arXiv subjects

Yuting Wang

Publications and source records attributed to Yuting Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Flat Topological Nodal Lines in Heavy-Fermion Compound CeCoGe$_3$

The interplay between strong electronic correlations, unconventional superconductivity, and symmetry-protected topology provides a fertile ground for discovering exotic quantum states. In this work, we investigate the correlated electronic structure and topological properties of the heavy fermion material CeCoGe$_3$ using density functional theory combined with dynamical mean-field theory calculations. Our results reveal a crossover from high temperature incoherent states to low temperature coherent heavy quasiparticles, accompanied by a mass enhancement of $m^*/m_{\text{DFT}}\sim 52.6$ at $T=25$ K. The interplay between electronic correlation, spin-orbit coupling and the noncentrosymmetric $I4mm$ crystal symmetry stabilize flat topological nodal lines within 10 meV of the Fermi level, which could contribute a significant density of states. The proximity of topological nodal lines to the Fermi surface suggests a potential role in mediating pressure induced unconventional superconductivity. Our work establishes CeCoGe$_3$ as a prototype topological nodal line Kondo semimetal. The coexistence of strong correlation, non-trivial band topology and superconductivity indicate CeCoGe$_3$ as a potential candidate for realizing topological superconductivity.

cond-mat.str-el↗

arXiv2026

RustCompCert: A Verified and Verifying Compiler for a Sequential Subset of Rust

We present our ongoing work on developing an end-to-end verified Rust compiler based on CompCert. It provides two guarantees: one is semantics preservation from Rust to assembly, i.e., the behaviors of source code includes the behaviors of target code, with which the properties verified at the source can be preserved down to the target; the other is memory safety ensured by the verifying compilation -- the borrow checking pass, which can simplify the verification of Rust programs, e.g., by allowing the verification tools focus on the functional correctness.

cs.PL↗

arXiv2026

J-PAS and PFS surveys in the era of dark energy and neutrino mass measurements

Fisher-matrix forecasts are presented for the cosmological surveys of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) and the Subaru Prime Focus Spectrograph (PFS). The wide, low-redshift coverage of J-PAS and the high-density, high-redshift mapping of PFS are strongly complementary: combining the two reduces marginalized uncertainties on all primary parameters compared with either survey individually. Adding the joint J-PAS+PFS data to next-generation CMB measurements from the Simons Observatory (SO) and \textsc{LiteBird} yields an expected precision of $σ(\sum m_ν)=0.017\,$eV in the $Λ$CDM$+\sum m_ν+N_{\rm eff}$ framework, sufficient to disfavour the inverted neutrino hierarchy at $2.34\,σ$ if the true mass sum equals the normal-ordering minimum. Motivated by recent DESI results, we also forecast within a $w_0w_a$CDM$+\sum m_ν+N_{\rm eff}$ cosmology, adopting the DESI\,DR2 best-fit values ($w_0=-0.758$, $w_a=-0.82$) as fiducial. The combination CMB+J-PAS+PFS then delivers $σ(w_0)=0.044$ and $σ(w_a)=0.18$, corresponding to a $5.1\,σ$ preference for a time-varying dark-energy equation of state. These findings show that J-PAS and PFS, especially when coupled with Stage-IV CMB observations, will provide competitive tests of neutrino physics and the dynamics of cosmic acceleration.

astro-ph.CO↗

arXiv2026

Large-scale EM Benchmark for Multi-Organelle Instance Segmentation in the Wild

Accurate instance-level segmentation of organelles in electron microscopy (EM) is critical for quantitative analysis of subcellular morphology and inter-organelle interactions. However, current benchmarks, based on small, curated datasets, fail to capture the inherent heterogeneity and large spatial context of in-the-wild EM data, imposing fundamental limitations on current patch-based methods. To address these limitations, we developed a large-scale, multi-source benchmark for multi-organelle instance segmentation, comprising over 100,000 2D EM images across variety cell types and five organelle classes that capture real-world variability. Dataset annotations were generated by our designed connectivity-aware Label Propagation Algorithm (3D LPA) with expert refinement. We further benchmarked several state-of-the-art models, including U-Net, SAM variants, and Mask2Former. Our results show several limitations: current models struggle to generalize across heterogeneous EM data and perform poorly on organelles with global, distributed morphologies (e.g., Endoplasmic Reticulum). These findings underscore the fundamental mismatch between local-context models and the challenge of modeling long-range structural continuity in the presence of real-world variability. The benchmark dataset and labeling tool will be publicly released soon.

cs.CV↗

arXiv2026

AutoBinder Agent: An MCP-Based Agent for End-to-End Protein Binder Design

Modern AI technologies for drug discovery are distributed across heterogeneous platforms-including web applications, desktop environments, and code libraries-leading to fragmented workflows, inconsistent interfaces, and high integration overhead. We present an agentic end-to-end drug design framework that leverages a Large Language Model (LLM) in conjunction with the Model Context Protocol (MCP) to dynamically coordinate access to biochemical databases, modular toolchains, and task-specific AI models. The system integrates four state-of-the-art components: MaSIF (MaSIF-site and MaSIF-seed-search) for geometric deep learning-based identification of protein-protein interaction (PPI) sites, Rosetta for grafting protein fragments onto protein backbones to form mini proteins, ProteinMPNN for amino acid sequences redesign, and AlphaFold3 for near-experimental accuracy in complex structure prediction. Starting from a target structure, the framework supports de novo binder generation via surface analysis, scaffold grafting and pose construction, sequence optimization, and structure prediction. Additionally, by replacing rigid, script-based workflows with a protocol-driven, LLM-coordinated architecture, the framework improves reproducibility, reduces manual overhead, and ensures extensibility, portability, and auditability across the entire drug design process.

q-bio.BM↗

arXiv2026

Deep Learning Superresolution for 7T Knee MR Imaging: Impact on Image Quality and Diagnostic Performance

Background: Deep learning superresolution (SR) may enhance musculoskeletal MR image quality, but its diagnostic value in knee imaging at 7T is unclear. Objectives: To compare image quality and diagnostic performance of SR, low-resolution (LR), and high-resolution (HR) 7T knee MRI. Methods: In this prospective study, 42 participants underwent 7T knee MRI with LR (0.8*0.8*2 mm3) and HR (0.4*0.4*2 mm3) sequences. SR images were generated from LR data using a Hybrid Attention Transformer model. Three radiologists assessed image quality, anatomic conspicuity, and detection of knee pathologies. Arthroscopy served as reference in 10 cases. Results: SR images showed higher overall quality than LR (median score 5 vs 4, P<.001) and lower noise than HR (5 vs 4, P<.001). Visibility of cartilage, menisci, and ligaments was superior in SR and HR compared to LR (P<.001). Detection rates and diagnostic performance (sensitivity, specificity, AUC) for intra-articular pathology were similar across image types (P>=.095). Conclusions: Deep learning superresolution improved subjective image quality in 7T knee MRI but did not increase diagnostic accuracy compared with standard LR imaging.

eess.IV↗

arXiv2026

RFAssigner: A Generic Label Assignment Strategy for Dense Object Detection

Label assignment is a critical component in training dense object detectors. State-of-the-art methods typically assign each training sample a positive and a negative weight, optimizing the assignment scheme during training. However, these strategies often assign an insufficient number of positive samples to small objects, leading to a scale imbalance during training. To address this limitation, we introduce RFAssigner, a novel assignment strategy designed to enhance the multi-scale learning capabilities of dense detectors. RFAssigner first establishes an initial set of positive samples using a point-based prior. It then leverages a Gaussian Receptive Field (GRF) distance to measure the similarity between the GRFs of unassigned candidate locations and the ground-truth objects. Based on this metric, RFAssigner adaptively selects supplementary positive samples from the unassigned pool, promoting a more balanced learning process across object scales. Comprehensive experiments on three datasets with distinct object scale distributions validate the effectiveness and generalizability of our method. Notably, a single FCOS-ResNet-50 detector equipped with RFAssigner achieves state-of-the-art performance across all object scales, consistently outperforming existing strategies without requiring auxiliary modules or heuristics.

cs.CV↗

arXiv2026

GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

We present GLM-4.1V-Thinking, GLM-4.5V, and GLM-4.6V, a family of vision-language models (VLMs) designed to advance general-purpose multimodal understanding and reasoning. In this report, we share our key findings in the development of the reasoning-centric training framework. We first develop a capable vision foundation model with significant potential through large-scale pre-training, which arguably sets the upper bound for the final performance. We then propose Reinforcement Learning with Curriculum Sampling (RLCS) to unlock the full potential of the model, leading to comprehensive capability enhancement across a diverse range of tasks, including STEM problem solving, video understanding, content recognition, coding, grounding, GUI-based agents, and long document interpretation. In a comprehensive evaluation across 42 public benchmarks, GLM-4.5V achieves state-of-the-art performance on nearly all tasks among open-source models of similar size, and demonstrates competitive or even superior results compared to closed-source models such as Gemini-2.5-Flash on challenging tasks including Coding and GUI Agents. Meanwhile, the smaller GLM-4.1V-9B-Thinking remains highly competitive-achieving superior results to the much larger Qwen2.5-VL-72B on 29 benchmarks. We open-source both GLM-4.1V-9B-Thinking and GLM-4.5V. We further introduce the GLM-4.6V series, open-source multimodal models with native tool use and a 128K context window. A brief overview is available at https://z.ai/blog/glm-4.6v. Code, models and more information are released at https://github.com/zai-org/GLM-V.

cs.CV↗

arXiv2025

Proper conflict-free choosability of planar graphs

A proper conflict-free coloring of a graph is a proper vertex coloring wherein each non-isolated vertex's open neighborhood contains at least one color appearing exactly once. For a non-negative integer $k$, a graph $G$ is said to be proper conflict-free (degree+$k$)-choosable if given any list assignment $L$ for $G$ where $|L(v)| = d(v) + k$ holds for every vertex $v \in V(G)$, there exists a proper conflict-free coloring $ϕ$ of $G$ such that $ϕ(v) \in L(v)$ for all $v \in V(G)$. Recently, Kashima, Škrekovski, and Xu proposed two related conjectures on proper conflict-free choosability: the first asserts the existence of an absolute constant $k$ such that every graph is proper conflict-free (degree+$k$)-choosable, while the second strengthens this claim by restricting to connected graphs other than the cycle of length 5 and reducing the constant to $k=2$. In this paper, we confirm the second conjecture for three graph classes: $K_4$-minor-free graphs with maximum degree at most 4, outer-1-planar graphs with maximum degree at most 4, and planar graphs with girth at least 12; we also confirm the first conjecture for these same graph classes, in addition to all outer-1-planar graphs (without degree constraints). Moreover, we prove that planar graphs with girth at least 12 and outer-1-planar graphs are proper conflict-free $6$-choosable.

math.CO↗

arXiv2025

J-PAS: Forecasting constraints on Neutrino Masses

The large-scale structure survey J-PAS is taking data since October 2023. In this work, we present a forecast based on the Fisher matrix method to establish its sensitivity to the sum of the neutrino masses. We adapt the Fisher Galaxy Survey Code (FARO) to account for the neutrino mass under various configurations applied to galaxy clustering measurements. This approach allows us to test the sensitivity of J-PAS to the neutrino mass across different tracers, with and without non-linear corrections, and under varying sky coverage. We perform our forecast for two cosmological models: $ΛCDM + \sum m_ν$ and $w_0w_a CDM + \sum m_ν$. We combine our J-PAS forecast with Cosmic Microwave Background (CMB) data from the Planck Collaboration and Type Ia supernova (SN) data from Pantheon Plus. Our analysis shows that, for a sky coverage of 8,500 square degrees, J-PAS galaxy clustering data alone will constrain the sum of the neutrino masses to an upper limit at 95% C.L of $\sum m_ν< 0.32$ eV for the $ΛCDM + \sum m_ν$ model, and $\sum m_ν< 0.36$ eV for the $w_0w_a CDM + \sum m_ν$ model. When combined with Planck data, the upper limit improves significantly. For J-PAS+Planck at 95% C.L, we find $\sum m_ν< 0.061$ eV for the $ΛCDM + \sum m_ν$ model, and for J-PAS+Planck+Pantheon Plus, we obtain $\sum m_ν< 0.12$ eV for the $w_0w_a CDM + \sum m_ν$ model. These results demonstrate that J-PAS clustering measurements can play a crucial role in addressing challenges in the neutrino sector, including potential tensions between cosmological and terrestrial measurements of the neutrino mass, as well as in determining the mass ordering.

astro-ph.CO↗

arXiv2025

MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents

Building general-purpose graphical user interface (GUI) agents has become increasingly promising with the progress in vision language models. However, developing effective mobile GUI agents with reinforcement learning (RL) remains challenging due to the heavy-tailed distribution of task difficulty and the inefficiency of large-scale environment sampling. We present an online agentic reinforcement learning framework MobileRL to enhance GUI agents in mobile environments. Its core component is the Difficulty-ADAptive GRPO (ADAGRPO) algorithm. In ADAGRPO, we design difficulty-adaptive positive replay and failure curriculum filtering to adapt the model to different task difficulties. We introduce the shortest-path reward adjustment strategy to reshape rewards concerning the task length in multi-turn agentic tasks. Those strategies jointly stabilize RL training, improve sample efficiency, and generate strong performance across diverse mobile apps and tasks. We apply MOBILERL to two open models (Qwen2.5-VL-7B-Instruct and GLM-4.1V-9B-Base). The resultant MOBILERL-9B model achieves state-of-the-art results in terms of success rates on both AndroidWorld (80.2%) and AndroidLab (53.6%). The MOBILERL framework is open-sourced at: https://github.com/THUDM/MobileRL.

cs.LG↗

arXiv2025

End-to-end Compositional Verification of Program Safety through Verified and Verifying Compilation

Program safety (i.e., absence of undefined behaviors) is critical for correct operation of computer systems. It is usually verified at the source level (e.g., by separation logics) and preserved to the target by verified compilers (e.g., CompCert), thereby achieving end-to-end verification of safety. However, modern safe programming languages like Rust pose new problems in achieving end-to-end safety. Because not all functionalities can be implemented in the safe language, mixing safe and unsafe modules is needed. Therefore, verified compilation must preserve a modular notion of safety which can be composed at the target level. Furthermore, certain classes of errors (e.g., memory errors) are automatically excluded by verifying compilation (e.g., borrow checking) for modules written in safe languages. As a result, verified compilation needs to cooperate with verifying compilation to ensure end-to-end safety. To address the above problems, we propose a modular and generic definition of safety called open safety based on program semantics described as open labeled transition systems (LTS). Open safety is composable at the boundary of modules and can be modularly preserved by verified compositional compilation. Those properties enable separate verification of safety for heterogeneous modules and composition of the safety results at the target level. Open safety can be generalized to partial safety (i.e., only a certain class of errors can occur). By this we formalized the correctness of verifying compilation as derivation of total safety from partial safety. We demonstrate how our framework can combine verified and verifying compilation by developing a verified compiler for an ownership language (called Owlang) inspired by Rust. We evaluate our approach on the compositional safety verification using a hash map implemented by Owlang and C.

cs.PL↗

arXiv2025

Introduction to the Chinese Space Station Survey Telescope (CSST)

The Chinese Space Station Survey Telescope (CSST) is an upcoming Stage-IV sky survey telescope, distinguished by its large field of view (FoV), high image quality, and multi-band observation capabilities. It can simultaneously conduct precise measurements of the Universe by performing multi-color photometric imaging and slitless spectroscopic surveys. The CSST is equipped with five scientific instruments, i.e. Multi-band Imaging and Slitless Spectroscopy Survey Camera (SC), Multi-Channel Imager (MCI), Integral Field Spectrograph (IFS), Cool Planet Imaging Coronagraph (CPI-C), and THz Spectrometer (TS). Using these instruments, CSST is expected to make significant contributions and discoveries across various astronomical fields, including cosmology, galaxies and active galactic nuclei (AGN), the Milky Way and nearby galaxies, stars, exoplanets, Solar System objects, astrometry, and transients and variable sources. This review aims to provide a comprehensive overview of the CSST instruments, observational capabilities, data products, and scientific potential.

astro-ph.IM↗

arXiv2025

Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measurements

Understanding whether cosmic acceleration arises from a cosmological constant or a dynamical component is a central goal of cosmology, and the Dark Energy Spectroscopic Instrument (DESI) enables stringent tests with high-precision distance measurements. We analyze baryon acoustic oscillation (BAO) measurements from DESI Data Release 1 (DR1) and Data Release 2 (DR2), combined with Type Ia supernovae and a cosmic microwave background (CMB) distance prior. With the larger statistical power and wider redshift coverage of DR2, the preference for dynamical dark energy does not diminish relative to DR1. Using both a shape-function reconstruction and non-parametric approaches with a Horndeski-motivated correlation prior, we find that the dark-energy equation of state $w(z)$ varies with redshift. BAO data alone yield modest constraints, but in combination with independent supernova compilations and the CMB prior they strengthen the evidence for dynamics. Bayesian model comparison shows moderate support for departures from $Λ$CDM when multiple degrees of freedom in $w(z)$ are allowed, corresponding to $\approx3σ$ tension with $Λ$CDM (and higher for some data sets). Despite methodological differences, our results are consistent with companion DESI papers, underscoring the complementarity of approaches. Possible systematics remain under study; forthcoming DESI, \emph{Euclid}, and next-generation CMB data will provide decisive tests.

astro-ph.CO↗

arXiv2025

Automatic Large Language Models Creation of Interactive Learning Lessons

We explore the automatic generation of interactive, scenario-based lessons designed to train novice human tutors who teach middle school mathematics online. Employing prompt engineering through a Retrieval-Augmented Generation approach with GPT-4o, we developed a system capable of creating structured tutor training lessons. Our study generated lessons in English for three key topics: Encouraging Students' Independence, Encouraging Help-Seeking Behavior, and Turning on Cameras, using a task decomposition prompting strategy that breaks lesson generation into sub-tasks. The generated lessons were evaluated by two human evaluators, who provided both quantitative and qualitative evaluations using a comprehensive rubric informed by lesson design research. Results demonstrate that the task decomposition strategy led to higher-rated lessons compared to single-step generation. Human evaluators identified several strengths in the LLM-generated lessons, including well-structured content and time-saving potential, while also noting limitations such as generic feedback and a lack of clarity in some instructional sections. These findings underscore the potential of hybrid human-AI approaches for generating effective lessons in tutor training.

cs.CY↗

arXiv2025

Measuring the redshift-space distortions by cross-correlating the density fields before and after reconstruction

In this work, we develop a theoretical model for the cross-power spectrum of the galaxy density field before and after standard baryonic acoustic oscillation (BAO) reconstruction. Using this model, we extract the redshift-space distortion (RSD) parameter from the cross-power spectrum. The model is validated against a suite of high-resolution $N$-body simulations, demonstrating its accuracy and robustness for cosmological analyses.

astro-ph.CO↗

arXiv2025

A cosmological full-shape power spectra analysis using pre- and post-reconstructed density fields

In this work, we investigate a joint fitting approach based on theoretical models of power spectra associated with density-field reconstruction. Specifically, we consider the matter auto-power spectra before and after baryon acoustic oscillation (BAO) reconstruction, as well as the cross-power spectrum between the pre- and post-reconstructed density fields. We present redshift-space models for these three power spectra at the one-loop level within the framework of standard perturbation theory (SPT), and perform a joint analysis using three types of power spectra, and quantify their impact on parameter constraints. When restricting the analysis to wavenumbers $k \leq 0.2\,h\,\mathrm{Mpc}^{-1}$ and adopting a smoothing scale of $R_{\mathrm{s}} = 15\,h^{-1}\,\mathrm{Mpc}$, we find that incorporating all three power spectra improves parameter constraints by approximately $11\%\text{--}16\%$ compared to using only the post-reconstruction power spectrum, with the Figure of Merit (FoM) increasing by $10.5\%$. These results highlight the advantages of leveraging multiple power spectra in BAO reconstruction, ultimately enabling more precise cosmological parameter estimation.

astro-ph.CO↗

arXiv2025

GeV-level $γ$-ray and positron beams produced by collisions of ultra-intense ultra-short laser on high-energy electron beam

Based on collisions between the 100 PW laser and 8 GeV superconducting linear accelerator constructing at the Shanghai hard X-ray free electron laser system (SHINE), the building of GeV-level $γ$-ray as well as positron beams are proposed according to particle-in-cell simulations. Key processes are considered involving the nonlinear inverse Compton scattering for $γ$-ray generation and the multiphoton Breit-Wheeler process for electron-positron pair production. Regardless of laser polarization, the simulations indicate that $γ$-ray beams achieve energy up to 8 GeV, brilliance around 10$^{27}$ photons/(s mm$^{2}$ mrad$^{2}$), and emittance as low as 0.1 mm mrad, while positron beams reach energy up to 7 GeV, brilliance around 4 $\times$ 10$^{24}$ positrons/(s mm$^{2}$ mrad$^{2}$), and emittance as low as 0.1 mm mrad. Various applications could benefit from the possible high-energy $γ$-ray and positron beams built at the SHINE facility, including fundamental physics of strong-field quantum electrodynamics theory validation, nuclear physics, radiopharmaceutical preparation, and imaging, etc.

physics.acc-ph↗