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Researcher profile

Lei Li

Lei Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputer VisionComputation and Languageastro-ph.SRcond-mat.mes-halleess.IVeess.SPInformation TheoryMachine Learningmath.ITphysics.med-phRoboticsSoftware EngineeringTissues and Organs

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Published work

22 published item(s)

preprint2026arXiv

A one-step generation model with a Single-Layer Transformer: Layer number re-distillation of FreeFlow

Currently, Flow matching methods aim to compress the iterative generation process of diffusion models into a few or even a single step, with MeanFlow and FreeFlow being representative achievements of one-step generation based on Ordinary Differential Equations (ODEs). We observe that the 28-layer Transformer architecture of FreeFlow can be characterized as an Euler discretization scheme for an ODE along the depth axis, where the layer index serves as the discrete time step. Therefore, we distill the number of layers of the FreeFlow model, following the same derivation logic as FreeFlow, and propose SLT (Single-Layer Transformer), which uses a single shared DiT block to approximate the depth-wise feature evolution of the 28-layer teacher. During training, it matches the teacher's intermediate features at several depth patches, fuses those patch-level representations, and simultaneously aligns the teacher's final velocity prediction. Through distillation training, we compress the 28 independent Transformer Blocks of the teacher model DiT-XL/2 into a single Transformer Block, reducing the parameter count from 675M to 4.3M. Furthermore, leveraging its minimal parameters and rapid sampling speed, SLT can screen more candidate points in the noise space within the same timeframe, thereby selecting higher-quality initial points for the teacher model FreeFlow and ultimately enhancing the quality of generated images. Experimental results demonstrate that within a time budget comparable to two random samplings of the teacher model, our method performs over 100 noise screenings and produces a high-quality sample through the teacher model using the selected points. Quality fluctuations caused by low-quality initial noise under a limited number of FreeFlow sampling calls are effectively avoided, substantially improving the stability and average generation quality of one-step generation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

AM$^3$Safety: Towards Data Efficient Alignment of Multi-modal Multi-turn Safety for MLLMs

Multi-modal Large Language Models (MLLMs) are increasingly deployed in interactive applications. However, their safety vulnerabilities become pronounced in multi-turn multi-modal scenarios, where harmful intent can be gradually reconstructed across turns, and security protocols fade into oblivion as the conversation progresses. Existing Reinforcement Learning from Human Feedback (RLHF) alignment methods are largely developed for single-turn visual question-answer (VQA) task and often require costly manual preference annotations, limiting their effectiveness and scalability in dialogues. To address this challenge, we present InterSafe-V, an open-source multi-modal dialogue dataset containing 11,270 dialogues and 500 specially designed refusal VQA samples. This dataset, constructed through interaction between several models, is designed to more accurately reflect real-world scenarios and includes specialized VQA pairs tailored for specific domains. Building on this dataset, we propose AM$^3$Safety, a framework that combines a cold-start refusal phase with Group Relative Policy Optimization (GRPO) fine-tuning using turn-aware dual-objective rewards across entire dialogues. Experiments on Qwen2.5-VL-7B-Instruct and LLaVA-NeXT-7B show more than 10\% decrease in Attack Success Rate (ASR) together with an increment of at least 8\% in harmless dimension and over 13\% in helpful dimension of MLLMs on multi-modal multi-turn safety benchmarks, while preserving their general abilities.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Amplitude analysis and branching fraction measurement of $J/ψ\to Λ\barΣ^0η+\mathrm{c.c}$

Based on a sample of $(10087\pm44)\times10^{6}$ $J/ψ$ events collected with the BESIII detector, a partial-wave analysis of $ J/ψ\to Λ\bar{ Σ}^0η+\mathrm{c.c} $ is performed for the first time. The dominant contributions are found to be excited $Λ$ states with $J^P=1/2^-$ and $J^P=1/2^+$ in the $ηΛ$ mass spectra. The measured masses and widths are $M=1668.8\pm3.1\pm21.2$ MeV/$c^2$ and $Γ=52.7\pm4.2\pm17.8$ MeV for the $Λ(1670)$, and $M=1881.5\pm16.5\pm20.3$ MeV/$c^2$ and $Γ=82.4\pm18.2\pm8.9$ MeV for the $Λ(1810)$, respectively. The branching fraction is determined to be $ \mathcal{B}(J/ψ\to Λ\bar{ Σ}^0η+\mathrm{c.c}) $ = $(3.44 \pm 0.11 \pm 0.13) \times 10^{-5}$. The first uncertainties are statistical and the second systematic.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD

Industrial Computer-Aided Design (CAD) code generation requires models to produce executable parametric programs from visual or textual inputs. Beyond recognizing the outer shape of a part, this task involves understanding its 3D structure, inferring engineering parameters, and choosing CAD operations that reflect how the part would be designed and manufactured. Despite the promise of Multimodal large language models (MLLMs) for this task, they are rarely evaluated on whether these capabilities jointly hold in realistic industrial CAD settings. We present BenchCAD, a unified benchmark for industrial CAD reasoning. BenchCAD contains 17,900 execution-verified CadQuery programs across 106 industrial part families, including bevel gears, compression springs, twist drills, and other reusable engineering designs. It evaluates models through visual question answering, code question answering, image-to-code generation, and instruction-guided code editing, enabling fine-grained analysis across perception, parametric abstraction, and executable program synthesis. Across 10+ frontier models, BenchCAD shows that current systems often recover coarse outer geometry but fail to produce faithful parametric CAD programs. Common failures include missing fine 3D structure, misinterpreting industrial design parameters, and replacing essential operations such as sweeps, lofts, and twist-extrudes with simpler sketch-and-extrude patterns. Fine-tuning and reinforcement learning improve in-distribution performance, but generalization to unseen part families remains limited. These results position BenchCAD as a benchmark for measuring and improving the industrial readiness of multimodal CAD automation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Building Digital Twins of Different Human Organs for Personalized Healthcare

Digital twins are virtual replicas of physical entities and are poised to transform personalized medicine through the real-time simulation and prediction of human physiology. Translating this paradigm from engineering to biomedicine requires overcoming profound challenges, including anatomical variability, multi-scale biological processes, and the integration of multi-physics phenomena. This survey systematically reviews methodologies for building digital twins of human organs, structured around a pipeline decoupled into anatomical twinning (capturing patient-specific geometry and structure) and functional twinning (simulating multi-scale physiology from cellular to organ-level function). We categorize approaches both by organ-specific properties and by technical paradigm, with particular emphasis on multi-scale and multi-physics integration. A key focus is the role of artificial intelligence (AI), especially physics-informed AI, in enhancing model fidelity, scalability, and personalization. Furthermore, we discuss the critical challenges of clinical validation and translational pathways. This study not only charts a roadmap for overcoming current bottlenecks in single-organ twins but also outlines the promising, albeit ambitious, future of interconnected multi-organ digital twins for whole-body precision healthcare.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Cross section measurement of $e^{+}e^{-}\rightarrow π^{0}π^{0}ψ(3686)$ from $\sqrt{s}=$ 4.008 GeV to 4.951 GeV

Using data samples with a total integrated luminosity of $22.1~\rm fb^{-1}$ at center-of-mass energies between 4.008 and 4.951~GeV collected with the BESIII detector, the cross sections of $e^{+}e^{-}\rightarrow π^{0}π^{0}ψ(3686)$ process are measured. The obtained cross sections are found to be approximately one-half of those of $e^{+}e^{-}\rightarrow π^{+}π^{-}ψ(3686)$, consistent with the isospin symmetry expectation. A coherent fit to the dressed cross sections is performed, with the $Y(4230)$~parameters fixed at the values measured in $e^{+}e^{-}\rightarrow π^{+}π^{-}ψ(3686)$. The significances of the $Y(4390)$ and $Y(4660)$ are both larger than $5σ$, and their masses and widths are consistent with the previous measurement in the $e^{+}e^{-}\rightarrow π^{+}π^{-}ψ(3686)$ process.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention

Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and subsequently apply fine-grained softmax attention on the selected tokens. However, the top-k operation assumes the number of relevant tokens for any query is fixed and it precludes the gradient flow between the sparse and dense stages. In this work, we propose DashAttention (Differentiable and Adaptive Sparse Hierarchical Attention), which leverages the adaptively sparse $α$-entmax transformation to select a variable number of blocks according to the current query in the first stage. This in turn provides a prior for the second-stage softmax attention, keeping the entire hierarchy fully differentiable. Contrary to other hierarchical attention methods, we show that DashAttention is non-dispersive, translating to better long-context modeling ability. Experiments with large language models (LLMs) show that DashAttention achieves comparable accuracy as full attention with 75% sparsity and a better Pareto frontier than NSA and InfLLMv2, especially in high-sparsity regimes. We also provide an efficient, GPU-aware implementation of DashAttention in Triton, which achieves a speedup of up to over FlashAttention-3 at inference time. Overall, DashAttention offers a cost-effective strategy to model long contexts.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DiffCap-Bench: A Comprehensive, Challenging, Robust Benchmark for Image Difference Captioning

Image Difference Captioning (IDC) generates natural language descriptions that precisely identify differences between two images, serving as a key benchmark for fine-grained change perception, cross-modal reasoning, and image editing data construction. However, existing benchmarks lack diversity and compositional complexity, and standard lexical-overlap metrics (e.g., BLEU, METEOR) fail to capture semantic consistency or penalize hallucinations, which together prevent a comprehensive and robust evaluation of multimodal large language models (MLLMs) on IDC. To address these gaps, we introduce DiffCap-Bench, a comprehensive IDC benchmark covering ten distinct difference categories to ensure diversity and compositional complexity. Furthermore, we propose an LLM-as-a-Judge evaluation protocol grounded in human-validated Difference Lists, enabling a robust assessment of models' ability to both capture and describe visual changes. Through extensive evaluation of state-of-the-art MLLMs, we reveal significant performance gaps between proprietary and open-source models, highlight the critical importance of reasoning capability, and identify clear limitations in model scaling. Our framework also demonstrates strong alignment with human expert judgments and strong correlation with downstream image editing data construction quality. These findings establish DiffCap-Bench as both a reliable IDC evaluation framework and a practical predictor of downstream utility. The benchmark and code will be made publicly available to support further research.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

First Measurement of the Absolute Branching Fraction of $η_c \to γγ$

We apply a tag-and-probe method to precisely measure the absolute branching fraction of the decay $η_c \to γγ$ with the BESIII experiment at BEPCII. Starting with a large initial sample of $2712.4\pm 14.3$ million $ψ(3686)$ events, a sample of 0.16 million $η_c$ events are tagged via the golden channel $ψ(3686)\to π^0 h_c$, $h_c\to γη_c$, effectively avoiding interference effects. The absolute branching fraction of $η_c \to γγ$ is measured for the first time to be $\mathcal{B}(η_c \to γγ) = (2.45 \pm 0.48_{\rm stat.} \pm 0.09_{\rm syst.}) \times 10^{-4}$. Using the world average value of the total width of the $η_c$, the partial decay width of $η_c \to γγ$ is calculated to be $Γ(η_c \to γγ) = (7.48 \pm 1.48_{\rm stat.} \pm 0.30_{\rm syst.})~{\rm keV}$.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

First Observation of $D^{0(+)}\to \bar Kωe^+ν_e$ and Determination of the Branching Fraction of $\bar K_1(1270)\to \bar K ω$

Using 20.3~fb$^{-1}$ of $e^+e^-$ annihilation data collected at a center-of-mass energy of 3.773~GeV with the BESIII detector, we report the first observation of the semileptonic decays $D^0\to K^-ωe^+ν_e$ and $D^+\to K_S^0ωe^+ν_e$ with significances of $8.0σ$ and $5.8σ$, respectively, including systematic uncertainties. Their decay branching fractions are measured to be ${\cal B}(D^0\to K^-ωe^+ν_e)=(9.3^{+2.1}_{-1.9}\pm 0.7)\times10^{-5}$ and ${\cal B}(D^+\to K_S^0ωe^+ν_e)=(6.6^{+2.0}_{-1.8}\pm 0.6)\times10^{-5}$. Combining with the latest measurements of $D^{0(+)}\to K^-π^+π^{-(0)} e^+ν_e$ and assuming $\bar{K}_1(1270)$ to be the sole mediating resonance in all processes, the branching ratios are determined to be $\frac{Γ(K_1(1270)^-\to K^-π^+π^-)}{Γ(K_1(1270)^-\to K^-ω)} = 3.4^{+0.8}_{-0.7} \pm 0.3$ and $\frac{Γ(\bar{K}_1(1270)^0\to K^-π^+π^0)}{Γ(\bar{K}_1(1270)^0\to \bar{K}^0ω)} = 9.6^{+3.0}_{-2.7} \pm 0.8$. The combined branching fraction is determined to be $\mathcal B(\bar{K}_1(1270)\to \bar{K}ω) = (7.5\pm 1.3 \pm 0.5)\%$, which is the most precise measurement from a collider experiment. The first uncertainties are statistical, and the second are systematic.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters

Vision-Language Models (VLMs) have demonstrated remarkable proficiency in general multi-modal understanding; yet they struggle to efficiently acquire continually evolving domain-specific skills. Conventional approaches to enhancing VLM capabilities, such as Supervised Fine-Tuning (SFT), require extensive dataset curation and substantial computational resources. Model merging has emerged as an efficient alternative that enables the transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead. Unlike conventional merging of homogeneous LLMs, which mainly aggregates existing capabilities, cross-modal skill injection aims to induce emergent cross-modal capabilities by integrating a domain-expert LLM into a VLM. However, existing research lacks a systematic analysis of the applicability and methodology of cross-modal skill injection. In this study, we investigate cross-modal skill injection across three main aspects: scenarios, methods, and hyperparameters. For scenarios, we find that cross-modal skill injection generally performs well in instruction-following and cross-lingual settings, yet struggles with mathematical reasoning. For methods, we find that classic approaches such as TA and DARE consistently achieve superior performance over alternative merging methods. We also provide a systematic and quantitative analysis of the hyperparameter tuning that these classic methods critically depend on.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Manipulating Anomalous Transport via Crystal Symmetry in 2D Altermagnets

Anomalous transports, including the anomalous Hall effect (AHE) and anomalous Nernst effect (ANE), are typical manifestations of time-reversal-symmetry-breaking responses in materials. In general, the two Hall states with opposite Hall conductivities can be regarded as time-reversal pairs coupled to magnetic order, and switching between them relies on reversing the magnetization via an external magnetic field or electric current. Here, we introduce a approach for manipulating anomalous transport through crystal symmetry engineering in two-dimensional (2D) altermagnetic systems. Based on symmetry analysis, we demonstrate that 2D altermagnets (AM) with out-of-plane Néel vectors will not host any anomalous Hall transport. Remarkably, breaking the symmetry connecting the two magnetic sublattices, an anomalous Hall response can emerge immediately, and the signs of the anomalous Hall and anomalous Nernst conductivities can be flexibly controlled by the symmetry-breaking term, thereby realizing tunable sign-reversible anomalous transport. Furthermore, the feasibility of the theoretical scheme is further verified by explicit lattice-model construction. Using first-principles calculations, we investigate the realization of crystal symmetry-controlled anomalous transport in a 2D AM material Cr$_{2}$O$_{2}$. The results indicate that Cr$_{2}$O$_{2}$ with out-of-plane Néel vectors can sequentially exhibit the AHE and quantum anomalous Hall effect (QAHE) under continuous uniaxial strain. Interestingly, the sign reversal between these two effects can be achieved by simply rotating the strain direction by C$_{4z}$ symmetry. The corresponding ANE and its sign reversal are also revealed. Our findings provide a new strategy to manipulate anomalous transport, and should have significant potential applications.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Measurements of the absolute branching fractions of the $Λ_{c}^{+}$ hadronic decays

Based on 4.5 fb$^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies between 4599.53 MeV and 4698.82 MeV with the BESIII detector, the absolute branching fractions of twelve $Λ_{c}^{+}$ hadronic decay modes are measured with a double-tag technique. A global least-square fit is implemented simultaneously among different decay modes at different energy points. This paper gives the most precise results on the branching fractions of different decay modes to date, with precision improved by a factor of 2 to 3. Among them, the branching fraction of $Λ_{c}^{+}\to pK^{-}π^+$ is determined to be $(6.61\pm0.11\pm0.12)\%$, where the first uncertainty is statistical and the second is systematic. In addition, the $e^+e^-\toΛ_c^+\barΛ_c^-$ Born cross sections and the effective form factors ($|G_{\rm eff}|$) at different energy points have been determined with the highest precision to date.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Measurements of the branching fractions of $χ_{cJ}\to 2K^+ 2K^- ω$ and $ϕK^+ K^- ω$ decays

Using a data sample of $(2712.4 \pm 14.3) \times 10^{6}$ $ψ(3686)$ events collected with the BESIII detector operating at the BEPCII collider, we report the first observation of the decays $χ_{cJ}\to 2K^+ 2K^- ω$ and $χ_{cJ}\to ϕK^{+}K^{-} ω$ ($J = 0,1,2$) via the radiative transitions $ψ(3686) \to γχ_{cJ}$. The branching fractions of these decays are measured for the first time, and the statistical significance for each signal exceeds $10σ$.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MiMo-V2-Flash Technical Report

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Observation of Polarization and Determination of Electric and Magnetic Moments of $Ξ(1530)^0$ in $ψ(3686)\toΞ(1530)^0\barΞ(1530)^0$

Using the data sample of $2.7\times10^9$ $ψ(3686)$ events collected with the BESIII detector at the BEPCII collider, we present an observation of the $Ξ(1530)^0$ polarization in the decay $ψ(3686)\toΞ(1530)^0\barΞ(1530)^0$ with a significance larger than $20σ$ compared with all other tested hypotheses. The helicity amplitudes for the process $ψ(3686)\toΞ(1530)^0\barΞ(1530)^0$ and the moduli of form factors including electric charge, magnetic dipole, electric quadrupole, and magnetic octupole are measured for the first time by performing an angular distribution analysis. Additionally, the polarization correlations between $Ξ(1530)^0$ and $\barΞ(1530)^0$ are measured.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PatchAlign3D: Local Feature Alignment for Dense 3D Shape understanding

Current foundation models for 3D shapes excel at global tasks (retrieval, classification) but transfer poorly to local part-level reasoning. Recent approaches leverage vision and language foundation models to directly solve dense tasks through multi-view renderings and text queries. While promising, these pipelines require expensive inference over multiple renderings, depend heavily on large language-model (LLM) prompt engineering for captions, and fail to exploit the inherent 3D geometry of shapes. We address this gap by introducing an encoder-only 3D model that produces language-aligned patch-level features directly from point clouds. Our pre-training approach builds on existing data engines that generate part-annotated 3D shapes by pairing multi-view SAM regions with VLM captioning. Using this data, we train a point cloud transformer encoder in two stages: (1) distillation of dense 2D features from visual encoders such as DINOv2 into 3D patches, and (2) alignment of these patch embeddings with part-level text embeddings through a multi-positive contrastive objective. Our 3D encoder achieves zero-shot 3D part segmentation with fast single-pass inference without any test-time multi-view rendering, while significantly outperforming previous rendering-based and feed-forward approaches across several 3D part segmentation benchmarks. Project website: https://souhail-hadgi.github.io/patchalign3dsite/

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PEMNet: Towards Autonomous and Enhanced Environment-Aware Mobile Networks

With 5G deployment and the evolution toward 6G, mobile networks must make decisions in highly dynamic environments under strict latency, energy, and spectrum constraints. Achieving this goal, however, depends on prior knowledge of spatial-temporal variations in wireless channels and traffic demands. This motivates a joint, site-specific representation of radio propagation and user demand that is queryable at low online overhead. In this work, we propose the perception embedding map (PEM), a localized framework that embeds fine-grained channel statistics together with grid-level spatial-temporal traffic patterns over a base station's coverage. PEM is built from standard-compliant measurements -- such as measurement report and scheduling/quality-of-service logs -- so it can be deployed and maintained at scale with low cost. Integrated into PEM, this joint knowledge supports enhanced environment-aware optimization across PHY, MAC, and network layers while substantially reducing training overhead and signaling. Compared with existing site-specific channel maps and digital-twin replicas, PEM distinctively emphasizes (i) joint channel-traffic embedding, which is essential for network optimization, and (ii) practical construction using standard measurements, enabling network autonomy while striking a favorable fidelity-cost balance.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Search for a dark baryon in the $Ξ^-\rightarrowπ^-+{\rm invisible}$ decay

A search for a dark baryon is performed for the first time in the two-body decay $Ξ^-\rightarrowπ^-+{\rm invisible}$ using $(10.087\pm0.044)\times10^{9}$ $J/ψ$ events collected at a center-of-mass energy of $\sqrt{s}=3.097\,\mbox{GeV}$ with the BESIII detector at the BEPCII collider. No significant signal is observed, and the 90% (95%) confidence level upper limits on the branching fraction $B(Ξ^-\rightarrowπ^-+{\rm invisible})$ are determined to be $4.2\times10^{-5}$ ($5.2\times10^{-5}$), $6.9\times10^{-5}$ ($8.4\times10^{-5}$), $6.5\times10^{-4}$ ($7.6\times10^{-4}$), $1.1\times10^{-4}$ ($1.3\times10^{-4}$) and $4.5\times10^{-5}$ ($5.5\times10^{-5}$), under the dark baryon mass hypotheses of 1.07$\,\mbox{GeV}/c^2$, 1.10$\,\mbox{GeV}/c^2$, $m_Λ$ (1.116$\,\mbox{GeV}/c^2$), 1.13$\,\mbox{GeV}/c^2$, and 1.16$\,\mbox{GeV}/c^2$, respectively. The constraints obtained on the Wilson coefficients $C_{u s, s}^L$ and $C_{u s, s}^R$ are more stringent than the previous limits derived from the LHC searches for the colored mediators.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Volume-Consistent Kneading-Based Deformation Manufacturing for Material-Efficient Shaping

Conventional subtractive manufacturing inevitably involves material loss during geometric realization, while additive manufacturing still suffers from limitations in surface quality, process continuity, and productivity when fabricating complex geometries. To address these challenges, this paper proposes a volume-consistent kneading-based forming method for plastic materials, enabling continuous and controllable three-dimensional deformation under mass conservation. An integrated kneading-based manufacturing system is developed, in which geometry-aware kneading command generation, layer-wise kneading execution, and in-process point-cloud scanning are tightly coupled to form a closed-loop workflow of scanning, forming, and feedback compensation. Target geometries are analyzed through layer-wise point-cloud processing and classified into enveloping and non-enveloping types. Accordingly, an Envelope Shaping First strategy and a Similar Gradient Method are adopted to ensure stable material flow and continuous deformation. An RMSE-based compensation scheme is further introduced to correct systematic geometric deviations induced by elastic rebound and material redistribution. Experimental validation on five representative geometries demonstrates high geometric fidelity, with material utilization consistently exceeding 98%. The results indicate that kneading-based forming provides a promising alternative manufacturing paradigm for low-waste, customizable production.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Measurement of branching fractions of $Λ_{c}^{+}$ decays to $Σ^{+} η$ and $Σ^{+} η'$

By analyzing $e^+e^-$ collision data taken at center-of-mass energies $\sqrt{s}$ between 4.600 and 4.699 GeV with the BESIII detector at the BEPCII collider, corresponding to an integrated luminosity of $\rm 4.5~fb^{-1}$, we study the hadronic decays $Λ_{c}^{+} \rightarrow Σ^{+} η$ and $Λ_{c}^{+} \rightarrow Σ^{+} η^{\prime}$ using the single-tag method. The branching fraction ratio of $Λ_{c}^+ \rightarrow Σ^+ η$ relative to $Λ_{c}^+ \rightarrow Σ^+ π^0$ is determined to be $0.305 \pm 0.046_{\rm stat.} \pm 0.007_{\rm syst.}$, and that of $Λ_{c}^+ \rightarrow Σ^+ η'$ relative to $Λ_{c}^+ \rightarrow Σ^+ ω$ is $0.336 \pm 0.094_{\rm stat.} \pm 0.037_{\rm syst.}$. The ratio of $\frac{\mathcal{B}\left(Λ_{c}^{+} \rightarrow Σ^{+} η'\right)}{\mathcal{B}\left(Λ_{c}^{+} \rightarrow Σ^{+} η\right)} $ is determined to be $1.73 \pm 0.22_{\rm stat.} \pm 0.16_{\rm syst.}$. These results enrich our knowledge of charmed baryon decays.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

The influence of rotation and metallicity on the explodability of massive stars

During the late stages of massive stellar evolution, failed supernovae (FSN) may form through core-collapse processes. The traditional evaluation criterion $ξ_{2.5}$ $=$ 0.45, primarily established using non-rotating progenitor models, suffers from significant inaccuracies when applied to rotating pre-supernova systems. The effects of metallicity and rotation on the explodability landscapes of massive stars lack robust quantification. We aim to investigate how rotation and metallicity influence the explodability of massive stars. We investigate how rotation and metallicity affect stellar explodability using MESA simulations with initial rotational velocities of $0$, $300$, and $600~\mathrm{km,s^{-1}}$ at three metallicities ($Z_{\odot}$, $1/10,Z_{\odot}$, $1/50,Z_{\odot}$). Core-collapse phases are simulated with GR1D to determine critical heating efficiencies. Our results yield revised $ξ_{2.5}$ criteria: 0.45 for non-rotating models; 0.48 for $300~\mathrm{km,s^{-1}}$; 0.47 for $600~\mathrm{km,s^{-1}}$ at solar metallicity; and 0.59 for low-metallicity models. Chemically homogeneous evolution in rapidly rotating low-metallicity stars significantly raises the compactness limit for successful explosions and narrows the zero-age main sequence mass range for failed supernovae. Rotation substantially affects the explodability of low-metallicity massive stars, underscoring the importance of incorporating rotational effects in models of core-collapse supernova progenitors.

0 citations0 reviewsNo code linkedOpen access