arXiv subjects

Ping Chen

Publications and source records attributed to Ping Chen.

At least 37 records · Page 2Linked to original sources

arXiv2026

Pandora cluster Lensing, AGN, and Transient Exploration (PLATE) from JWST Multi-Epoch Imaging. I. Discovery of a type II supernova candidate in a spiral galaxy at $z=0.7$

We report the discovery and multi-wavelength analysis of a $z\sim0.7$ transient PLATE-23a in the Abell 2744 field, as the first results of the Pandora Lensing, AGN, and Transient Exploration (PLATE) project. Using multi-epoch JWST NIRCam imaging spanning from 2022 to 2025, we detect PLATE-23a in 12 filters. Difference-imaging analysis reveals its rising and declining phases. The host galaxy of PLATE-23a is a barred spiral at $z=0.688$ with a stellar mass of $\sim 10^{10.4}M_{\odot}$ and a star formation rate of $\sim5.1M_{\odot}~\rm yr^{-1}$, placing it on the star-forming main sequence. Bayesian light-curve classification strongly favors a type IIP supernova (SN) origin. It lies $\sim 13\rm kpc$ (after lensing correction) from the galaxy center in a region of low local star formation, suggesting the progenitor may have migrated from a distant star-forming clump. Physical properties derived from blackbody modeling indicate a temperature decreasing from $\sim8010\rm K$ to $\sim6100\rm K$ at around 65 days after the explosion; the late-time SED is consistent with entering the radioactive decay phase at about one rest-frame year. This work demonstrates the power of deep, multi-epoch JWST observations for studying transients at cosmological distances.

astro-ph.GA↗

arXiv2026

Bernhard-1: An Eccentric Binary Periodically Obscured by its Misaligned Circumbinary Disk

Bernhard-1 is a proposed KH 15D-like circumbinary disk occultation (CBO) system, but its binary nature and disk geometry have not previously been confirmed. We present new optical and near-infrared spectroscopy together with multi-band photometric monitoring of the system. The radial velocities confirm that Bernhard-1 hosts a highly eccentric binary with $e = 0.80 \pm 0.09$, confirming that the periodic photometric variability arises from occultation by a misaligned circumbinary disk. Joint modeling of the spectra and phase-dependent spectral energy distributions yields pre-main-sequence components with masses of $\sim 1.1\,M_\odot$ and $\sim 0.8\,M_\odot$. Combining stellar isochrones with the measured lithium abundance yields a system age of $\sim$ 10 Myr. Together with the spatial, astrometric, and metallicity properties of Bernhard-1, this suggests that Bernhard-1 is probably a member of the open cluster Dolidze 42. By combining the RV orbit with a semi-transparent occultation-screen model, we infer a disk--binary mutual inclination of roughly $50^\circ$ or $130^\circ$, with the degeneracy arising from the unknown disk rotation direction. This geometric method can be applied to any CBO system once radial velocity monitoring yields an orbital solution. The new light curves deviate from earlier model predictions, consistent with ongoing disk precession, while the phase-dependent H$α$ profiles indicate pulsed accretion near periastron. Bernhard-1 therefore joins KH 15D and Bernhard-2 as a rare spectroscopically confirmed CBO system.

astro-ph.SR↗

arXiv2026

Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks

Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones. That separation is then read as evidence that the score will also catch the attacks that succeed. Harmful intent is a property of the prompt. Jailbreak success is an outcome produced later by a particular target model, decoding policy, and judge. A filter tuned on a score that measures the wrong quantity spends its false positive budget on attacks that would have failed anyway. In this paper we audit that inference. Attention based measurements are usually read from prompt dependent locations, so a wrapper changes both the content being judged and the place the signal is taken from. We therefore introduce Active Attention Probing, which supplies a fixed content independent measurement coordinate. We pair every base goal with a plain and a wrapped version and generate real completions from the target models. On Llama, wrapping raises harmful generation from 0.05 to 0.27 while harmful intent AUROC falls from 0.936 to 0.803, so the attacks grow more dangerous while the prompts look safer to the score. Among wrapped harmful prompts the outcome AUROC is 0.220, which places the attacks that succeeded below the attacks that failed. Rare token, passive, and detector derived channels reproduce the reversal on the same matched design, and the reversal itself persists across three target models, seven attack families, and two independent judges. Distribution shift then degrades calibration and threshold transfer before it degrades ranking.

cs.CL↗

arXiv2026

SegDem: Segmentation helps Demosaicing

Image demosaicing reconstructs a full-color image from incomplete color measurements produced by a sensor covered with a color filter array (CFA). Most existing methods formulate demosaicing as pixel-level reconstruction and mainly rely on local textures, cross-channel correlations, and low-level image statistics. Our core insight is that reconstruction and visual understanding can be viewed as complementary views of shared scene structure: both are grounded in the same underlying physical world, and therefore the structural and physical information inferred from an image should remain consistent across the two tasks. We instantiate this idea with instance segmentation and propose \emph{SegDem}, a cross-task decoder representation transfer framework for demosaicing. SegDem first learns region- and boundary-aware representations through instance-aware structural pretraining and then transfers the decoder to RAW-conditioned reconstruction. Segmentation- and demosaicing-conditioned features are further anchored to a shared frozen DINOv2 representation space to preserve structural organization across tasks. We instantiate SegDem with convolutional, Transformer-based, and state-space backbones for unified Single- and Quad-Bayer demosaicing. Extensive experiments on synthetic, external, and challenging datasets demonstrate consistent improvements across different architectures and CFA layouts.

cs.CV↗

arXiv2026

Structural Guidance for Unified Joint Demosaicing and Denoising

Joint demosaicing and denoising is a fundamental step in camera image signal processing, yet remains challenging because different Bayer-like color filter arrays (CFAs) and sensor noise jointly corrupt both color sampling and image content. Existing unified restoration networks explicitly model CFA geometry but are still driven primarily by pixel-level supervision, making them prone to structural degradation around edges, repetitive textures, and moiré patterns where local evidence is unreliable. We attribute this limitation partly to the absence of explicit structural guidance beyond pixel-level reconstruction supervision. Motivated by this observation, we propose a structural-guided unified restoration framework that injects pretrained structural knowledge into CFA-aware image restoration. Our model receives a unified five-channel observation consisting of the raw mosaic, CFA masks, and a noise-level map. A SwinIR restoration branch reconstructs pixel details under CFA-conditioned modulation, while a parallel structural reasoning branch extracts complementary structural cues from a sparse pseudo-RGB observation. To bridge the substantial domain gap between sparse noisy sensor data and the natural-image pretraining domain of the structural encoder, we introduce a lightweight trainable adapter before residually fusing structural and restoration features. A shared decoder jointly predicts the restored RGB image and an auxiliary clean mosaic, providing supervision in both image and sensor domains. Extensive experiments across multiple CFA patterns and noise levels demonstrate consistent improvements over state-of-the-art unified and CFA-specific methods, indicating that adapted structural priors can enhance robust camera image restoration. The source codes and dataset are provided in the supplementary material.

eess.IV↗

arXiv2026

Stochastic Scalar Conservation Laws on Moving Hypersurfaces

We establish the well-posedness of stochastic scalar conservation laws on moving hypersurfaces driven by Brownian motion. To handle the interaction between stochastic forcing and evolving geometry, we derive an Itô formula on moving surfaces and introduce the notion of generalized entropy solutions incorporating the relevant stochastic interaction terms. A martingale entropy solution is constructed via the vanishing-viscosity method, based on a uniform $L^\infty$-bound in space and time, an $L^1$-estimate for the spatial gradient, an $L^1$-continuity estimate in time, and a suitable tightness argument. Pathwise uniqueness is established by adapting Kruzhkov's doubling-of-variables method to moving hypersurfaces, yielding an $L^1$-contraction property. Finally, together with the Yamada-Watanabe theorem, these results yield the well-posedness of the problem.

math.PR↗

arXiv2026

Token-Operations-Oriented Inference Optimization Techniques for Large Models

Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large model services. Centered on token-oriented inference optimization technology, this paper proposes for the first time a four-layer technical architecture consisting of Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion. It systematically reviews the key technologies and current industry status across these four levels and analyzes the application value of related technologies in real-world business scenarios. This paper provides a practical technical path for reducing token production costs, improving token service efficiency, ensuring the stability of token supply, and driving the transition of large model services from being merely callable to being operable.

cs.SE↗

arXiv2026

Rewriting the Response Path: Silent Tampering and Provider-Signed Defense in BYOK LLM Agents

LLM agents convert model outputs into consequential actions, including communications, code changes, and financial transactions. Developers often trust evidence such as test results and execution logs. We identify a response path integrity gap in Bring Your Own Key configurations used by roughly 88 percent of mainstream agents. Because traffic passes through a user-authorized relay, the relay can modify plaintext LLM responses after alignment but before execution without breaking encryption. A minimal attack rewrites one execution bearing field and regenerates the remaining response using the user key while preserving the model style. Experiments reveal false green verification, where malicious code modifications pass public tests while silently defeating security checks. On APPS, 99.7 percent of publicly passing solutions retained downgraded behavior without developer-visible warnings. Tests on SWE bench, AgentDojo, and ASB across five frontier models show that single-field rewriting can redirect agents while preserving apparent task completion. We propose sign-c, a server-side scheme that authenticates execution bearing fields and outgoing queries. A local shim verifies them before action, while encryption protects confidentiality. The defense rejected all tampered responses with zero false rejections and only 0.0167 percent latency overhead.

cs.CR↗

arXiv2026

Multiwavelength Analysis of Six Luminous Fast Blue Optical Transients

We present multiwavelength observations and analysis of six luminous fast blue optical transients (LFBOTs) discovered in Zwicky Transient Facility (ZTF) survey data. We identified these LFBOTs from their fast light-curve evolution ($t_{1/2}\leq 12 $d), blue colors at peak brightness ($g-r\leq-0.5 $mag), a visible host galaxy, high optical luminosity ($M_g<-20$), and an X-ray or radio detection. With the exception of AT2024aehp (ZTF24abygbss), these transients exhibit peaks in their $10\,$GHz radio light curves at $t_{\text{rest}} \approx 50-100$ d, with peak radio luminosities ranging from $10^{38}-10^{40}$ erg s$^{-1}$. Modeling the radio emission as synchrotron radiation indicates a fast ($v=0.1-0.3c$) shock in a dense ($n_e\approx10^{3}-10^{4}$ cm$^{-3}$) medium. The X-ray emission varies by $\approx2$ orders of magnitude in luminosity ($10^{42}-10^{44}$ erg s$^{-1}$) at $t_{\text{rest}}\sim20 $d. Analysis of the host-galaxy photometry and spectroscopy for each transient shows that they are predominantly nonnuclear (a few kpc offset) with star-forming host galaxies of stellar masses $10^{9}-10^{11} ,M_\odot$. Unlike all other LFBOTs to date, AT2024aehp exhibited a luminous ($M<-19 $mag) plateau in the optical light curve; spectra during this plateau phase showed a featureless blue continuum. The $6-15$ GHz radio emission of AT2024aehp brightened by over an order of magnitude from $t_{\text{rest}} \approx70 $d to $t_{\mathrm{rest}} \approx130 $d. The mostly consistent radio behavior between optically selected LFBOTs implies a similar circumburst medium, leading us to prefer a progenitor scenario in which mass is lost in a consistent way shortly prior to the terminal event, such as a massive star merging with a compact object.

astro-ph.HE↗

arXiv2026

When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems

While enabling effective collaboration on complex tasks, LLM-based Multi-Agent Systems (MAS) face critical security challenges due to vulnerabilities at the agent and interaction levels. Most existing MAS security defenses are built upon two core assumptions: semantically-explicit malicious attacks and explicit graph-based modeling of the MAS topology and agent-level interactions. In practice, real-world attacks are becoming more semantically stealthy, while MAS execution is typically asynchronous without the temporal alignment assumed by graph-based propagation models. To address these limitations, we propose AcMAS, an activation-based framework for malicious-behavior detection in MAS. By analyzing internal reasoning states in the activation space of local agents, AcMAS detects even stealthy attacks in a synchronization-robust fashion, without relying on explicit interaction graphs. Moreover, our activation analysis provides critical signals to guide AcMAS in restoring the functionality of compromised agents, rather than the disruptive agent isolation commonly used by the state-of-the-art methods. Comprehensive evaluation demonstrates that AcMAS significantly outperforms graph-based baselines against stealthy attacks, by +0.22 F1 in synchronous settings (0.94 vs. 0.72) and by +0.55 F1 in asynchronous settings (0.93 vs. 0.38), with generalization across diverse open-source LLM backbones, attack intensity, and MAS scale.

cs.CR↗

arXiv2026

Prior-Anchored Debiasing for Long-Tailed Multi-Organ Pathology Report Generation

Automated pathology report generation from Whole Slide Images (WSIs) has attracted increasing attention in digital pathology. However, existing methods are predominantly developed under single-organ settings, overlooking the multi-organ scenarios encountered in clinical practice, where organ types typically follow a long-tailed distribution. To address this gap, we identify two critical biases: (1) visual representation bias, where the encoder favors head-class patterns over tail-class discriminative features, and (2) textual decoding bias, where the decoder overfits to head-class narrative patterns, yielding diagnostically unreliable outputs for tail-class organs. To mitigate these two biases, we propose a novel Prior-anchored multi-Organ pathology report Generation framework (PriOrGen). Specifically, a Visual-Prototype Anchored Bottleneck module leverages the information bottleneck principle with learnable anchor representations to selectively retain diagnostically relevant visual information while filtering out head-biased redundancy. Secondly, a Meta-Report Anchored Bank module constructs an organ-specific meta-report anchored bank and retrieves organ-faithful textual priors to steer the decoder away from head-class narrative patterns. Extensive experiments on a multi-organ pathology dataset demonstrate that our method effectively mitigates long-tail biases and achieves superior report generation performance across both head and tail organ categories compared to state-of-the-art methods.

cs.CV↗

arXiv2026

Decoding the Early-Time Light Curves of Type Ia Supernovae. II. Population Parameters of One Thousand ZTF Supernovae

Early-time light curves of Type Ia Supernovae (SNe Ia) encode critical information about their progenitor systems. We characterize the rise of normal SNe Ia using a volume-complete sample of 972 events from the Zwicky Transient Facility Data Release 2, an order of magnitude larger than any previous dataset for similar analyses. Fitting light curves up to $30\%$ of peak flux with a power-law model under a hierarchical Bayesian framework, we provide robust population-level constraints on the rise time ($t_\mathrm{rise}$; $μ=18.55\pm0.08$ days, $σ=1.42\pm0.07$ days), rise index ($α$; $μ=2.10\pm0.04$, $σ=0.48\pm0.03$ in ZTF $r$), and $g-r$ color evolution ($α_g - α_r$; $μ=0.20\pm0.02$, $σ=0.17\pm0.02$). These power-law fits are sensitive to the chosen truncation epoch if data beyond $\sim$$40\%$ of peak flux are included, but generally converge when restricted to earlier epochs. The relation between rise morphology and light-curve width ($\texttt{SALT2}$ $x_1$ stretch) bifurcates into two distinct regimes: high-stretch SNe Ia show clear trends where a higher $x_1$ correlates with shallower rises and more persistent blue colors, whereas low-stretch SNe Ia lack such trends. While rise times correlate positively with $x_1$ overall, this relation flattens significantly within the high-stretch population. Searching for anomalies, we identify several normal SNe Ia with unusually long rise times, which potentially exhibit short-duration ($\lesssim$2 days) flux excesses over a smooth rise. Long-duration ($\sim$5 days) flux excesses appear common within the high-stretch population and are tied to the shallow rises and early blue colors, pointing to widespread outward $^{56}$Ni mixing. Multi-dimensional explosion models with more realistic progenitor setups are needed to fully reproduce the observed dichotomy in rise morphology and stretch.

astro-ph.HE↗

arXiv2026

Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment

Coding agents powered by large language models are becoming central modules of modern IDEs, helping users perform complex tasks by invoking tools. While powerful, tool invocation opens a substantial attack surface. Prior work has demonstrated attacks against general-purpose and domain-specific agents, but none have focused on the security risks of tool invocation in coding agents. To fill this gap, we conduct the first systematic red-teaming of six popular real-world coding agents: Cursor, Claude Code, Copilot, Windsurf, Cline, and Trae. Our red-teaming proceeds in two phases. In Phase 1, we perform prompt leakage reconnaissance to recover system prompts. We discover a general vulnerability, ToolLeak, which allows malicious prompt exfiltration through benign argument retrieval during tool invocation. In Phase 2, we hijack the agent's tool-invocation behavior using a novel two-channel prompt injection in the tool description and return values, achieving remote code execution (RCE). We adaptively construct payloads using security information leaked in Phase 1. In emulation across five backends, our method outperforms baselines on Claude-Sonnet-4, Claude-Sonnet-4.5, Grok-4, and GPT-5. On real agents, our approach succeeds on 19 of 25 agent-LLM pairs, achieving leakage on every agent using Claude and Grok backends. For tool-invocation hijacking, we obtain RCE on every tested agent-LLM pair, with our two-channel method delivering the highest success rate. We provide case studies on Cursor and Claude Code, analyze security guardrails of external and built-in tools, and conclude with practical defense recommendations.

cs.CR↗

arXiv2026

Human and AI collaboration for pulmonary nodule segmentation

Medical expert annotators are scarce, and blind reliance on artificial intelligence (AI) can be misleading, motivating approaches in which humans, particularly junior medical trainees or even non-medical personnel, collaborate with AI to achieve robust medical segmentation. Although the Segment Anything Model (SAM) shows promise for general-purpose image segmentation, its performance in human-AI collaboration for specialized medical tasks has not been thoroughly evaluated. Here we present Hi-Seg, a human-in-the-loop segmentation framework for pulmonary nodules built on SAM. Humans iteratively refine prompts through trial-and-error learning and semantic reasoning, progressively guiding SAM toward higher-quality masks. Using chest CT scans from 1,179 patients across 12 centers, we conducted the first large-scale external validation of collaborative human-SAM segmentation. Across all annotator groups, Hi-Seg achieved a mean Dice score of almost 85%, outperforming five state-of-the-art deep learning models by 10-22% and 13 SAM variants by 1-29%. Hi-Seg improved segmentation accuracy while reducing annotation time for medical annotators, and briefly trained non-medical annotators achieved performance comparable to that of the junior medical student. These findings suggest that human-in-the-loop segmentation can reduce clinician workload, enable scalable crowdsourced annotation, and transform clinical workflows by facilitating the safe and efficient integration of foundation models into routine clinical practice.

cs.CV↗

arXiv2026

Detection of persistent helium absorption in the 91bg-like type Ia Supernova 2022an

We present optical and near-infrared observations of the fast-declining Type Ia supernova (SN Ia) 2022an. The photometric and spectroscopic properties identify it as a standard 91bg-like event; however, our data reveal a relatively narrow absorption feature with a full width at half maximum (FWHM) of 75 angstroms near $1.037\,μ$m in the rest frame of the observed spectra that persists from around 30 days to nearly 90 days after maximum light. We attribute this feature to He I $1.083\,μ$m line with a blueshifted velocity of $1.3\times10^{4}$ km s$^{-1}$ and a FWHM of $2.1\times10^{3}$ km s$^{-1}$, supported by the detection of multiple optical He I transitions in earlier epochs at a higher velocity around $1.5\times10^{4}$ km s$^{-1}$. The high velocity of the helium could not be explained by helium external to the progenitor at the explosion, such as the stripped surface helium from a companion star. The properties of the helium absorption in SN 2022an spectra instead point to unburnt material in the outer ejecta, thus providing the most compelling evidence to date for helium-bearing ejecta in a 91bg-like SN Ia. Such helium has been predicted for sub-Chandrasekhar-mass double-detonation explosions involving a surface helium shell. No theoretical calculations of modern helium-shell double detonation have been performed at epochs similar to those observed for SN 2022an to study the effect of helium on their spectra, revealing a gap between observations and theoretical calculations in understanding the manifestation of helium in SNe Ia. Nevertheless, the discovery of persistent helium absorption in SN 2022an demonstrates the diagnostic power of NIR spectroscopy for understanding thermonuclear supernova explosions by probing the abundance and structure of their ejecta.

astro-ph.HE↗

arXiv2026

A Disappearing Act: Constraints From "Missing" Flares of Repeating Partial TDE Candidates

Recurrent tidal disruption events (rTDEs) are sources that exhibit multiple TDE-like flares; many are likely powered by the recurring partial disruption of a bound star, in a repeating partial TDE (rpTDE). Two such sources, TDE 2022dbl (ASASSN-22ci) and TDE 2020vdq (ZTF20acaazkt), each exhibited two UV/optical flares and, under the assumption of periodicity, both were expected to exhibit a third flare in early 2026. Neither exhibited such a flare, to limits of $L_{\textrm{UV/optical}} \lesssim 10^{42}$ erg s$^{-1}$, $\sim$30$\times$ fainter than the previous flares. Here, we examine several possible explanations. Observing two independent TDEs from the same galaxy within $\sim$2 yr has a probability of $\lesssim$0.5% for measured average TDE rates and currently expected rate enhancements, unless there is extreme intrinsic dispersion in the rates. Theoretical predictions for a double TDE of both stars in a binary are inconsistent with the observed flares. We therefore conclude that TDE 2022dbl and TDE 2020vdq are rpTDEs. To produce only two observable flares with similar energetics, our semi-analytical modeling strongly favors a main-sequence star promptly placed on a bound orbit with a deep initial tidal encounter at pericenter. These results suggest that the majority of rTDEs with multiple flares over a few-year baseline are likely to be rpTDEs, and that a significant fraction of systems may produce only two observable flares. This has important implications for the use of r(p)TDEs as probes of TDE physics and dynamical processes in the nuclei of other galaxies, in addition to the expected yield from upcoming surveys.

astro-ph.HE↗

arXiv2026

Capture and Stability of Resonant Planet Pairs in Turbulent Disk

We present a theoretical framework for the resonance capture and stability of two-planet systems in turbulent disks. By incorporating stochastic forcing (parameterized by $κ$) alongside laminar angular momentum and eccentricity damping timescales ($τ_{\rm m}, τ_{e}$), we derive an analytical criterion for the general $j:j-1$ mean motion resonances, and validate it through N-body simulations. The outcome is mapped in $κ$-$τ_{\rm m}/τ_{e}$ parameter space, revealing two distinct regimes: resonance trapping and turbulence-induced disruption -- which occurs either directly cross or via temporary capture followed by escape through turbulent diffusion. Crucially, our analysis identifies turbulence as a universal destabilizer. It amplifies the intrinsic overstability mechanism: In laminar disks, escape requires $τ_{\rm m}/τ_{e}$ to drop below a critical limit due to excessive eccentricity excitation. We demonstrate that turbulent diffusion lowers this limit, demanding stronger damping (larger $τ_{\rm m}/τ_{e}$) for stability. Thus, greater turbulence promotes escape, and sufficiently strong diffusion precludes resonance retention irrespective of eccentricity damping.

astro-ph.EP↗

arXiv2026

Building a physics-aware AI ecosystem for solid-state hydrogen storage materials

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By embedding physical constraints and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.

cond-mat.mtrl-sci↗