 125 Myr OC Pleiades, this work combines cluster membership from Gaia Data Release 3, rotation periods from the K2 mission, and orbital solutions of the binary population from a long-term spectroscopic survey, to investigate the degree of tidal synchronization in each binary by comparing the pseudo-synchronization period to the rotation period of the primary stars. Among 42 binaries with reliable orbital periods Porb and rotation periods, we identify seven tidally synchronized systems with Porb < 8.6 days, including one early-type system and six late-type systems. For binaries with longer Porb, primaries generally are super-synchronized, and most systems are eccentric. We find a synchronization transition near Porb ~ 8.6-14 days, comparable to the known circularization period (Porb ~ 7.2 days) in the Pleiades, which suggests similar critical period scales for synchronization and circularization in this coeval population. Synchronization depends much more strongly on mass ratio than on primary mass. Most synchronized systems in Pleiades have high mass ratios and are likely to evolve into double white dwarf systems. Tides likely impose strong rotational braking on close early-type binaries, while their influence on late-type close binaries is weaker, and their spins largely follow the single-star sequence.

astro-ph.SR↗
arXiv2026

Lepton flavor of four-fermion operator and fermion portal dark matter

We study the ultraviolet realization of semileptonic four-fermion operator $O_{ledq}^{αβ11}$ that incorporates Majorana dark matter (DM) in both lepton-flavor-conserving (LFC) and lepton-flavor-violating (LFV) scenarios at the one-loop level via box diagram, which effectively alleviates the lower bounds on the new physics scale. The interplay between the model-independent constraints on the Wilson coefficients and DM direct detection, relic density, and collider searches in the context of fermion portal DM model with two mediators is investigated. We find that both the projected future constraint on the LFC Wilson coefficient $C_{ledq}^{2211}/Λ^2< (12.3~\text{TeV})^{-2}$ from the measurements of neutrino non-standard interaction in the next-generation neutrino oscillation experiments, and LFV constraint $C_{ledq}^{1211}/Λ^2< \left(2.2\times 10^3~\text{TeV} \right)^{-2}$ from ongoing charged-lepton-flavor-violation searches, provide a complementary exploration of the parameter space encompassing the DM mass and scalar mass. With the colored mediator mass typically around $2~\text{TeV}$, the sensitivity of the indirect constraints on the four-fermion operator could surpass those of collider searches and DM direct detection, in scenarios where the masses of the DM and scalar are close. By ensuring the correct DM relic density, however, we obtain that the collider searches and DM direct detection are more sensitive to the electroweak scale DM and scalar compared to the indirect constraints.

hep-ph↗
arXiv2026

On the Stability of Spatially Distributed Cavity Laser and Boundary of Resonant Beam SLIPT

Spatially distributed cavity (SDC) lasers are a promising technology for simultaneous light information and power transfer (SLIPT), offering benefits such as increased mobility and intrinsic safety, which are advantageous for various Internet of Things (IoT) devices. \mll However, achieving beam transmission over meter-level long working distances presents significant challenges from cavity stability constraints, manufacturing/assembly tolerances, and diffraction losses\mrr. This paper conducts a theoretical investigation of the fundamental restrictions limiting long-range resonant beam generation. We investigate cavity stability and beam characteristics, and propose a binary-search-based Monte Carlo simulation algorithm as well as a linear approximation algorithm to quantify the maximum acceptable tolerances for stable operation. \mll Numerical results indicate that the stable region contracts sharply as distance increases. For fixed-component systems, an acceptable tolerance of 0.01 mm restricts the achievable transmission distance to less than 2 m. \mrr To address this limitation, we also prove the feasibility of long-range beam formation using precision adjustable elements, paving the way for advanced engineering applications. \mll Experimental results verified this assumption, demonstrating that by tuning the stable region during assembly, the transmission distance could be extended to 2.8 m. \mrr This work provides essential theoretical insights and practical design guidelines for realizing stable, long-range SDC systems.

eess.SY↗
arXiv2026

Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology

Recent years have witnessed remarkable progress in multimodal learning within computational pathology. Existing models primarily rely on vision and language modalities; however, language alone lacks molecular specificity and offers limited pathological supervision, leading to representational bottlenecks. In this paper, we propose STAMP, a Spatial Transcriptomics-Augmented Multimodal Pathology representation learning framework that integrates spatially-resolved gene expression profiles to enable molecule-guided joint embedding of pathology images and transcriptomic data. Our study shows that self-supervised, gene-guided training provides a robust and task-agnostic signal for learning pathology image representations. Incorporating spatial context and multi-scale information further enhances model performance and generalizability. To support this, we constructed SpaVis-6M, the largest Visium-based spatial transcriptomics dataset to date, and trained a spatially-aware gene encoder on this resource. Leveraging hierarchical multi-scale contrastive alignment and cross-scale patch localization mechanisms, STAMP effectively aligns spatial transcriptomics with pathology images, capturing spatial structure and molecular variation. We validate STAMP across six datasets and four downstream tasks, where it consistently achieves strong performance. These results highlight the value and necessity of integrating spatially resolved molecular supervision for advancing multimodal learning in computational pathology. The code is included in the supplementary materials. The pretrained weights and SpaVis-6M are available at: https://github.com/Hanminghao/STAMP.

cs.CV↗
arXiv2026

Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints

Constrained optimization with multiple functional inequality constraints has significant applications in machine learning. This paper examines a crucial subset of such problems where both the objective and constraint functions are weakly convex. Existing methods often face limitations, including slow convergence rates or reliance on double-loop algorithmic designs. To overcome these challenges, we introduce a novel single-loop penalty-based stochastic algorithm. Following the classical exact penalty method, our approach employs a {\bf hinge-based penalty}, which permits the use of a constant penalty parameter, enabling us to achieve a {\bf state-of-the-art complexity} for finding an approximate Karush-Kuhn-Tucker (KKT) solution. We further extend our algorithm to address finite-sum coupled compositional objectives, which are prevalent in artificial intelligence applications, establishing improved complexity over existing approaches. Finally, we validate our method through experiments on fair learning with receiver operating characteristic (ROC) fairness constraints and continual learning with non-forgetting constraints.

cs.LG↗
arXiv2026

DALI: A Workload-Aware Offloading Framework for Efficient MoE Inference on Local PCs

Mixture of Experts (MoE) architectures significantly enhance the capacity of LLMs without proportional increases in computation, but at the cost of a vast parameter size. Offloading MoE expert parameters to host memory and leveraging both CPU and GPU computation has recently emerged as a promising direction to support such models on resourceconstrained local PC platforms. While promising, we notice that existing approaches mismatch the dynamic nature of expert workloads, which leads to three fundamental inefficiencies: (1) Static expert assignment causes severe CPUGPU load imbalance, underutilizing CPU and GPU resources; (2) Existing prefetching techniques fail to accurately predict high-workload experts, leading to costly inaccurate prefetches; (3) GPU cache policies neglect workload dynamics, resulting in poor hit rates and limited effectiveness. To address these challenges, we propose DALI, a workloaDAware offLoadIng framework for efficient MoE inference on local PCs. To fully utilize hardware resources, DALI first dynamically assigns experts to CPU or GPU by modeling assignment as a 0-1 integer optimization problem and solving it efficiently using a Greedy Assignment strategy at runtime. To improve prefetching accuracy, we develop a Residual-Based Prefetching method leveraging inter-layer residual information to accurately predict high-workload experts. Additionally, we introduce a Workload-Aware Cache Replacement policy that exploits temporal correlation in expert activations to improve GPU cache efficiency. By evaluating across various MoE models and settings, DALI achieves significant speedups in the both prefill and decoding phases over the state-of-the-art offloading frameworks.

cs.DC↗
arXiv2026

GLAP: General contrastive audio-text pretraining across domains and languages

Contrastive Language Audio Pretraining (CLAP) is a widely-used method to bridge the gap between audio and text domains. Current CLAP methods enable sound and music retrieval in English, ignoring multilingual spoken content. To address this, we introduce general language audio pretraining (GLAP), which expands CLAP with multilingual and multi-domain abilities. GLAP demonstrates its versatility by achieving competitive performance on standard audio-text retrieval benchmarks like Clotho and AudioCaps, while significantly surpassing existing methods in speech retrieval and classification tasks. Additionally, GLAP achieves strong results on widely used sound-event zero-shot benchmarks, while simultaneously outperforming previous methods on speech content benchmarks. Further keyword spotting evaluations across 50 languages emphasize GLAP's advanced multilingual capabilities. Finally, multilingual sound and music understanding is evaluated across four languages. Checkpoints and Source: https://github.com/xiaomi-research/dasheng-glap.

cs.SD↗
arXiv2026

Dipion transitions from $X(3872)$ to $χ_{cJ}\ (J=0,1,2)$

In this work, we investigate the dipion transition processes $X(3872)\to ππχ_{cJ} (J=0,1,2)$ within the framework of heavy hadron chiral perturbation theory, treating $X(3872)$ as a molecular state composed of $D\bar{D}^*$+ H.c. components. By analyzing the box and triangle loop diagrams with the nonrelativistic effective field theory power-counting rule, we demonstrate that box diagrams dominate these dipion transition processes. Branching ratios are calculated as functions of the mixing angle $θ$, which parametrizes the neutral and charged meson compositions of the $X(3872)$. Our results indicate that the branching fractions for $X(3872)\toππχ_{c0}$, $X(3872)\to ππχ_{c1}$, and $X(3872)\to ππχ_{c2}$ are of the orders of $10^{-4}$, $10^{-3}$, and $10^{-5}$, respectively. We also predict the ratios ${\mathcal{B}[X(3872)\rightarrow ππχ_{c0/2}]}/{\mathcal{B}[X(3872)\rightarrow ππχ_{c1}]}$ and ${\mathcal{B}[X(3872)\rightarrow π^+π^-χ_{cJ}]}/{\mathcal{B}[X(3872)\rightarrow π^0π^0χ_{cJ}]}$. The latter deviates from isospin-symmetry expectations, revealing various degrees of isospin violation. By studying the $π^+π^-$ and $π^+χ_{cJ}$ invariant mass spectra, we find a double-bump structure in the $π^ + π^-$ invariant mass distributions of the process $X(3872)\to π^+π^-χ_{c1}$ and $π^+χ_{c0}$ invariant mass distribution of the process $X(3872)\to π^+π^-χ_{c0}$, which could be tested by future experimental measurements.

hep-ph↗
arXiv2026

Uncertainty Analysis of Experimental Parameters for Reducing Warpage in Injection Molding

Injection molding is a critical manufacturing process, but controlling warpage remains a major challenge due to complex thermomechanical interactions. Simulation-based optimization is widely used to address this, yet traditional methods often overlook the uncertainty in model parameters. In this paper, we propose a data-driven framework to minimize warpage and quantify the uncertainty of optimal process settings. We employ polynomial regression models as surrogates for the injection molding simulations of a box-shaped part. By adopting a Bayesian framework, we estimate the posterior distribution of the regression coefficients. This approach allows us to generate a distribution of optimal decisions rather than a single point estimate, providing a measure of solution robustness. Furthermore, we develop a Monte Carlo-based boundary analysis method. This method constructs confidence bands for the zero-level sets of the response surfaces, helping to visualize the regions where warpage transitions between convex and concave profiles. We apply this framework to optimize four key process parameters: mold temperature, injection speed, packing pressure, and packing time. The results show that our approach finds stable process settings and clearly marks the boundaries of defects in the parameter space.

stat.ME↗