arXiv subjects

Shu Li

Publications and source records attributed to Shu Li.

At least 19 recordsLinked to original sources

arXiv2026

Foundation model for particle collision data analysis

Data-intensive collider experiments increasingly require models that transfer across high-dimensional, partially observed measurements, yet most analyses still optimize separate models for each task. Here, to address this bottleneck, we introduce EVENET, an event-level foundation model pretrained on 500 million simulated collision events using self-supervised learning and physics-informed supervision. The model constructs a shared particle-cloud representation from the variable-length set of physics objects in each event. Across searches for heavy resonances and exotic Higgs decays, EVENET outperforms both established and state-of-the-art baselines, particularly in low-statistics regimes. We validate transfer to experimental data by rediscovering the $Υ$ meson in CMS Open Data. The pretrained model also supports precise extraction of quantum-correlation observables that remain stable under the tested systematic variations. Together, these results establish a reusable backbone for diverse collider analyses, improving downstream adaptation after a substantial one-time pretraining investment.

hep-ex↗

arXiv2026

Multi-Agent Path Planning via Compatible Encounter-Space Allocation in Crowded Spaces Dynamically Occupied by Work Activities

Lifelong multiagent path planning on construction floors must accommodate continually changing goals and work occupancy. Existing planners can update paths and timing online, but work motion space coupling changes the spatial basis of unfinished encounters, while rerouting affects later encounters. Planning must therefore construct and maintain local encounter possibility. We propose Width based Planning with Compatible Encounter Space Allocation (WP), which constructs shared passage relationships from actual encounter demands and real time widths. Shared channels, ordered anchors, and urgency based updates coordinate successive encounters while retaining individual greedy motion between them. A necessary and sufficient width condition for fixed-point position exchange and a necessary directional condition for composite exchange provide geometric grounds for channel construction and encounter relocation. In crowded floor computational experiments, WP maintained route continuation after occupancy changes and enabled multi agent clearing, with infrequent, brief near stops. Complete run comparisons with four methods showed 25.2% to 52.2% more mean goal arrivals than the best baseline at each configuration of 5 to 10 agents, with lower mean computation time than the rolling horizon K_CBS adaptation. The contribution is a planning principle that constructs and continuously maintains local passage relationships from real time space, unifying shared spatial allocation and conditional local clearing to extend lifelong planning to work motion space coupling.

cs.CE↗

arXiv2026

Bounds on the maximum number of limit cycles of piecewise linear Lienard systems II. The discontinuous case

This paper concerns the planar Liénard system $\dot x=y-F(x),\ \dot y=-x$, where $F(x)$ is a piecewise linear function with exactly $m$ jump points and no fold points. Tonnelier [SIAM J. Appl. Math. 63 (2002)] conjectured that the maximum number of limit cycles of the system is $2m$. The conjecture was confirmed for $m=1$ in [J. Lond. Math. Soc. 113 (2026)], and a lower bound of $2m$ for arbitrary $m$ was established by Chen et al. [arXiv:2608.19542]. In this paper, we construct systems with at least $4m-2$ hyperbolic crossing limit cycles for every positive integer $m$, thereby disproving Tonnelier's conjecture for $m\geq2$. We also establish the uniform upper bound $2^{224(m+1)^2}$ for the total number of crossing, grazing, sliding, and composite limit cycles.

math.DS↗

arXiv2026

PLADOS: A Parameter-free Landing Algorithm for Decentralized Optimization on the Stiefel Manifold

Decentralized optimization on the Stiefel manifold has broad applications in machine learning and signal processing. We propose PLADOS, a parameter-free landing algorithm for decentralized optimization on the Stiefel manifold. Each iteration combines local stochastic gradient updates with a single retraction-free communication step over the communication graph. A key convex-like property of the intersection of the nonconvex Stiefel manifold and consensus constraints is characterized through a restricted secant inequality for the penalty-only scheme, ensuring local contraction of the manifold consensus error without introducing additional parameters that require tuning. For $n$ nodes using independent local batches of size $b$, we prove that PLADOS achieves linear speedup with respect to the number of nodes with an asymptotic convergence rate of $O(σ/\sqrt{nbK}+(nξ^2)^{1/3}/K^{2/3})$ after $K$ iterations, where $σ^2>0$ is the sampling variance and $ξ^2$ quantifies gradient heterogeneity. Experiments demonstrate stability across tested stepsizes and networks, time efficiency, and linear speedup with respect to the number of nodes.

math.OC↗

arXiv2026

Stochastic Subgradient Descent at Sharply Repulsive Points: A Bounded Noise Counterexample and Gaussian Noise Avoidance

Motivated by the open question of Bianchi, Hachem, and Schechtman (Bianchi et al., 2024, Remark 4), we show that in the non-weakly convex definable setting, omnidirectional noise with conditional fourth-moment control does not suffice for universal almost-sure avoidance of sharply repulsive critical points. We demonstrate this failure by constructing a globally Lipschitz, coercive, definable objective that is not weakly convex. For this objective and its sufficiently small linear perturbations, stochastic subgradient descent with independent noise uniformly distributed on a ball in $\mathbb{R}^2$ converges with probability $1$ to a nonminimal sharply repulsive critical point from every initial point in a specified ball. A two-cycle argument establishes uniform bounds on the rescaled iterates, yielding convergence to the sharply repulsive critical point. In contrast, for locally Lipschitz definable objectives, Gaussian noise ensures simultaneous almost-sure avoidance of any finite collection of sharply repulsive points. Through a construction of difference quotient functions, we show that convergence to any such point with positive probability would produce a stationary distribution with expected descent $0$, whereas a global positive lower bound on the Fréchet subgradient norms of the limiting functions forces strictly positive expected descent under the same distribution. This contradiction proves avoidance, with the everywhere positive Gaussian density playing a key role.

math.OC↗

arXiv2026

HBFlex: A Flexible Memory System for Bridging Fine-Grained LLM States and Coarse-Grained HBF Parallel Execution

Large language models (LLMs) require increasing memory capacity to accommodate growing model weights and KV caches. High-Bandwidth Flash (HBF) offers high memory density and aggregate read bandwidth through massive plane-level parallelism, making it an attractive option for LLM serving. However, serving LLMs entirely from HBF introduces three challenges: fine-grained KV reads create placement and access imbalance, incremental writes interfere with foreground reads, and mixed KV lifetimes amplify garbage collection. Hybrid HBM/HBF designs retain HBM to support dynamic KV management, but this allocation reduces the HBF resources available under a fixed packaging budget, limiting aggregate HBF bandwidth. We present HBFlex, a full-HBF memory system with coordinated optimizations for KV reads, writes, and reclamation. HBFlex balances KV placement and attention accesses to improve plane utilization. It aggregates incremental updates and schedules writeback within sufficiently long compute windows to reduce write--read interference. It also combines lifetime-guided block packing with deferred reclamation to reduce valid-page migration. We evaluate HBFlex through trace-driven simulation across different configurations. HBFlex achieves average throughput speedups of up to 1.58$\times$ over FlashAccel and 3.30$\times$ over H3, benefiting from higher HBF bandwidth and more efficient management of dynamic KV-cache reads, writes, and erases.

cs.AR↗

arXiv2026

Prospective study of light dark matter search in positron beam mode with DarkSHINE experiment initiative

We perform the prospective study on searching for invisibly decayed dark photons ($A^{\prime}$) in positron-on-fixed-target (POT) scheme with the DarkSHINE experiment simulation setup. In the positron-on-fixed-target scheme, two additional production channels of dark photons via annihilation with extranuclear electrons are present: non-resonant t-channel ($e^+e^-N \to γA^{\prime}N$) and resonant s-channel ($e^+e^-N \to A^{\prime}N$), alongside bremsstrahlung emission similar to electron-on-fixed-target scheme. Based on a GEANT4 full detector simulation of an 8 GeV positron beam with $3 \times 10^{14}$ POT events, we developed an analysis strategy using missing energy and momentum signatures across three orthogonal signal regions optimized for these channels. Our results indicate that the additional annihilation channels of dark photon production enhance the dark photon invisible decay search sensitivity, improving the exclusion limits by approximately two orders of magnitude compared to previous constraints in the electron-on-fixed target (EOT) scheme, particularly near the center-of-mass energy threshold for annihilation production of dark photons.

hep-ex↗

arXiv2026

ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System

Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated research systems remain predominantly focused on empirically driven domains with quantitative benchmarks, leaving theory-driven discovery, particularly in mathematically grounded disciplines requiring rigorous proofs and synthesis of domain knowledge, largely underexplored. Key challenges include the difficulty of verifying theoretical reasoning at scale, insufficient reasoning ability for autonomous frontier exploration, and a scarcity of procedural heuristics in the literature. We introduce ReasFlow, an end-to-end autonomous agent system for reasoning-centric scientific discovery that operationalizes a collaborative paradigm where the human expert acts as Principal Investigator while the agent executes rigorous derivations as a capable graduate student. ReasFlow incorporates (i) a robust internal verification loop that audits logical coherence and corrects fundamental errors prior to human inspection, and (ii) an automated knowledge retrieval and self-improvement mechanism that proactively surfaces both declarative facts and overlooked procedural heuristics, substantially reducing expert intervention. The system unifies literature synthesis, algorithm design, theorem proving, experimentation, and manuscript preparation in a single system. Deployed to autonomously generate five complete research papers with rigorous theoretical and empirical content from minimal prompts, ReasFlow consistently achieves the highest evaluation scores among state-of-the-art open-access baselines under a curated LLM-based review rubric. ReasFlow is publicly accessible via the ReasLab platform, providing a collaborative workspace for AI-assisted theoretical research. Github repo: https://github.com/reaslab/ReasFlow.git.

cs.AI↗

arXiv2026

Potential Applications of HBF in LLM Serving Systems

LLM serving is increasingly constrained by memory capacity as model weights, KV caches, and the number of served model variants continue to grow. This report examines High-Bandwidth Flash (HBF) as a capacity-oriented extension to HBM-based serving systems. We first discuss how HBF can be integrated into the GPU memory hierarchy without undermining the bandwidth expected by the compute die. We then model the system-level value of added capacity as expanded residency for read-mostly model-state objects. Under this view, HBF can improve MoE serving by enabling more expert replicas and can improve multi-model serving by reducing model loading and supporting hot-model replication. Our simulation results show that these benefits depend on preserving the HBM-resident execution path while using HBF to expand the resident set of model weights.

cs.AR↗

arXiv2026

Spectral and Trajectory Regularization for Diffusion Transformer Super-Resolution

Diffusion transformer (DiT) architectures show great potential for real-world image super-resolution (Real-ISR). However, their computationally expensive iterative sampling necessitates one-step distillation. Existing one-step distillation methods struggle with Real-ISR on DiT. They suffer from fundamental trajectory mismatch and generate severe grid-like periodic artifacts. To tackle these challenges, we propose StrSR, a novel one-step adversarial distillation framework featuring spectral and trajectory regularization. Specifically, we propose an asymmetric discriminative distillation architecture to bridge the trajectory gap. Additionally, we design a frequency distribution matching strategy to effectively suppress DiT-specific periodic artifacts caused by high-frequency spectral leakage. Extensive experiments demonstrate that StrSR achieves state-of-the-art performance in Real-ISR, across both quantitative metrics and visual perception. The code and models will be released at https://github.com/jkwang28/StrSR .

cs.CV↗

arXiv2026

A Retraction-Free EXTRA Method for Decentralized Optimization on the Stiefel Manifold

Decentralized optimization provides a fundamental framework for large-scale learning and signal processing with distributed data. We study decentralized optimization with orthogonality constraints on the Stiefel manifold and propose RF-EXTRA, a distributed retraction-free primal-dual method on static undirected networks. The method combines an approximate gradient mapping for orthogonality-constrained optimization with an EXTRA-based decentralized recursion, thereby avoiding retractions while preserving a simple communication pattern. On the theoretical side, the analysis considers \revise{the joint error} $(\mathbf{X}_k-\overline{\mathbf X}_k,\mathbf{s}_k-\overline{\mathbf s}_k)$ in the local variables and local directions, and establishes a contractive recursion for the joint error. This contractivity ensures that the joint error can be controlled using small yet constant step sizes, thus leading to an exact $\mathcal{O}(1/K)$ convergence rate of RF-EXTRA to a stationary point. Experiments on PCA and low-rank matrix completion show that RF-EXTRA compares favorably with the reported decentralized baselines and exhibits strong communication efficiency on the tested tasks on the Stiefel manifold.

math.OC↗

arXiv2026

SiPM non-linearity studies in beam tests with scintillating crystals

High-granularity homogeneous electromagnetic calorimeters based on scintillating crystals and silicon photomultipliers (SiPMs) are a promising option for future $e^{+}e^{-}$ Higgs factories, where both excellent energy resolution and a very large dynamic range are required. In this work, the non-linear response of high-pixel-density SiPMs with pixel pitches of 6--10~$μ$m coupled to BGO and BSO crystals is studied under realistic beam conditions. A dual-end readout scheme with an attenuated reference SiPM was employed to precisely calibrate the deposited energy and the corresponding number of photoelectrons over a wide dynamic range. Beam tests were carried out at the CERN SPS H2 beamline using high-energy electrons, with a tungsten pre-shower and variable incident angles to enhance energy deposition. The measurements directly quantify the non-linear response of SiPMs to scintillation light over an extended dynamic range. For BGO-coupled Hamamatsu SiPMs, deviations from linearity of about 20\% are observed at $5\times10^{5}$ photoelectrons, while larger deviations are measured for the tested NDL devices and for configurations with faster BSO scintillation.

physics.ins-det↗

arXiv2026

Conceptual Design of a Novel Highly Granular Crystal Electromagnetic Calorimeter for Future Higgs Factories

Next-generation high-energy electron-positron colliders, operating as Higgs factories, require an unprecedented jet energy resolution for precision measurements of Higgs and Z/W bosons. To address this challenge, a conceptual design is presented for a novel high-granularity crystal electromagnetic calorimeter that combines the superior intrinsic energy resolution of a homogeneous calorimeter with the fine segmentation required for particle-flow reconstruction. The crystal electromagnetic calorimeter design is based on orthogonally arranged long scintillating crystal bars read out by silicon photomultipliers (SiPMs) at both ends. Key design specifications were established through comprehensive simulation studies. Critical technical considerations, including crystal choices, photosensors, electronics, mechanical support, and radiation damage, are discussed. A dedicated digitisation framework was developed to realistically model effects from the crystal, SiPMs, and readout electronics. The performance of a single calorimeter module was evaluated using simulated electron showers. Simulation results for a single module demonstrate an excellent electromagnetic energy resolution of $1.12\%/\sqrt{E(\mathrm{GeV})}\oplus0.22\%$ and an energy linearity within $\pm0.5\%$ for electrons from 3 GeV to 100 GeV. The performance significantly exceeds the design requirement of $\leq 3\%/\sqrt{E(\mathrm{GeV})}\oplus1\%$. The results establish the feasibility of the proposed high-granularity crystal calorimeter concept and point to a promising pathway toward the precision calorimetry required for future high-energy electron-positron collider experiments.

physics.ins-det↗

arXiv2026

LCUDiff: Latent Capacity Upgrade Diffusion for Faithful Human Body Restoration

Existing methods for restoring degraded human-centric images often struggle with insufficient fidelity, particularly in human body restoration (HBR). Recent diffusion-based restoration methods commonly adapt pre-trained text-to-image diffusion models, where the variational autoencoder (VAE) can significantly bottleneck restoration fidelity. We propose LCUDiff, a stable one-step framework that upgrades a pre-trained latent diffusion model from the 4-channel latent space to the 16-channel latent space. For VAE fine-tuning, channel splitting distillation (CSD) is used to keep the first four channels aligned with pre-trained priors while allocating the additional channels to effectively encode high-frequency details. We further design prior-preserving adaptation (PPA) to smoothly bridge the mismatch between 4-channel diffusion backbones and the higher-dimensional 16-channel latent. In addition, we propose a decoder router (DeR) for per-sample decoder routing using restoration-quality score annotations, which improves visual quality across diverse conditions. Experiments on synthetic and real-world datasets show competitive results with higher fidelity and fewer artifacts under mild degradations, while preserving one-step efficiency. The code and model will be at https://github.com/gobunu/LCUDiff.

cs.CV↗

arXiv2026

Measurement of diboson production and precision EFT constraints in ATLAS

These proceedings summarize the latest progress by the ATLAS Experiment at the LHC in measuring diboson production and related searches for physics beyond Standard Model via anomalous gauge couplings with the latest Effective Field Theory approach. The most recent measurements of $W^{+}W^{-} \to \ell^{+}ν\ell^{-}\barν$, $W^{\pm}Z \to 3\ell1ν$ and $Z(\to \ell^{+}\ell^{-})γ$ measurements with ATLAS full Run 2 dataset are presented, along with the highlights of the first evidence of $W^{+}W^{-}$ charge asymmetry, the first measurement of CP-violation sensitive observables in $WZ$, and, for the first time at the LHC, $SU(2)_L \otimes U(1)_Y$ fully gauge invariant anomalous neutral triple gauge coupling limits with $Z(\to \ell^{+}\ell^{-})γ$ process.

hep-ex↗

arXiv2025

Development of the CEPC analog hadron calorimeter prototype

The Circular Electron Positron Collider (CEPC) is a next-generation electron$-$positron collider proposed for the precise measurement of the properties of the Higgs boson. To emphasize boson separation and jet reconstruction, the baseline design of the CEPC detector was guided by the particle flow algorithm (PFA) concept. As one of the calorimeter options, the analogue hadron calorimeter (AHCAL) was proposed. The CEPC AHCAL comprises a 40-layer sandwich structure using steel plates as absorbers and scintillator tiles coupled with silicon photomultipliers (SiPM) as sensitive units. To validate the feasibility of the AHCAL option, a series of studies were conducted to develop a prototype. This AHCAL prototype underwent an electronic test and a cosmic ray test to assess its performance and ensure it was ready for three beam tests performed in 2022 and 2023. The test beam data is currently under analysis, and the results are expected to deepen our understanding of hadron showers, validate the concept of Particle Flow Algorithm (PFA), and ultimately refine the design of the CEPC detector.

physics.ins-det↗

arXiv2025

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

physics.ins-det↗

arXiv2025

Towards Universal Material Property Prediction with Deep Learning and Single-Descriptor electronic Density

Owing to its high scalability and computational efficiency, machine learning methods have been increasingly integrated into various scientific research domains, including ab initio-based materials design. It has been demonstrated that, by incorporating modern machine learning algorithms, one can predict material properties with practically acceptable accuracy. However, one of the most significant limitations that restrict the widespread application of machine learning is its lack of transferability, as a given framework is typically applicable only to a specific property. The origin of this limitation is rooted in the fact that a material's properties are determined by multiple degrees of freedom -- and their complex interplay -- associated with nuclei and electrons, such as atomic type, structural symmetry, and the number and quantum states of the valence electrons, among others. The inherent complexity rules out the possibility of a single machine learning framework providing a full description of these critical quantities. In this paper, we develop a universal machine learning framework based solely on a physically grounded and theoretically rigorous descriptor -- electronic charge density. Our framework not only enables accurate prediction of eight different material properties (with R$^2$ values up to 0.94), but also demonstrates outstanding multi-task learning capability, as prediction accuracy improves when more target properties are incorporated into a single training process, thereby indicating excellent transferability. These results represent a significant step toward realizing the long-standing goal of a universal machine learning framework for the unified prediction of all material properties.

cond-mat.mtrl-sci↗