arXiv subjects

Kun Hu

Publications and source records attributed to Kun Hu.

At least 19 recordsLinked to original sources

arXiv2026

Is the hard X-ray polarization of Cyg X-1 stable?

We present results from hard X-ray observations of Cyg X-1 in the hard state, conducted during, and proximate to, the July 2024 balloon flight of XL-Calibur. Temporal and spectral observations from NICER, NuSTAR, and Swift-BAT have been included, complementing XL-Calibur polarimetric observations in the $\sim$19-64 keV range. We perform joint NICER+NuSTAR+Swift-BAT spectral analyses in the 1.0-195.0 keV range and reference these to the day-by-day polarization analysis. XL-Calibur previously reported (5.0$^{+2.7}_{-3.0}$)\% polarization degree (PD) and polarization angle (PA) aligned with the jet axis, when integrating over the four observing days. In this paper, we present day-by-day polarimetry results, which indicate variation in the polarization parameters. For July 12-13, the PD is ($16.8\pm4.9$)\%, at PA $\sim$25$^{\circ}$ shifted from the jet, while for the remaining days, the PD and PA are consistent with the integrated values. At the same time, we observed that the reflection fraction parameter ($\mathrm{R_{f}}$) of spectroscopic model increases by factor of two. These changes place specific requirements on the PD and flux of additional component responsible for them. Such requirements are more likely to be satisfied by geometric changes in corona near the BH than by scattering in the vicinity of the stellar companion.

astro-ph.HE↗

arXiv2026

Do correct answers have stable latent knowledge support?

Large language models are typically evaluated in terms of the correctness of their output. However, output correctness alone does not confirm whether the latent knowledge supporting an answer has stable internal representations. We therefore propose latent state stability (LSS) and knowledge topology stability (KTS) to investigate the stability of individual knowledge states and their relational structure, respectively. Specifically, we first separately average the model's internal representations of the same knowledge under each elicitation condition. Using the mean representation under the baseline condition as a reference, we remove shared shifts to obtain corrected representations. We then formulate LSS as the distance between each corrected representation and its reference representation, normalized against the typical distance between knowledge items of the same type. We formulate KTS as the cross-condition consistency of the ordered pairwise representation distances among knowledge items of the same type. Next, we construct EpiStableBench, which comprises 4,411 items spanning factual knowledge and mathematical reasoning, with 76,766 controlled queries. Across 16 LLMs from four families, representation stability varies substantially even among items answered correctly under the base condition. Within models, higher stability generally corresponds to higher accuracy under held-out elicitation conditions; across models, stability does not consistently increase with model size or overall accuracy. Meanwhile, we present LatentDPO to learn a knowledge-editing policy that accounts for the stability of latent knowledge. Experimental results show that integrating LatentDPO with multiple existing editing methods consistently improves editing success and generalization across different expressions.

cs.CL↗

arXiv2026

X-ray Polarization Signatures from Comptonization by Magnetic Reconnection Plasmoids

Emission from X-ray binaries in the hard spectral state is dominated by high-energy radiation attributed to the Compton scattering of seed photons. The prevalent model of the Comptonization by hot electrons or pairs faces the problem of rapid radiative cooling of the emitting particles. A proposed alternative mechanism is the Comptonization by scattering off fast plasmoids formed during magnetic reconnection. In this work, we simulate a simplified model of the plasmoid chain with Monte Carlo radiation transport and report on spectropolarimetric properties. We find that the Comptonization off trans-relativistic bulk plasmoids is not only able to reproduce the 100 keV spectral cutoff, but furthermore produces X-rays that are above 1 keV strongly polarized perpendicular to the reconnection layer. The polarization is stronger than that from the Comptonization by an isotropic hot plasma owing to the confinement of the motion of the scattering plasmoids in the plane of the reconnection layer. The dependence of polarization on azimuthal viewing angle is discussed, along with possible locations for the plasmoid chain in an equatorial current sheet or the sheath of the black hole's relativistic jet.

astro-ph.HE↗

arXiv2026

Energy-Resolved Limits on Orbital X-ray Polarization Modulation in Cygnus X-1

Reflection off the companion star and its focused stellar wind is predicted to modulate the X-ray polarization of black hole X-ray binaries at half the orbital period ($P_{\rm orb}/2$), with an energy-dependent amplitude. We test this prediction against all publicly available IXPE observations of Cygnus X-1, comprising 26 time bins of up to one day from 12 observation IDs spanning 2022-2024. Since the normalized Stokes parameters correlate linearly with the spectral hardness ratio in all three energy bands (2-4, 4-6, and 6-8 keV), we employ a simultaneous harmonic regression that decouples spectral variability from orbital modulation at both $P_{\rm orb}/2$ and $P_{\rm orb}$, complemented by direct fitting of 3D Monte Carlo radiative transfer stellar companion and wind-scattering templates. After removing the spectral hardness trend, neither approach reveals statistically significant orbital modulation: permutation tests yield $p > 0.01$ in all bands, with 99% confidence upper limits of 0.25%, 0.63%, and 1.96% on the $P_{\rm orb}$ amplitude and 0.42%, 0.97%, and 2.78% on the $P_{\rm orb}/2$ amplitude in the 2-4 keV, 4-6 keV, and 6-8 keV bands, respectively. The best-fit stellar companion and wind-scattering amplitude scaling factors in the three bands of $A = 0.67 \pm 0.85$, $0.97 \pm 0.62$, and $-1.06 \pm 1.11$ are consistent with a null result. These non-detections are sensitivity-limited, as the predicted stellar companion and wind-scattering RMS amplitudes in the three bands of $\approx$0.10%, $\approx$0.33%, and $\approx$0.49% lie below the statistical noise floor of $\sim$0.18%, $\sim$0.38%, and $\sim$1.01%. We quantify the additional exposure required to detect the predicted signal and constrain the wind physics.

astro-ph.HE↗

arXiv2026

FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement

Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies remains highly challenging. Dense smoke and dust cause substantial loss of visual information and degrade LiDAR point-cloud features, while long, self-similar corridors induce geometric degeneration, leading to pronounced odometry drift. To address these issues, we propose FIRE-LIVWO: Failure-Immune mmWave Radar-Enhanced LiDAR-Inertial-Visual-Wheel Odometry, a tightly coupled multi-modal odometry framework based on an iterated error-state Kalman filter (IESKF). The framework fuses 4D mmWave radar, LiDAR, and visual features within a unified VoxelMap and jointly constructs LiDAR-radar point-to-plane residuals and sparse visual photometric residuals. In smoke-filled environments, we exploit the strong penetration of 4D mmWave radar and introduce pointwise Doppler velocity constraints to preserve state observability. In geometrically degenerate corridors, we tightly couple wheel odometry using non-holonomic constraints (NHC) and online lever-arm compensation to reduce drift. Our central contribution is a degeneration detection and adaptive fusion model switching strategy grounded in geometric and visual observability analysis, which quantifies observability online and dynamically adjusts modality weights. Real-world experiments in underground coal mines demonstrate that FIRE-LIVWO accurately identifies failure boundaries, enabling reliable modality switching under extreme conditions. Compared with baselines, it achieves superior accuracy and robustness (average localization error of 5.677m). We open source our code on Github to benefit the robotics community.

cs.RO↗

arXiv2026

Magnetar Fireballs and Short Bursts: Curved Spacetime Lensing, QED Effects, Spectra, Polarization, and Impulse Responses

Magnetar short bursts (SBs) are hard X-ray transients of durations $0.01-1$ s peaking at $\sim 10-100$ keV, and are prime targets for new high-energy missions and polarimeters. The recent association of SBs with bright radio bursts in SGR 1935+2154 has broadened interest in SB physics. We present new advanced fireball models combining general relativistic light bending, polarized transport in magnetized photospheres, magnetic photon splitting attenuation, and magnetospheric vacuum birefringence. These models also have relevance to trapped fireballs in magnetar giant flare pulsating tails. We adopt confined flux tube geometries consistent with adiabatic fireballs, and anisotropic/polarized emergent intensities to produce spectra and polarizations, and energy-time Stokes impulse responses. We predict that most fireballs are highly linearly polarized, especially when vacuum birefringence is important. There is rich potential for diagnostics: coexisting direct and lensed delayed images, gaps by occultation of the neutron star surface, and Shapiro+Rømer delay with temporal caustics. These effects can imprint spin phase dependence of the spectral and polarization character of bursts. Predicted signatures depend strongly on viewing geometry, fireball configuration, and photon splitting assumptions, yielding large variance in model high-energy spectral shapes and cutoffs, and energy-dependent polarization. The models can reproduce established double-blackbody SB spectral phenomenology, and we find that the unusual April 2020 radio-associated SB from SGR 1935+2154 is broadly consistent with a footpoint close to the magnetic pole, and possibly near pole-on viewing geometry. Our models motivate reverberation-style analyses for SBs and suggest that high-quality data might constrain source geometry, burst crustal footpoints, and, potentially, neutron star masses and radii.

astro-ph.HE↗

arXiv2026

Resonant Inverse Compton Scattering and Hard X-ray Emission in Magnetar Magnetospheres

Magnetars are a subclass of neutron stars with ultra-strong surface magnetic fields. Some magnetars exhibit persistent hard X-ray emission, characterized by power-law tails with photon indices around 1--1.5, extending from ${\sim}$10 keV to several hundred keV. The leading explanation for this hard X-ray component is resonant Compton scattering, in which the thermal seed photons are upscattered by relativistic electron-positron pairs flowing along magnetic field lines in the magnetosphere. In this work, we adopt the pair outflow framework of the magnetar magnetosphere and calculate the resonant Compton scattering opacity, as well as the spectrum and polarization of the upscattered emission. We find that resonant cooling can substantially modify the magnetospheric plasma density and impose strong optical depth constraints on the hard X-ray emission regions. Under the viewing geometry inferred from IXPE, an equatorial twist near the stellar surface provides a viable configuration for the NuSTAR hard X-ray spectrum of 4U 0142+61, while a polar-twist geometry is disfavored. Joint spectral, timing, and polarimetric modeling will be essential for distinguishing between the magnetospheric scattering geometries and understanding the physical properties of the pair plasma.

astro-ph.HE↗

arXiv2026

Stable Attention Response for Reliable Precipitation Nowcasting

Precipitation nowcasting remains challenging due to the highly localized, rapidly evolving, and heterogeneous nature of atmospheric dynamics. Although recent methods increasingly adopt attention-based architectures in both unimodal and multimodal settings, they mainly emphasize stronger representation learning and prediction capacity, while paying less attention to the stability of attention responses across samples. In this work, we show that cross-sample instability of attention-response energy is an important and previously underexplored source of forecasting unreliability. Empirically, inaccurate forecasts are associated with larger attention-response energy variance across heads and layers. Theoretically, we show that cross-sample variability can propagate through self-attention, and enlarge a lower bound on prediction error. Based on this insight, we propose HARECast, a Head-wise Attention Response Energy-regulated framework for precipitation nowcasting. HARECast explicitly models head-wise attention-response energy and stabilizes it through a group-wise regularization objective that reduces cross-sample fluctuations. The proposed formulation is generic and applicable to both unimodal and multimodal nowcasting architectures. We instantiate HARECast in a standard forecasting pipeline with reconstruction branches and a diffusion-based predictor, and evaluate it on commonly used benchmarks--SEVIR and MeteoNet. Experimental results demonstrate that HARECast achieves state-of-the-art performance.

cs.LG↗

arXiv2026

A New Approach for Testing Einstein's Theory of Gravity Close to Rapidly Spinning Black Holes

The Penrose process and the collisional Penrose process involve particles decaying or interacting very close to a spinning black hole, respectively, during which some particles are pushed into negative energy trajectories and fall into the black hole while others gain that energy and escape the system. These two processes are difficult to observe as they occur very rarely by chance. Here we report a new observational signature of similar, but less extreme processes occurring in and near the ergospheres of rapidly spinning black holes. We find that the reflection of the thermal emission from a geometrically thin, optically thick accretion disk can lead to the formation of a power-law component, even in the absence of a corona. Unlike the well-known Penrose processes, the scattering particles lose a good fraction of their energy, but are not pushed into negative energy trajectories. We emphasize that this new component has distinct spectral and polarimetric properties that can be used for its identification as long as it out-competes other power-law emission components. We emphasize that the new component needs to be taken into account when interpreting spectral and spectropolarimetric observations of black holes. The detection and unambiguous identification of the new component with current or future broadband X-ray spectral and spectropolarimetric missions can open a new window into testing Einstein's theory of gravity close to the edges of black holes, and opens up new opportunities to constrain black hole spins and inclinations.

astro-ph.HE↗

arXiv2026

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers

As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate LLM agents' tool-using capabilities. However, these benchmarks overlook the continuous evolution of tool interfaces and functionalities within MCP servers, resulting in flawed assessments that fail to capture the agent's adaptability in changing tool landscapes. To bridge this gap, we introduce \textbf{MCPEvol-Bench}, a novel benchmark for evaluating the task-solving capabilities of LLM agents under dynamic toolset evolution. Inspired by large-scale empirical study, we propose 11 mutation operators to simulate realistic tool evolution within 123 MCP servers. We benchmark 12 state-of-the-art LLMs on multiple versions of MCP servers, revealing that even frontier models struggle to adapt to evolving tools. For instance, GPT-5.4 and Claude-Sonnet-4-6 exhibit performance declines of 13.7\% and 14.4\% in evolved MCP servers, respectively, accompanied by substantial increases in planning and reasoning errors. These findings highlight the vulnerability of LLM-driven workflows, establishing MCPEvol-Bench as a standard for evaluating agent adaptability in dynamic tool environments.

cs.AI↗

arXiv2026

ProfMalPlus: Agent-Coordinated Detection of Malicious NPM Packages via Static-Dynamic Analysis Synergy

Open source software is vulnerable to supply-chain attacks through transitive dependencies, especially malicious code injected into NPM packages. Existing detectors often inadequately model obfuscated behavior, overlook JavaScript's object-centric features, poorly coordinate static and dynamic analysis, and lose semantic information during behavior abstraction. We propose ProfMalPlus, a malicious NPM package detector combining object-sensitive behavior graphs with coordinated LLM reasoning over annotated code slices. It identifies installation commands and entry files, then constructs graphs capturing sensitive APIs, third-party calls, and unresolved calls. From these graphs, ProfMalPlus extracts security-relevant slices and adds inline static analysis evidence. Local judge agents independently assess each slice. Self-consistency consolidates repeated judgements to reduce LLM variance, while a global judge synthesizes their reports into an entry-level verdict. For undetermined cases, a router selects either third-party enrichment, which adds registry derived module and method semantics, or dynamic augmentation, which executes the package in a sandbox to resolve runtime dependent behavior. The enriched evidence is fed back for reassessment. Finally, a localization agent reports malicious code snippets with explanations. ProfMalPlus achieves a 98.1% F1-score, outperforming state-of-the-art detectors by 3.5% to 52.6%. It also identified 597 previously unknown malicious packages, all confirmed and removed from NPM.

cs.SE↗

arXiv2026

Q-BridgeNet: A Quantization Network for Cross-Lingual Sign Language Translation

Most sign language translation (SLT) methods focus on isolated native sign-spoken pairs (e.g., American Sign Language - English). Extending language-specific SLT models to multilingual translation would improve accessibility by enabling communication across diverse sign and spoken language communities. However, existing multilingual SLT approaches still struggle to learn a unified model that minimizes cross-lingual conflicts while capturing shared cross-lingual semantics and preserving language-specific variations across different sign languages. Therefore, we propose Q-BridgeNet, a unified framework for multilingual SLT that jointly mitigates cross-lingual conflicts across both the sign language and spoken language sides. On the sign language side, Q-BridgeNet learns discrete Q-units via adaptive segmentation and residual vector quantization: a shared base codebook provides language-agnostic semantic primitives, while language-specific residual codebooks refine heterogeneous signing semantics. On the spoken language side, a multilingual LLM is fine-tuned to operate in the Q-unit space, leveraging cross-lingual priors to enable a unified SLT model. Experiments on PHOENIX14T, How2Sign, and CSL-Daily show that Q-BridgeNet effectively mitigates cross-lingual conflicts, achieving state-of-the-art performance on native sign-spoken pairs while also demonstrating strong generalization to non-native pairs. Our source code is publicly available at: https://github.com/FengLiQ/Q-BridgeNet

cs.CL↗

arXiv2026

CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation

Behavior Trees (BTs) offer a powerful paradigm for designing modular and reactive robot controllers. BT planning, an emerging field, provides theoretical guarantees for the automated generation of reliable BTs. However, BT planning typically assumes that a well-designed BT system is already grounded -- comprising high-level action models and low-level control policies -- which often requires extensive expert knowledge and manual effort. In this paper, we formalize the BT Grounding problem: the automated construction of a complete and consistent BT system. We analyze its complexity and introduce CABTO (Context-Aware Behavior Tree grOunding), the first framework to efficiently solve this challenge. CABTO leverages pre-trained Large Models (LMs) to heuristically search the space of action models and control policies, guided by contextual feedback from BT planners and environmental observations. Experiments spanning seven task sets across three distinct robotic manipulation scenarios demonstrate CABTO's effectiveness and efficiency in generating complete and consistent behavior tree systems.

cs.RO↗

arXiv2026

GeoRoPE: Ground-Aware Rotary Adaptation for Remote Sensing Foundation Models

Remote-sensing foundation models (RSFMs) benefit from pretraining on imagery from multiple sensors and ground sampling distances (GSDs), but such exposure alone does not resolve scale mismatch during downstream adaptation. A fixed token-grid offset can correspond to different ground distances across sensors, making grid-based positional priors physically inconsistent. Meanwhile, heterogeneous spatial granularity means that compact urban regions and homogeneous landscapes may require different positional sensitivities even under the same GSD. Therefore, we propose {GeoRoPE}, a ground-aware, RoPE-compatible, and parameter-efficient spatial adaptation method for RSFMs. GeoRoPE recalibrates token-level positional interactions from two complementary aspects. First, \textit{Geo-Coordinate Calibration (GCC)} rescales raw token-grid offsets according to the ground distance represented by one token-grid step, producing geo-calibrated relative coordinates across GSDs. Second, \textit{Geo-Frequency Calibration (GFC)} adjusts the native RoPE frequency with a relation-specific factor, enabling position sensitive adaptation to scene-dependent spatial granularity. GeoRoPE is injected into pretrained RSFMs through a lightweight adapter, preserving the frozen spatial prior while adding geo-aware positional corrections. Experiments across multiple RSFMs, sensors, resolutions, and downstream tasks demonstrate that GeoRoPE improves cross-resolution robustness and scale-sensitive representation learning.

cs.CV↗

arXiv2026

Predictions for the X-ray polarisation modulation in Cygnus X-1 from reflection off the stellar companion and its wind

Context. Cyg X-1 is one of the brightest X-ray binaries and has been observed multiple times with the Imaging X-ray Polarimetry Explorer (IXPE). Recent studies report tentative evidence for a polarisation modulation with the orbital period P, but a half-period (P/2) signal, expected from reflection off the companion star and its stellar wind, has not been reported. Aims. We aim to quantify the reflection-induced variations of the polarisation degree PD and polarisation angle PA in Cyg X-1 as a function of orbital phase and energy, and interpret these in terms of binary geometry and wind structure. Methods. We set up a radiative transfer model combining a general relativistic description of the polarised source emission (kerrC) with a focussed stellar wind model for the binary medium. Using the 3D X-ray radiative transfer code SKIRT, we simulate broadband Stokes I, Q, and U fluxes, surface brightness maps, and linear polarisation maps over one binary orbit. Results. We find a prominent double-peaked (P/2) polarisation modulation, with a peak-to-peak PD amplitude of 0.25, 0.81, and 1.24 percentage points in the 2-4, 4-6, and 6-8 keV bands, respectively, with a strong energy dependence. The PA modulation is more modest, with |ΔPA| < 4.6°. Crucially, X-ray reprocessing reduces the overall PD relative to the source polarisation. Conclusions. The modulation is driven by reflection off the companion star and the focussed wind, which induces a polarisation signal that alternately reinforces and counteracts the source polarisation throughout the orbit. The diffuse scattering halo surrounding the source systematically reduces the PD, an effect that should be accounted for in all wind-fed XRBs. The PD amplitude increases with energy as absorption disproportionately attenuates the distant-reflection signal; as the extinction drops, the reflection signal becomes increasingly important.

astro-ph.HE↗

arXiv2026

SRENet: Spectral Re-Entry Network for Point Cloud Action Recognition

Recognizing human actions from point cloud sequences is critical for 3D perception driven applications such as autonomous driving and human-computer interaction. However, the irregular structure and temporal inconsistency of point clouds pose unique challenges for spatio-temporal representation learning, especially in capturing both global motion context and fine-grained temporal dynamics. We propose SRENet, a spectral-aware framework designed to explicitly learn both global context and fine-grained temporal dynamics of motion from a frequency perspective for action recognition. SRENet introduces a Spectral Decomposition Block (SDeBlock) that performs wavelet-based analysis along temporal and spatial axes, disentangling features into low- and high-frequency components with frequency-specific attention. To recover residual dynamics and re-align temporal frequency structures distorted during semantic fusion, a Spectral Re-entry Block (SReBlock) performs secondary temporal decomposition. Furthermore, a spectral-aware learning strategy is devised to enhance discriminability in both frequency subspaces via contrastive loss and a curriculum schedule that gradually shifts focus from low- to high-frequency spaces in line with coarse to detailed motion patterns. Extensive experiments on MSR-Action3D, NTU-RGBD and NTU-RGBD120 demonstrate that SRENet achieves state-of-the-art performance, validating the effectiveness of frequency modeling in point cloud-based action understanding.

cs.CV↗

arXiv2026

PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction

Accurate crop yield prediction is crucial for sustainable agriculture and global food security. While existing methods are predominantly developed for single-crop prediction, they often struggle to generalize across diverse crop types, without addressing the unique crop phenological responses that are dynamically modulated by complex weather patterns. In this paper, we propose PhenoYieldNet, a multi-crop yield prediction framework that learns crop-specific phenology by explicitly modeling their responses with temporal drivers. Specifically, we develop a crop-aware temporal decoder consisting of a Crop Phenology Bank (CPB) and a Crop Phenology Attention (CPA) module. The CPB integrates a set of learnable embeddings, which leverage a query to guide the CPA module to learn the most relevant phenology patterns for the specific crop. And the CPA module explicitly captures multi-scale trend and variation components to construct temporal contexts, enabling the model to dynamically adjust the attention across different phenological stages. To learn robust and generalizable features for multi-crop prediction, the encoder is initialized with a pre-trained foundation model, and further adapted via a self-supervised Temporal Contrastive Adaptation strategy to align with agricultural temporal dynamics. Extensive experiments conducted on multi-crop datasets indicate that our proposed method significantly outperforms state-of-the-art methods, exhibiting strong generalization capabilities across different regions and crops.

cs.CV↗

arXiv2026

KAN Text to Vision? The Exploration of Kolmogorov-Arnold Networks for Multi-Scale Sequence-Based Pose Animation from Sign Language Notation

Sign language production from symbolic notation offers a scalable route to accessible sign animation. We present KANMultiSign, a multi-scale sequence generator that translates HamNoSys notation into two-dimensional human pose sequences. Our framework makes two complementary contributions. First, we introduce a coarse-to-fine generation strategy with multi-scale supervision: the model is first guided by an intermediate body--hand--face scaffold to encourage global structural coherence, and then refines fine-grained hand articulation to improve finger-level detail. Second, we investigate integrating Kolmogorov--Arnold Network modules into a Transformer backbone, using learnable univariate function primitives to model the highly non-linear mapping from discrete phonological symbols to continuous body kinematics with a compact parameterization. Experiments on multiple public corpora spanning Polish, German, Greek, and French sign languages show consistent reductions in dynamic time warping based joint error compared with a strong notation-to-pose baseline, while using substantially fewer parameters. Controlled ablations further indicate that KAN-based variants substantially reduce parameter count while maintaining competitive performance when coupled with multi-scale supervision, rather than serving as the main driver of accuracy gains. These findings position multi-scale supervision as the key mechanism for improving notation-conditioned pose generation, with KAN offering a compact alternative for efficient modeling. Our code will be publicly available.

cs.CV↗