arXiv subjects

Jie Liao

Publications and source records attributed to Jie Liao.

At least 19 recordsLinked to original sources

arXiv2026

Magnetic rigidity reveals the PeVatron acceleration region in SS 433

PeVatrons are cosmic accelerators capable of driving particles to petaelectronvolt (PeV) energies. Recently, microquasar jets have emerged as compelling Galactic PeVatron candidates. This is especially the case for SS 433 as its $>100$ TeV gamma-ray emission is spatially coincident with an atomic cloud. However, the exact region where PeV protons are accelerated and injected within these jets remains unresolved. Here we report, using archival, multi-frequency VLBA observations, the magnetic field profile $B(H)$ along the SS 433 inner jet on tens of AU scale, where $H$ is the distance from the central compact object. We find that the field declines as $B(H) \propto H^{-0.50\pm0.12}$, demonstrating that the magnetic rigidity $B(H)R_{\rm acc}$ grows with $H$ for a conical jet. This implies the Hillas limit ($E_{\rm max} \propto BH$) to lie well beyond a PeV at a few hundred-AU scale, which becomes a highly potential site for accelerating protons to energies $E_{\rm cut} \simeq 2.6$ PeV inferred from the LHAASO gamma-ray spectrum. These results reveal a hidden PeVatron within the baryonic ejecta of microquasar SS 433, well upstream of the extended TeV-emitting lobes.

astro-ph.HE↗

arXiv2026

ElasticBack: Stealthy Conditional Backdoor in LLM-Agent Skills via Coupled Trigger-Rule Optimization

Agent skills, bundles of instructions and resources that an LLM agent loads on demand, form an emerging supply chain where a single poisoned skill can persistently compromise every agent that installs it. However, existing skill attacks either fire on every request or rely on fine-tuned weights or multiple skills, leaving a conditional and low-cost backdoor unexplored. In this work, we present ElasticBack, an effective conditional single-skill backdoor that plants a rule R in the skill document and a benign-looking trigger T in the user query, so the malicious payload fires only when both co-occur. ElasticBack binds the two sides through a trigger-as-switch construction, generating R via semantic-anchored rule injection. It then freezes R and evolves T against it with a stealth-constrained genetic search, so that effectiveness and stealth are optimized, keeping the backdoor weight-free and dormant on benign inputs. Extensive experiments across three target behaviors (50 skills each) and four agent LLMs show that ElasticBack attains a high attack success rate at a near-zero false-positive rate with preserved clean accuracy, transfers across models, and evades deployment-time defenses. These results motivate stronger defenses for the skill supply chain.

cs.AI↗

arXiv2026

A second-scale periodicity in an active repeating fast radio burst source

Fast radio bursts (FRBs) are fierce radio flashes from the deep sky. Abundant observations have indicated that highly magnetized neutron stars might be involved in these energetic bursts, but the underlying trigger mechanism is still enigmatic. Especially, the widely expected periodicity connected to the spin of the central engine has never been discovered, which leads to further debates on the nature of FRBs. Here we report the first discovery of a $\sim$ 1.7 s period in the repeating source of FRB 20201124A. This is an active repeater, from which more than 2800 bursts have been detected over a total of 49 days. The phase-folding method is adopted to analyze the bursts on each day separately. While no significant periodic signal is found in most days, a clear periodicity does appear on two specific days: a period of 1.706024(13) s on MJD 59310, and a slightly larger period of 1.707968(9) s on MJD 59347. A global Monte Carlo analysis based on all single-day datasets yields a significance level of $5.5 σ$ for the periodicity. A period derivative of $6.11(5)\times10^{-10}$ s s$^{-1}$ can be derived from these two periods, which further implies a surface magnetic field strength of $1.03\times10^{15}$ G and a spin-down age of $44$ years for the central engine. It is concluded that FRB 20201124A should be associated with a young magnetar.

astro-ph.HE↗

arXiv2026

SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents

Agent skills extend LLM agents with task-specific instructions, executable scripts, and auxiliary resources, improving reusability but creating a new supply-chain attack surface. A malicious or compromised skill can be repeatedly loaded as trusted guidance and steer downstream tool use. Existing skill-based prompt-injection attacks are often manual and brittle, because explicit malicious instructions are rejected or ignored when they are not aligned with the original workflow. We propose SkillJect, the first automated framework for generating poisoned skills against skill-enabled agent systems. SkillJect uses two coordinated channels. In the artifact channel, it hides the payload inside an auxiliary helper script. In the instruction channel, it rewrites SKILL.md with a front-loaded inducement strategy, placing injected content at the beginning and framing the helper script as a mandatory prerequisite or initialization step. The rewritten instruction explicitly references the helper-script path and provides an executable example command, making the helper appear to be a legitimate setup step before normal skill operations. SkillJect further adopts a closed-loop multi-agent process to improve attack effectiveness. An Attack Agent generates poisoned skills, a Victim Agent executes downstream tasks with the poisoned skill, and an Evaluate Agent inspects execution traces to determine whether the hidden payload was executed. The Attack Agent then uses this feedback to diagnose failure causes and rewrite SKILL.md, while keeping the payload fixed. Experiments across skill-enabled platforms, backend LLMs, and attack categories show that SkillJect substantially outperforms naive direct injection and prior manual skill-injection attacks, highlighting poisoned skills as a persistent threat in reusable skill ecosystems.

cs.CR↗

arXiv2026

Seeing Is Not Screening: Multimodal Hidden Instruction Attacks on Agent Skill Scanners

Agent skills are emerging as an important attack surface in LLM-based systems. Through an empirical study of existing skill scanners, we find that current defenses primarily rely on textual descriptions, manifests, and source code as the main signals for security analysis, which can leave visually conveyed malicious intent insufficiently examined. This creates a practical blind spot: harmful operational instructions hidden in images may bypass scanning while still being recoverable by multimodal agents during deployment. To systematically investigate this threat, we propose SkillCamo, a document-mediated multimodal instruction attack that conceals malicious instructions within images bundled with a skill while rewriting the surrounding documentation to naturally reference those images as part of the normal workflow. Thus, the attack does not rely on the image alone, but on the joint interpretation of textual guidance and visual payload at execution time. To defend against such attacks, we further propose ExecScan, an execution-grounded multimodal scanning module that performs intent extraction, behavior reconstruction, abuse assessment, and deliberative execution simulation over skill artifacts. ExecScan jointly analyzes documentation, code, referenced resources, and visual content to recover hidden instructions, reconstruct executable behavior chains, and identify downstream risks such as exfiltration, destruction, persistence, deception, and privilege escalation. Extensive experiments show that image-hidden malicious instructions challenge existing skill scanners, while ExecScan can improve the skill scanning performance.

cs.CR↗

arXiv2025

OmniSafeBench-MM: A Unified Benchmark and Toolbox for Multimodal Jailbreak Attack-Defense Evaluation

Recent advances in multi-modal large language models (MLLMs) have enabled unified perception-reasoning capabilities, yet these systems remain highly vulnerable to jailbreak attacks that bypass safety alignment and induce harmful behaviors. Existing benchmarks such as JailBreakV-28K, MM-SafetyBench, and HADES provide valuable insights into multi-modal vulnerabilities, but they typically focus on limited attack scenarios, lack standardized defense evaluation, and offer no unified, reproducible toolbox. To address these gaps, we introduce OmniSafeBench-MM, which is a comprehensive toolbox for multi-modal jailbreak attack-defense evaluation. OmniSafeBench-MM integrates 13 representative attack methods, 15 defense strategies, and a diverse dataset spanning 9 major risk domains and 50 fine-grained categories, structured across consultative, imperative, and declarative inquiry types to reflect realistic user intentions. Beyond data coverage, it establishes a three-dimensional evaluation protocol measuring (1) harmfulness, distinguished by a granular, multi-level scale ranging from low-impact individual harm to catastrophic societal threats, (2) intent alignment between responses and queries, and (3) response detail level, enabling nuanced safety-utility analysis. We conduct extensive experiments on 10 open-source and 8 closed-source MLLMs to reveal their vulnerability to multi-modal jailbreak. By unifying data, methodology, and evaluation into an open-source, reproducible platform, OmniSafeBench-MM provides a standardized foundation for future research. The code is released at https://github.com/jiaxiaojunQAQ/OmniSafeBench-MM.

cs.CR↗

arXiv2025

Magnetic Reconnection as a Potential Driver of X-ray Variability in Active Galactic Nuclei

We present a systematic analysis on the X-ray variability in 13 bright quasars at z > 4.5, combining recent Swift observations from 2021 to 2023 and archival multi-epoch observations. Upper limits of the luminosity measurements were included in the analysis by using the Kaplan-Meier estimator method. It is found that the high-z quasars exhibit X-ray variability on both short-term (hours-to-days) and intermediate-term (weeks-to-months) timescales, with short-term variability dominating the overall variation. A linear correlation exists between the global mean ($μ_{\mathrm{L_{2-10\,keV}}}$) and standard deviation ($σ_{\mathrm{L_{2-10\,keV}}}$) of X-ray luminosities, which is independent of the X-ray photon index and optical-to-X-ray spectral slope. The localized stochastic magnetic reconnection mechanism is strongly favored, which can naturally lead to a scale-invariant power-law energy distribution and satisfactorily explain the correlation. The $σ$-$μ$ correlation parallels with the well-documented rms-flux relation of low-z active galactic nuclei (AGNs), implying the magnetic reconnection mechanism could drive short-timescale X-ray variability in both high- and low-z AGNs. The highest-z quasar in our sample, J142952+544717 (z = 6.18), shows a luminosity distribution extending to ${10}^{47}\ \rm{erg\ {s}^{-1}}$ with a not conspicuous median luminosity. On the other hand, J143023+420436 (z = 4.7), which hosts the most relativistic jet among known high-z blazars, is dominated in the high-luminosity regime (${10}^{47}\ \rm{erg\ {s}^{-1}}$ ), making it an ideal target for multi-wavelength follow-up observations. J090630+693030 is found to have a rest-frame period of 182.46 days and J143023+420436 has a period of 16.89 days, both could be explained by the global evolution of plasmoid chains, in which magnetic islands formed during reconnection may merge successively.

astro-ph.HE↗

arXiv2025

The Mathematical Theory of Behavioural Swarms: Towards Modelling the Collective Dynamics of Living Systems

Classical swarm models, exemplified by the Cucker--Smale framework, provide foundational insights into collective alignment but exhibit fundamental limitations in capturing the adaptive, heterogeneous behaviours intrinsic to living systems. This paper formalises the mathematical theory of \textit{Behavioural Swarms}, a comprehensive framework where each particle's state incorporates a dynamic internal variable, the \textit{activity} that co-evolves with position and velocity through nonlocal interactions. We demonstrate how this approach transcends prior models by integrating adaptive decision-making mechanisms and heterogeneous behavioural states into rigorous differential systems. Through applications in behavioural economics and crowd dynamics, we establish the theory's capacity to predict emergent macroscopic patterns from individual behavioural states. Our critical analysis positions this framework against kinetic theories of active particles and agent-based approaches, revealing distinct advantages for modelling systems where individual agency drives collective outcomes.

nlin.AO↗

arXiv2025

New Trends in Kinetic Theory Towards the Complexity of Living Systems

The development of a mathematics for living systems is one of the most challenging prospects of this century. The search began with the pioneering contribution of Ilia Prigogine, who developed methods from statistical physics to describe the dynamics of vehicular traffic. This visionary seminal research contribution has given rise to a great deal of research activity, which began at the end of the last century and has been further developed in this century by several authors who have developed mathematical methods, generally focused on applications. These methods are somewhat inspired by the classical kinetic theory, but significant differences have led to the concept of active particles and to a kinetic theory that is ultimately very different from the classical theory. Different approaches have been developed, each of which is in some way an alternative to the others. This paper develops a critical analysis of the scientific activity after Prigogine with the aim of developing a unified mathematical theory, taking into account the conceivable interactions that a mathematical theory of living systems can have with studies of artificial intelligence.

math.AP↗

arXiv2025

Tracking the jet-like corona of black hole Swift J1727.8-1613 during a flare state through Type-C quasi-periodic oscillations

Type-C quasi-periodic oscillations (QPOs) in black hole X-ray transients typically manifest in the low-hard and hard-intermediate states. This study presents a detailed spectral and temporal analysis of the black hole candidate Swift J1727.8-1613 using NICER observations from August and September 2023, with a focus on the first flare period. We detected Type-C QPOs whose centroid frequency increased from 0.33 Hz to 2.63 Hz. An additional increase in frequency was observed when the outburst entered a flare period. The time-averaged spectra, along with the rms and phase-lag spectra of the QPOs, were jointly fitted using the time-dependent Comptonization model vkompthdk to examine the geometry of the corona during this flare. Correlations between spectral and temporal properties suggest that the detected type-C QPOs are primarily modulated by Lense-Thirring precession. Leveraging simultaneous radio observations that indicate discrete jet ejections, we proposed a scenario to describe the co-evolution of the disk-corona-jet during a flare (~3 days). This scenario is partially supported for the first time by polarization data in the soft gamma-ray band from INTEGRAL/IBIS. A phenomenological analysis of the corona scenario was also conducted.

astro-ph.HE↗

arXiv2024

Whispering-Gallery-Mode Resonators for Detection and Classification of Free-Flowing Nanoparticles and Cells through Photoacoustic Signatures

Micro and nanoscale particles are crucial in various fields, from biomedical imaging to environmental processes. While conventional spectroscopy and microscopy methods for characterizing these particles often involve bulky equipment and complex sample preparation, optical micro-sensors have emerged as a promising alternative. However, their broad applicability is limited by the need for surface binding and difficulty in differentiating between sensing targets. This study introduces an optofluidic, high-throughput optical microresonator sensor that captures subtle acoustic signals generated by particles absorbing pulsed light energy. This novel approach enables real-time, label-free detection and interrogation of particles and cells in their native environments across an extended sensing volume. By leveraging unique optical absorption properties, our technique selectively detects and classifies flowing particles without surface binding, even in complex matrices like whole blood samples. We demonstrate the measurement of gold nanoparticles with diverse geometries and different species of red blood cells amidst other cellular elements and proteins. These particles are identified and classified based on their photoacoustic fingerprint, which captures shape, composition, and morphology features. This work opens new avenues for rapid, reliable, and high-throughput particle and cell identification in clinical and industrial applications, offering a valuable tool for understanding complex biological and environmental systems.

physics.bio-ph↗

arXiv2024

Tests of the Kerr Hypothesis with MAXI J1803-298 Using Different RELXILL_NK Flavors

Iron line spectroscopy has been one of the leading methods not only for measuring the spins of accreting black holes but also for testing fundamental physics. Basing on such a method, we present an analysis of a dataset observed simultaneously by NuSTAR and NICER for the black hole binary candidate MAXI J1803-298, which shows prominent relativistic reflection features. Various relxill_nk flavors are utilized to test the Kerr black hole hypothesis. The results obtained from our analysis provide stringent constraints on Johannsen deformation parameter $α_{13}$ with the highest precise to date, namely $α_{13}=0.023^{+0.071}_{-0.038}$ from relxillD_nk and $α_{13}=0.006^{+0.045}_{-0.022}$ from relxillion_nk respectively in 3-$σ$ credible lever, where Johannsen metric reduces to Kerr metric when $α_{13}$ vanishes. Furthermore, we investigate the best model-fit results using Akaike Information Criterion and assess its systematic uncertainties.

astro-ph.HE↗

arXiv2023

SAILOR: Structural Augmentation Based Tail Node Representation Learning

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the key operation of message propagation, highly depends on the quality of the topology structure. Most of the graphs in real-world scenarios follow a long-tailed distribution on their node degrees, that is, a vast majority of the nodes in the graph are tail nodes with only a few connected edges. GNNs produce inferior node representations for tail nodes since they lack structural information. In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes. Extensive experiments on public benchmark datasets demonstrate that SAILOR can significantly improve the tail node representations and outperform the state-of-the-art baselines.

cs.LG↗

arXiv2023

GUARD: Graph Universal Adversarial Defense

Graph convolutional networks (GCNs) have been shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks. However, current defense approaches are typically designed to prevent GCNs from untargeted adversarial attacks and focus on overall performance, making it challenging to protect important local nodes from more powerful targeted adversarial attacks. Additionally, a trade-off between robustness and performance is often made in existing research. Such limitations highlight the need for developing an effective and efficient approach that can defend local nodes against targeted attacks, without compromising the overall performance of GCNs. In this work, we present a simple yet effective method, named Graph Universal Adversarial Defense (GUARD). Unlike previous works, GUARD protects each individual node from attacks with a universal defensive patch, which is generated once and can be applied to any node (node-agnostic) in a graph. GUARD is fast, straightforward to implement without any change to network architecture nor any additional parameters, and is broadly applicable to any GCNs. Extensive experiments on four benchmark datasets demonstrate that GUARD significantly improves robustness for several established GCNs against multiple adversarial attacks and outperforms state-of-the-art defense methods by large margins.

cs.LG↗

arXiv2020

1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation

In this technical report, we introduce our winning solution "HorizonLiDAR3D" for the 3D detection track and the domain adaptation track in Waymo Open Dataset Challenge at CVPR 2020. Many existing 3D object detectors include prior-based anchor box design to account for different scales and aspect ratios and classes of objects, which limits its capability of generalization to a different dataset or domain and requires post-processing (e.g. Non-Maximum Suppression (NMS)). We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the 3D attributes, and to learn an end-to-end point cloud object detection without the need of hand-engineering or learning the anchors. AFDet serves as a strong baseline in our winning solution and significant improvements are made over this baseline during the challenges. Specifically, we design stronger networks and enhance the point cloud data using densification and point painting. To leverage camera information, we append/paint additional attributes to each point by projecting them to camera space and gathering image-based perception information. The final detection performance also benefits from model ensemble and Test-Time Augmentation (TTA) in both the 3D detection track and the domain adaptation track. Our solution achieves the 1st place with 77.11% mAPH/L2 and 69.49% mAPH/L2 respectively on the 3D detection track and the domain adaptation track.

cs.CV↗

arXiv2020

1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking

This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges. An efficient and pragmatic online tracking-by-detection framework named HorizonMOT is proposed for camera-based 2D tracking in the image space and LiDAR-based 3D tracking in the 3D world space. Within the tracking-by-detection paradigm, our trackers leverage our high-performing detectors used in the 2D/3D detection challenges and achieved 45.13% 2D MOTA/L2 and 63.45% 3D MOTA/L2 in the 2D/3D tracking challenges.

cs.CV↗

arXiv2020

2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection

A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons. In this report, we introduce a state-of-the-art 2D object detection system for autonomous driving scenarios. Specifically, we integrate both popular two-stage detector and one-stage detector with anchor free fashion to yield a robust detection. Furthermore, we train multiple expert models and design a greedy version of the auto ensemble scheme that automatically merges detections from different models. Notably, our overall detection system achieves 70.28 L2 mAP on the Waymo Open Dataset v1.2, ranking the 2nd place in the 2D detection track of the Waymo Open Dataset Challenges.

cs.CV↗

arXiv2019

Highly efficient optical add-drop filter with an angle-polished fiber coupler

Microbubble whispering-gallery resonators have shown great promise in fiber-optic communications because of their low confinement loss and hollow cores, which allow for facile stress-based tunability. Usually, the transmission spectrum of taper-coupled microbubbles contains closely spaced modes due to the relatively large radii and oblate geometry of microbubbles. In this letter, we develop an optical add-drop filter using a microbubble coupled to fiber taper and angle-polished fiber waveguides. Because of the extra degree of freedom in the angle of its polish surface, the angle-polished fiber can be used for the discriminatory excitation of certain radial-order modes in the optical microcavity, reducing the high modal density and enhancing add-drop selectivity. Our robust and tunable add-drop filter demonstrated a drop efficiency of 85.9% and quality factor of 2 x 10^7, corresponding to a linewidth of 9.68 MHz. As a proof of concept, the drop frequency was tuned using internal aerostatic pressure at a rate of 7.3 +/- 0.2 GHz/bar with no diminishing effects on the add-drop filter performance.

physics.app-ph↗