arXiv subjects

Xin Cheng

Publications and source records attributed to Xin Cheng.

At least 55 records · Page 3Linked to original sources

arXiv2026

Forbidding edge-critical graphs as trace in uniform hypergraphs

We say a hypergraph $\mathcal{H}$ contains a graph $G$ as trace if there exists a vertex subset $S \subseteq V(\mathcal{H})$ such that $|S| = V(G)$ and $\{e \cap S \mid e \in E(\mathcal{H})\}$ contains $G$ as a subgraph. We use $\mathrm{ex}(n, Tr_r(G))$ to denote the maximum number of edges in an $r$-uniform hypergraph on $n$ vertices not containing $G$ as trace. The study of Turán numbers for traces was initiated by Mubayi and Zhao~(2017) who studied $\mathrm{ex}(n, Tr_r(K_{s+1}))$ where $K_{s+1}$ is a clique on $s+1$ vertices and conjectured the exact value of $\mathrm{ex}(n, Tr_r(K_{s+1}))$. When $r \le s$, the conjecture was covered by a result of Pikhurko~(2013) who gave the exact value of Turán numbers for expanded cliques. Then Gerbner and Picollelli~(2023) gave the exact value for book graphs~($K_{1,1,t}$, the complete tripartite graph with two parts of size one and one part of size $t \ge 2$). We say $G$ is edge-critical if there exists an edge $e \in E(G)$ such that $χ(G - e) < χ(G)$ where $χ(G)$ is the chromatic number of $G$. The definition of edge-critical was given by Simonovits~(1974), who proved that for an edge-critical graph $G$ with $χ(G) = s+1 \ge 3$, the Turán graph $T(n,s)$ is the unique extremal graph for $ex(n,G)$ as $n$ is sufficiently large. In this paper, we further generalize the results of Gerbner and Picollelli~(2023) to edge-critical graphs. More precisely, we prove that for an edge-critical graph $G$ with $χ(G) = s+1$, when $s \ge r \ge 3$ and $n$ is sufficiently large, the $r$-uniform Turán graph $T_r(n,s)$ is the unique extremal hypergraph.

math.CO↗

arXiv2026

Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks

Group-based reinforcement learning (RL), such as GRPO, has advanced the capabilities of large language models on long-horizon agentic tasks. To enable more fine-grained policy updates, recent research has increasingly shifted toward stepwise group-based policy optimization, which treats each step in a rollout trajectory independently while using a memory module to retain historical context. However, we find a key issue in estimating stepwise relative advantages, namely context inconsistency, where steps within the same group may differ in their historical contexts. Empirically, we reveal that this issue can lead to severely biased advantage estimation, thereby degrading policy optimization significantly. To address the issue, in this paper, we propose Hierarchy-of-Groups Policy Optimization (HGPO) for long-horizon agentic tasks. Specifically, within a group of rollout trajectories, HGPO assigns each step to multiple hierarchical groups according to the consistency of historical contexts. Then, for each step, HGPO computes distinct advantages within each group and aggregates them with an adaptive weighting scheme. In this way, HGPO can achieve a favorable bias-variance trade-off in stepwise advantage estimation, without extra models or rollouts. Evaluations on two challenging agentic tasks, ALFWorld and WebShop with Qwen2.5-1.5B-Instruct and Qwen2.5-7B-Instruct, show that HGPO significantly outperforms existing agentic RL methods under the same computational constraints. Code is available at https://github.com/langfengQ/verl-agent/tree/master/recipe/hgpo.

cs.LG↗

arXiv2026

The Turán number of Berge paths

A Berge path of length $k$ in an $r$-uniform hypergraph is a collection of $k$ hyperedges $h_1,\dots,h_k$ and $k+1$ vertices $v_1,\dots,v_{k+1}$ such that $v_i, v_{i+1}\in h_i$ for each $1\le i\le k$. Győri, Katona and Lemons [\textit{European J. Combin. 58 (2016) 238--246}] generalized the Erdős-Gallai theorem to Berge paths and established bounds for the Turán number of Berge paths. However, these bounds are sharp only when some divisibility conditions hold. Gy\H ori, Lemons, Salia and Zamora [\textit{J. Combin. Theory Ser. B 148 (2021) 239--250}] determined the exact value of the Turán number of Berge paths in the case $k\le r$. In this paper, we settle the final open case $k>r$, thereby completing the determination of the Turán number of Berge paths.

math.CO↗

arXiv2026

Envisioning Audio Augmented Reality in Everyday Life

While visual augmentation dominates the augmented reality landscape, devices like Meta Ray-Ban audio smart glasses signal growing industry movement toward audio augmented reality (AAR). Hearing is a primary channel for sensing context, anticipating change, and navigating social space, yet AAR's everyday potential remains underexplored. We address this gap through a collaborative autoethnography (N=5, authoring) and an online survey (N=74). We identify ten roles for AAR, grouped into three categories: task- and utility-oriented, emotional and social, and perceptual collaborator. These roles are further layered with a rhythmic and embodied collaborator framing, mapping them onto micro-, meso-, and macro-rhythms of everyday life. Our analysis surfaces nuanced tensions, such as blocking distractions without erasing social presence, highlighting the need for context-aware design. This paper contributes a foundational and forward-looking framework for AAR in everyday life, providing design groundwork for systems attuned to daily routines, sensory engagement, and social expectations.

cs.HC↗

arXiv2025

Two-stage primary acceleration in filament initial eruption under a fan-spine magnetic configuration

Understaning the filament rising process is crucial for unveiling the triggering mechanisms of the coronal mass ejections and forecasting the space weather. In this paper, we present a detailed study on the filament initial eruption under a fan-spine structure. It was found that the filament underwent two distinct acceleration stages corresponding to a calss M1.0 and M4.6 flare event, respectively. The first acceleration stage commenced with the filament splitting, after which the upper portion was subsequently heated being a hot channel and slow rose at an average speed of 22 km/s. A set of hot reverse C-shaped loops appeared repeatedly during the filament splitting and a hook structure was recognized at this phase, suggesting ongoing growth of the magnetic flux rope (MFR). When it reached a certain altitude, the hot channel appeared to get into a quasi-static phase with its upper edge seriously decelerated and lower edge expanding downward. Approximately 30 minutes later, as a distinct annular ribbon appeared outside the hook structure, the hot channel rose again at a velocity over 50 km/s accompanied with rapid footpoints drifting, and experienced the second acceleration stage with its axial flux increased to 1.1 X 10^{21} Mx. It is deduced that the filament initial eruption under a magnetic dome possess multi kinetic process. We suggest that the magnetic reconnection taken place within and beneath the filament continues to trigger the growth of pre-eruptive MFR and the first acceleration, when the magnetic reconnection above the filament plays a key role in the second acceleration.

astro-ph.SR↗

arXiv2025

VSpeechLM: A Visual Speech Language Model for Visual Text-to-Speech Task

The task of Visual Text-to-Speech (VisualTTS), also known as video dubbing, aims to generate speech synchronized with the lip movements in an input video, in additional to being consistent with the content of input text and cloning the timbre of a reference speech. Existing VisualTTS models typically adopt lightweight architectures and design specialized modules to achieve the above goals respectively, yet the speech quality is not satisfied due to the model capacity and the limited data in VisualTTS. Recently, speech large language models (SpeechLLM) show the robust ability to generate high-quality speech. But few work has been done to well leverage temporal cues from video input in generating lip-synchronized speech. To generate both high-quality and lip-synchronized speech in VisualTTS tasks, we propose a novel Visual Speech Language Model called VSpeechLM based upon a SpeechLLM. To capture the synchronization relationship between text and video, we propose a text-video aligner. It first learns fine-grained alignment between phonemes and lip movements, and then outputs an expanded phoneme sequence containing lip-synchronization cues. Next, our proposed SpeechLLM based decoders take the expanded phoneme sequence as input and learns to generate lip-synchronized speech. Extensive experiments demonstrate that our VSpeechLM significantly outperforms previous VisualTTS methods in terms of overall quality, speaker similarity, and synchronization metrics.

cs.MM↗

arXiv2025

The Solar Close Observations and Proximity Experiments (SCOPE) mission

The Solar Close Observations and Proximity Experiments (SCOPE) mission will send a spacecraft into the solar atmosphere at a low altitude of just 5 R_sun from the solar center. It aims to elucidate the mechanisms behind solar eruptions and coronal heating, and to directly measure the coronal magnetic field. The mission will perform in situ measurements of the current sheet between coronal mass ejections and their associated solar flares, and energetic particles produced by either reconnection or fast-mode shocks driven by coronal mass ejections. This will help to resolve the nature of reconnections in current sheets, and energetic particle acceleration regions. To investigate coronal heating, the mission will observe nano-flares on scales smaller than 70 km in the solar corona and regions smaller than 40 km in the photosphere, where magnetohydrodynamic waves originate. To study solar wind acceleration mechanisms, the mission will also track the process of ion charge-state freezing in the solar wind. A key achievement will be the observation of the coronal magnetic field at unprecedented proximity to the solar photosphere. The polar regions will also be observed at close range, and the inner edge of the solar system dust disk may be identified for the first time. This work presents the detailed background, science, and mission concept of SCOPE and discusses how we aim to address the questions mentioned above.

astro-ph.SR↗

arXiv2025

Data-constrained magnetohydrodynamic simulation of global solar corona including solar wind effects within 2.5 $R_\odot$

Total solar eclipses (TSEs) provide a unique opportunity to observe the large-scale solar corona. The solar wind plays an important role in forming the large-scale coronal structure and magnetohydrodynamic (MHD) simulations are used to reproduce it for further studying coronal mass ejections (CMEs). We conduct a data-constrained MHD simulation of the global solar corona including solar wind effects of the 2024 April 8 TSE with observed magnetograms using the Message Passing Interface Adaptive Mesh Refinement Versatile Advection Code (MPI-AMRVAC) within 2.5 $R_\odot$. This TSE happened within the solar maximum, hence the global corona was highly structured. Our MHD simulation includes the energy equation with a reduced polytropic index $γ=1.05$. We compare the global magnetic field for multiple magnetograms and use synchronic frames from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager to initialize the magnetic field configuration from a magneto-frictionally equilibrium solution, called the Outflow field. We detail the initial and boundary conditions employed to time-advance the full set of ideal MHD equations such that the global corona is relaxed to a steady state. The magnetic field, the velocity field, and distributions of the density and thermal pressure are successfully reproduced. We demonstrate direct comparisons with TSE images in white-light and Fe XIV emission augmented with quasi-separatrix layers, the integrated current density, and the synthetic white-light radiation, and find a good agreement between simulations and observations. This provides a fundamental background for future simulations to study the triggering and acceleration mechanisms of CMEs under solar wind effects.

astro-ph.SR↗

arXiv2025

Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks

Video Models have achieved remarkable success in high-fidelity video generation with coherent motion dynamics. Analogous to the development from text generation to text-based reasoning in language modeling, the development of video models motivates us to ask: Can video models reason via video generation? Compared with the discrete text corpus, video grounds reasoning in explicit spatial layouts and temporal continuity, which serves as an ideal substrate for spatial reasoning. In this work, we explore the reasoning via video paradigm and introduce VR-Bench -- a comprehensive benchmark designed to systematically evaluate video models' reasoning capabilities. Grounded in maze-solving tasks that inherently require spatial planning and multi-step reasoning, VR-Bench contains 7,920 procedurally generated videos across five maze types and diverse visual styles. Our empirical analysis demonstrates that SFT can efficiently elicit the reasoning ability of video model. Video models exhibit stronger spatial perception during reasoning, outperforming leading VLMs and generalizing well across diverse scenarios, tasks, and levels of complexity. We further discover a test-time scaling effect, where diverse sampling during inference improves reasoning reliability by 10--20%. These findings highlight the unique potential and scalability of reasoning via video for spatial reasoning tasks.

cs.CV↗

arXiv2025

Observatory Science with eXTP

Scheduled for launch in 2030, the enhanced X-ray Timing and Polarization (eXTP) telescope is a Chinese space-based mission aimed at studying extreme conditions and phenomena in astrophysics. eXTP will feature three main payloads: Spectroscopy Focusing Arrays (SFAs), Polarimetry Focusing Arrays (PFAs), and a Wide-field Camera (W2C). This white paper outlines observatory science, incorporating key scientific advances and instrumental changes since the publication of the previous white paper [1]. We will discuss perspectives of eXTP on the research domains of flare stars, supernova remnants, pulsar wind nebulae, cataclysmic variables, X-ray binaries, ultraluminous X-ray sources, AGN, and pulsar-based positioning and timekeeping.

astro-ph.IM↗

arXiv2025

WildSpoof Challenge Evaluation Plan

The WildSpoof Challenge aims to advance the use of in-the-wild data in two intertwined speech processing tasks. It consists of two parallel tracks: (1) Text-to-Speech (TTS) synthesis for generating spoofed speech, and (2) Spoofing-robust Automatic Speaker Verification (SASV) for detecting spoofed speech. While the organizers coordinate both tracks and define the data protocols, participants treat them as separate and independent tasks. The primary objectives of the challenge are: (i) to promote the use of in-the-wild data for both TTS and SASV, moving beyond conventional clean and controlled datasets and considering real-world scenarios; and (ii) to encourage interdisciplinary collaboration between the spoofing generation (TTS) and spoofing detection (SASV) communities, thereby fostering the development of more integrated, robust, and realistic systems.

cs.SD↗

arXiv2025

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs

The emergence of multimodal large language models (MLLMs) presents promising opportunities for automation and enhancement in Electronic Design Automation (EDA). However, comprehensively evaluating these models in circuit design remains challenging due to the narrow scope of existing benchmarks. To bridge this gap, we introduce MMCircuitEval, the first multimodal benchmark specifically designed to assess MLLM performance comprehensively across diverse EDA tasks. MMCircuitEval comprises 3614 meticulously curated question-answer (QA) pairs spanning digital and analog circuits across critical EDA stages - ranging from general knowledge and specifications to front-end and back-end design. Derived from textbooks, technical question banks, datasheets, and real-world documentation, each QA pair undergoes rigorous expert review for accuracy and relevance. Our benchmark uniquely categorizes questions by design stage, circuit type, tested abilities (knowledge, comprehension, reasoning, computation), and difficulty level, enabling detailed analysis of model capabilities and limitations. Extensive evaluations reveal significant performance gaps among existing LLMs, particularly in back-end design and complex computations, highlighting the critical need for targeted training datasets and modeling approaches. MMCircuitEval provides a foundational resource for advancing MLLMs in EDA, facilitating their integration into real-world circuit design workflows. Our benchmark is available at https://github.com/cure-lab/MMCircuitEval.

cs.LG↗

arXiv2025

Microwave Imaging Spectroscopy Diagnosis of the Slow-rise Precursor of a Major Solar Eruption

In this Letter, taking advantage of microwave data from the Expanded Owens Valley Solar Array and extreme-ultraviolet (EUV) data from the Atmospheric Imaging Assembly, we present the first microwave imaging spectroscopy diagnosis for the slow-rise precursor of a major coronal mass ejection (CME) on 2022 March 30. The EUV images reveal that the CME progenitor, appearing as a hot channel above the polarity inversion line, experiences a slow rise and heating before the eruption. The microwave emissions are found to mainly distribute along the hot channel, with high-frequency sources located at the ends of the hot channel and along precursor bright loops underneath the hot channel. The microwave spectroscopic analysis suggests that microwave emissions in the precursor phase are dominated by thermal emission, largely different from the main phase when a significant non-thermal component is present. These results support the scenario that the precursor reconnection, seeming to be moderate compared with the flare reconnection during the main phase, drives the build-up, heating, and slow rise of CME progenitors toward the explosive eruption.

astro-ph.SR↗

arXiv2025

CGEarthEye:A High-Resolution Remote Sensing Vision Foundation Model Based on the Jilin-1 Satellite Constellation

Deep learning methods have significantly advanced the development of intelligent rinterpretation in remote sensing (RS), with foundational model research based on large-scale pre-training paradigms rapidly reshaping various domains of Earth Observation (EO). However, compared to the open accessibility and high spatiotemporal coverage of medium-resolution data, the limited acquisition channels for ultra-high-resolution optical RS imagery have constrained the progress of high-resolution remote sensing vision foundation models (RSVFM). As the world's largest sub-meter-level commercial RS satellite constellation, the Jilin-1 constellation possesses abundant sub-meter-level image resources. This study proposes CGEarthEye, a RSVFM framework specifically designed for Jilin-1 satellite characteristics, comprising five backbones with different parameter scales with totaling 2.1 billion parameters. To enhance the representational capacity of the foundation model, we developed JLSSD, the first 15-million-scale multi-temporal self-supervised learning (SSL) dataset featuring global coverage with quarterly temporal sampling within a single year, constructed through multi-level representation clustering and sampling strategies. The framework integrates seasonal contrast, augmentation-based contrast, and masked patch token contrastive strategies for pre-training. Comprehensive evaluations across 10 benchmark datasets covering four typical RS tasks demonstrate that the CGEarthEye consistently achieves state-of-the-art (SOTA) performance. Further analysis reveals CGEarthEye's superior characteristics in feature visualization, model convergence, parameter efficiency, and practical mapping applications. This study anticipates that the exceptional representation capabilities of CGEarthEye will facilitate broader and more efficient applications of Jilin-1 data in traditional EO application.

cs.CV↗

arXiv2025

On the Turán number of the expansion of the $t$-fan

The $t$-fan is the graph on $2t+1$ vertices consisting of $t$ triangles which intersect at exactly one common vertex. For a given graph $F$, the $r$-expansion $F^r$ of $F$ is the $r$-uniform hypergraph obtained from $F$ by adding $r-2$ distinct new vertices to each edge of $F$. We determine the Turán number of the 3-expansion of the $t$-fan for sufficiently large $n$.

math.CO↗

arXiv2025

Initiation Route of Coronal Mass Ejections: II. The Role of Filament Mass

The thorough understanding on the initiation of coronal mass ejections (CMEs), which is manifested as a slow rise of pre-eruptive structures before the impulsive ejection in kinematics, is the key for forecasting the solar eruptions. In our previous work, we showed that the slow rise of a hot flux rope with coronal mass density is caused by the moderate magnetic reconnection occurring in the hyperbolic flux tube (HFT) combined with the torus instability. However, it remains unclear how the initiation process varies when a filament is present in the pre-eruptive flux rope. In this work, we reveal the complete initiation route of a CME containing filament mass with a state-of-the-art full-magnetohydrodynamics simulation. The comprehensive analyses show that the filament mass has an important impact on the CME initiation through triggering and driving the slow rise of flux rope with its drainage, besides the contributions of HFT reconnection and torus instability. Finally, in combination with our previous work, we propose that the enhanced drainage of filament mass and various features related to the HFT reconnection, such as, the split of pre-eruptive structure and the pre-flare loops and X-ray emissions, can serve as the precursors of CME initiation in observations.

astro-ph.SR↗

arXiv2025

AFlow: Automating Agentic Workflow Generation

Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing these workflows requires significant human effort, limiting scalability and generalizability. Recent research has sought to automate the generation and optimization of these workflows, but existing methods still rely on initial manual setup and fall short of achieving fully automated and effective workflow generation. To address this challenge, we reformulate workflow optimization as a search problem over code-represented workflows, where LLM-invoking nodes are connected by edges. We introduce AFlow, an automated framework that efficiently explores this space using Monte Carlo Tree Search, iteratively refining workflows through code modification, tree-structured experience, and execution feedback. Empirical evaluations across six benchmark datasets demonstrate AFlow's efficacy, yielding a 5.7% average improvement over state-of-the-art baselines. Furthermore, AFlow enables smaller models to outperform GPT-4o on specific tasks at 4.55% of its inference cost in dollars. The code is available at https://github.com/FoundationAgents/AFlow.

cs.AI↗

arXiv2025

Basic Pattern of Three-dimensional Magnetic Reconnection within Strongly Turbulent Current Sheets

Magnetic reconnection is a fundamental mechanism of driving eruptive phenomena of different scales and may be coupled with turbulence as suggested by recent remote-sensing and in-situ observations. However, the specific physics behind the complex three-dimensional (3D) turbulent reconnection remains mysterious. Here, we develop a novel methodology to identify and analyze multitudes of multi-scale reconnection fragments within a strongly turbulent current sheet (CS) and apply it to a state-of-the-art numerical simulation of turbulent reconnection for solar flares. It is determined that the reconnection fragments tend to appear as quasi-2D sheets forming along local magnetic flux surfaces, and, due to strong turbulence, their reconnection flow velocities and reconnection rates are significantly broadened statistically but are scale-independent. Each reconnection fragment is found to be surrounded by strongly fluctuated in/out-flows and has a widely distributed reconnection rate, mainly in the range of 0.01-0.1. The results, for the first time, provide quantitative measurements of 3D magnetic reconnection in strongly turbulent flare CSs, offering insights into the cascading laws of 3D reconnection in other turbulent plasmas.

astro-ph.SR↗