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Zeyu Chen

Publications and source records attributed to Zeyu Chen.

At least 37 records · Page 2Linked to original sources

Filling and Interlayer Superexchange Control Superconductivity in La$_3$Ni$_2$O$_7$

A central puzzle in bilayer nickelate superconductors is why pressure, epitaxial strain, oxygen stoichiometry, and chemical substitution produce systematic but apparently different changes in the superconducting T$_\text{c}$. Here we show that these trends can be organized by a two-parameter control principle based on the $d_{x^2-y^2}$-orbital filling $n_x$ and the effective interlayer antiferromagnetic superexchange $J_\perp$. Starting from a physical picture in which the nearly half-filled $d_{z^2}$ orbital supplies localized spin correlations while the nearly quarter-filled $d_{x^2-y^2}$ orbital carries superconductivity, we study an effective bilayer $t-J_\parallel-J_\perp$ model with parameters constrained by first-principles calculations. Combined slave-boson mean-field and density-matrix renormalization group calculations place realistic La$_3$Ni$_2$O$_7$ in an overdoped-cuprate-like regime where T$_\text{c}$ is governed mainly by the pairing scale. In this regime, hole doping reduces $n_x$ and suppresses T$_\text{c}$, whereas tuning routes that enhance $J_\perp$ raise T$_\text{c}$. This framework accounts for the suppression by over-oxidation and Ca/Sr substitution, the half-dome oxygen-stoichiometry response in the film, the enhancement by Nd/Sm substitution in the pressurized bulk, the right-triangle-like pressure dependence in the bulk, and the enhancement by compressive strain in the film. It also separates clean carrier doping from oxygen-vacancy tuning: clean electron doping mainly increases $n_x$, whereas oxygen vacancies weaken the apical-oxygen-mediated exchange path and introduce disorder. This leads to a falsifiable materials-design prediction: clean electron doping should enhance the pairing scale if introduced without oxygen vacancies or strong pair-breaking disorder.

cond-mat.supr-con↗

Environmental Imprints on the Assembly of the Cool Gas around Bright Cluster Galaxies

Galaxy clusters represent extreme cosmic laboratories where environmental processes dramatically reshape their constituent galaxies, yet their effect on the gaseous halos of central galaxies remains poorly constrained. Here we present the first statistical mapping of cool gas around massive brightest cluster galaxies (BCGs) at $z\approx0.55$. Using Mg II absorption in stacked sight-line spectra from over a million background quasars observed by the Dark Energy Spectroscopic Instrument, we compare BCGs to a matched sample of field galaxies and trace the radial profile from 40 kpc to 15 Mpc. Our analysis reveals a striking dual environmental signature: within 200 kpc, the circumgalactic medium (CGM) around BCGs is significantly suppressed compared to that of field galaxies, while at larger radii (200 kpc to 10 Mpc) a pronounced excess of cool gas emerges. This clear transition from suppression in the core to enhancement on such large scales delineates a novel observed pattern for gas regulation by the dense environment. It suggests that clusters may not only strip gas in the core but also facilitate its accumulation in the outskirts. Our results provide key observational constraints on theoretical models of environmental processing in and around the most massive dark matter halos.

astro-ph.GA↗

RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities

LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable snippets, focus on binary classification (vulnerable or not), and do not account for the risk that publicly-released datasets are part of model training corpora. We introduce RustMizan, a benchmarking framework for Rust vulnerability analysis that addresses these gaps. RustMizan contains compilable code variants at the crate, file, and function levels, with annotations for binary vulnerability detection, CWE classification, and function- and line-level localization. A paired mutation framework produces semantics-preserving code mutants for contamination testing and robustness probing. Across four frontier models in an agentic setup with command-line access, binary classification sits in the 56-65% range, but line localization F1 stays near 20%, and adversarial cues drop line F1 by about 27%.

cs.CR↗

Stellar feedback drives the baryon deficiency in low-mass galaxies

Stellar feedback, as a key process regulating the baryon cycle, is thought to greatly redistribute baryonic material inside and outside the dark matter halos (DMHs), however the observational evidences are lacking. Through stacking analyses of ~400,000 galaxy spectra from Dark Energy Spectroscopic Instrument (DESI), we find star formation driven cool outflows in Mg II absorption line. Assuming only gravity acts on the launched gas, our calculations reveal that outflows from low mass galaxies ($M_*<10^{10}\,\rm M_\odot$) are capable of escaping beyond the DMHs, which aligns well with our finding in the circumgalactic medium (CGM) absorption along the minor-axes of galaxies using background quasars. This research offers indirect evidence that stellar feedback drives the low baryon retention rate in low-mass haloes, implicating that baryonic processes within galaxies are connected with the diffuse matter beyond the DMHs.

astro-ph.GA↗

Cooler Phases of the Circumgalactic Medium Are More Centrally Concentrated: Constraints from Multiphase Absorption Lines

We present a systematic study of the multiphase circumgalactic medium (CGM) around galaxies and quasars, traced by Ca II $λ\lambda3934,3969$, Mg II $λ\lambda2796,2803$, and C IV $λ\lambda1548,1550$, using the Year 1 dataset from the Dark Energy Spectroscopic Instrument. These three doublets trace CGM gas across a range of temperatures, from cold to warm phases, and we employ a stacking technique to measure the corresponding absorption signals using background sources. We show that CGM structure is strongly phase-dependent: ions tracing progressively cooler gas exhibit increasingly steep radial profiles in equivalent width ($W_i$). These trends are broadly consistent with predictions from cosmological simulations, supporting a phase-stratified CGM in which cooler gas is more centrally concentrated. Specifically, halos of emission-line galaxies exhibit a strong radial transition from cool to warm gas, whereas halos of quasars show a more uniform distribution, likely regulated by active galactic nuclei feedback; in contrast, the cold gas traced by Ca II in low-redshift galaxies is tightly confined to inner regions. We further demonstrate that the radial scaling $W_i \propto D^α$ is primarily set by host stellar mass, particularly for the cool-phase medium, suggesting efficient heating processes in massive halos. By jointly leveraging multiple absorption tracers from observations and simulations, we map the CGM from cold to warm phases and place new constraints on the baryon cycle governing galaxy evolution.

astro-ph.GA↗

Learning from the Self-future: On-policy Self-distillation for dLLMs

On-policy self-distillation (OPSD) has proven effective for post-training large language models (LLMs), yet its application to diffusion LLMs (dLLMs) remains unexplored. Existing OPSD methods are inherently autoregressive-centric. They inject privileged information via left-to-right prefix conditioning with token-level divergence supervision, a design that fundamentally conflicts with the arbitraryorder generation of dLLMs. We introduce d-OPSD, the first OPSD framework tailored for dLLMs. Our approach makes two core contributions. First, we reframe self-teacher construction by using self-generated answers as suffix conditioning, enabling the student model to learn from "self future-experience" rather than privileged prefixes. Second, we shift supervision from token-level to step-level, aligning training with the iterative denoising process of dLLMs. Experiments across four reasoning benchmarks show that d-OPSD consistently outperforms RLVR and SFT baselines with superior sample efficiency, requiring only around 10% of the optimization steps by RLVR and opening a promising pathway for dLLM posttraining. The code is available at https://github.com/xingzhejun/d-OPSD.

cs.CL↗

CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook

Multimodal representation alignment is pivotal for large language models and robotics. Traditional methods are often hindered by cross-modal information discrepancies and data scarcity, leading to suboptimal alignment spaces that overlook modality-unique features. We propose CodeBind, a framework that optimizes multimodal representation spaces through a modality-shared-specific codebook design. By incrementally aligning target and bridging modalities, CodeBind bypasses the need for fully paired data. Unlike traditional hard alignment, CodeBind decomposes features into shared components for semantic consistency and specific components for modality-unique details. This design utilizes a compositional vector quantization scheme, where a shared codebook bridges modality gaps and modality-specific codebooks mitigate representation bias by preventing dominant modalities from overshadowing others. Validated across nine modalities (text, image, video, audio, depth, thermal, tactile, 3D point cloud, EEG), CodeBind achieves state-of-the-art performance in multimodal classification and retrieval tasks.

cs.CV↗

Beyond Detection: A Structure-Aware Framework for Scene Text Tracking

Modern visual object trackers show impressive results on general targets, yet their performance drops substantially when dealing with scene text. Although currently underexplored, tracking text in videos is essential for dynamic text manipulations such as segmentation, removal, and editing. To fill this gap, this paper formalizes this specific task as Scene Text Tracking and presents the first systematic work for it. We identify three primary challenges in this task: 1) severe geometric distortions from perspective shifts, 2) high visual ambiguity across different instances, and 3) high sensitivity to fine-grained structural details. To address these issues, we propose SymTrack, a unified detection-free framework with synergistic dual-branch design. It integrates a Cross-Expert Calibration mechanism to reduce semantic bias, along with a Predictive Token Rectification mechanism to correct structural imbalances, complemented by an Adaptive Inference Engine that stabilizes predictions under motion constraints. Considering the lack of dedicated benchmarks for this task, we utilize three datasets from video text spotting to construct a benchmark with high-quality annotations. Extensive experiments demonstrate that SymTrack sets the new state-of-the-art on all three benchmarks, outperforming previous best trackers by up to 11.97\% AUC on $ \text{BOVText}_{\text{SOT}} $. Overall, our work promotes efficient and thorough text tracking, paving the way toward more generalized video text manipulation.

cs.CV↗

DRS-GUI: Dynamic Region Search for Training-Free GUI Grounding

GUI agents powered by Multimodal Large Language Models (MLLMs) have demonstrated impressive capability in understanding and executing user instructions. However, accurately grounding instruction-relevant elements from high-resolution screenshots cluttered with irrelevant UI components remains challenging for existing approaches. Inspired by how humans dynamically adjust their perceptual scope to locate task-related regions on complex screens, we propose DRS-GUI, a training-free dynamic region search framework for GUI grounding that can be seamlessly integrated into existing MLLMs. DRS-GUI introduces a lightweight UI Perceptor that performs three human-like perceptual actions (Focus, Shift, and Scatter) to progressively explore the interface and generate region proposals. To dynamically schedule these actions, we further design an Action Planner based on Monte Carlo Tree Search (MCTS). A region quality reward is employed to evaluate and select the highly instruction-relevant region, efficiently pruning redundant UI elements. Experiments demonstrate that DRS-GUI yields a 14\% improvement on ScreenSpot-Pro for general and GUI-specific MLLMs (Qwen2.5-VL-7B and UGround-V1-7B), significantly enhancing grounding performance and generalization.

cs.AI↗

StyleTextGen: Style-Conditioned Multilingual Scene Text Generation

Style-conditioned scene text generation faces unique challenges in extracting precise text styles from complex backgrounds and maintaining fine-grained style consistency across characters, especially for multilingual scripts. We propose StyleTextGen, a novel framework that learns to perceive and replicate visual text styles across different languages and writing systems. Our approach features three key contributions: First, we introduce a dual-branch style encoder dedicated to style modeling, yielding robust multilingual text style representations in complex real-world scenes. Second, we design a text style consistency loss that enhances style coherence and improves overall visual quality. Third, we develop a mask-guided inference strategy that ensures precise style alignment between generated and reference text. To facilitate systematic evaluation, we construct StyleText-CE, a bilingual scene text style benchmark covering both monolingual and cross-lingual settings. Extensive experiments demonstrate that StyleTextGen significantly outperforms existing methods in style consistency and cross-lingual generalization, establishing new state-of-the-art performance in multilingual style-conditioned text generation.

cs.CV↗

On the Simulation Cost of Quantum Finite Automata

This paper identifies exact probabilistic simulation cost as the natural quantitative measure of quantum advantage for finite automata under strict cutpoints. It gives sharp simulation laws for two representative models. A one-way finite automaton with $c$ classical states and a $q$-dimensional quantum register has exact probabilistic simulation cost $Θ(cq^2)$, while an $n$-dimensional measure-once one-way quantum finite automaton has worst-case cost $Θ(n^2)$. The proofs develop a prepare--test framework, in which prefixes generate the relevant real operator degrees of freedom and suffixes convert them into strict-cutpoint tests. The same obstruction is recast through finite sign-rank matrices, clarifying the role of Forster's spectral method. Placed beside the surrounding two-way separations, these results give a clean hierarchy of finite-automata quantum advantage.

quant-ph↗

Rational-Valued Affine Verifiers in Arthur--Merlin Proof Systems

Affine automata provide a finite-state computational model that preserves the linear-algebraic structure of quantum computation while operating entirely over the reals. Recent work has shown that affine automata can far surpass classical probabilistic finite-state verifiers. However, prior constructions relied on arbitrary real-valued transition matrices, leaving open whether the observed power stems from the affine mechanism itself or from computational resources implicitly encoded in irrational or infinite-precision parameters. This paper studies one-way and two-way automata with deterministic and affine states as verifiers in Arthur--Merlin proof systems under the restriction that every affine transition matrix has rational entries, and shows that the resulting rational model still supports the main verification advantages of affine finite-state verification. At the one-way level, we verify benchmark nonregular languages that are provably hard or impossible for classical two-way probabilistic verifiers. At the two-way level, we achieve weak verification of every Turing-recognizable language, strong bounded-error verification for every language in $\mathbf{ATIME}(2^{O(n)})$, and perfect-completeness strong verification for every language in $\mathbf{PSPACE}$. These results establish that the remarkable verification power of affine finite-state automata is structural.

cs.FL↗

Exact Separation of Words via Trace Geometry

A basic question in the study of measure-once quantum finite automata is whether two distinct input words can be separated with certainty. The exact separation problem reduces to a trace-vanishing question in \(SU(2)\). The main difficulty lies in the genuinely nonabelian regime, where \(u\) and \(v\) have the same abelianization. This paper develops a slice-driven framework that converts algebraic invariants of the word -- prefix statistics, metabelian polynomials, and slope specializations -- into explicit low-dimensional families in \(SU(2)^2\) on which the trace-vanishing question can be analyzed effectively. A quadratic trace-deficit identity on a principal one-parameter family provides the main algebraic-to-geometric bridge. Building on this framework, the paper establishes three core certified slice criteria: a dihedral criterion, equivalently readable through a signed \(a\)-count; a quaternionic criterion; and a local one-row criterion. Together with a supplementary interior-point test and a binary-dihedral slice, these results sharply reduce the unresolved portion of the problem to a residual super-degenerate class, while also clarifying the limitations of certification strategies based only on finitely many finite-subgroup evaluations.

cs.FL↗

First Statistical Detection of MgII-traced Cool Gas Outflows with JWST Towards Cosmic Dawn

Galactic-scale outflows are a crucial component of galaxy evolution, yet their properties in the early universe remain poorly constrained. We present the first statistical investigation of \ion{Mg}{2}-traced cool gas outflows in galaxies spanning a wide cosmic timeline from $z \approx 1$ to $z > 6$ (with sample coverage extending to $z \sim 10$). Using thousands of public JWST/NIRSpec spectra, we employ a signal-to-noise weighted spectral stacking technique on the \ion{Mg}{2} $λ\lambda2796, 2803$ absorption doublet. We robustly detect blueshifted \ion{Mg}{2} absorption in nearly all stellar mass and redshift bins, with the exception of the lowest-mass systems at $z \sim 1-2$. The outflow equivalent width exhibits a positive correlation with stellar mass ($M_*$) at all epochs, with the fitted slope of $1.21 \pm 0.35$. Our work provides the first statistical constraints on \ion{Mg}{2}-traced cool outflows in the low-mass ($M_* \lesssim 10^{9.5} \, \mathrm{M}_\odot$), high-redshift ($z > 3$) regime. We also find that the outflow velocities generally remain below the host halo escape velocities, consistent with a galactic fountain scenario. The consistency of the stellar mass-outflow equivalent width relation across $z \sim 2-6$ suggests a persistent, unevolving feedback mechanism governing the baryon cycle towards cosmic dawn.

astro-ph.GA↗

Efficient endometrial carcinoma screening via cross-modal synthesis and gradient distillation

Early detection of myometrial invasion is critical for the staging and life-saving management of endometrial carcinoma (EC), a prevalent global malignancy. Transvaginal ultrasound serves as the primary, accessible screening modality in resource-constrained primary care settings; however, its diagnostic reliability is severely hindered by low tissue contrast, high operator dependence, and a pronounced scarcity of positive pathological samples. Existing artificial intelligence solutions struggle to overcome this severe class imbalance and the subtle imaging features of invasion, particularly under the strict computational limits of primary care clinics. Here we present an automated, highly efficient two-stage deep learning framework that resolves both data and computational bottlenecks in EC screening. To mitigate pathological data scarcity, we develop a structure-guided cross-modal generation network that synthesizes diverse, high-fidelity ultrasound images from unpaired magnetic resonance imaging (MRI) data, strictly preserving clinically essential anatomical junctions. Furthermore, we introduce a lightweight screening network utilizing gradient distillation, which transfers discriminative knowledge from a high-capacity teacher model to dynamically guide sparse attention towards task-critical regions. Evaluated on a large, multicenter cohort of 7,951 participants, our model achieves a sensitivity of 99.5\%, a specificity of 97.2\%, and an area under the curve of 0.987 at a minimal computational cost (0.289 GFLOPs), substantially outperforming the average diagnostic accuracy of expert sonographers. Our approach demonstrates that combining cross-modal synthetic augmentation with knowledge-driven efficient modeling can democratize expert-level, real-time cancer screening for resource-constrained primary care settings.

cs.CV↗

ERNIE 5.0 Technical Report

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio. All modalities are trained from scratch under a unified next-group-of-tokens prediction objective, based on an ultra-sparse mixture-of-experts (MoE) architecture with modality-agnostic expert routing. To address practical challenges in large-scale deployment under diverse resource constraints, ERNIE 5.0 adopts a novel elastic training paradigm. Within a single pre-training run, the model learns a family of sub-models with varying depths, expert capacities, and routing sparsity, enabling flexible trade-offs among performance, model size, and inference latency in memory- or time-constrained scenarios. Moreover, we systematically address the challenges of scaling reinforcement learning to unified foundation models, thereby guaranteeing efficient and stable post-training under ultra-sparse MoE architectures and diverse multimodal settings. Extensive experiments demonstrate that ERNIE 5.0 achieves strong and balanced performance across multiple modalities. To the best of our knowledge, among publicly disclosed models, ERNIE 5.0 represents the first production-scale realization of a trillion-parameter unified autoregressive model that supports both multimodal understanding and generation. To facilitate further research, we present detailed visualizations of modality-agnostic expert routing in the unified model, alongside comprehensive empirical analysis of elastic training, aiming to offer profound insights to the community.

cs.CL↗

Reinforcement Learning Meets Masked Generative Models: Mask-GRPO for Text-to-Image Generation

Reinforcement learning (RL) has garnered increasing attention in text-to-image (T2I) generation. However, most existing RL approaches are tailored to either diffusion models or autoregressive models, overlooking an important alternative: masked generative models. In this work, we propose Mask-GRPO, the first method to incorporate Group Relative Policy Optimization (GRPO)-based RL into this overlooked paradigm. Our core insight is to redefine the transition probability, which is different from current approaches, and formulate the unmasking process as a multi-step decision-making problem. To further enhance our method, we explore several useful strategies, including removing the KL constraint, applying the reduction strategy, and filtering out low-quality samples. Using Mask-GRPO, we improve a base model, Show-o, with substantial improvements on standard T2I benchmarks and preference alignment, outperforming existing state-of-the-art approaches. The code is available on https://github.com/xingzhejun/Mask-GRPO

cs.CV↗

Multi-Server FL with Overlapping Clients: A Latency-Aware Relay Framework

Multi-server Federated Learning (FL) has emerged as a promising solution to mitigate communication bottlenecks of single-server FL. In a typical multi-server FL architecture, the regions covered by different edge servers (ESs) may overlap. Under this architecture, clients located in the overlapping areas can access edge models from multiple ESs. Building on this observation, we propose a cloud-free multi-server FL framework that leverages Overlapping Clients (OCs) as relays for inter-server model exchange while uploading the local updated model to ESs. This enables ES models to be relayed across multiple hops through neighboring ESs by OCs without introducing new communication links. We derive a new convergence upper bound for non-convex objectives under non-IID data and an arbitrary number of cells, which explicitly quantifies the impact of inter-server propagation depth on convergence error. Guided by this theoretical result, we formulate an optimization problem that aims to maximize dissemination range of each ES model among all ESs within a limited latency. To solve this problem, we develop a conflict-graph-based local search algorithm optimizing the routing strategy and scheduling the transmission times of individual ESs to its neighboring ESs. This enables ES models to be relayed across multiple hops through neighboring ESs by OCs, achieving the widest possible transmission coverage for each model without introducing new communication links. Extensive experimental results show remarkable performance gains of our scheme compared to existing methods.

cs.NI↗