arXiv subjects

Xiaofeng Li

Publications and source records attributed to Xiaofeng Li.

At least 19 recordsLinked to original sources

arXiv2026

Generics-Aware Fuzz Target Generation for Rust Libraries via Structured API Analysis

Fuzzing Rust library APIs requires constructing well-typed, compilable call sequences that satisfy ownership rules, generic parameters, and trait bounds; existing tools ignore these constraints or use shallow heuristics, yielding low coverage. We present GRAFT, which extracts structured API information from Rust documentation, builds an API dependency graph via recursive generics-aware type matching, and uses topology-guided traversal plus LLM synthesis with compiler-error feedback to produce compilable fuzz targets. On 13 crates from crates.io, GRAFT achieves 80.75% macro-average API coverage at 96.19% compilation success, outperforming RULF and RPG by 4.76x and 2.43x, and reaching 1.41x the average API coverage of deepSURF on crates with unsafe-reaching APIs.

cs.SE↗

arXiv2026

Mi-Memory: A Lifecycle Memory Framework for Personal AI

Personal AI is moving beyond chat-only interaction toward continuous services that span phones, cars, homes, wearables, cameras, and tools. In this setting, memory cannot remain a cache of prior conversations. It should serve as a continuity and governance substrate: preserving durable user state, grounding answers in multimodal and device evidence, supporting correction and forgetting, bounding policy evolution, and remaining deployable under latency, cost, privacy, and edge-cloud constraints. This technical report presents Mi-Memory, a lifecycle memory framework for Personal AI organized around four roles: Structure, Expansion, Evolution, and Deployment. A shared audit contract links these roles through four recurring artifact families: typed evidence payloads preserve source identity and provenance, diagnostic traces localize evidence loss across the serving pipeline, strategy artifacts make memory-policy changes explicit, and gate/rollback records bound accepted evolution. MiMemory instantiates the roles through MemStack, MemSense/MemFuse, D$^{2}$ACCI/E$^{2}$MEND, and LiteMem. In controlled-reference Structure evaluations, MemStack reaches 93.59%, 57.24%, and 87.47% on LoCoMo, PersonaMem-V2, and LongMemEval, respectively; other tracks report module-level, preliminary/internal, transfer-feasibility, or design-only evidence with explicit boundaries. MiMemory is a step toward auditable, evidence-gated, and deployment-aware memory systems for Personal AI. Project homepage: https://darwin-agent.github.io/Mi-Memory/ .

cs.AI↗

arXiv2026

A Jet from a Nearly Dormant Black Hole

Most galaxies host supermassive black holes (SMBHs) that remain weakly accreting or dormant for much of their lifetimes. At the lowest accretion rates, these systems may represent the transition between active nuclei and dormant black holes, but whether they can still launch collimated jets remains unclear. The nuclei in our Galaxy (\sgra) and M31 are key examples of this regime, although no clear jet structure has yet been detected in either source. Here we report multi-frequency very long baseline interferometric observations of \Msixty\ (NGC~4649), a nearby elliptical galaxy hosting a nearly dormant SMBH with an Eddington ratio of $\sim10^{-8}$. We detect a compact two-sided jet with an unusually steep synchrotron spectrum, demonstrating that collimated outflows can persist even under nearly dormant accretion conditions. The apparent radio core exhibits an unprecedentedly steep frequency-dependent position shift toward the SMBH, locating the central engine only $\sim57\,\mu$as, corresponding to a projected distance of $\sim10$ Schwarzschild radii, upstream of the 8.37-GHz core. The observed jet morphology and steep core-shift behaviour are reproduced by general relativistic magnetohydrodynamic and radiative-transfer simulations, indicating a magnetically dominated, non-equipartition jet-launching region that departs from the standard conical equipartition picture. These results provide direct observational evidence that jet production can survive near the dormant SMBHs and establish \Msixty\ as a unique laboratory for probing jet formation on event-horizon scales in the lowest-accretion SMBH regime.

astro-ph.HE↗

arXiv2026

AxiomOcean: Forecasting the Three-Dimensional Structure of the Upper Ocean

Short-term ocean forecast skill depends strongly on the three-dimensional ocean structure of the upper ocean, which governs stratification, subsurface heat storage, and the response of the ocean to atmospheric forcing. However, AI ocean forecasting models often fail to preserve this vertical structure, resulting in over-smoothed subsurface features and weak physical consistency under strong forcing. Here, we present AxiomOcean, a global AI ocean forecasting model that explicitly represents vertical hierarchy and cross-layer dependence within the water column. By combining a fully three-dimensional encoder-backbone-decoder architecture with surface atmospheric forcing, AxiomOcean jointly predicts upper-ocean temperature, salinity, and three-dimensional currents at global 1/12{\deg} resolution down to 643 m depth. In 10-day forecasts, AxiomOcean outperforms an advanced AI comparison model across variables and lead times, reducing day-1 RMSE by approximately 20 to 35% while maintaining higher anomaly correlation. The gain is not achieved through excessive smoothing: AxiomOcean better preserves eddy kinetic energy, temperature and salinity variance. Its advantage also extends through the water column and remains evident across the equatorial Pacific, Kuroshio Extension, and Southern Ocean, yielding a more realistic reconstruction of upper-ocean heat content. These results show that explicitly preserving upper-ocean three-dimensional structure can improve both forecast accuracy and physical fidelity in AI ocean prediction.

cs.LG↗

arXiv2026

A Radio Changing-state Jet in the Narrow-line Seyfert 1 Galaxy J1105+1452

We report the discovery of a radio-quiet to radio-loud transition in the narrow-line Seyfert 1 galaxy J1105+1452. The source has undergone a long-term evolution from a radio-quiet state in the 1990s to a persistently radio-bright state after 2017. Post-2017 flux densities in the $0.8$-$7$ GHz range cluster between $32$ and $43$ mJy, whereas the $144$ MHz flux density is only $1.94 \pm 0.23$ mJy. This indicates strong low-frequency suppression from a compact, absorbed component. Modeling the radio spectral energy distribution with a synchrotron self-absorption model yields a turnover frequency $\nu_{\rm p} = 0.48 \pm 0.03$ GHz and a peak flux density $S_{\rm p} = 38.9 \pm 4.7$ mJy. These parameters classify J1105+1452 as a megahertz peaked-spectrum source, consistent with the new episode of an early-stage compact jet. Under the assumption of equipartition, we derive an intrinsic physical radius $R \sim 0.68$ pc and an average apparent expansion velocity $\beta_{\rm app} \approx 0.64$. The observed brightness temperature $T_b \approx 6.0 \times 10^{11}$ K necessitates a Doppler factor $\delta \approx 12$, implying a relativistic jet viewed at $\theta \lesssim 5^\circ$. Despite the dramatic radio evolution, the X-ray spectrum remains stable and steep ($\Gamma \simeq 3.0$), suggesting that the X-ray emission remains dominated by the disk-corona, while the radio band has become jet-dominated. Our results identify J1105+1452 as a rare radio changing-state NLSy1, providing a unique laboratory for studying the birth and early evolution of relativistic jets at high Eddington ratios.

astro-ph.HE↗

arXiv2026

Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data

Deep learning-based tropical cyclone (TC) forecasting methods have demonstrated significant potential and application advantages, as they feature much lower computational cost and faster operation speed than numerical weather prediction models. However, existing deep learning methods still have key limitations: they can only process a single type of sequential trajectory data or homogeneous meteorological variables, and fail to achieve accurate forecasting of abnormal deflected TCs. To address these challenges, we present two groundbreaking contributions. First, we have constructed a multimodal and multi-source dataset named AOT-TCs for TC forecasting in the Northwest Pacific basin. As the first dataset of its kind, it innovatively integrates heterogeneous variables from the atmosphere, ocean, and land, thus obtaining a comprehensive and information-rich meteorological dataset. Second, based on the AOT-TCs dataset, we propose a forecasting model that can handle both normal and abnormally deflected TCs. This is the first TC forecasting model to adopt an explicit atmosphere-ocean-terrain coupling architecture, enabling it to effectively capture complex interactions across physical domains. Extensive experiments on all TC cases in the Northwest Pacific from 2017 to 2024 show that our model achieves state-of-the-art performance in TC forecasting: it not only significantly improves the forecasting accuracy of normal TCs but also breaks through the technical bottleneck in forecasting abnormally deflected TCs.

cs.LG↗

arXiv2026

IPV-Bench: Benchmarking Image Protection Methods under Diverse Image-to-Video Generation Scenarios

Image-to-video (I2V) generation models can be misused to animate a single image into a convincing fake video, motivating perturbation-based image protection methods that aim to disrupt such generation. Yet these methods remain difficult to compare: they are reported under inconsistent metrics and generation settings, are often validated only on the single generator they were optimized against, and are evaluated on narrow, single-domain image sets that do not reflect real misuse. To address these challenges, we introduce IPV-Bench (Image Protection against Video generation), the first systematic benchmark for image protection in I2V generation scenarios. IPV-Bench couples a unified protocol that jointly scores protection effectiveness, visual fidelity, and robustness to preprocessing attacks together with IPV-500, a prompt-paired dataset spanning five misuse-relevant domains. Based on this benchmark, we evaluate five representative protection methods across four I2V models covering distinct architectures and both open-source and commercial systems. Extensive experiments show a consistently sobering picture: image protection and video disruption trade off against each other, most methods fail to disrupt generation beyond noise, protection rarely transfers across generators, and the few effective cases are broken by simple preprocessing. We further find that image content governs the perceptual cost of protection but not its benefit: no image domain offers an easier target. Overall, IPV-Bench provides a rigorous, reproducible, and extensible foundation for developing protection methods that work in practice.

cs.CV↗

arXiv2026

A Delayed Radio Flare Traces Kinetic Energy Injection in the SMBHB Candidate SDSS~J143016.05+230344.4

SDSS~J143016.05+230344.4 ($z=0.08105$) has been proposed as a candidate pre-coalescence supermassive black hole binary and shows remarkable multiwavelength variability. Its radio evolution provides a direct probe of the compact emitting region and of the physical origin of the late-time activity. We aim to localize the variable radio emission, characterize its spectral evolution, and constrain whether the radio brightening is produced by a newly emerging compact component, external absorption, or dissipation in a structured circumnuclear environment. At all epochs, the radio emission is dominated by a single unresolved milliarcsecond core with $T_{\rm B} \gtrsim 10^{7}$ K, constraining the variable emission to $\lesssim 0.3$ pc. The broadband spectra require two synchrotron self-absorbed components: a persistent low-frequency component with $\nu_{\rm p,steady} \approx 0.74$ GHz and $S_{\rm p,steady} \approx 1.22$ mJy, and a flare component whose turnover evolves from $(6.35 {\rm GHz}, 0.18 {\rm mJy})$ in 2022 February-May to $(8.61 {\rm GHz}, 0.38 {\rm mJy})$ in 2022 December, and then to $(5.83 {\rm GHz}, 0.25 {\rm mJy})$ in 2023 March-April. The flare contribution at 15 GHz reaches $\sim 80\%$ and matches the near-epoch VLBI recovery fraction, showing that the high-frequency brightening arises from a newly formed compact synchrotron component. A second brightening of the 15.2 GHz VLBI core is detected between 2023 September and 2024 February, while the source remains unresolved. Equipartition scalings imply characteristic radii of $\sim 5 \times 10^{-4}$ pc for the flare and $\sim 9 \times 10^{-3}$ pc for the steady component, and indicate a steep inner circumnuclear density profile, $n \propto R^{-1.7}$. The delayed radio flare is best explained by dissipation in an outflow or jet-base disturbance propagating through a structured circumnuclear medium.

astro-ph.HE↗

arXiv2026

Evaluating Large Language Models for Time Series Anomaly Detection in Aerospace Software

Time series anomaly detection (TSAD) is essential for ensuring the safety and reliability of aerospace software systems. Although large language models (LLMs) provide a promising training-free alternative to unsupervised approaches, their effectiveness in aerospace settings remains under-examined because of complex telemetry, misaligned evaluation metrics, and the absence of domain knowledge. To address this gap, we introduce ATSADBench, the first benchmark for aerospace TSAD. ATSADBench comprises nine tasks that combine three pattern-wise anomaly types, univariate and multivariate signals, and both in-loop and out-of-loop feedback scenarios, yielding 108,000 data points. Using this benchmark, we systematically evaluate state-of-the-art open-source LLMs under two paradigms: Direct, which labels anomalies within sliding windows, and Prediction-Based, which detects anomalies from prediction errors. To reflect operational needs, we reformulate evaluation at the window level and propose three user-oriented metrics: Alarm Accuracy (AA), Alarm Latency (AL), and Alarm Contiguity (AC), which quantify alarm correctness, timeliness, and credibility. We further examine two enhancement strategies, few-shot learning and retrieval-augmented generation (RAG), to inject domain knowledge. The evaluation results show that (1) LLMs perform well on univariate tasks but struggle with multivariate telemetry, (2) their AA and AC on multivariate tasks approach random guessing, (3) few-shot learning provides modest gains whereas RAG offers no significant improvement, and (4) in practice LLMs can detect true anomaly onsets yet sometimes raise false alarms, which few-shot prompting mitigates but RAG exacerbates. These findings offer guidance for future LLM-based TSAD in aerospace software.

cs.SE↗

arXiv2025

A Tale of 1001 LoC: Potential Runtime Error-Guided Specification Synthesis for Verifying Large-Scale Programs

Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to long-context reasoning limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This paper presents Preguss -- a modular, fine-grained framework for automating the generation and refinement of formal specifications. Preguss synergizes between static analysis and deductive verification by steering two components in a divide-and-conquer fashion: (i) potential runtime error-guided construction and prioritization of verification units, and (ii) LLM-aided synthesis of interprocedural specifications at the unit level. We show that Preguss substantially outperforms state-of-the-art LLM-based approaches and, in particular, it enables highly automated RTE-freeness verification for real-world programs with over a thousand LoC, with a reduction of 80.6%~88.9% human verification effort.

cs.SE↗

arXiv2025

Near-field perturbation of laser filament enabling simultaneous far-field THz diagnosis and broadband calculus processing

Terahertz (THz) wave manipulation based on laser filaments-plasma channels formed by femtosecond laser-induced air ionization-has emerged as a promising platform for free-space THz applications. However, in-situ characterization of the spatially confined THz modes within filaments faces significant challenges due to the plasma's ultra-high intensity, which not only hinders direct near-field probing but also limits reliance on indirect far-field reconstruction. Here, we introduce a non-invasive near-field modulation scheme where a metal plate approaches the filament at submillimeter distances (comparable to THz wavelengths), perturbing the dielectric environment to convert the symmetric annular THz mode into an asymmetric state. This controlled transition enables far-field detection of broadband calculus behaviors (first- and second-order differentiation/integration) on time-domain THz waveforms and characteristic spectral transfer functions with 1/f, 1/f^2, f or f^2 dependency (where f is the THz frequency), thereby diagnosing the near-field THz mode confinement. Hence, the proposed approach synergizes near-field modulation efficiency with far-field detection robustness, advancing fundamental understanding of plasma-THz interactions and enabling novel all-optical signal processing for filament-based THz technologies.

physics.optics↗

arXiv2025

Tracing Footsteps of Similar Cities: Modeling Urban Economic Vitality with Dynamic Inter-City Graph Embeddings

Urban economic vitality is a crucial indicator of a city's long-term growth potential, comprising key metrics such as the annual number of new companies and the population employed. However, modeling urban economic vitality remains challenging. This study develops ECO-GROW, a multi-graph framework modeling China's inter-city networks (2005-2021) to generate urban embeddings that model urban economic vitality. Traditional approaches relying on static city-level aggregates fail to capture a fundamental dynamic: the developmental trajectory of one city today may mirror that of its structurally similar counterparts tomorrow. ECO-GROW overcomes this limitation by integrating industrial linkages, POI similarities, migration similarities and temporal network evolution over 15 years. The framework combines a Dynamic Top-K GCN to adaptively select influential inter-city connections and an adaptive Graph Scorer mechanism to dynamically weight cross-regional impacts. Additionally, the model incorporates a link prediction task based on Barabasi Proximity, optimizing the graph representation. Experimental results demonstrate ECO-GROW's superior accuracy in predicting entrepreneurial activities and employment trends compared to conventional models. By open-sourcing our code, we enable government agencies and public sector organizations to leverage big data analytics for evidence-based urban planning, economic policy formulation, and resource allocation decisions that benefit society at large.

cs.AI↗

arXiv2025

Deep Learning Based Concurrency Bug Detection and Localization

Concurrency bugs, caused by improper synchronization of shared resources in multi-threaded or distributed systems, are notoriously hard to detect and thus compromise software reliability and security. The existing deep learning methods face three main limitations. First, there is an absence of large and dedicated datasets of diverse concurrency bugs for them. Second, they lack sufficient representation of concurrency semantics. Third, binary classification results fail to provide finer-grained debug information such as precise bug lines. To address these problems, we propose a novel method for effective concurrency bug detection as well as localization. We construct a dedicated concurrency bug dataset to facilitate model training and evaluation. We then integrate a pre-trained model with a heterogeneous graph neural network (GNN), by incorporating a new Concurrency-Aware Code Property Graph (CCPG) that concisely and effectively characterizes concurrency semantics. To further facilitate debugging, we employ SubgraphX, a GNN-based interpretability method, which explores the graphs to precisely localize concurrency bugs, mapping them to specific lines of source code. On average, our method demonstrates an improvement of 10\% in accuracy and precision and 26\% in recall compared to state-of-the-art methods across diverse evaluation settings.

cs.SE↗

arXiv2025

Integrating Symbolic Execution with LLMs for Automated Generation of Program Specifications

Automatically generating formal specifications including loop invariants, preconditions, and postconditions for legacy code is critical for program understanding, reuse and verification. However, the inherent complexity of control and data structures in programs makes this task particularly challenging. This paper presents a novel framework that integrates symbolic execution with large language models (LLMs) to automatically synthesize formally verified program specifications. Our method first employs symbolic execution to derive precise strongest postconditions for loop-free code segments. These symbolic execution results, along with automatically generated invariant templates, then guide the LLM to propose and iteratively refine loop invariants until a correct specification is obtained. The template-guided generation process robustly combines symbolic inference with LLM reasoning, significantly reducing hallucinations and syntactic errors by structurally constraining the LLM's output space. Furthermore, our approach can produce strong specifications without relying on externally provided verification goals, enabled by the rich semantic context supplied by symbolic execution, overcoming a key limitation of prior goal-dependent tools. Extensive evaluation shows that our tool SESpec outperforms the existing state-of-the-art tools across numerical and data-structure benchmarks, demonstrating both high precision and broad applicability.

cs.SE↗

arXiv2025

Revealing the terahertz-laser velocity effect during air filamentation via travelling-wave-antenna model

During femtosecond laser filamentation in air, the velocity ratio (K) between the terahertz (THz) phase velocity and the laser group velocity plays a crucial role in THz waves generation. However, K is typically assumed to be unity and its impact has been long overlooked due to the more attention paid to the more easily controlled filament length. Here, we investigate the obscured contribution of K to the THz radiation characteristics by using the improved travelling-wave-antenna (TWA) model. It has been found that, under both single- and two-color laser pumping schemes, K significantly determines the far-field spatial distribution of forward or backward THz radiation, as well as a transition from Bessel- to Cherenkov-type THz emission patterns. These results establish the TWA model as a reliable theoretical tool for studying the mechanisms of THz beam shaping via the designed K. Moreover, for cases of K not being controlled, its value can also be inferred by the proposed TWA model, which could be an effective method to confirm whether the laser ionization front is superluminal or subluminal compared with the generated THz waves.

physics.optics↗

arXiv2025

PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification

The fusion of Synthetic Aperture Radar (SAR) and RGB imagery for land cover classification remains challenging due to modality heterogeneity and underexploited spectral complementarity. Existing approaches often fail to decouple shared structural features from modality-complementary radiometric attributes, resulting in feature conflicts and information loss. To address this, we propose Phase-Amplitude Decoupling (PAD), a frequency-aware framework that separates phase (modality-shared) and amplitude (modality-complementary) components in the Fourier domain. This design reinforces shared structures while preserving complementary characteristics, thereby enhancing fusion quality. Unlike previous methods that overlook the distinct physical properties encoded in frequency spectra, PAD explicitly introduces amplitude-phase decoupling for multi-modal fusion. Specifically, PAD comprises two key components: 1) Phase Spectrum Correction (PSC), which aligns cross-modal phase features via convolution-guided scaling to improve geometric consistency; and 2) Amplitude Spectrum Fusion (ASF), which dynamically integrates high- and low-frequency patterns using frequency-adaptive multilayer perceptrons, effectively exploiting SAR's morphological sensitivity and RGB's spectral richness. Extensive experiments on WHU-OPT-SAR and DDHR-SK demonstrate state-of-the-art performance. This work establishes a new paradigm for physics-aware multi-modal fusion in remote sensing. The code will be available at https://github.com/RanFeng2/PAD.

cs.CV↗

arXiv2025

Resolving the black hole sphere of influence in a hyper-luminous obscured quasar at redshift 4.6

Supermassive black holes (SMBHs) imprint gravitational signatures on the matter within their sphere of influence (SoI). Nuclear gas dynamics can hence be used to accurately measure the mass of an SMBH, yet such measurements remain elusive in the early Universe. We report the first dynamical measurement of an SMBH mass at $z >$ 2, based on high spatial resolution observations of the [C II]157.7um and CO (12-11) 216.93um emission lines that resolve the SoI in an obscured quasar at $z$ = 4.6. The radial profile of the velocity dispersion reveals a clear Keplerian rise, requiring the presence of an approximately 6 $\times$ 10$^9~\rm M_{\odot}$ SMBH. We propose that obscured quasarsallow tracers like [C II] to survive in the inner regions, and may be ideal targets for increasing dynamical SMBH mass estimates in the early Universe.

astro-ph.GA↗

arXiv2025

The orbital period of the long-period and colliding-wind binary WR 146 from radio interferometry of the shock cone

We report the first measurement of the orbital period of a long-period colliding-wind binary (CWB) system WR 146, derived by tracing the rotational morphology of its wind-colliding region (WCR) and the relative orientation of the two binary components. This result is based on our imaging observations using the Very Long Baseline Array (VLBA) and the European Very Long Baseline Interferometry (VLBI) Network (EVN), combined with archival data from VLBA, EVN, the Very Large Array (VLA), the enhanced Multi-Element Radio-Linked Interferometer Network (eMERLIN) arrays, and optical images from the Hubble Space Telescope (HST). We evaluated two methods for determining the binary's orbital period based on the images of the WCR: (I) fitting the shock cone of the WCR and (II) stacking images using the cross-correlation function. Using these techniques, we find orbital period estimates of 810+120-90 years from method I and 1120+540-270 years from method II, both of which support a long orbital period of approximately 1,000 years. Furthermore, we analyzed archival spectral data of WR 146 to estimate the stellar wind velocities of the binary components, finding no significant orbital phase lag between the binary orientation and the WCR rotation. We also estimate the range of the binary's mass using the currently measured parameters.

astro-ph.SR↗