arXiv subjects

Yuan Fang

Publications and source records attributed to Yuan Fang.

At least 19 recordsLinked to original sources

arXiv2026

The Milky Way-like radial structure in nearby galaxies

The Milky Way exhibits a distinctive plateau in its inner surface brightness profile around one effective radius, challenging the traditional view of a purely exponential disk. To explore the origin of this feature, we identify 65 nearby galaxies ($<50$ Mpc) with similar plateau structures and analyze their multi-band surface brightness and color profiles using SDSS, DESI, 2MASS, and Mephisto data. Of the 65 galaxies with Milky Way-like plateaus, 42 have bar classifications (25 barred, 17 non-barred). Barred galaxies show radial color variations more consistent with stellar population ageing, while non-barred galaxies align more with the dust reddening direction, suggesting dust attenuation contributes to plateau formation in non-barred systems. The broad range of color gradients among barred galaxies suggests bars influence plateaus through multiple pathways, including stellar population variations and dynamical stellar-mass redistribution. Environment may also contribute: both barred and non-barred galaxies tend to reside in denser environments than a comparison sample without plateaus. The low occurrence rate ($\sim$3.6\%) may indicate that plateau structures mark a relatively uncommon evolutionary stage. The Milky Way, with both strong stellar population dependence and a persistent stellar mass profile feature, may represent an intermediate case where both stellar population variations and dynamical processes associated with the bar shape its plateau.

astro-ph.GA↗

arXiv2026

Annealing-Induced Structural and Optical Evolution of Ion-Beam-Sputtered Ta2O5 Films for High-Power Laser Applications

Tantalum pentoxide (Ta2O5) thin films deposited by ion beam sputtering (IBS) are essential components in high-power laser systems, where post-deposition annealing is routinely used to optimize film quality. In this work, we systematically investigate the structural and optical evolution of IBS-deposited Ta2O5 single layers subjected to post-deposition annealing at temperatures from 400 to 800 $^{\circ}\mathrm{C}$. The films undergo an amorphous-to-polycrystalline transition between 600 $^{\circ}\mathrm{C}$ and 700 $^{\circ}\mathrm{C}$, accompanied by a marked increase in surface roughness. Argon bubbles inherently incorporated during IBS serve as structural markers: their size correlates positively with the surface roughness from the as-deposited to 700 $^{\circ}\mathrm{C}$ but decouples at 800 $^{\circ}\mathrm{C}$, revealing a transition from matrix-relaxation-dominated to surface-diffusion-dominated behavior. Transmittance spectra show a red shift at 400 and 600 $^{\circ}\mathrm{C}$ which can be explained by physical thickness increase. Weak absorption decreases from ~6 ppm in as-deposited films to ~2 ppm at 400-600 $^{\circ}\mathrm{C}$ due to annihilation of oxygen vacancies, rises markedly at 700 $^{\circ}\mathrm{C}$ due to grain-boundary defects and then drops again at 800 $^{\circ}\mathrm{C}$ as grain boundaries relax. We identify 400 $^{\circ}\mathrm{C}$ as the optimal annealing window for IBS-deposited Ta2O5 single layers, with the lowest surface roughness and effectively reducing weak absorption while preserving the amorphous phase.

cond-mat.mtrl-sci↗

arXiv2026

Pathway to lowest-energy structures and stress relaxation for the surface triple junction of copper

The behavior of surface triple junctions (STJ) at emergent grain boundaries on free surfaces is critical to the microstructure evolution, and therefore to the stability of next generation interconnects. Yet, despite this significant importance, its lowest-energy structure and local stress have remained persistently unknown. Here, we fill this critical gap through high-resolution experimental mapping of the local surface deformation at STJs of a symmetrical tilt grain boundaries with [111] rotation axis and misorientation angles 13.17°, the analysis of the local core-shifted structure and stress relaxation, and ergodic searching metastable structures accordingly with the molecular-statics (MS) calculation. The computation clearly shows the zipped Y-shaped notch as the lowest-energy structures for this group of STJs, the line profile of which is consistent with the experimental observation. This energetic preference was well explained by the stress delocalization mechanism and was tested for a wide range of boundaries with even deeper core-shift depth using machine learning methods based on the data obtained from MS calculation. By revealing the elusive thermodynamics of STJs, our findings advance the research field by redefining the energetic framework for capillary driven structure evolution and providing foundation for understanding kinetically diffusive deformation and for engineering thin-film interconnects and related materials.

cond-mat.mtrl-sci↗

arXiv2026

SN 2025dys: A luminous supernova explosion from a partially stripped helium star

We present well-sampled multi-band imaging and low- to medium-resolution spectroscopic observations of the early-discovered Type~II supernova (SN)~2025dys. The photometric evolution exhibits a rise time of $\sim$\,18 days ($r$ band) and a rapid decline transitioning to the radioactive decay tail at $\sim$\,70 days. SN~2025dys is among the high-luminosity events with a peak pseudo-bolometric luminosity of $L_{\rm opt}=8.57\times10^{42}\,\mathrm{erg\,s^{-1}}$. Early X-ray emission was identified, displaying a luminosity of $7.55\times10^{40}\,\mathrm{erg\,s^{-1}}$. The observational investigation points to a dense, hydrogen-rich circumstellar medium with a mass of $M_{\rm CSM}\approx0.5$--$0.7\,\mathrm{M_\odot}$, suggesting a partially stripped, hydrogen-poor inner ejecta with a relatively low mass of $M_{\rm ej}\approx2.5\,\mathrm{M_\odot}$. The explosion possessed a kinetic energy of $E_{\rm k}\approx0.3\text{--}0.5$~foe and yielded a synthesized nickel mass of $M_{\rm Ni}\approx0.042_{-0.011}^{+0.012}\,\mathrm{M_\odot}$. These physical parameters indicate an extreme mass-loss episode before the explosion, characterized by a high mass-loss rate of $\dot{M} \gtrsim 0.01\,\mathrm{M_\odot\,yr^{-1}}$. SN~2025dys represents a stripped progenitor, intrinsically resembling a Type~IIb or even a Type~Ib SN, that exploded within a dense, hydrogen-rich circumstellar environment.

astro-ph.HE↗

arXiv2026

Simultaneous Real-Color Time-Domain Survey of the Coma Field with the Multi-channel Photometric Survey Telescope (Mephisto)

We present the results from a high-cadence, multi-channel time-domain survey targeting the Coma galaxy cluster, utilizing the newly commissioned 1.6-m Multi-channel Photometric Survey Telescope (Mephisto). Mephisto's pioneering mosaic focal-plane design enables strictly simultaneous imaging in three distinct optical bands ($u/v, g/r, i/z$). This unique capability allows for the extraction of true, instantaneous spectral energy distributions (SEDs) of fast-evolving transients. Our survey continuously monitors a 20-square-degree footprint centered on the Coma cluster. By strategically targeting this massive local structure, we exploit cosmic anisotropy to significantly amplify the volumetric discovery rate of stellar explosions. During the initial six-month pilot operation, we successfully detected and tracked nine new supernovae (SNe). Remarkably, our high-cadence, multi-band strategy allowed us to capture ultra-early rising light curves and pre-discovery signals (e.g., SN 2026dyr) hours to days prior to formal discovery alerts. These instantaneous early-phase colors allow us to reconstruct intrinsic SEDs without temporal interpolation, providing reliable constraints on the physical parameters of early supernova (SN) evolution. Furthermore, our observed supernova yield shows good agreement with theoretical forecasts based on local stellar mass and star-formation rate overdensities. These pilot results validate the efficiency of the Mephisto Coma survey, demonstrating its objective capability to systematically explore the local transient population and provide rapid optical localizations for future multi-messenger events.

astro-ph.HE↗

arXiv2026

Atmospheric Parameter Determination of RR Lyrae Stars With Mephisto Multi-band Photometry

Phase-resolved atmospheric parameters of pulsating stars, including RR Lyrae stars and Cepheids, are essential for studying stellar structure and pulsation physics and for establishing accurate stellar benchmarks for Galactic structure and assembly history. We present a photometric framework that simultaneously infers Teff, log g, and [Fe/H] from Mephisto multi-epoch, multi-band (u, v, g, r, i, z) photometry. A supervised neural-network forward model is built by training on synthetic colors derived from theoretical stellar atmosphere models. It reproduces the synthetic colors to a precision of 0.01 per cent. The model is applied to observed multi-epoch colors to derive phase-dependent atmospheric parameters. We apply the method to six RRab and four RRc stars in three globular clusters observed with Mephisto. We find typical per-phase uncertainties of about 62 K in effective temperature (Teff), 0.10 dex in surface gravity (log g), and 0.16 dex in metallicity ([Fe/H]). Repeated measurements across the pulsation cycle reduce the typical formal uncertainty of the final [Fe/H] estimates to roughly 0.03 dex. A sensitivity analysis indicates that the five Mephisto colors provide distinct and complementary constraints on the three stellar atmospheric parameters: (u-v) provides robust constraints on log g, (v-g) is informative for estimating [Fe/H], and (u-v), (v-g), (g-r), (r-i), and (i-z) all provide information on Teff. The results demonstrate that Mephisto multi-epoch, multi-band photometry can deliver precise, cost-effective phase-resolved atmospheric parameters for RR Lyrae stars, offering an efficient way to construct large samples of stellar benchmarks for studies of stellar pulsation physics and Galactic structure and evolution.

astro-ph.SR↗

arXiv2026

A Shell Collision Scenario for EP251222b/GRB 251222A: Connecting Prompt Emission and Double-Peaked Afterglow

GRB 251222A is a long-duration gamma-ray burst at a spectroscopic redshift of $z=3.171$. It was detected by the EP as the soft X-ray transient EP251222b before and during the gamma-ray emission. We performed a multi-wavelength (EP/WXT, fermi/GBM, swift/XRT and optical/NIR) investigation of this event. Its prompt soft X-ray and gamma-ray features, together with two afterglow rebrightenings, allow us to examine a possible connection between the prompt ejecta and the afterglow. We consider a multiple-shell scenario in which a faster, narrower shell catches up with a slower, wider shell. We assume that the selected pulse groups trace different ejecta and that the late X-ray flare marks their collision. Under these assumptions, we represent the post-collision outflow by an effective merged core and an outer annular component. A joint fit to the multi-band afterglow gives a Lorentz factor of $Γ_c\approx293$ for the merged core and $Γ_{1,w}\approx228$ for the outer component. The sample-wise collision mapping gives an energy release of $E_{\rm diss}=(1.6^{+2.1}_{-0.9})\times10^{52}$ erg and a dynamical dissipation efficiency of $0.37^{+0.07}_{-0.14}\%$. EP251222b can therefore be used to test a prompt--afterglow connection through shell collisions, although it does not uniquely establish this interpretation.

astro-ph.HE↗

arXiv2026

Eye, Robot: Learning Expressive Gaze Dynamics via a VLM-Guided Transformer-VAE

Gaze coordinates attention in human interaction through coupled eye, head, and eyelid motion. A humanoid robot must choose where to look while generating a suitable movement toward that target; fixed tracking rules alone do not model the temporal structure of human gaze. We separate these tasks with an asynchronous vision-language model that selects a scene referent, a reactive perception module that maintains fixation, and a Realistic Gaze Transformer (RGT) that generates target-conditioned motion. RGT is a Transformer-VAE trained on markerless human recordings retargeted to the robot's 11-DoF joint space: self-supervised pretraining encodes recent full-head history, and autoregressive fine-tuning exposes the model to its own predictions during rollout. At inference, a four-dimensional binocular-eye trajectory conditions coordinated neck and eye motion; an auxiliary blink predictor triggers eyelid templates during playback. On a two-second benchmark in which all controllers receive the same ground-truth target, RGT-Base obtains the lowest reported overall seven-joint MSE, and physical demonstrations show the integrated targeting and control pipeline. The evidence establishes robot-space kinematic fidelity and real-time RGT inference; perceived naturalness and open-vocabulary target-selection accuracy remain questions for controlled evaluation.

cs.RO↗

arXiv2026

Chiral Weyl-Kondo semimetallic state through enhanced correlation in CeGaGe

Strongly correlated chiral materials have been proposed to host a chiral Weyl-Kondo semimetal (cWKSM) state, in which Kondo hybridization pins chirality-induced Kramers-Weyl point crossings, together with additional symmetry-enforced band crossings, near the Fermi level. Realizing this state requires a material that combines crystal chirality, non-symmorphic symmetry, and Kondo correlations, and currently there are no known materials that combine all these requirements. Here we report evidence for a cWKSM state in CeGaGe, a member of the RXY (R = rare earth; X = Al, Si; Y = Ga, Ge) family. Magnetization measurements confirm long-range antiferromagnetic order below T$_N$ = 4.7 K with a complex, canted magnetic structure. Unique to CeGaGe among the known RXY systems is a structural transition from an achiral tetragonal I4$_{1}$md structure (at high temperatures) to the chiral tetragonal P4$_{3}$ structure (at low temperatures). The structural transition in CeGaGe allows us to highlight the role of chirality in stabilizing the Kramers cWKSM state. Hall resistivity measurements with $H || c$ reveal an anomalous Hall conductivity (AHC) that is constant below the Kondo temperature $T_K \approx 7$ K and drops sharply above it. This crossover tracks the onset of Kondo coherence rather than the magnetic ordering, which occurs at T$_N$ = 4.7 K. Together with first-principles calculations for the $P4_3$ structure, this identifies the AHC as an intrinsic, Berry-curvature-driven contribution generated by Kondo hybridization, rather than a consequence of extrinsic scattering or dynamic scalar spin chirality. These results establish CeGaGe as a rare experimental platform in which crystal chirality, strong electronic correlations, and non-trivial band topology coexist, providing the first material realization of a Kondo-driven cWKSM state.

cond-mat.str-el↗

arXiv2026

Navigating Route Latent Space for Synthesizable Molecular Design

Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, limiting their real-world utility. Prior synthesizability-aware methods either project generated molecules back to synthesizable analogs that deviate from the intended target, or optimize directly in discrete synthesis spaces that lack a continuous landscape for efficient search. We argue that this limitation mainly comes from the search space rather than the optimizer. To address this, we propose RouteFlow, a framework that reformulates synthesizable molecular design as a search over a continuous route latent space, where each latent maps back to a complete synthesis route and synthesizability is inherently preserved. To navigate this space, we adopt reward-guided flow matching as an efficient sampler that steers toward high-property regions. Since reward optimization may push latents off the manifold of real synthesis routes, where decoding becomes unreliable, we further introduce a cycle-consistency mechanism to stabilize fine-tuning. Across 16 optimization tasks from Therapeutic Data Commons, RouteFlow achieves the best sample efficiency among synthesizability-aware baselines, with the best synthetic accessibility and the highest retrosynthesis success rate. Our results also confirm that the proposed cycle-consistency reliably keeps optimization on-manifold while improving target properties, supporting effective synthesizable molecular discovery.

cs.AI↗

arXiv2026

Find Something You Can't Do: Agentic Real-World Reinforcement Learning for Self-Improving VLA Models

Vision--language--action (VLA) models provide strong priors for robotic manipulation but are typically deployed as frozen policies, unable to improve from their own failures. Real-world reinforcement learning (RL) offers a path to continued improvement, yet manual environment resets and task-success supervision hinder autonomous learning. We introduce \textbf{FIND}, an agentic real-world RL framework that closes the loop between scene understanding, weakness-aware practice, self-evaluation, and policy improvement in a persistent workspace. FIND reframes autonomous practice as a scene-conditioned, performance-aware task-selection problem: instead of restoring a predefined scene after each rollout, it uses the resulting scene to determine what to practice next. A vision--language agent identifies feasible tasks from a predefined library, prioritizes those with lower recent success rates, and evaluates outcomes using paired pre- and post-execution observations. We instantiate FIND with a frozen $π_{0.5}$ VLA and residual off-policy RL. Across eight real-world manipulation tasks, the independent human-assessed success rate improves from $55\%$ to $71.9\%$. A representative run completes 456 autonomous episodes within 6 hours of interaction, requiring 30 scene-recovery interventions and no human-provided reward labels during online learning. Ablations and systematic evaluations further examine key design choices, agent evaluation accuracy, and human intervention requirements. Our website is made publicly available at: FIND.github.io.

cs.RO↗

arXiv2026

KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation

Generative recommendation, has been attracted a surge of attentions in industrial and academic research community, towards to build more smart system to build next-generation recommender. Under the significant developing wave of large language model, our team have been developed Semantic ID based OneRec/OneRec-V2. These models have been widely deployed in production and demonstrate the scaling potential of the autoregressive next-item prediction paradigm for industrial recommender systems. Building on the success of OneRec, we further explored a series of models, including OneRec-Think, OpenOneRec, and OneReason, that connect item Semantic IDs with natural language in a unified representation space and seek to unlock the potential of natural-language chain-of-thought (CoT) reasoning for recommendation. However, our preliminary works found that introducing reasoning CoT does not always improve the recommendation performance. To address this issue, OneReason strengthens the semantic alignment between items and language, introduces structured template-based supervision for interest reasoning, and applies advanced reinforcement learning techniques to make reasoning more beneficial to recommendation. As a frontier topic to building recommendation foundation models, we believe this topic has significant research value and hope to encourage more researchers to explore it together. To this end, together with the SIGIR 2026 community, we organized the KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation.

cs.IR↗

arXiv2026

Can AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets

AI-based trading methods have rapidly evolved from machine learning and reinforcement learning to large language models (LLMs) and trading agents, yet their performance is still predominantly assessed through historical backtesting. Such evaluations provide limited evidence of whether a method can generalize to unseen future markets or whether its backtested performance can be sustained in realistic trading frictions (e.g., latency, slippage, liquidity constraints, and market impact). We present a unified benchmark that evaluates representative machine learning, reinforcement learning, LLM-based, and agent-based trading methods in cryptocurrency markets through three progressively more realistic stages: historical backtesting, prospective exchange-based paper trading, and real-money live trading. These stages jointly increase temporal realism by moving from historical to unseen future markets, and execution realism by moving from offline simulation toward live trading. This protocol enables us to quantify the backtest-to-realization gap, identify when performance begins to deteriorate, and compare how this gap differs across major classes of AI trading methods. We further provide a unified open-source system supporting all three evaluation stages, together with a public platform that continuously updates benchmark results. Code is available at https://github.com/Starlien95/Awesome-TradingAI.

cs.AI↗

arXiv2026

Chiral Weyl--Kondo semimetal and circular photogalvanic effect in a prototype Kondo lattice system

Chiral Weyl--Kondo semimetals (cWKSM) provide a setting in which chiral Weyl quasiparticles emerge in the immediate vicinity of the Fermi energy from a Kondo-driven reconstruction of the strongly correlated electronic states in chiral heavy fermion systems (K.-S. Lin et al., arXiv:2602.22185). A defining characteristic of this strongly correlated topological state is the Kramers chiral Weyl fermions in the low-energy quasiparticle states. Recently, experiments in CeGaGe have emerged as a concrete realization of the proposed effect (Arushi et al., preprint). Motivated by these findings, here we go beyond the materials-specific effects by constructing a prototype Kondo lattice model; it incorporates only the essential couplings that respect the associated tetragonal crystalline symmetries. This simplification allows us to robustly demonstrate the symmetry-enforced Kramers Weyl fermions and related topological nodal states in the spectrum of heavy quasiparticles. Furthermore, the simplification provides a tractable setting to determine the salient features in the system's nonlinear optical response, the circular photogalvanic effect, in chiral Weyl--Kondo semimetals. Both analytical and numerical calculations identify sharp peaks in the frequency domain as signatures of the Kondo-driven chiral Weyl nodes; the sharpness of the spectrum reflects the resonant nature of the underlying strongly correlated electronic excitations. Thus, cWKSM provides a unique setting to spectroscopically identify topological fermions that are induced by strong electron correlations. As such, our results are expected to bring about much needed new insights into the understanding of strongly correlated gapless topological matter.

cond-mat.str-el↗

arXiv2026

From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge

How does a language model's dependence on query-routing information and target knowledge change as it answers a question? We study this question through layerwise interventions on the hidden state at the end of the question. Across Qwen, Llama, and Gemma, we compare country-continent questions with noun, adjective, and code answers while keeping several fitted measurements distinct. A pair-conditioned request direction describes which country is queried in natural single-country questions; a global request direction describes first- versus second-country requests in paired questions; separate selection candidates test control among contents already available in the hidden state. A diagnostic reanalysis of frozen Qwen natural-question states shows that the pair-conditioned direction grows stronger before interventions on it begin to alter later fitted knowledge, with this causal window opening while answer-supporting content is still forming. The paired three-model trajectories are not uniform: Gemma shows a partially overlapping mid-layer routing-content profile, whereas Llama has no sustained routing-effect window under the same gates. In the paired protocol, dependence on the global request direction decreases from fixed earlier to later layer sets while dependence on fitted content persists. A matched Qwen comparison shows that the pair-conditioned direction retains a late effect, so this operational handoff concerns the global fitted direction rather than all request information. These results separate early readability, natural strength, causal steering, and later content dependence.

cs.AI↗

arXiv2026

HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs

Graph neural networks (GNNs) have demonstrated success in modeling relational data primarily under the assumption of homophily. However, many real-world graphs exhibit heterophily, where linked nodes belong to different categories or possess diverse attributes, such as webpages, Wikipedia articles, social networks, and e-commerce platforms. Additionally, nodes in many domains are associated with textual descriptions, forming heterophilic text-attributed graphs (TAGs). Despite their significance, heterophilic TAGs remain underexplored due to the lack of dedicated benchmarks that jointly capture heterophilic structures and rich textual attributes. To address this gap, we introduce the \textbf{He}terophilic \textbf{T}ext-attributed \textbf{G}raph \textbf{B}enchmark (HeTGB), a novel benchmark comprising five real-world heterophilic graph datasets from diverse domains, with nodes enriched by extensive textual descriptions. HeTGB enables systematic evaluation of GNNs, pre-trained language models (PLMs) and co-training methods on the node classification task. Through extensive benchmarking experiments, we showcase the utility of text attributes in heterophilic graphs, analyze the challenges posed by heterophilic TAGs and the limitations of existing models, and provide insights into the interplay between graph structures and textual attributes.

cs.CL↗

arXiv2026

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder

Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated strong performance, they overlook the rich molecular domain knowledge associated with submolecular instances (atoms and bonds). While molecular pre-training approaches incorporate such knowledge into their pre-training objectives, they typically employ designs tailored to a specific type of knowledge, lacking the flexibility to integrate diverse knowledge present in molecules. Hence, reusing widely available and well-validated pre-trained 2D encoders, while incorporating molecular domain knowledge during downstream adaptation, offers a more practical alternative. In this work, we propose MolGA, which adapts pre-trained 2D graph encoders to downstream molecular applications by flexibly incorporating diverse molecular domain knowledge. First, we propose a molecular alignment strategy that bridge the gap between pre-trained topological representations with domain-knowledge representations. Second, we introduce a conditional adaptation mechanism that generates instance-specific tokens to enable fine-grained integration of molecular domain knowledge for downstream tasks. Finally, we conduct extensive experiments on eleven public datasets, demonstrating the effectiveness of MolGA.

cs.LG↗

arXiv2026

Rapid Variability and Broadband Spectral Modeling in the Flaring Activity of BL Lacertae

We report a multi-wavelength study of two flaring episodes of the blazar BL Lacertae during MJD 60500-60800 (9 July 2024 - 5 May 2025). The source reached a daily-averaged $γ$-ray flux of $(1.03 \pm 0.05) \times 10^{-5} \, \mathrm{ph \, cm^{-2} \, s^{-1}}$ ($E > 100$ MeV) on MJD 60588 (5 October 2024). Using orbit-binned data from the Large Area Telescope (LAT) onboard the \textit{Fermi Gamma-ray Space Telescope}, we identify a minimum flux halving timescale of $τ= 1.33 \pm 0.29$ hr. This constrains the upper limit on the $γ$-ray emitting region size to $R \le 2.0 \times 10^{15}$ cm, as well as its distance from the central supermassive black hole to $R_\mathrm{H} \le 5.9 \times 10^{16}$ cm, assuming a Doppler factor of $δ= 14.8$ derived from the spectral energy distribution (SED) modeling. We find tentative evidence for sub-minute $γ$-ray variability with a minimum doubling time of $0.7 \pm 0.2$ min ($p$-value = 0.03). This may originate from an extremely compact region with a size of $R \le 1.8 \times 10^{13}$ cm, suggesting that the emission arises from magnetohydrodynamic substructures, such as plasmoids within a magnetic reconnection zone. Spectral analysis reveals a significant ``softer-when-brighter'' trend ($r = 0.96, p = 4.5 \times 10^{-4}$) during the minute-scale flare peaks, indicating a complex interplay between particle acceleration and radiative cooling. The SED is reproduced using a one-zone leptonic model, in which synchrotron self-Compton (SSC) and external Compton (EC) scattering effectively account for the high-energy emissions. The reduced magnetic field strengths and hard electron injection spectral indices observed during the flaring states suggest enhanced particle acceleration efficiency, possibly associated with relativistic magnetic reconnection.

astro-ph.HE↗