Searcharxiv⌕ Search

arXiv subjects

Xiaoping Zhang

Publications and source records attributed to Xiaoping Zhang.

At least 19 recordsLinked to original sources
arXiv2026

A Compact Residual-Screened Local Reduced-Order Model for Diffuse-Domain Shape Optimization

We generalize the compact residual-screened local reduced-order model (CL-ROM) from Brinkman-penalized flow to shape optimization with the diffuse domain method (DDM). As with Brinkman penalization, DDM extends evolving-domain problems to a fixed rectangular domain, allowing one grid for discretization and directly compatible snapshots from different designs. Unlike Brinkman penalization, DDM uses a phase field to weight operators and sources and treats Dirichlet, Neumann and Robin conditions through diffuse boundary terms. The CL-ROM mechanism carries over: recent full-order and corrected solutions determine local reduced spaces, while residual screening, correction with retained factors and objective verification decide when a full-order solve is needed. We derive reduced operators, residuals and sensitivities for each diffuse model, using Galerkin projection for the symmetric problems and least-squares Petrov--Galerkin projection for nonlinear flow. For the symmetric problems we prove a local approximation estimate that admits inexact, corrected snapshots. In numerical tests of shape identification, drag minimization in Navier--Stokes channel flow and compliance minimization of a cantilever, CL-ROM uses reduced solutions for nearly all design updates, reproduces the full-order objectives with relative differences below $5\times10^{-6}$, and is $3.85$ to $7.65$ times faster than a full-order baseline that reuses factorizations as preconditioners.

math.NA↗
arXiv2026

Numerical estimation of the capture ability of Neptunian mean motion resonances II. Influence of eccentricity and inclination

Evaluating the ability of Neptunian mean-motion resonances (MMRs) to capture small bodies is an important step in reconstructing the evolutionary history of the early Solar System. Building on a previous planar model, this study extends the investigation of MMR capture during Neptune's migration to the three-dimensional case, systematically analyzing the effects of the small objects' eccentricity, e, and inclination, i. Through numerical simulations with different migration rates, we obtain the distribution of capture probabilities for 12 MMRs on the e-i plane. The results show that increasing i narrows the ``inevitable capture zone'' (strong MMRs and/or slow migration) and raises the lower e threshold for capture (weak MMRs and/or fast migration), while it may also expand the upper e limit for capture. When the resonance order reaches three, the e-i plane further differentiates into several regions, which are closely related to the types of resonant angles. The emerging i-dominated region, along with its inevitable capture zone, makes third-order MMRs the most likely to benefit from higher inclination, as well as the most likely to host mixed-type resonators. Finally, using a hypothetical planetesimal distribution, we calculated the capture probability and efficiency for each MMR, finding that a cold disk favors strong MMRs, whereas a hot disk reduces differences among MMRs and across migration rates. In the future, a better determination of the population associated with different MMRs and different types of objects will help us understand the dynamical excitation state of the primordial disk in the early Solar System.

astro-ph.EP↗
arXiv2026

Post-perihelion dust activity and a candidate icy-grain halo in long-period comet C/2023 A3 (Tsuchinshan--ATLAS)

We analyze the post-perihelion coma of the Great Comet of 2024, C/2023 A3 (Tsuchinshan--ATLAS) to characterize dust activity and assess the contribution of sublimating icy grains to a candidate near-nucleus halo. Using archival Zwicky Transient Facility images from 15 February to 26 May 2025, we performed aperture photometry within a fixed projected radius of 40,000 km and derived azimuthally averaged surface-brightness profiles. The comet faded from 12.86 to 14.77 mag as heliocentric distance increased from 2.64 to 3.90 au, with two short-lived brightenings. The effective dust cross section decreased by a factor of 2.4, following $C_{\rm e}\propto r_{\rm h}^{-3.1}$, while the dust mass production rate scaled as $\dot{M}_{\rm d}\propto r_{\rm h}^{-3.41}$, consistent with a contribution from water-ice-driven activity. Extrapolating the fitted relation to perihelion implies a model-dependent cumulative dust mass loss of $3.0\times10^{11}$ kg, equivalent to a globally averaged erosion depth of $\sim1.4$ m for an assumed nucleus radius of 5.9 km. Coma profiles were shallower than the canonical steady-state expectation, suggesting radial changes in dust distribution or scattering properties, possibly due to delayed aggregate fragmentation. A persistent central brightness excess, coincident with less-red inner-coma colors, is consistent with an icy-grain halo. Among the models explored, icy grains of radius $\sim10~μ$m with carbon fraction $\sim0.1\%$ yield halo radii comparable to those observed during enhanced activity on 21--25 February 2025, with grain lifetimes of $2.8\times10^5$--$3.2\times10^5$ s. No single fixed grain size and composition reproduces the full halo evolution; an increasing carbon fraction could explain the broad halo-radius trend, possibly reflecting progressive refractory enrichment of near-surface material.

astro-ph.EP↗
arXiv2026

Dust Activity Evolution and Constraints on Icy Grains in the Interstellar Comet 3I/ATLAS

We present Zwicky Transient Facility observations of the interstellar comet 3I/ATLAS to characterize its dust-activity evolution before and after perihelion. Before perihelion, the effective scattering cross section $C_{\rm e}$ brightened across the inner $2\times10^4$ km as $C_{\rm e}\propto r_{\rm H}^{-γ}$, with $γ$ increasing from $1.63\pm0.14$ to $1.88\pm0.34$ with aperture size. After perihelion, the heliocentric index decreased from $2.44\pm0.09$ in the $5\times10^3$ km aperture to $1.54\pm0.09$ in the $8\times10^4$ km aperture, indicating faster fading of the inner coma. Under adopted grain-size and residence-time assumptions, the implied dust mass-loss rates are $\sim123$--$203~{\rm kg~s^{-1}}$ before perihelion and $\sim596$--$1412~{\rm kg~s^{-1}}$ after perihelion. The morphology yields a sunward ejection-speed scale of $v_{\rm ej}\sim(190\pm3)a_{μ{\rm m}}^{-1/2}~{\rm m~s^{-1}}$ and an effective perpendicular velocity of $v_\perp=(79\pm6)a_{μ{\rm m}}^{-1/2}~{\rm m~s^{-1}}$. Post-perihelion surface-brightness profiles transiently steepened to $k\sim-2.7$ in late November and early December, then became shallower and approached $k=-1.5$ by January. Matched $g-r$ profiles show that this phase coincided with an anomalously blue outer coma that disappeared by January. We characterize the fading component with a projected destruction length of $\ell_{\rm DE}\sim(6.4$--$7.2)\times10^4$ km. Thermophysical sublimation calculations indicate that this scale, together with the order of magnitude of the water production near the main imaging epoch, is consistent with $a_0\sim5$--$10~μ{\rm m}$ icy grains containing a small absorbing carbonaceous fraction of $f_{\rm C}\sim0.1\%$. These results suggest that the early post-perihelion coma likely contained a short-lived, volatile-bearing icy-grain component whose fading contributed to the outer-coma brightness and color evolution

astro-ph.EP↗
arXiv2026

AECSF: Adaptive Ensemble Conditional Score Filtering for High-Dimensional Nonlinear Data Assimilation

Bayesian state estimation for high-dimensional nonlinear dynamical systems entails a fundamental tension between statistical fidelity and computational tractability, as particle weights can collapse, while Gaussian ensemble updates can miss non-Gaussian posterior structure. Score-based diffusion filters offer a sampling-based alternative, but existing training-free score filters often rely on heuristic likelihood corrections, which can compromise posterior accuracy by neglecting uncertainty about the system state associated with each noisy reverse particle. To address these issues, we propose AECSF, a training-free adaptive ensemble conditional score filter. AECSF constructs an analytically tractable score estimator from the conditional Tweedie identity, which recasts noisy posterior score estimation as estimating the conditional mean of the system state given a noisy reverse particle and the observation. To estimate these conditional means efficiently, AECSF employs a shared adaptive weighted proposal ensemble, while particle-specific conditional weights yield an estimate for each noisy reverse particle without separate proposal sampling. The proposal ensemble is updated using reverse-particle information within the same reverse-diffusion run to improve conditional-mean estimation. Theoretically, we characterize when a fixed weighted proposal measure yields the exact noisy posterior score. Under stated assumptions, we establish a bound relating conditional-mean estimation errors to reverse-sampling endpoint error. Numerical experiments demonstrate that AECSF improves the accuracy of posterior sampling and nonlinear filtering in high-dimensional problems with limited forecast ensembles.

stat.ML↗
arXiv2026

Efficient and Accurate Lift-and-Decoupling Generalized Transferable Neural Networks for Elliptic and Moving Interface Problems

In this paper, we propose a highly efficient and accurate method, called ``lift-and-decoupling generalized transferable neural network" (LD-GTransNet), for numerical solution of elliptic and moving interface problems. Our method makes use of the multi-layer GTransNet approach and incorporates a novel {lift-and-decoupling} strategy, in which the hidden-layer neuron parameters are preset and the physical coordinates and the added auxiliary variable are processed by separate subnetworks. Such design enables a unified global approximation of piecewise smooth solutions without explicit domain decomposition, while preserving network expressivity and avoiding the redundancy inherent in conventional {lift} approaches. The resulting formulation reduces to a linear least-squares problem, thereby eliminating the need of nonlinear training and ensuring the high computational efficiency and accuracy. Extensive numerical experiments on benchmark interface problems in two and three dimensions demonstrate that, compared with existing neural network-based and traditional numerical methods, our proposed LD-GTransNet consistently delivers superior performance, particularly for problems with high-contrast coefficients and complex interface geometries.

math.NA↗
arXiv2026

INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment

As large language models (LLMs) are deployed as autonomous agents, safety failures increasingly involve consequential actions. We study agentic misalignment, where agents take harmful actions under goal conflicts and pressures. Using chain-of-thought (CoT) monitoring, we find that harmful execution is often preceded by intent signals in reasoning. However, post-hoc CoT labels are too coarse to show how intent changes during generation. We introduce INTENT-AS-A-TOOL, an approach that adds intent-targeted tools to give the model a dedicated channel for expressing commitment to a target behavior. The probability of calling an intent tool provides a judge-free, fine-grained signal of the model's tendency to pursue that behavior. Our results show that INTENT-AS-A-TOOL complements CoT monitoring, expands post-hoc CoT labels into dense trajectories, and identifies critical steps for online intervention. These findings suggest that action preferences are useful for tracking agentic misalignment during reasoning. Our code and data are accessible: https://github.com/RebeccaZhang22/intent-as-a-tool.

cs.CL↗
arXiv2026

Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States

Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.g., malicious side-tasks hidden in external tool results. While many efforts have sought to address this threat, little is known about the internals of agentic LLMs when they are exposed to IPI attacks. For simplicity, we refer to this condition as IPI exposure. In this paper, we study IPI exposure from three perspectives. (1) Probing: Across eight models, including the 753B-parameter GLM-5.2 and the 2.8T-parameter Kimi-K3, simple linear probes trained on pre-generation hidden states can predict LLMs' IPI exposure. These probes achieve 0.90+ AUROC on unseen attacks, agent instructions, and task suites; they remain robustly predictive under adaptive attacks and in cross-lingual settings. (2) Defense: We reveal and diagnose a knowledge-action gap: post-trained LLMs encode signals predictive of IPI exposure, yet do not reliably bind these signals to safe agentic actions. We therefore introduce a probe-gated reasoning-based defense to bridge this gap at test time. On difficult AgentDojo settings, it substantially reduces attack success rate, e.g., from 34.6% to 0% on Qwen3.5-27B, and better preserves clean-task utility than the baselines. (3) Explanation: We introduce an analysis framework that identifies natural-language explanations strongly correlated with probe-captured signals. The resulting profiles differ across models: latent signals can align with either direct IPI-exposure sensing or indirect operational cues. Code is available at https://github.com/jianshuod/IPI-exposure-signal.

cs.CR↗
arXiv2026

The Model's Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges

LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. However, processing these contexts alongside user queries creates an attack surface: adversarial inputs can induce models to disclose them. Prior probing studies suggest that leakage-related signals emerge in hidden states, yet the need to extract these states poses additional deployment challenges. In this paper, we explore whether this internal signal leaves a more accessible ``tell'' before decoding. We propose LeakGauge, which probes this response by appending a suffix that gauges leakage behavior and mapping its prefill token probabilities to an attack-risk score. While a direct gauge uses the initial tokens of confidential content, we find that a content-agnostic one that verbalizes leakage behavior yields more robust signals. Across 11 LLMs, including GLM-5.2 (753B) and Kimi-K3 (2.8T), LeakGauge reaches an AUROC range of 0.944--0.996 on unseen attacks. The signal remains stable when the content changes language or the attack shifts from verbatim to semantic disclosure. By activation-steering interventions, we further show that the risk score is sensitive to an internal leakage-related direction, relating the observable signal to the model's internal representation. In addition, LeakGauge enables an input detector with fewer than 0.5K extra parameters and added latency of 10.34 ms. Code: \href{https://github.com/yeasen-z/LeakGauge}.

cs.CR↗
arXiv2026

ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing

This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing. The challenge is motivated by two key limitations of existing industrial defect inspection systems: (1) current deep learning-based methods often suffer significant performance degradation when deployed in unseen production scenarios, and (2) most benchmarks neglect severity-aware assessment, which is critical for risk control and yield optimization. To address these limitations, we design two complementary tracks: Track 1 (Cross-Scenario Defect Detection) targets accurate defect detection, localization, and classification across diverse unseen production environments; Track 2 (Fine-Grained Severity Grading) requires assigning each detected defect an industry-standard severity level, including Acceptable, Marginal NG, NG, and Gross NG. We construct a large-scale industrial dataset of high-resolution microscopic images spanning seven representative defect categories, comprising over 3,800 images with pixel-level instance annotations for Track 1 and over 2,600 images with severity-grade labels for Track 2. The challenge attracted 86 registered participants with 130 submissions; during the final testing phase, 21 teams submitted results and 12 teams provided models with technical reports. The resulting benchmark, together with the diverse and effective solutions contributed by participating teams, sets a new standard for industrial defect analysis research.

cs.CV↗
arXiv2026

Numerical estimation of the capture ability of Neptunian mean motion resonances

Resonant populations of trans-Neptunian objects serve as crucial dynamical archives for unraveling the early migratory history of the Solar System. A quantitative assessment of the capture efficiency into various mean motion resonances (MMRs) during migration is essential for understanding the origins of these populations, constraining migration parameters, and reconstructing of the primordial planetesimal disk. Using numerical simulations, this study systematically investigates the capture capability of exterior MMRs during Neptune's outward migration in a planar model. For a specific p:q MMR, the small bodies can be captured only when their eccentricities surpass a certain threshold, which increases with faster migration rates, greater distances of MMRs, and higher resonance orders. On the other hand, as long as a particle's eccentricity is suitable, its capture efficiency shows little dependence on the migration rate; instead, it mainly depends on the p value and heliocentric distance, decaying exponentially as either parameter increases. Based on our simulation results, we derive for the first time a simple empirical expression to calculate eccentricity threshold and the capture efficiency. This research provides a systematic quantitative framework for understanding capture into Neptunian MMRs during migration. Future integrations of more comprehensive observational data will facilitate a more precise reconstruction of the Solar System's early dynamical evolution.

astro-ph.EP↗
arXiv2026

Lunar ejecta as the missing piece to resolve the lunar cratering asymmetry

The leading-trailing asymmetry in lunar crater distribution provides a critical record of inner solar system dynamics, yet the long-standing discrepancy between the observed higher asymmetry and lower theoretical predictions indicates a gap in our understanding of the impactor population. This paper hypothesizes that lunar impact ejecta, which can enter Earth-like orbits and return, constitute a previously unaccounted-for component. Through numerical simulations, we find that ~25% of escaped ejecta will re-impact the Earth-Moon system within 3 Myr, with about 1.2% striking the Moon. Crucially, these lunar impacts exhibit an extreme leading-trailing asymmetry with a ratio of 5.9. Our results indicate that lunar ejecta, if comprising ~15% of total impactors, can fully explain the observed asymmetry, leading to their recognition as active agents shaping the lunar impact record. This work provides new constraints for understanding the impact environment of the Earth-Moon system, with direct relevance to the interpretation of lunar geology, the transport of lunar material to Earth, and ongoing space exploration missions.

astro-ph.EP↗
arXiv2026

LeakDojo: Decoding the Leakage Threats of RAG Systems

Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to leverage external knowledge, but also exposes valuable RAG databases to leakage attacks. As RAG systems grow more complex and LLMs exhibit stronger instruction-following capabilities, existing studies fall short of systematically assessing RAG leakage risks. We present LeakDojo, a configurable framework for controlled evaluation of RAG leakage. Using LeakDojo, we benchmark six existing attacks across fourteen LLMs, four datasets, and diverse RAG systems. Our study reveals that (1) query generation and adversarial instructions contribute independently to leakage, with overall leakage well approximated by their product; (2) stronger instruction-following capability correlates with higher leakage risk; and (3) improvements in RAG faithfulness can introduce increased leakage risk. These findings provide actionable insights for understanding and mitigating RAG leakage in practice. Our codebase is available at https://github.com/yeasen-z/LeakDojo.

cs.CR↗
arXiv2026

Generalized Transferable Neural Networks for Steady-State Partial Differential Equations

Deep learning has emerged as a compelling framework for scientific and engineering computing, motivating growing interest in neural network-based solvers for partial differential equations (PDEs). Within this landscape, network architectures with deterministic feature construction have become an appealing approach, offering both high accuracy and computational efficiency in practice. Among them, the transferable neural network (TransNet) is a special class of shallow neural networks (i.e., single-hidden-layer architectures), whose hidden-layer parameters are predetermined according to the principle of uniformly distributed partition hyperplanes. Although TransNet has demonstrated strong performance in solving PDEs with relatively smooth solutions, its accuracy and stability may deteriorate in the presence of highly oscillatory solution structures, where activation saturation and system conditioning issues become limiting factors. In this paper, we propose a generalized transferable neural network (GTransNet) for solving steady-state PDEs, which augments the original TransNet design with additional hidden layers while preserving its interpretable feature-generation mechanism. In particular, the first hidden layer of GTransNet retains TransNet's parameter sampling strategy but incorporates an additional symmetry constraint on the neuron biases, while the subsequent hidden layers omit bias terms and employ a variance-controlled sampling strategy for selecting neuron weights.

math.NA↗
arXiv2026

Developing and characterizing a new-generation regolith simulant "IGCAS-AST01" for the Tianwen-2 target asteroid (469219) Kamo'oalewa

China plans to return samples from the near-Earth asteroid (469219) Kamo'oalewa, which we previously identified as an LL-chondrite-compositional, highly space-weathered object with fine-grained regolith. In this study, we developed 10 mL of Kamo'oalewa regolith simulant, designated "IGCAS-AST01", by irradiating LL5/6 chondrite (Kheneg Ljou^ad) powder with a high-energy pulsed laser. We then analyzed the composition, grain size distribution, density, porosity, visible to near-infrared reflectance spectrum, thermal emission spectrum, thermal diffusivity, specific heat capacity, and microstructural features of both the fresh (unirradiated) powder and IGCAS-AST01. IGCAS-AST01 is composed of 57.8 vol.% olivine, 19.9 vol.% orthopyroxene, 5.6 vol.% diopside, 12.2 vol.% plagioclase, 2.6 vol.% troilite, and minor amounts of other phases. It has a mean size of 26.99 um, a median size of 23.19 um, a density of 700 kg m^-3, and a porosity of 79.1%. Additionally, IGCAS-AST01 exhibits a low reflectance of 0.1 at 0.55 um and an extremely steep spectral slope. In the temperature range of 253.15-473.15 K, its thermal diffusivity and specific heat capacity range from 3.6-4.7 x 10^-6 m^2 s^-1 and 718.43-890.20 J kg^-1 K^-1, respectively. Furthermore, thick amorphous rims and abundant nanophase metallic iron particles are observed in olivine and pyroxene grains of IGCAS-AST01. These results could support the Tianwen-2 mission's payload calibration, sampling operations, on-orbit scientific data interpretation, and future sample analysis.

astro-ph.EP↗
arXiv2026

Tianwen-2 target asteroid (469219) Kamo'oalewa probably develops an Itokawa-compositional but ultra-highly space-weathered surface

China's Tianwen-2 mission plans to return samples from a small, rapidly spinning Earth quasi-satellite (469219) Kamo'oalewa. Previous studies linked Kamo'oalewa to lunar composition and origin. Here, we propose another scenario. We reanalyzed the reflectance spectrum of Kamo'oalewa and obtained an absorption band center at 1.001+-0.028 um (error is 1sigma), consistent with LL chondrites. We then conducted space weathering (SW) experiments on meteorites and found that highly space-weathered LL chondrite powder (but not slab) successfully reproduced the reflectance spectrum of Kamo'oalewa. We further traced the dynamical origin of Kamo'oalewa and found that it probably originated from the v6 secular resonance, and more specifically, the Flora family. Kamo'oalewa exhibits a similar composition to Itokawa and 7 objects in the Flora family, but with a higher degree of space weathering. We, therefore, proposed that Kamo'oalewa probably originated from the Flora family and developed an Itokawa-compositional, highly space-weathered, fine-regolith-dominated surface.

astro-ph.EP↗
arXiv2026

Shape, regolith size and thickness, SMFe^0 content, and spectral type of Tianwen-2 target asteroid (469219) Kamo'oalewa

China's Tianwen-2 spacecraft will return samples from the near-Earth asteroid (469219) Kamo'oalewa. We previously reported that Kamo'oalewa develops an LL-chondrite-compositional, highly space-weathered surface. This study aims to estimate Kamo'oalewa's shape, regolith grain size and thickness, sub-micrometer iron (SMFe0) content, and spectral type. Using the lightcurve data and the Cellinoid model, we modeled Kamo'oalewa's shape, rotation period, and pole orientation. We then estimated its global distribution of regolith critical size using the balance method of gravity, cohesive force, and centrifugal force. Furthermore, in the temperature range of 253.15 to 473.15 K, we measured the thermal parameters of laser-irradiated LL chondrite powder that best matches Kamo'oalewa's spectrum, estimating Kamo'oalewa's thermal inertia and skin depth (lower limit of regolith thickness). Using the radiative transfer mixing model, we also estimated the content of SMFe0 in Kamo'oalewa's regolith. Finally, using the MIT online spectral classification tool for the laser-irradiated LL chondrite powder, we obtained a virtual spectral type of Kamo'oalewa. Our model gives a size of 68 m x 46 m x 39 m, a rotation period of 27.66 minutes, and a pole orientation of 134.7 degrees longitude and -11.4 degrees latitude for Kamo'oalewa. Regolith grains with a size <2 cm can remain stable over 93.8% of the global surface area of Kamo'oalewa. Laser-irradiated LL chondrite powder shows a low thermal inertia (95.5 to 135.1 J m^-2 K^-1 s^-1/2), corresponding to a thermal skin depth of 3 to 3.5 mm on Kamo'oalewa. An SMFe0 content of 0.29 +- 0.05 wt.% is required to match Kamo'oalewa's spectrum. The virtual spectral type of Kamo'oalewa is given as "Sqw".

astro-ph.EP↗
arXiv2026

Anomalously Strong Localized First Ionization Potential Effect Associated with a Solar Subflare

Plasma composition in the solar corona commonly differs from that of the photosphere, with the enhancement of low--first-ionization-potential (FIP) elements referred to as the FIP effect. This phenomenon provides important diagnostics of energy and mass transport between different layers of the solar atmosphere. In this work, we analyze an anomalously strong, localized FIP effect observed in active region 13486 associated with a subflaring episode on 2023 November 17, using multiwavelength observations combining high energy-resolution soft X-ray disk-integrated spectra obtained by the Macao Science Satellite-1B with spatially resolved EUV/UV and H$α$ imaging from Hinode/EIS, SDO/AIA and HMI, and CHASE/HIS. By investigating the temporal evolution of plasma composition in response to changes in magnetic field orientation, we provide new insight into the physical processes linking magnetic reconnection, ponderomotive force fractionation, and coronal abundance anomalies. This work reveals that the anomalously strong enhancement of low-FIP elements is localized in regions with strongly inclined magnetic fields despite a subflare. We interpret these observations within the framework of the ponderomotive force fractionation model and propose that the inclined magnetic geometry enhances the transmission of upward-propagating magnetohydrodynamic waves by reducing reflection near the plasma-$β$$\simeq$1 layer, enhancing FIP fractionation associated with a consequential upward-directed ponderomotive force. In addition, sustained chromospheric heating associated with chromospheric reconnection and flux cancellation appears to maintain the enhanced FIP effect for tens of minutes following the event.

astro-ph.SR↗