arXiv subjects

Meng Jin

Publications and source records attributed to Meng Jin.

At least 19 recordsLinked to original sources

arXiv2026

ESCAPE: a small explorer mission to study the stellar drivers of exoplanet evolution

The long-term stability of exoplanetary atmospheres depends critically on the extreme-ultraviolet (EUV) photon and high-energy particle fluxes from the host star, which are poorly constrained. To address this key gap in our understanding of atmospheric retention, we present the Extreme-ultraviolet Stellar Characterization for Atmospheric Physics and Evolution (ESCAPE) mission, a NASA Small Explorer concept proposed in 2026. ESCAPE employs extreme- and far-ultraviolet spectroscopy (80 - 1650 Ang) to provide the first comprehensive study of the stellar EUV history and stellar coronal mass ejection (CME) environments that control atmospheric mass-loss and determine the habitability of rocky exoplanets. This paper outlines both the primary science goals of the mission, the breadth of future general observer investigations, and a detailed design study of the mission's instrumentation. The ESCAPE instrument comprises a grazing incidence telescope that feeds multiple diffraction gratings and a photon-counting detector. We describe a demonstration of the Hettrick-Bowyer telescope, etched silicon diffraction gratings, the microchannel plate detector and housing, and gold and zirconium coatings. We present a STOP analysis that verifies ESCAPE's ability to meet its structural integrity, thermal stability, and optical performance requirements throughout the mission environment.

astro-ph.EP↗

arXiv2026

From Causal Discovery to Implementation: An Agentic AI Framework for E-Scooter Mobility Hub Planning Across 29 German Cities

Existing approaches to e-scooter mobility hub planning lack city-type-specific causal evidence. Demand models are typically correlational, built on proprietary trip data, and do not distinguish how driver profiles vary across urban typologies. This paper presents a three-phase agentic AI framework that constructs a Causal Template Library from public GBFS data across 29 German cities, encoding which environmental features causally drive hotspot demand for each combination of city type (large, university, industrial, hilly) and cluster type (core, peripheral). A large language model (LLM) orchestrated causal discovery pipeline adapts algorithm selection to local data conditions across 57 city-cluster units. The library reveals systematic variation. Core demand is driven by activity access and transit proximity, while peripheral demand responds to built form, with city-type-specific patterns supporting transferable siting templates. A planning tool built on the library scores candidate sites, calibrates infrastructure recommendations to local demographics, and generates practitioner-ready reports. In Heilbronn, Germany, two hub sites informed by the framework's causal evidence are currently under construction, illustrating how the outputs can support real-world siting decisions.

cs.CY↗

arXiv2026

Extreme Ultraviolet Microflashes at Plume Bases: A Candidate for Powering the Corona and Solar Wind?

Solar plumes - outflows of bright coronal plasma - are a major component of the open-magnetic-field corona and solar wind, but their driving mechanism remains uncertain. Here we report on network microflashes, fine-scale bright bursts captured by Solar Orbiters Extreme Ultraviolet Imager in 174A images encompassing magnetic network at the base of plumes. Because they sit in evidently unipolar magnetic flux, they are evidently a new, previously unidentified, kind of network event. Approximately 20 microflashes are ongoing within a plume base, with a new microflash starting every second. The energy for an average microflash is 1024 erg, in the range of nanoflares. A 3D data-driven global MHD model yields open magnetic field with fast solar wind for the investigated plumes. From our findings, we suggest that network microflashes result from fine-scale bursts of reconnection of crossed legs of unipolar magnetic field, that the bursts are often triggered by 5-minute p-mode oscillations, and that the bursts are candidates for powering the open-field corona and solar wind. That is, unipolar microflashes such as ours are plausibly from unipolar-network-field reconnection bursts that sustain the heliosphere.

astro-ph.SR↗

arXiv2026

Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent

Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution, yet in-situ observations remain costly and sparse. While vast historical records exist, they lie buried within the unstructured content of academic papers, creating "data silos" inaccessible to comprehensive analysis. Manual extraction is unscalable, while general-purpose Large Language Models (LLMs) lack the necessary domain-specific knowledge, leading to hallucinations and scientifically invalid outputs. To address this, we introduce an expert-guided adaptation approach that enables LLMs to perform rigorous scientific data extraction without fine-tuning. We operationalize this approach through Compass, an LLM agent framework enhanced by a Knowledge Tree co-designed with marine scientists, which decomposes complex tasks into verifiable steps, guiding the agent's reasoning to ensure scientific validity. Deploying Compass across a corpus of over 230,000 relevant open-access papers, we successfully extract 3,751 previously unincorporated Pb records. This effort establishes the largest integrated marine Pb database to date. Beyond standard metrics, Compass demonstrates superior reliability through multi-layered validation, achieving 92% accuracy as confirmed through expert manual verification. The newly integrated data expand coverage in previously under-sampled regions such as the East China Sea and the Southern Ocean, providing an enriched data foundation for future scientific discoveries. We release an interactive visualization platform to facilitate open scientific access. Our work demonstrates that expert-guided agents can effectively bridge the gap between general-purpose LLMs and high-stakes scientific domains, enabling scalable data discovery in geosciences.

cs.AI↗

arXiv2026

Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle

Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essential structural and semantic properties of the original graph. However, most existing methods rely on pair-wise similarity matching, where each node independently searches for its best partner based on global information. This selfishness matching paradigm incurs substantial computational and memory overhead. To address this problem, we shift to a non-selfishness principle that prioritizes the collective interference of neighborhood in coarsening, and propose an efficient method named NOPE, which achieves linear memory consumption and near-linear computational complexity in the number of nodes. Furthermore, we derive a faster variant NOPE*, which reduces O(\delta \dot d) interference evaluation to O(d) based on the local isotropy assumption, and consequently alleviates the computational bottleneck for high-degree nodes. Experimental results show that NOPE* achieves 1.8-10\times speedup over NOPE and surpass almost all baselines with 1-3 orders of magnitude acceleration. Meanwhile, learning on coarsened graphs yields comparable performance to original graphs, and can even show superior performance over LLM-based graph reasoning owing to compact graph information. The code can be available at https://github.com/dazonglian/NOPE-main.

cs.LG↗

arXiv2026

SLASH the Sink: Sharpening Structural Attention Inside LLMs

Large Language Models (LLMs) show remarkable semantic understanding but often struggle with structural understanding when processing graph topologies in a serialized format. Existing solutions rely on training external graph-based adapters or fine-tuning, which incur high costs and lost generalizability. In this work, we investigate the internal mechanisms of LLMs and present a critical finding: LLMs spontaneously reconstruct the graph's topology internally, evidenced by a distinct "sawtooth" pattern in their attention maps that structurally aligns with the "token-level adjacency matrix". However, this intrinsic structural understanding is diluted by the attention sink. We theoretically formalize this dilution as a representation bottleneck, stemming from a fundamental conflict: the model's anisotropic bias, essential for language tasks, suppresses the topology-aware local aggregation required for graph reasoning. To address this, we propose a training-free solution, named StructuraL Attention SHarpening (SLASH), which amplifies this internal structural understanding via a plug-and-play attention redistribution. Experiments on pure graph tasks and molecular prediction validate that SLASH delivers significant and consistent performance gains across diverse LLMs.

cs.AI↗

arXiv2026

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence

As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment interaction. Existing agenticinfrastructure remain fragmented across evaluation, data management, and agent evolution, making it difficult to discover risks systematically and improve models in a continuous closed loop. In this report, we present \textbf{Safactory}, a scalable agent factory for trustworthy autonomous intelligence. Safactory integrates three tightly coupled platforms: a \textbf{Parallel Simulation Platform} for trajectory generation, a \textbf{Trustworthy Data Platform} for trajectory storage and experience extraction, and an \textbf{Autonomous Evolution Platform} for asynchronous reinforcement learning and on-policy distillation. As far as we know, Safactory is the first framework to propose a unified evolutionary pipeline for next-generation trustworthy autonomous intelligence.

cs.AI↗

arXiv2026

Stellar Coronal Mass Ejections with HWO: A Science Case Concept

The primary mission of the Habitable World Observatory (HWO) will be to constrain the prevalence of life on Earth-like planets. These planets will be subject to impacts by energetic particles generated from coronal mass ejection (CME) shocks that can dramatically deplete ozone, a key biosignature gas. Other biosignatures are also likely vulnerable, though not yet studied. Here, we make a conceptual case for factoring sensitivity to stellar coronal mass ejections into the design of HWO. We drive design considerations by requiring that HWO constrain the rate of CMEs producing 10% or greater depletions of total ozone column to fewer than one per decade, the timescale over which ozone returns to pre-event levels. As CME detection methods, we consider coronal dimming, doppler shifted emission, high contrast imaging, and planetary aurora. We explore coronal dimming most thoroughly of the four, though with appropriate design considerations each of these may be possible with HWO.

astro-ph.IM↗

arXiv2026

AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting

Accurate weather forecasting is more than grid-wise regression: it must preserve coherent synoptic structures and physical consistency of meteorological fields, especially under autoregressive rollouts where small one-step errors can amplify into structural bias. Existing physics-priors approaches typically impose global, once-for-all constraints via architectures, regularization, or NWP coupling, offering limited state-adaptive and sample-specific controllability at deployment. To bridge this gap, we propose Agent-Guided Cross-modal Decoding (AGCD), a plug-and-play decoding-time prior-injection paradigm that derives state-conditioned physics-priors from the current multivariate atmosphere and injects them into forecasters in a controllable and reusable way. Specifically, We design a multi-agent meteorological narration pipeline to generate state-conditioned physics-priors, utilizing MLLMs to extract various meteorological elements effectively. To effectively apply the priors, AGCD further introduce cross-modal region interaction decoding that performs region-aware multi-scale tokenization and efficient physics-priors injection to refine visual features without changing the backbone interface. Experiments on WeatherBench demonstrate consistent gains for 6-hour forecasting across two resolutions (5.625 degree and 1.40625 degree) and diverse backbones (generic and weather-specialized), including strictly causal 48-hour autoregressive rollouts that reduce early-stage error accumulation and improve long-horizon stability.

cs.AI↗

arXiv2026

Quasi-Periodic Fast-Mode Wave Trains Associated with the 2015-Jun-22 M6.5 Flare in AR~12371: Observations and 3D MHD Modeling

Quasi-periodic fast-propagating (QFP) wave trains are a distinctive form of magnetohydrodynamic disturbance frequently observed in the solar corona. Yet their excitation mechanism and propagation characteristics are not well understood. In this study, we investigate a well-observed QFP wave event associated with an M6.5-class flare and coronal mass ejection that occurred in active region (AR) 12371 on 2015 June 22 by combining multi-wavelength observations from SDO/AIA and HMI with data-inspired 3D MHD simulations. The QFP wave trains propagating at high speeds of 1140$-$1760 km~s$^{-1}$ are detected in the AIA 171 \AA\ channel, following global EUV wave fronts visible at 171 and 193~\AA\ traveling at considerably lower speeds of 300$-$510 km~s$^{-1}$. Wavelet analysis reveals consistent 2--4 minutes periodicities in both the QFPs and flare quasi-periodic pulsations (QPPs) observed in UV/EUV and hard X-ray emissions, suggesting a common origin likely linked to intermittent magnetic reconnection. Guided by these observations, we construct realistic 3D MHD models incorporating dense fan-loop structures and periodic drivers applied at different locations. The simulations reproduce the key characteristics of the observed wave trains. Comparison between cases with and without a coronal background (non-loop plasma emission) indicates that coronal density structuring significantly modifies the detected wave amplitudes and propagation patterns. Our results highlight the importance of realistic coronal magnetic configurations in modeling QFP dynamics and suggest that their observed association with fan loops in AIA 171 \AA\ may represent a temperature-dependent visibility effect rather than a genuine confinement of the waves.

astro-ph.SR↗

arXiv2026

: One LLM Token for Explicit Graph Structural Understanding

Large language models show great potential in unstructured data understanding, but still face significant challenges with graphs due to their structural hallucination. Existing approaches mainly either verbalize graphs into natural language, which leads to excessive token consumption and scattered attention, or transform graphs into trainable continuous embeddings (i.e., soft prompt), but exhibit severe misalignment with original text tokens. To solve this problem, we propose to incorporate one special token to fully represent the Structure Of Graph within a unified token space, facilitating explicit topology input and structural information sharing. Specifically, we propose a topology-aware structural tokenizer that maps each graph topology into a highly selective single token. Afterwards, we construct a set of hybrid structure Question-Answering corpora to align new structural tokens with existing text tokens. With this approach, empowers LLMs to understand, generate, and reason in a concise and accurate manner. Extensive experiments on five graph-level benchmarks demonstrate the superiority of our method, achieving a performance improvement of 9.9% to 41.4% compared to the baselines while exhibiting interpretability and consistency. Furthermore, our method provides a flexible extension to node-level tasks, enabling both global and local structural understanding. The codebase is publicly available at https://github.com/Jingyao-Wu/SOG.

cs.CL↗

arXiv2025

The impact of Solar magnetic field configurations on the production of gamma rays at the Solar disk

The Sun produces a steady signal of high-energy gamma rays through interactions of Galactic cosmic rays (GCRs) with its atmosphere. Observations with Fermi-LAT and HAWC have revealed a gamma-ray flux significantly higher than early theoretical predictions, with unexpected temporal and spectral features that suggest a crucial role of the solar magnetic field. In this work, we model GCR-induced gamma-ray emission at the solar disk using the CRPropa framework with realistic hadronic interactions, chromospheric density profiles, and several magnetic field configurations over the solar cycle. This allows us to quantify the gamma-ray emission of the entire solar disk for different phases of the solar activity cycle and we present, for the first time, maps of the production locations of gamma rays on the solar surface. We consider both mono-energetic and realistic power-law injection spectra in a simplified dipole-quadrupole current sheet model and potential-field source surface (PFSS) extrapolations for Carrington rotations during solar maximum and minimum. Our results show that magnetic mirroring and large-scale field topology strongly affect the spectral shape and spatial distribution of the emission, with slightly enhanced fluxes predicted at solar minimum. While our simulated baseline fluxes remain below observations, additional effects, such as heavier nuclei, Parker-field mirroring, and deeper atmospheric interactions, could result in further enhancements of fluxes closer to observational values. Hadronic interactions do not only produce gamma rays but also neutrinos. We estimate the expected neutrino flux from the Sun based on our gamma-ray predictions. We find that the expected flux is slightly below current upper limits from IceCube.

astro-ph.HE↗

arXiv2025

The Exospace Weather Frontier

Space weather is among the most powerful and least understood forces shaping planetary atmospheres. In our Solar System, we observe its effects directly: atmospheric escape, chemical disruption, and spectacular auroral displays. Yet for exoplanets, we lack the tools and data to comprehensively assess the impacts of space weather, especially invisible elements like stellar winds, coronal mass ejections, energetic particles, and variable interplanetary magnetic fields. This problem lies at the intersection of four key fields: heliophysics, planetary science, astrobiology, and astrophysics. In 2023--2025, experts from these four fields convened at the W. M. Keck Institute for Space Studies to explore pathways for advancing the study of exospace weather. Organizing the subject into five core themes -- planets and their stellar particle environments, stellar magnetism and space weather modeling, quasi-steady stellar winds, transient events, and programmatic pathways -- our team synthesized concepts from across relevant fields and identified a wide array of opportunities for progress. This report is the product of that effort. It assembles cross-disciplinary knowledge; highlights outstanding theoretical challenges; explores promising innovations in observation, modeling, methodology, and instrumentation; and makes recommendations for accelerating community-wide progress. Together, these lay out a path to transforming the challenging, yet tractable problem of exospace weather into a foundational element of our understanding exoplanetary systems, and our own Solar System, in their entirety.

astro-ph.IM↗

arXiv2025

Modeling the Excitation, Propagation and Damping of Quasi-Periodic Fast Magnetosonic Waves in Realistic Coronal Active Region Magnetic Field Structures

Quasi-periodic fast propagating magnetosonic waves (QFPs) were discovered in the solar corona in EUV since the launch of SDO spacecraft more than a decade ago. The QFP waves are associated with flares and coronal mass ejections (CMEs) providing information on flare pulsations as well as on the magnetic field by MHD wave seismology. Previous models of QFP waves used primarily idealized magnetic active region structures. However, more realistic active region numerical models are needed to improve the application of coronal seismology to observations of waves in coronal structures. Here, we extend the previous models by including realistic magnetic configuration based on an observed coronal active region in a case study using AR 11166 observed on March 10, 2011 by SDO/AIA, using potential field extrapolation of photospheric magnetic field with realistic gravitationally stratified density structure { with typical coronal temperature} in our resistive 3D MHD model. We aim at reproducing the observed QFPs properties, such as directionality, propagation, reflection, nonlinearity, and damping of these waves. We model various forms of excitation of QFPs through time dependent boundary conditions, and localized pulses at the base of the corona. We produce synthetic emission measure (EM) maps from the 3D MHD modeling results to facilitate comparison to EUV observations. We find that the present more realistic model provides better qualitative agreement with observations compared to previous idealized models, improving the study of QFP wave excitation, propagation and damping in coronal ARs, with potential applications to coronal seismology.

astro-ph.SR↗

arXiv2025

Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models

While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalating computational requirements associated with their expanding scale increasingly hinder practical deployment. Current Parameter-Efficient Fine-Tuning (PEFT) methods, designed for vision or language tasks, fail to address the unique challenges of weather downstream tasks, such as variable heterogeneity, resolution diversity, and spatiotemporal coverage variations, leading to suboptimal performance when applied to WFMs. To bridge this gap, we introduce WeatherPEFT, a novel PEFT framework for WFMs incorporating two synergistic innovations. First, during the forward pass, Task-Adaptive Dynamic Prompting (TADP) dynamically injects the embedding weights within the encoder to the input tokens of the pre-trained backbone via internal and external pattern extraction, enabling context-aware feature recalibration for specific downstream tasks. Furthermore, during backpropagation, Stochastic Fisher-Guided Adaptive Selection (SFAS) not only leverages Fisher information to identify and update the most task-critical parameters, thereby preserving invariant pre-trained knowledge, but also introduces randomness to stabilize the selection. We demonstrate the effectiveness and efficiency of WeatherPEFT on three downstream tasks, where existing PEFT methods show significant gaps versus Full-Tuning, and WeatherPEFT achieves performance parity with Full-Tuning using fewer trainable parameters. The code of this work is available at https://github.com/ShileiCao/WeatherPEFT.

cs.LG↗

arXiv2025

Detecting Stellar Coronal Mass Ejections via Coronal Dimming in the Extreme Ultraviolet

Stellar flares and coronal mass ejections (CMEs) can strip planetary atmospheres, reducing the potential habitability of terrestrial planets. While flares have been observed for decades, stellar CMEs remain elusive. Extreme ultraviolet (EUV) emissions are sensitive to both flares and CME-induced coronal dimming. We assess the detectability of stellar CME-induced EUV dimming events by adapting a known "Sun-as-a-star" dimming technique -- validated by the Solar Dynamics Observatory's EUV Variability Experiment (EVE) -- to stellar conditions. We adapt the solar data to reflect a range of stellar intensities, accounting for intrinsic brightness, distance, and interstellar medium (ISM) attenuation. We generate synthetic light curves for two different missions: the legacy EUV Explorer (EUVE) and the proposed ESCAPE mission. Our results indicate that dimming detections are well within reach. EUVE's broadband imager was capable of detecting stellar CMEs -- albeit with limited spectral (temperature) resolution -- but that was not part of the observing plan. EUVE's spectroscopic survey lacked sufficient sensitivity for CME detections. Optimizing modern instrument design for this task would make the observation fully feasible. In this work, we present a tool to explore the stellar-CME detection parameter space. Our tool shows that an instrument with performance similar to ESCAPE, setting a 600-second integration period, and integrating the spectra into bands, any star with an X-ray flux $\geq 2.51 \times 10^{-12}$ergs$^{-1}$~cm$^{-2}$ should have a $\geq 3\sigma$ detection even for a modest few-percent dimming profile, regardless of ISM attenuation. Such measurements would be crucial for understanding the space weather environments of exoplanet host stars and, ultimately, for evaluating planetary habitability.

astro-ph.SR↗

arXiv2025

Coronal dimmings and what they tell us about solar and stellar coronal mass ejections

Coronal dimmings associated with coronal mass ejections (CME) from the Sun have gained much attention since the late 1990s when they were first observed in high-cadence imagery of the SOHO/EIT and Yohkoh/SXT instruments. They appear as localized sudden decreases of the coronal emission at extreme ultraviolet (EUV) and soft X-ray (SXR) wavelengths, that evolve impulsively during the lift-off and early expansion phase of a CME. Coronal dimmings have been interpreted as "footprints" of the erupting flux rope and also as indicators of the coronal mass loss by CMEs. However, these are only some aspects of coronal dimmings and how they relate to the overall CME/flare process. The goal of this review is to summarize our current understanding and observational findings on coronal dimmings, how they relate to CME simulations, and to discuss how they can be used to provide us with a deeper insight and diagnostics of the triggering of CMEs, the magnetic connectivities and coronal reconfigurations due to the CME as well as the replenishment of the corona after an eruption. In addition, we go beyond a pure review by introducing a new, physics-driven categorization of coronal dimmings based on the magnetic flux systems involved in the eruption process. Finally, we discuss the recent progress in studying coronal dimmings on solar-like and late-type stars, and to use them as a diagnostics for stellar coronal mass ejections and their properties.

astro-ph.SR↗

arXiv2025

TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State

Accurate Subseasonal-to-Seasonal (S2S) forecasting is vital for decision-making in agriculture, energy production, and emergency management. However, it remains a challenging and underexplored problem due to the chaotic nature of the weather system. Recent data-driven studies have shown promising results, but their performance is limited by the inadequate incorporation of climate states and a model tendency to degrade, progressively losing fine-scale details and yielding over-smoothed forecasts. To overcome these limitations, we propose TianQuan-S2S, a global S2S forecasting model that integrates initial weather states with climatological means via incorporating climatology into patch embedding and enhancing variability capture through an uncertainty-augmented Transformer. Extensive experiments on the Earth Reanalysis 5 (ERA5) reanalysis dataset demonstrate that our model yields a significant improvement in both deterministic and ensemble forecasting over the climatology mean, traditional numerical methods, and data-driven models. Ablation studies empirically show the effectiveness of our model designs. Remarkably, our model outperforms skillful numerical ECMWF-S2S and advanced data-driven Fuxi-S2S in key meteorological variables. The code implementation can be found in https://github.com/zhangminglang42/TianQuan.

cs.LG↗