arXiv subjects

Xing Liu

Publications and source records attributed to Xing Liu.

At least 19 recordsLinked to original sources

arXiv2026

EP250302a: violent shell collision in a soft-X-ray-selected GRB-like transient

The Einstein Probe opens a previously unexplored soft X-ray window onto gamma-ray bursts, filling a critical observational gap in the soft X-ray coverage of their prompt emission. In this letter, we present EP250302a, a soft-X-ray-selected, GRB-like transient at $z=1.131$ detected by the Einstein Probe. Follow-up observations from X-ray to radio reveal a narrow X-ray flare at $\sim 1.1$\, ks and subsequent achromatic optical and X-ray rebrightening. These features challenge a standard single-component afterglow model and indicate the need for multiple ejecta components. A violent collision between a late relativistic shell and the decelerated leading blast wave provides a plausible interpretation: the flare arises from internal dissipation of the late ejecta, while the rebrightening is powered by the shocked emission produced in the collision. Quantitative modeling constrains the kinetic energy ratio between the late shell and the initial ejecta to $E_{\rm k,iso,2}/E_{\rm k,iso,1} \sim 5$ (with $E_{\rm k,iso,2} \sim 10^{53}$~erg and $E_{\rm k,iso,1} \sim 2\times10^{52}$~erg), as well as the Lorentz factor contrast to $Γ_{2,0}/Γ_{1,0} \approx 0.98$--$2.27$, required to reproduce the observed flare luminosity and rebrightening amplitude. Such an energetic late shell can be launched in a radiatively inefficient second episode of central-engine activity. Thanks to the well-sampled, early-time multiband coverage facilitated by the EP trigger, EP250302a provides a valuable case to test the physical connection between central-engine activity and shell collisions.

astro-ph.HE↗

arXiv2026

Residual Fault Adaptation for Dexterous In-Hand Manipulation Under Runtime Joint Faults

Dexterous in-hand manipulation requires coordinated control of multiple actuated joints, and a runtime joint fault can abruptly disrupt the contact configuration required for successful manipulation. In this work, we propose residual fault adaptation (RFA), a teacher-anchored framework for compensating for hidden command-channel faults. RFA retains a frozen healthy teacher to provide nominal behavior and trains a recurrent residual policy to infer corrective actions from proprioceptive and command-response history. During training, fault-injection domain randomization (FIDR) varies the fault mode, affected joint, severity, and onset time, while adaptive sampling increases the frequency of fault modes associated with lower recent performance. A frozen Direct FIDR policy provides a distributional reference only on fault-active training samples and is absent from deployment. The deployed controller receives neither fault labels nor controller-switching signals. Simulation experiments on the dexterous hand indicate that RFA can improve manipulation performance relative to the healthy policy under a fixed mixed-fault protocol. Real-robot experiments with software-injected faults further demonstrate zero-shot deployment of the learned adaptation policy.

cs.RO↗

arXiv2026

ICM-Bench: Person-Level Identity Reasoning in Multimodal Agents with Long-Term Memory

Long-horizon multimodal agents should remember not only what happened but also who participated. This capability depends on linking recurring faces, voices, names, person-associated objects, events, and social relations to consistent identities over time. Existing long-video and multimodal-agent benchmarks measure broad memory question answering, but they do not isolate the ability to maintain recurring person identities and reason over their cross-time relations. We introduce ICM-Bench (Identity-Centric Memory Benchmark), which, to the best of our knowledge, is the first benchmark specifically designed to evaluate identity-centric reasoning over long video memories in multimodal agents. The benchmark contains 839 synthetic clips spanning 141 minutes and 1,217 open-ended questions about six recurring adults in a one-year life album. A theme-configurable pipeline generates the video collection and associates each question with its target identities and traceable supporting evidence. We compare direct caption-memory baselines, memory-augmented agents, and graph-retrieval systems. Gemini 3.1 Pro achieves the highest overall accuracy of 74.0%, yet its score falls to 60.3% on questions that require long-term identity profiles. The results show that current systems recover many event-level memories but remain less reliable when evidence must be accumulated around a stable person.

cs.CV↗

arXiv2026

Integrated Positioning and Communication via LEO Satellites: A Contemporary Overview

Low Earth orbit (LEO) satellites, as a prominent technology in the sixth generation non-terrestrial network, offer both positioning and communication capabilities. While these two applications have each been extensively studied and have achieved substantial progress in recent years, the potential synergistic benefits of integrating them remain an underexplored yet promising avenue. This article provides a contemporary overview of integrated positioning and communication (IPAC) systems enabled by LEO satellites. Leveraging the distinct characteristics of LEO satellites, we examine how communication systems can enhance positioning accuracy and, conversely, how positioning information can be exploited to improve communication efficiency. A representative case study is included to illustrate enhanced LEO positioning signal acquisition with the assistance of communication-side information. Finally, several key open research challenges for LEO-based IPAC systems are discussed.

eess.SP↗

arXiv2026

Orbital Errors in LEO Satellite Positioning: Modeling, Analysis, and Bayesian Calibration

Low-Earth orbit (LEO) satellites offer a promising alternative to global navigation satellite systems for precise positioning. However, their relatively low altitudes make them more susceptible to orbital perturbations, which in turn degrade positioning accuracy. In this work, we study (i) the mechanisms through which orbital errors affect positioning performance and (ii) the fundamental calibration strategies for mitigating low-Earth orbital errors. We first model the satellite orbit and analyze historical two-line element data to characterize the statistical behavior of low-Earth orbital errors. Based on these real-data statistics, we derive the misspecified Cramér-Rao bound to quantify the impact of orbital errors on positioning performance. Subsequently, we develop a Bayesian orbit calibration framework that leverages the learned orbital error statistics as prior information to enhance calibration accuracy. This work sheds light on orbital error modeling, real-data-based evaluation, theoretical impact analysis, and calibration methodologies. Extensive simulations show that stale orbital information can severely degrade positioning performance, whereas learning and incorporating orbital error statistics can effectively enhance orbit calibration and ultimately improve positioning accuracy.

eess.SP↗

arXiv2026

Coordination of Ground-to-Space Reference Networks for High-Precision GNSS

High-precision Global Navigation Satellite System (GNSS) services rely on accurate orbit, clock, atmospheric, and hardware-bias corrections generated from reference observations. These products are traditionally derived from terrestrial reference networks, whose performance strongly depends on the density and geographic distribution of ground stations. Consequently, sparse or regionally concentrated networks can suffer from tracking gaps and limited global observability, reducing their ability to support globally consistent high-precision products. Low Earth Orbit (LEO) constellations equipped with onboard GNSS receivers and inter-satellite links (ISLs) can serve as a network of spaceborne reference stations, offering a promising way to extend terrestrial reference networks into space, thereby improving observability for ground networks and enabling future direct correction broadcast. However, most existing network-based GNSS correction-generation workflows assume that observations can be centrally collected and processed. This assumption fails in practical ground--to--space architectures, where dynamic satellite geometry and system constraints render communication links intermittent, asymmetric, capacity-limited, and lossy. To address these limitations, this paper proposes a decentralized processing architecture that coordinates the ground-to-space GNSS reference network. By modeling ground stations and LEO satellites as interacting subnetworks over a dynamic graph, our approach allows frequent intra-tier communication while restricting cross-tier exchanges to compact estimation summaries transmitted opportunistically under probabilistic link availability....

eess.SP↗

arXiv2026

GRB 260310A / SN 2026fgk: A Multi-Wavelength Study of a Nearby Underluminous Long GRB and SN with a Complex Afterglow

We present a comprehensive multi-wavelength study of GRB 260310A / SN 2026fgk, a nearby ($z=0.153$), long-duration gamma-ray burst (GRB) with an exceptionally underluminous prompt $γ$-ray emission and a Comptonized spectrum. The burst occurred at the edge of a blue host galaxy at a projected distance of 15 kpc, which is one of the largest offsets reported for a long GRB. The bright optical afterglow, with dense coverage from COLIBRÍ, likely peaked at a few to several hours post-burst, followed by a shallow decay not expected from canonical afterglow models. Both the optical and X-ray light curves show a brief chromatic plateau from $4-7$ days. We show that the subsequent rebrightening observed at $\sim20$ days is best explained by the combined contribution of the associated Type Ic-BL supernova, identified in GTC spectra, and a late-time refreshed shock. The broadband optical to X-ray spectral energy distribution is well described by synchrotron emission from the forward shock, while the radio observations demand an additional emission component. We model the afterglow using (a) an on-axis uniform jet from a dirty fireball with late-time energy injection and (b) a misaligned jet with power-law angular structure, both having material emitting along our line-of-sight (LOS) moving with an initial Lorentz factor of $Γ_0\sim20-35$. We conclude that at more typical GRB distances ($z\gtrsim0.5$) the prompt $γ$-ray emission from this source would likely have escaped detection, whereas its optical afterglow would have remained observable, making the event appear as an orphan afterglow or a gamma-ray quiet fast X-ray transient.

astro-ph.HE↗

arXiv2026

HonestFace: Towards Honest Face Restoration with One-Step Diffusion Model

Face restoration has achieved significant advancements through the years of development. However, maintaining high fidelity and authenticity while avoiding artifacts remains challenging, especially in extreme degradation scenarios. This highlights the need for models that are more ``honest'' in their reconstruction from low-quality inputs, accurately reflecting original characteristics. In this work, we propose HonestFace, a novel approach for honest face restoration with identity consistency and realistic textures. To achieve this, HonestFace incorporates several key components. First, we propose an identity embedder to effectively capture and preserve crucial identity features from both the low-quality input and multiple reference faces. Second, a masked face alignment method is presented to enhance fine-grained details and textural authenticity. Furthermore, we present a new real-world reference-based face restoration dataset, \textbf{MultiRefCeleb-Test}, with multiple high-quality references and low-quality inputs. Leveraging these contributions within a one-step diffusion model framework, HonestFace delivers excellent restoration results in terms of facial fidelity and realism. Experiments demonstrate that our approach surpasses state-of-the-art methods, achieving superior performance in both visual quality and quantitative assessments. The code and pre-trained models are available at https://github.com/jkwang28/HonestFace .

cs.CV↗

arXiv2026

Curiosity-Diffuser: Curiosity Guide Diffusion Models for Reliability

One of the bottlenecks in robotic intelligence is the instability of neural network models. This leads to risks when applying intelligence in the physical world. Specifically, imitation policy based on neural network may generate hallucinations, leading to inaccurate behaviors that impact the safety of real-world applications. To address this issue, this paper proposes the Curiosity-Diffuser, aimed at guiding the conditional diffusion model to generate trajectories with lower curiosity, thereby improving the reliability of policy. The core idea is to use a Random Network Distillation (RND) curiosity module to assess whether the model's behavior aligns with the training data, and then minimize curiosity by classifier guidance diffusion to reduce overgeneralization during inference. Additionally, we propose a computationally efficient metric for evaluating the reliability of the policy, measuring the similarity between the generated behaviors and the training dataset, to facilitate research about reliability learning. Finally, simulations and real-world experiments verify the effectiveness and applicability of the proposed method to a variety of scenarios, showing that Curiosity-Diffuser significantly improves task performance and produces behaviors that are more similar to the training data. The code for this work is available at: github.com/CarlDegio/Curiosity-Diffuser

cs.RO↗

arXiv2026

Anchoring on Reality: Breaking the Pseudo-Target Ceiling in Makeup Transfer

Makeup transfer applies a reference cosmetic style to a source face while preserving its identity and geometry. However, this task is severely hindered by the lack of real paired training data. Current methods rely on either weak priors or synthetic pseudo-targets from large-scale editing models. These paradigms provide suboptimal guidance, often leading to degraded fine-grained details, synthetic artifacts, and identity drift. To this end, we propose Anchoring on Reality Makeup Transfer (ART), a two-stage framework with a reality-anchored refinement cycle. In Stage I, the model is initialized with pseudo-targets to establish basic semantic alignment and global makeup placement. Crucially, Stage II shifts supervision from pseudo-targets to the real reference, reconstructing it from its bare-skin counterpart through a differentiable cycle that penalizes any omitted detail and overrides synthetic artifacts. Furthermore, we introduce MakeupFaces2K (MF2K), the first 2K-resolution in-the-wild makeup portrait dataset comprising 8,573 images. Extensive experiments demonstrate that our method achieves superior makeup fidelity, strong background stability, and robust identity preservation, especially for complex makeup styles.

cs.CV↗

arXiv2026

STAGE: STyle-controllable Action GEneration for personalized autonomous driving

Driving style refers to the behavioral preferences that drivers maintain during driving, shaped by their diverse experiences, habits, and needs, and is typically reflected in varying levels of aggressiveness. If humans choose to use autonomous driving systems, they would expect the driving style of the systems to closely resemble their own habit. However, this is challenging for current industrial autonomous driving systems. To address this, we developed a style controllable action generation method, STAGE, for driving tasks. Its training process is based on imitation learning, incorporating both style value and latent value action modality encoding. Preference learning is then used to identify the user's driving style as a continuous, monotonic style value. And to reduce the cost of human involvement in the preference training process, we also developed a set of rules to compare driving style in data pairs. Then, during inference, the user inputs the style value to control the generated action patterns, dynamically meeting the user's expectations. Using the STAGE method, we verified that the style-controlled action generation results in several typical road scenarios significantly align with human expectations. Furthermore, through comparisons between the STAGE method and various other approaches, we reveal the unique functionalities of STAGE, including its style controllability, style continuity, driving style alignment capability and driving safety. The code for this work is available at: https://github.com/CarlDegio/STAGE

cs.RO↗

arXiv2026

How Does Urban Context Relate to Residential Building Health? A Vision-POI Fusion Framework for Building-Level Housing Inspection

Housing-level urban physical examination is essential for identifying residential building problems and supporting targeted urban renewal. Existing automated inspection studies primarily rely on individual images and rarely examine whether surrounding urban functional context can provide supplementary information for building-level assessment. This study proposes a vision-POI fusion framework that combines multi-view visual inspection with POI-derived neighborhood context for residential building health assessment. The empirical dataset covers 92 old residential communities, 3,237 residential buildings, and 25,608 field-acquired inspection images in Qingdao, China, encompassing seven categories of housing-related issues. First, multiple object detection models are evaluated to extract issue locations, categories, and confidence scores from individual images. The image-level outputs are subsequently aggregated across multiple views to construct interpretable building-level representations. Second, POI features are extracted within 500m, 1,000m, and 1,500m neighborhood buffers to characterize surrounding functional environments. Pearson and Spearman correlation analyses, combined with false discovery rate correction, are used to identify candidate contextual features. Finally, visual and POI features are integrated using a cost-sensitive Random Forest classifier under community-isolated spatial cross-validation. The results show that multi-view aggregation provides the main performance improvement, increasing the building-level Macro-F1 from 60.84% under Direct Detection to 74.95%. Incorporating POI context further increases Macro-F1 to 76.79%, although the additional gain is modest and category-dependent. POI information therefore functions as a supplementary contextual prior rather than a substitute for direct visual evidence or a causal determinant of building condition.

cs.CV↗

arXiv2026

X-rays breaking out of pre-explosion ejecta mark a supernova's first light

Massive stars die as core-collapse supernovae, whose optical light emerges days after the implosion. Theory predicts that the initial collapse-driven shock, upon breaking through the star and dense circumstellar medium, emits a brief thermal flash of soft X-rays and ultraviolet. Yet these elusive first signals have remained largely undetected, owing to limited wide-field soft X-ray monitoring. Here we report the discovery of a soft X-ray flash, EP260321a, followed days later by a broad-lined supernova from an envelope-stripped progenitor. Its X-ray spectrum, best modeled with blackbody, establishes it as the long-sought archetypal shock breakout. The burst's duration and energetics place the breakout at a radius of 300 solar radii, tracing a dense surrounding shell and revealing abrupt mass ejection within the final month before collapse.

astro-ph.HE↗

arXiv2026

Phase-field modeling of elastically driven abnormal grain growth

Grain-refined metals typically exhibit high strength, yet their engineering applications are often constrained by grain coarsening under thermo-mechanical loading. Recent experiments have revealed abnormal grain growth (AGG) in ultrafine-grained Ni thin films subjected to cyclic loading at room temperature. Unlike conventional AGG, which generally requires significant plastic deformation or high temperatures, this phenomenon occurs within the regime of macroscopic elastic deformation. This AGG is characterized by the preferential growth of grains with an in-plane <100> orientation aligned with the loading direction. Here, we investigate the underlying physical mechanisms by combining phase-field simulations with micromechanical analysis. The results indicate that elastic energy reduction provides a thermodynamically plausible driving force for this orientation-selective grain growth. Phase-field simulations reveal the evolution kinetics of AGG and confirm that local grain geometry and stress states play critical roles in determining the grain growth pathway. By applying this framework to systems with varying elastic anisotropy, we establish a general approach for investigating elastically driven AGG in polycrystalline materials.

cond-mat.mtrl-sci↗

arXiv2026

Decentralized GNSS at Global Scale via Graph-Aware Diffusion Adaptation

Network-based Global Navigation Satellite Systems (GNSS) underpin critical infrastructure and autonomous systems, yet typically rely on centralized processing hubs that limit scalability, resilience, and latency. Here we report a global-scale, decentralized GNSS architecture spanning hundreds of ground stations. By modeling the receiver network as a time-varying graph, we employ a deep linear neural network approach to learn topology-aware mixing schedules that optimize information exchange. This enables a gradient tracking diffusion strategy wherein stations execute local inference and exchange succinct messages to achieve two concurrent objectives: centimeter-level self-localization and network-wide consensus on satellite correction products. The consensus products are broadcast to user receivers as corrections, supporting precise point positioning (PPP) and precise point positioning-real-time kinematic (PPP-RTK). Numerical results demonstrate that our method matches the accuracy of centralized baselines while significantly outperforming existing decentralized methods in convergence speed and communication overhead. By reframing decentralized GNSS as a networked signal processing problem, our results pave the way for integrating decentralized optimization, consensus-based inference, and graph-aware learning as effective tools in operational satellite navigation.

eess.SP↗

arXiv2026

EP251023a: A fast X-ray transient featuring a magnetar-powered optical internal plateau followed by a steep decay

EP251023a is an extragalactic fast X-ray transient (eFXT) detected solely by EP without a gamma-ray counterpart. The prompt emission consists of a main emission with a duration $T_{90}=292\pm19$ s, followed by a long-lasting tail emission that persists until the observation ends at $T_0+1571$ s. With the upper limit of Konus--Wind, we derived a conservative upper limit on the isotropic gamma-ray energy $E_{γ,\rm{iso}}$ of $5.7 \times 10^{52}$ erg for the main emission phase. A redshift of $z = 2.232\pm0.001$ is identified from strong absorption features in the Keck spectrum, which also indicate a relatively low host-galaxy HI column density. Based on the broadband spectral energy distribution, the late-time light curves show an achromatic plateau, followed by an extremely steep decay with a slope of 3.99 after a break at about 49 ks, which is consistent with a rapidly spinning millisecond magnetar engine. Under the isotropic wind scenario, we obtain the initial period $P_0<2.27$~ms and the magnetic field strength $B_p<8.33\times10^{14}$~G for the magnetar; whereas considering a jet collimation with a typical opening angle of 0.1 rad relaxes these constraints to $P_0<32.15$~ms and $B_p<1.18\times10^{16}$~G. Together with GRB\,070707, EP251023a may represent a rare class of optical magnetar-powered internal plateaus with little external-shock contamination, unlike previous examples detected primarily in X-rays. Future discoveries of similar events will help clarify the relationship between magnetar-powered internal emission observed in the optical band and that detected only in X-rays.

astro-ph.HE↗

arXiv2026

GRB 250424A: A Case Study of Energy Injection with Multiwavelength Observations

We present a comprehensive multiwavelength analysis of the long-duration gamma-ray burst (GRB) 250424A. Our dataset spans from the prompt gamma-ray emission to late-time optical monitoring, including spectra obtained with the Keck 10\,m telescope. We find that the afterglow light curves display a prominent, simultaneous shallow decay phase in both X-ray and optical bands, followed by an achromatic transition to a standard decay regime. The broadband spectral energy distributions are well-modeled by a single power-law function, indicating a common synchrotron origin for the emission across frequencies. We interpret the afterglow evolution within the framework of a relativistic forward shock refreshed by continuous energy injection. This scenario successfully reproduces the observed temporal and spectral behavior, yielding an isotropic equivalent kinetic energy of $E_{\rm K,iso} \approx 5.5 \times 10^{52}$ erg and an injection index of $q\approx 0.34$ in a constant-density circumburst environment. The shallow decay phase is consistent with sustained energy injection lasting $\sim$ 9 ks. Despite the relatively low redshift, late-time optical observations reveal no distinct supernova component; however, our derived upper limits do not strictly rule out the presence of a typical GRB-associated supernova.

astro-ph.HE↗

arXiv2026

SSR-Merge: Subspace Signal Routing for Training-Free LoRA Merging in Diffusion Models

Low-Rank Adaptation (LoRA) merging can efficiently combine diverse generative capabilities from multiple trained LoRAs for a diffusion model. However, existing LoRA merging techniques often suffer from severe parameter interference, causing destructive collisions in the shared parameter space. To address this, we propose Subspace Signal Routing (SSR), which resolves interference by routing internal signals instead of performing parameter-space merge. Specifically, SSR first constructs a unified subspace by concatenating candidate LoRAs along the rank dimension. Next, SSR employs an inverse correlation matrix to decorrelate mixed signals within this space. Finally, a directional guide matrix steers these purified signals into their respective task-specific subspaces. We provide a rigorous theoretical analysis proving that SSR aligns with the Ordinary Least Squares (OLS) solution, thereby ensuring mathematical optimality. We utilize the additivity of sufficient statistics to design a streaming algorithm. This enables on-the-fly updates that significantly reduce memory overhead and computation time. Extensive experiments validate that SSR significantly outperforms state-of-the-art methods while maintaining comparable efficiency. Code is available at https://github.com/nagara214/SSR-Merge.

cs.CV↗