arXiv subjects

Lingzhi Wang

Publications and source records attributed to Lingzhi Wang.

At least 19 recordsLinked to original sources

arXiv2026

SN 2025fhm: A central-engine powered Ic-BL supernova associated with X-ray transient EP250304a

We present X-ray, optical, and radio follow-up observations of EP250304a, an extragalactic fast X-ray transient (EFXT) discovered by the Einstein Probe. Its X-ray light curve exhibits two broad pulses with comparable peak fluxes within the first $\sim$1~ks, a feature rarely seen among low-luminosity gamma-ray bursts or EFXTs. Optical follow-up observations were carried out using the Korea Microlensing Telescope Network, the Thai Robotic Telescope, the Las Cumbres Observatory 1~m global network, the Gemini Multi-Object Spectrograph on Gemini south telescope, and the Global Supernova Network. The fast-cooling phase (within 3 days) of optical data can be well fitted by a shocked cocoon model. However, during the supernova phase (SN 2025fhm, from 3 to 88 days), the late-time light curve cannot be explained solely by radioactive $^{56}$Ni decay, as demonstrated by a grid of simulations using the one-dimensional Lagrangian radiation hydrodynamics code SNEC, which reveals a significant energy excess at late epochs. To account for this excess, a central engine like a rapidly spinning, highly magnetized neutron star is needed to provide additional energy injection. This model yields a best-fit spin period of $\sim$12.60~ms and magnetic field strength of $\sim 3.52\times10^{15} \rm G$, and it successfully explains both the late-time bolometric light curve and the early X-ray pulse structures. Our results indicate that EP250304a/SN 2025fhm is likely powered by a central magnetar rather than by radioactive decay alone, offering new insights into the energy budget and physical origin of EFXTs and their associated supernovae.

astro-ph.HE↗

arXiv2026

What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents

LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain consistency among environments, tasks, interactions, and success signals while producing experience that is useful rather than merely abundant. Existing work spans many agent domains, but domain-centered organization and heterogeneous evaluation often obscure common generation mechanisms and conflate candidate construction with verification and selection. This work develops a two-level framework for the field. First, we represent agentic data as a common factorized object $(E,q,τ,v)$, comprising an environment specification, task signal, interaction realization, and optional verifier. We organize generation paradigms by their primary anchor and dependency structure. Second, we formulate generation as constrained distribution design through the Accuracy-Complexity-divErsity (ACE) lens. Accuracy establishes the feasible support of grounded and internally consistent data. Within this support, Complexity places learning mass relative to the capability of a declared learner and execution configuration, while divErsity controls coverage and redundancy of data. Using this framework, we explore how prior work verifies generated experience, constructs and calibrates difficulty, and expands behavioral coverage. The literature reveals a shift toward execution-grounded accuracy, learner-relative complexity, and diversity beyond surface variation or dataset size. We further discuss broader directions and emerging trends in agentic data generation through the ACE lens, including their implications for scaling, data sources, training regimes and adaptive learning. Overall, the central challenge is not simply to generate more data, but to continually allocate valid, informative, and non-redundant experience as agents and environments evolve.

cs.AI↗

arXiv2026

CTIFoundry: An Agent-Native Corpus Scaffold for Cyber Threat Intelligence

Cyber threat intelligence (CTI) is increasingly consumed not by human analysts but by LLM agents that compose multi-step investigations at query time. The harness side of this shift has matured rapidly (planning loops, tool protocols, context management), but the corpus side has not: threat reports and vulnerability databases are still packaged for retrieval-augmented generation, as opaque chunks behind an embedding index. We argue that this substrate, not model capability, is the bottleneck on agentic CTI investigation, and present CTIFoundry, an agent-native corpus scaffold. At build time, CTIFoundry materializes the latent structure of a CTI corpus: a deterministic ontology graph over four authoritative knowledge bases (CVE, CWE, CAPEC, ATT&CK) whose official cross-references become typed, traversable edges; a span-grounded report layer whose canonical, alias-resolved cross-vendor entities index provenance-carrying chunks; and hybrid dense+lexical retrieval surfaces. At query time this structure is exposed through seven typed tools and three procedural skills mounted on a stock open-source agent harness. On the public CTIConnect benchmark, swapping only the action surface lifts the identically-harnessed agent by +0.19 to +0.28 overall F1 across a four-model, two-provider panel: a small model on CTIFoundry surpasses a flagship on the flat substrate, and the gain is not bought with search effort, since on both Claude models the scaffolded agent is more accurate at roughly half the tool calls. An ablation attributes it: typed structure carries the larger share, procedural skills convert structure into discipline, and the two compose super-additively, because skills bind only to structure that exists.

cs.AI↗

arXiv2026

Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding

Modeling human cognitive states is essential for advanced artificial intelligence. Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection, and fail to capture interactions among cognitive dimensions defined in psychology, including emotion, thinking style, stance, and intention. To bridge this gap, we construct CognitiveBench, the first benchmark with unified annotations across the above four dimensions. Experiments on CognitiveBench show that although LLMs perform well on single dimension tasks, their performance drops sharply in joint multi-dimensional modeling. Using Gromov $δ$-hyperbolicity analysis, we find that CognitiveBench exhibits a strong hierarchical structure. We attribute the performance bottleneck to ``Cognitive Crowding'', where hierarchical cognitive states require exponential representational space, while the Euclidean space of LLMs grows only polynomially, causing representation overlap and degraded performance. To address this mismatch, we propose HyCoLLM, which models cognitive states in hyperbolic space and aligns LLM representations via Hyperbolic Guided Alignment Tuning. Results show that HyCoLLM substantially improves multi-dimensional cognitive understanding, allowing 8B parameter model to outperform strong baselines, including GPT-4o.

cs.CL↗

arXiv2026

Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation

Emotional Support Conversation (ESC) plays a critical role in mental health assistance by providing accessible psychological support in real-world applications. Large Language Models (LLMs) have shown strong empathetic abilities in ESC tasks. Yet, existing methods overlook the issue of cognitive distortions in help-seekers' expressions. As a result, current models can only provide basic emotional comfort, rather than helping help-seekers address their psychological distress at a deeper cognitive level. To address this challenge, we construct the CogBiasESC dataset, the first dataset that expands existing ESC datasets by adding labels for cognitive distortions, includes their type, intensity, and safe risk level. Furthermore, we propose the Cognitive Policy-driven Large Language Model framework (CoPoLLM) to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers. We also analyze the safety advantages of CoPoLLM from a theoretical perspective. Experimental results show that CoPoLLM significantly outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control.

cs.CL↗

arXiv2026

JWST Nebular Spectroscopy of SN 2023qov: Circumstellar Dust Emission in a Normal Type Ia Supernova

We present panchromatic observations of the Type Ia supernova (SN Ia) 2023qov, ranging from $\sim$2 weeks before to $\sim$1 year after maximum light. \textit{JWST} near- and mid-infrared spectra at $+$276 and $+$363~days show $\sim$400 K dust emission that cools by $\sim$75 K between epochs, the first unambiguous spectroscopic detection of dust emission in a normal SN Ia. We find that the emission is well described by models of carbonaceous dust placed within $\sim$1 light year of the SN, with a dust mass of $\sim$$10^{-4}$ M$_{\odot}$. We do not see evidence of active dust creation, suggesting an infrared light echo by pre-existing circumstellar dust as the likely source of the emission. The \textit{JWST} nebular line profiles suggest asymmetric, stratified ejecta, similar to other normal SNe Ia, though a slight double-horn structure in the argon lines indicate a toroidal enhancement. SN 2023qov exhibits a slightly red, fast-declining early light curve ($Δm_{15}(B) = 1.47 \pm 0.05$ mag), from which we determine a $^{56}$Ni mass of $M_{56} = 0.21 \pm 0.04$ M$_{\odot}$, and a distance of $d = 36.0 \pm 1.8$ Mpc to the SN and its host, NGC 7029.

astro-ph.HE↗

arXiv2026

SN 2024abvb: A Type Icn Supernova in the Outskirts of its Host Galaxy

We present multiband photometric and spectroscopic observations of supernova (SN) 2024abvb, which exhibits early-time prominent photoionized narrow emission lines of C II superposed on a blue continuum. The absence of Balmer features indicates that the SN exploded within hydrogen-poor circumstellar matter (CSM). Together with the lack of explicit evidence of helium signatures, we tentatively identify SN 2024abvb as a Type Icn SN (SN Icn). After correcting for extinction, we estimate an r-band peak absolute magnitude of -19.7, placing SN 2024abvb in the luminous regime of SNe Icn. We adopted a hybrid model that accounts for both the energy released by the ejecta-CSM interaction and the radioactive decay of nickel synthesized in the SN ejecta to fit the light curve of SN 2024abvb. The best-fit model to the multiband light curves within the first ~ 40 days after explosion suggests that the CSM, radioactive nickel, and ejecta masses to be 0.28 Msun, < 3.8 * 10^-2 Msun, and 0.12 Msun, respectively. Such a low ejecta mass indicates that the progenitor star of SN 2024abvb experienced a significant mass-stripping process, consistent with the hydrogen-poor and helium-poor spectral features. SN 2024abvb provides important insights into the physical origins of the rare subclass of SNe Icn.

astro-ph.HE↗

arXiv2026

ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction

Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions, often involving complex function calls and dynamic user-agent exchanges. Existing simulation-based data generation methods for such scenarios rely heavily on costly autoregressive interactions between multiple LLM agents, thereby compromising the practical efficiency of agentic data generation. In this paper, we propose ToolACE-MT, a novel Non-Autoregressive Iterative Generation framework for constructing high-quality multi-turn agentic dialogues. ToolACE-MT generates full conversational trajectories through three stages: coarse-grained initialization, iterative refinement, and offline verification. The initialization phase builds a structurally complete yet semantically coarse dialogue skeleton; the iterative refinement phase introduces realistic complexities and continued refinement via mask-and-fill operations; and the offline verification phase ensures correctness and coherence via rule- and model-based checks. Experiments demonstrate that ToolACE-MT enables efficient, effective and generalizable agentic data generation, offering a new paradigm for high-quality data construction in tool-augmented LLM scenarios.

cs.CL↗

arXiv2026

From Sands to Mansions: Towards Automated Cyberattack Emulation with Classical Planning and Large Language Models

Evolving attacker capabilities demand realistic and continuously updated cyberattack emulation for threat-informed defense and security benchmarking. Towards automated attack emulation, this paper defines modular attack actions and a linking model to organize and chain heterogeneous attack tools into causality-preserving cyberattacks. Building on this foundation, we introduce Aurora: an automated cyberattack emulation system powered by symbolic planning and large language models (LLMs). Aurora crafts actionable, causality-preserving attack chains tailored to Cyber Threat Intelligence (CTI) reports and target environments, and automatically executes these emulations. Using Aurora, we generated an extensive cyberattack emulation dataset from 250 attack reports, 15 times larger than the leading expert-crafted dataset. Our evaluation shows that Aurora significantly outperforms existing methods in creating actionable, diverse, and realistic attack chains. We release the dataset and use it to evaluate three state-of-the-art intrusion detection systems, whose performance differed notably from results on older datasets, highlighting the need for up-to-date, automated attack emulation.

cs.CR↗

arXiv2026

SN 2024abfl: A Low-Luminosity Type IIP Supernova in NGC 2146 from a Low-Mass Red Supergiant Progenitor

Type IIP supernovae (SNe IIP) exhibit a significant diversity in their explosion properties, yet the physical mechanisms driving this diversity remain unknown. In this work, we present photometric and spectroscopic observations of SN 2024abfl, a SN IIP in NGC 2146 with a directly detected red supergiant (RSG) progenitor. We find it has a low plateau luminosity ($M_V \sim -15$ mag) and a relatively long plateau length ($\sim 126.5$ days). By fitting a semi-analytical model, we estimated a $^{56}$Ni mass of $\sim 0.009 M_\odot$, an initial kinetic energy of $\sim 0.42$ foe, an initial thermal energy of $\sim 0.03$ foe and an ejecta mass of $\sim 8.3 M_\odot$. The spectral evolution of SN 2024abfl is similar to those of other SNe IIP, except for much lower ejecta velocities at similar epochs. At later epochs, we find a relatively high-velocity H$α$ absorption feature at $\sim -4000$ km s$^{-1}$, possibly due to a fast-moving plume of matter in the inner ejecta, and two emission features at $\pm 2000$ km s$^{-1}$, possibly caused by CSM interaction. We estimate the progenitor mass to be $\le 15 M_\odot$ based on nebular spectra. We conclude that SN 2024abfl is a low-luminosity SN IIP originating from a low-mass RSG progenitor.

astro-ph.HE↗

arXiv2026

ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation

Current test-time scaling (TTS) techniques enhance large language model (LLM) performance by allocating additional computation at inference time, yet they remain insufficient for agentic settings, where actions directly interact with external environments and their effects can be irreversible and costly. We propose ARTIS, Agentic Risk-Aware Test-Time Scaling via Iterative Simulation, a framework that decouples exploration from commitment by enabling test-time exploration through simulated interactions prior to real-world execution. This design allows extending inference-time computation to improve action-level reliability and robustness without incurring environmental risk. We further show that naive LLM-based simulators struggle to capture rare but high-impact failure modes, substantially limiting their effectiveness for agentic decision making. To address this limitation, we introduce a risk-aware tool simulator that emphasizes fidelity on failure-inducing actions via targeted data generation and rebalanced training. Experiments on multi-turn and multi-step agentic benchmarks demonstrate that iterative simulation substantially improves agent reliability, and that risk-aware simulation is essential for consistently realizing these gains across models and tasks.

cs.CL↗

arXiv2026

Swelling-Induced Stress-Assisted Transfer of Nanodiamond Arrays with a PVA Carrier Tape for Conformal Bio-Integrated Sensing and Labelling

The conformal integration of nitrogen-vacancy (NV) center nanodiamond arrays onto soft, hydrated, curvilinear biological interfaces remain a fundamental challenge for in vivo quantum sensing and imaging. Conventional transfer techniques often fail due to reliance on high temperature, corrosive chemicals, or mechanical peeling, leading to pattern damage, low fidelity, or poor biocompatibility. Here, we report a transfer strategy utilizing polyvinyl alcohol (PVA) carrier soluble tape, enabling rapid, residue-free, high-fidelity transfer of nanodiamond patterns onto diverse biointerfaces. The success of this method is rooted in a unique "hydrate-soften-expand-self-peel" mechanism of the soluble tape with PVA backing. In situ mechanical tracking reveals non-uniform PVA swelling upon hydration generates transient local normal and shear stresses at the interface. These stresses delaminate the tape within 3 minutes at room temperature while promoting adhesion of the nanodiamond array to the substrate. In contrast, conventional water-soluble tapes with composite structures undergo passive dissolution and collapse, causing residue contamination and reduced efficiency. Leveraging this mechanism, we achieve conformal patterning on ultra-soft hydrogels (~0.6 kPa) and highly curved bio-surfaces (hair, 100 μm^-1). Additionally, we demonstrate a dual-identity verification system integrating data storage and physical unclonable functions on a hydrogel contact lens. This work provides a versatile tool for bio-interface engineering and a general framework for gentle, efficient transfer of functional nanomaterials.

physics.bio-ph↗

arXiv2026

ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning

Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabilities. However, existing approaches primarily focus on data synthesis for fine-tuning LLMs to invoke tools effectively, largely ignoring how to fully stimulate the potential of the model. In this paper, we propose ToolACE-R, a novel framework that includes both model-aware iterative training and adaptive refinement for tool learning. ToolACE-R features a model-aware iterative training procedure that progressively adjust training samples based on the model's evolving capabilities to maximize its potential. Additionally, it incorporates self-refinement training corpus which emphasizes LLM's ability to iteratively refine their tool calls, optimizing performance without requiring external feedback. Furthermore, we introduce adaptive self-refinement mechanism for efficient test-time scaling, where the trained model can autonomously determine when to stop the process based on iterative self-refinement. We conduct extensive experiments across several benchmark datasets, showing that ToolACE-R achieves competitive performance compared to advanced API-based models. The performance of tool invocation can be further improved efficiently through adaptive self-refinement. These results highlight the effectiveness and generalizability of ToolACE-R, offering a promising direction for more efficient and scalable tool learning.

cs.CL↗

arXiv2026

Nanodiamond-Enabled Torsion Microscopy Uncovers Multidimensional Cell-Matrix Mechanical Interactions

Traditional cellular force-sensing techniques, such as traction force microscopy (TFM), are predominantly limited to measuring linear tractions, overlooking and technically unable to capture the nanoscale torsional forces that are critical in cell-matrix interactions. Here, we introduce a nanodiamond-enabled torsion microscopy (DTM) that integrates nitrogen-vacancy (NV) centers as orientation markers with micropillar arrays to decouple and quantify nanoscale rotational and translational motions induced by cells. This approach achieves high precision (~1.47 degree rotational accuracy and ~3.13*10-15 Nm torque sensitivity), enabling reconstruction of cellular torsional force fields and twisting energy distributions previously underestimated. Our findings reveal the widespread presence of torsional forces in cell-matrix interactions, introducing "cellular mechanical modes" where different adhesion patterns dictate the balance between traction- and torque- mediated mechanical energy transferred to the substrate. Notably, in immune cells like macrophages that generally exert low linear tractions, torque overwhelmingly dominates traction, highlighting a unique mechanical output for specific cellular functions. By uncovering these differential modes, DTM provides a versatile tool to advance biomechanical investigations, with potential applications in disease diagnostics and therapeutics.

physics.bio-ph↗

arXiv2025

SN 2024aecx: a fast-evolving Type IIb supernova with a prominent shock-cooling peak

SN 2024aecx is a nearby ($\sim$11 Mpc) Type IIb SN discovered within $\sim$1 d after explosion. In this paper we report high-cadence photometric (typically 0.5$\sim$1 day) and spectroscopic follow-up observations, conducted from as early as 0.27 d post discovery out to the nebular phase at 158.4 d. We analyze the environment of SN 2024aecx and derive a new distance (11.3$\pm$1.1 Mpc), metallicity and host extinction. The light curve exhibits a hot and luminous shock-cooling peak at the first few days, followed by a main peak with very rapid post-maximum decline. The earliest spectra are blue and featureless, while from 2.3 d after discovery prominent P-Cygni profiles emerge. At nebular phase, the emission lines exhibit asymmetric and double-peaked profiles, indicating asphericity and/or early dust formation in the ejecta. Nebular spectral modelling indicates a blueshifted O-rich clump moving toward observer, and the $[\text{OI}]/[\text{CaII}]$ line ratio suggests an intermediate-mass progenitor. We simulated the progenitor and explosion using a two-component model of shock cooling and radioactive $^{56}$Ni heating; our model favors an extended, low-mass H-rich envelope with $M_{\mathrm{e}} = 0.04\pm{0.01} M_{\odot}$ and a low ejecta mass of$M_{\mathrm{ej}} = 1.55^{+0.18}_{-0.14} M_{\odot}$. And the nebular-phase spectra and light-curve modelling both suggest that it most likely originated from an intermediate-mass binary progenitor system. The comprehensive monitoring of SN 2024aecx, coupled with the detailed characterization of its local environment, establishes it as a benchmark event for probing the progenitors and explosion mechanisms of Type IIb SNe.

astro-ph.SR↗

arXiv2025

Automated Penetration Testing with LLM Agents and Classical Planning

While penetration testing plays a vital role in cybersecurity, achieving fully automated, hands-off-the-keyboard execution remains a significant research challenge. In this paper, we introduce the "Planner-Executor-Perceptor (PEP)" design paradigm and use it to systematically review existing work and identify the key challenges in this area. We also evaluate existing penetration testing systems, with a particular focus on the use of Large Language Model (LLM) agents for this task. The results show that the out-of-the-box Claude Code and Sonnet 4.5 exhibit superior penetration capabilities observed to date, substantially outperforming all prior systems. However, a detailed analysis of their testing processes reveals specific strengths and limitations; notably, LLM agents struggle with maintaining coherent long-horizon plans, performing complex reasoning, and effectively utilizing specialized tools. These limitations significantly constrain its overall capability, efficiency, and stability. To address these limitations, we propose CHECKMATE, a framework that integrates enhanced classical planning with LLM agents, providing an external, structured "brain" that mitigates the inherent weaknesses of LLM agents. Our evaluation shows that CHECKMATE outperforms the state-of-the-art system (Claude Code) in penetration capability, improving benchmark success rates by over 20%. In addition, it delivers substantially greater stability, cutting both time and monetary costs by more than 50%.

cs.CR↗

arXiv2025

SN 2025coe: A Multiple-Peaked Calcium-Strong Transient from A White-Dwarf Progenitor

SN 2025coe is a calcium-strong transient located at an extremely large projected offset $\sim$39.3 kpc from the center of its host, the nearby early-type galaxy NGC 3277 at a distance of $\sim$25.5 Mpc. In this paper, we present multi-band photometric and spectroscopic observations spanning $\sim$100 days post-discovery. Its multi-band light curves display {multiple} distinct peaks: (1) an initial peak at $t \approx 1.6$ days attributed to shock cooling emission, (2) a secondary peak of $M_{R, \, peak} \approx$ $-$15.8 mag at $t \approx 10.2$ days powered by radioactive decay, and (3) a {possible} late-time bump at $t \approx 42.8$ days likely caused by ejecta-circumstellar material/clump interaction. Spectral evolution of SN 2025coe reveals a fast transition to the nebular phase within 2 months, where it exhibits an exceptionally high [Ca II]/[O I] ratio larger than 6. Modeling of the bolometric light curve suggests an ejecta mass of $M_{\rm ej} = 0.29^{+0.14}_{-0.15} \, M_{\odot}$, a $^{56}$Ni mass of $M_{\rm ^{56}Ni} = 2.4^{+0.06}_{-0.05} \times 10^{-2} M_{\odot}$, and a progenitor envelope with mass $M_e = 1.4^{+6.9}_{-1.2} \times 10^{-3} \, M_{\odot}$ and radius $R_e = 13.5^{+64.1}_{-11.1} \, R_{\odot}$. The tidal disruption of a hybrid HeCO white dwarf (WD) by a low-mass CO WD provides a natural explanation for the low ejecta mass, the small fraction of $^{56}$Ni, and the presence of an extended, low-mass envelope.

astro-ph.HE↗

arXiv2025

IndiTag: An Online Media Bias Analysis System Using Fine-Grained Bias Indicators

In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. However, without the experts' analysis, it is hard for the readers to distinguish bias from the news articles. This paper presents IndiTag, an innovative online media bias analysis system that leverages fine-grained bias indicators to dissect and distinguish bias in digital content. IndiTag offers a novel approach by incorporating large language models, bias indicators, and vector database to detect and interpret bias automatically. Complemented by a user-friendly interface facilitating automated bias analysis for readers, IndiTag offers a comprehensive platform for in-depth bias examination. We demonstrate the efficacy and versatility of IndiTag through experiments on four datasets encompassing news articles from diverse platforms. Furthermore, we discuss potential applications of IndiTag in fostering media literacy, facilitating fact-checking initiatives, and enhancing the transparency and accountability of digital media platforms. IndiTag stands as a valuable tool in the pursuit of fostering a more informed, discerning, and inclusive public discourse in the digital age. We release an online system for end users and the source code is available at https://github.com/lylin0/IndiTag.

cs.CY↗