arXiv subjects

Yang Liu

Publications and source records attributed to Yang Liu.

At least 739 records · Page 41Linked to original sources

arXiv2026

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

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

cs.SE↗

arXiv2026

Embracing Aleatoric Uncertainty in Medical Multimodal Learning with Missing Modalities

Medical multimodal learning faces significant challenges with missing modalities prevalent in clinical practice. Existing approaches assume equal contribution of modality and random missing patterns, neglecting inherent uncertainty in medical data acquisition. In this regard, we propose the Aleatoric Uncertainty Modeling (AUM) that explicitly quantifies unimodal aleatoric uncertainty to address missing modalities. Specifically, AUM models each unimodal representation as a multivariate Gaussian distribution to capture aleatoric uncertainty and enable principled modality reliability quantification. To adaptively aggregate captured information, we develop a dynamic message-passing mechanism within a bipartite patient-modality graph using uncertainty-aware aggregation mechanism. Through this process, missing modalities are naturally accommodated, while more reliable information from available modalities is dynamically emphasized to guide representation generation. Our AUM framework achieves an improvement of 2.26% AUC-ROC on MIMIC-IV mortality prediction and 2.17% gain on eICU, outperforming existing state-of-the-art approaches.

cs.LG↗

arXiv2026

Convergent sum of EFT corrections to Schwarzschild metric requires UV locality

Corrections to vacuum black hole solutions of general relativity (GR) are considered in an effective field theory (EFT) framework, perturbatively in EFT coefficients, focusing on the Schwarzschild solution of GR. We find dominant corrections to the Schwarzschild metric in all orders in the derivative expansion far away from the horizon. These corrections can be summed up in a closed form through EFT coefficients up to all orders in derivatives and to the second order in curvature. It occurs that such a summation is convergent only for localizable theories, making a direct connection between the graviton scattering amplitudes properties and the applicability of a perturbative treatment of an EFT of gravity. We further apply our results to logarithmic form-factors which appear in the 1-loop effective action for GR in four dimensions. We find out that the corresponding corrections to the Schwarzschild metric are stronger than those from the tree-level EFT operators. The developed framework can be extended to account for the corrections to the other BH solutions in GR, such as the Kerr metric.

hep-th↗

arXiv2026

Robust Multimodal Representation Learning in Healthcare

Medical multimodal representation learning aims to integrate heterogeneous data into unified patient representations to support clinical outcome prediction. However, real-world medical datasets commonly contain systematic biases from multiple sources, which poses significant challenges for medical multimodal representation learning. Existing approaches typically focus on effective multimodal fusion, neglecting inherent biased features that affect the generalization ability. To address these challenges, we propose a Dual-Stream Feature Decorrelation Framework that identifies and handles the biases through structural causal analysis introduced by latent confounders. Our method employs a causal-biased decorrelation framework with dual-stream neural networks to disentangle causal features from spurious correlations, utilizing generalized cross-entropy loss and mutual information minimization for effective decorrelation. The framework is model-agnostic and can be integrated into existing medical multimodal learning methods. Comprehensive experiments on MIMIC-IV, eICU, and ADNI datasets demonstrate consistent performance improvements.

cs.LG↗

arXiv2026

Composition and Space Weathering Characteristics of Tianwen-2 Mission's First Target Near-Earth Asteroid (469219) Kamo`oalewa

The near-Earth asteroid Kamo`oalewa, a quasi-satellite of the Earth and the target for sample return by China's Tianwen-2 mission, exhibits distinctive spectral characteristics. This study re-analyzes the visible and near-infrared reflectance spectrum of Kamo`oalewa published by B. N. L. Sharkey et al. (2021), obtained using the Large Binocular Telescope, to infer its mineral composition and space weathering characteristics. Spectral similarity analysis is performed by comparing the spectrum of Kamo`oalewa to the mean spectra of various types in the Bus-DeMeo taxonomy to make a preliminary constraint on the combined characteristics of surface mineralogy and space weathering effects. To further characterize the mineral composition, a detailed analysis of the 1 μm band center is conducted based on spectral data below 1.25 μm that have higher signal-to-noise ratios. Empirical models for normalized spectra are developed to estimate the Is/FeO content. The results suggest that asteroid Kamo`oalewa has higher olivine abundance than that of typical S-type asteroids and the Moon, exhibiting an immature to submature degree of space weathering. These findings enhance our understanding of the evolution of similar quasi-satellites and provide important implication for the future exploration of Tianwen-2 mission.

astro-ph.EP↗

arXiv2026

Promptware Engineering: Software Engineering for Prompt-Enabled Systems

Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.

cs.SE↗

arXiv2026

Beyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffraction

Crystal property prediction, governed by quantum mechanical principles, is computationally prohibitive to solve exactly for large many-body systems using traditional density functional theory. While machine learning models have emerged as efficient approximations for large-scale applications, their performance is strongly influenced by the choice of atomic representation. Although modern graph-based approaches have progressively incorporated more structural information, they often fail to capture long-range atomic interactions due to finite receptive fields and local encoding schemes. This limitation leads to distinct crystals being mapped to identical representations, hindering accurate property prediction. To address this, we introduce PRDNet that leverages unique reciprocal-space diffraction besides graph representations. To enhance sensitivity to elemental and environmental variations, we employ a data-driven pseudo-particle to generate a synthetic diffraction pattern. PRDNet ensures full invariance to crystallographic symmetries. Extensive experiments are conducted on Materials Project, JARVIS-DFT, and MatBench, demonstrating that the proposed model achieves state-of-the-art performance. The code is openly available at https://github.com/Bin-Cao/PRDNet.

cond-mat.mtrl-sci↗

arXiv2026

Cutting the Gordian Knot: Detecting Malicious PyPI Packages via a Knowledge-Mining Framework

The Python Package Index (PyPI) has become a target for malicious actors, yet existing detection tools generate false positive rates of 15-30%, incorrectly flagging one-third of legitimate packages as malicious. This problem arises because current tools rely on simple syntactic rules rather than semantic understanding, failing to distinguish between identical API calls serving legitimate versus malicious purposes. To address this challenge, we propose PyGuard, a knowledge-driven framework that converts detection failures into useful behavioral knowledge by extracting patterns from existing tools' false positives and negatives. Our method utilizes hierarchical pattern mining to identify behavioral sequences that distinguish malicious from benign code, employs Large Language Models to create semantic abstractions beyond syntactic variations, and combines this knowledge into a detection system that integrates exact pattern matching with contextual reasoning. PyGuard achieves 99.50% accuracy with only 2 false positives versus 1,927-2,117 in existing tools, maintains 98.28% accuracy on obfuscated code, and identified 219 previously unknown malicious packages in real-world deployment. The behavioral patterns show cross-ecosystem applicability with 98.07% accuracy on NPM packages, demonstrating that semantic understanding enables knowledge transfer across programming languages.

cs.CR↗

arXiv2026

EPAS: Efficient Training with Progressive Activation Sharing

We present a novel method for Efficient training with Progressive Activation Sharing (EPAS). This method bridges progressive training paradigm with the phenomenon of redundant QK (or KV ) activations across deeper layers of transformers. EPAS gradually grows a sharing region during training by switching decoder layers to activation sharing mode. This results in throughput increase due to reduced compute. To utilize deeper layer redundancy, the sharing region starts from the deep end of the model and grows towards the shallow end. The EPAS trained models allow for variable region lengths of activation sharing for different compute budgets during inference. Empirical evaluations with QK activation sharing in LLaMA models ranging from 125M to 7B parameters show up to an 11.1% improvement in training throughput and up to a 29% improvement in inference throughput while maintaining similar loss curve to the baseline models. Furthermore, applying EPAS in continual pretraining to transform TinyLLaMA into an attention-sharing model yields up to a 10% improvement in average accuracy over state-of-the-art methods, emphasizing the significance of progressive training in cross layer activation sharing models.

cs.LG↗

arXiv2026

From South to North: Leveraging Ground-Based LATs for Full-Sky CMB Delensing and Constraints on $r$

Delensing--the process of mitigating the lensing-induced B-mode contamination in cosmic microwave background (CMB) observations--will be a pivotal challenge for next-generation CMB experiments seeking to detect primordial gravitational waves (PGWs) through B-mode polarization. This process requires an accurate lensing tracer, which can be obtained either through internal reconstruction from high-resolution CMB observations or from external large-scale structure (LSS) surveys. Ground-based large-aperture telescopes (LATs) are crucial for internal reconstruction, yet existing and planned facilities are confined to the southern hemisphere, limiting effective delensing to that region. In this work, we assess the impact of introducing a northern hemisphere LAT, assumed to be situated near AliCPT (hence termed Ali-like LAT, or LATN), on delensing performance and PGW detection, using simulations. Our baseline setup includes a space-based small-aperture mission (LiteBIRD-like, SAT) and a southern LAT (SO-like, LATS). External LSS tracers, which have been shown to play an important role in delensing before the availability of ultra-sensitive polarization data, are also considered. We find that southern-hemisphere internal delensing reduces the uncertainty in r by approximately 21% compared to the no-delensing case. Adding LATN enables full-sky internal delensing, achieving a further ~19% reduction--comparable to that from including LSS tracers (~17%). Once LATN is included, the marginal benefit of LSS tracers drops to ~10%. These results highlight the significant role of LATN in advancing delensing capabilities and improving PGW constraints.

astro-ph.CO↗

arXiv2026

Nowcast3D: Reliable precipitation nowcasting via gray-box learning

Reliable nowcasting of extreme precipitation remains difficult because convective systems are strongly nonlinear, multiscale, and nonstationary in 3D. Radar is the backbone of nowcasting, yet existing methods struggle to predict extremes: physics-based extrapolation cannot capture growth and decay, deterministic learning tends to oversmooth and underestimate peaks, and purely generative models often lack physical consistency. Hybrid schemes help but are mostly limited to 2D composite reflectivity, collapsing the atmosphere into one layer and discarding vertical structure critical for height-dependent dynamics. We introduce Nowcast3D, a gray-box, fully 3D framework that works directly on volumetric radar reflectivity. The end-to-end model couples physically constrained neural operators (advection, local diffusion, and microphysics) with a conditional diffusion model to generate ensemble forecasts with quantified uncertainty. Trained on provincial-scale 3D volumes over a $10.24^\circ \times 10.24^\circ$ region and fine-tuned on a $2.56^\circ \times 2.56^\circ$ city region ($0.01^\circ \approx 1$ km), Nowcast3D provides near-real-time forecasts up to 3 h and outperforms competitive baselines in cross-region and temporal out-of-sample tests. It can also infer wind fields without labeled supervision, supporting physically plausible transport. In a nationwide blind evaluation by 160 meteorologists, Nowcast3D ranked first and was preferred in 57% of post-hoc assessments, surpassing the leading baseline (27%). These results highlight its reliability and operational value for extreme precipitation nowcasting.

cs.LG↗

arXiv2026

Two-stage Estimation of Latent Variable Regression Models: A General, Root-N Consistent Solution

Latent variable (LV) models are widely used in psychological research to investigate relationships among unobservable constructs. When one-stage estimation of the overall LV model is challenging, two-stage factor score regression (FSR) serves as a convenient alternative: the measurement model is fitted to obtain factor scores in the first stage, which are then used to fit the structural model in the subsequent stage. However, naive application of FSR is known to yield biased estimates of structural parameters. In this paper, we develop a generic bias-correction framework for two-stage estimation of parametric statistical models and tailor it specifically to FSR. Unlike existing bias-corrected FSR solutions, the proposed method applies to a broader class of LV models and does not require computing specific types of factor scores. We establish the root-n consistency of the proposed bias-corrected two-stage estimator under mild regularity conditions. To ensure broad applicability and minimize reliance on complex analytical derivations, we introduce a stochastic approximation algorithm for point estimation and a Monte Carlo-based procedure for variance estimation. In a sequence of Monte Carlo experiments, we demonstrate that the bias-corrected FSR estimator performs comparably to the ``gold standard'' one-stage maximum likelihood estimator. These results suggest that our approach offers a straightforward yet effective alternative for estimating LV models.

stat.ME↗

arXiv2026

Photon rest mass from localized fast radio bursts with improved distribution of dispersion measure from extragalactic gas

The assumption that photons are massless is a foundational postulate of modern physics, yet it remains subject to experimental verification. Fast radio bursts (FRBs), with their cosmological distances and precisely measured dispersion, offer an excellent laboratory for testing this hypothesis. In this work, we propose an improved distribution function for the dispersion measure arising from extragalactic gas and demonstrate that it provides an excellent fit to mock data. We then apply this distribution to constrain the photon rest mass under the $Λ$CDM, $w$CDM, and $w_{0}w_{a}$CDM cosmological models, the last of which is favored by recent DESI baryon acoustic oscillation observations. The corresponding 1$σ$ upper limits on the photon mass are found to be $4.83\times10^{-51}\,\mathrm{kg}$, $4.71\times10^{-51}\,\mathrm{kg}$, and $4.86\times10^{-51}\,\mathrm{kg}$, respectively, which are the most stringent constraints derived from FRBs to date. These results indicate that the choice of cosmological model has only a minor impact on photon-mass bounds, demonstrate that FRBs provide robust and reliable constraints, and offer strong empirical support for the massless nature of the photon.

astro-ph.CO↗

arXiv2026

REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering

The rapid development of large language models is transforming software development. Beyond serving as code auto-completion tools in integrated development environments, large language models increasingly function as foundation models within coding agents in vibe-coding scenarios. In such settings, prompts play a central role in agent-based intelligent software development, as they not only guide the behavior of large language models but also serve as carriers of user requirements. Under the dominant conversational paradigm, prompts are typically divided into system prompts and user prompts. System prompts provide high-level instructions to steer model behavior and establish conversational context, while user prompts represent inputs and requirements provided by human users. Despite their importance, designing effective prompts remains challenging, as it requires expertise in both prompt engineering and software engineering, particularly requirements engineering. To reduce the burden of manual prompt construction, numerous automated prompt engineering methods have been proposed. However, most existing approaches neglect the methodological principles of requirements engineering, limiting their ability to generate artifacts that conform to formal requirement specifications in realistic software development scenarios. To address this gap, we propose REprompt, a multi-agent prompt optimization framework guided by requirements engineering. Experiment results demonstrate that REprompt effectively optimizes both system and user prompts by grounding prompt generation in requirements engineering principles.

cs.SE↗

arXiv2026

Noise-immune and AI-enhanced DNA storage via adaptive partition mapping of digital data

Encoding digital information into DNA sequences offers an attractive potential solution for storing rapidly growing data under the information age and the rise of artificial intelligence. However, practical implementations of DNA storage are constrained by errors introduced during synthesis, preservation, and sequencing processes, and traditional error-correcting codes remain vulnerable to noise levels that exceed predefined thresholds. Here, we developed a Partitioning-mapping with Jump-rotating (PJ) encoding scheme, which exhibits exceptional noise resilience. PJ removes cross-strand information dependencies so that strand loss manifests as localized gaps rather than catastrophic file failure. It prioritizes file decodability under arbitrary noise conditions and leverages AI-based inference to enable controllable recovery of digital information. For the intra-strand encoding, we develop a jump-rotating strategy that relaxes sequence constraints relative to conventional rotating codes and provides tunable information density via an adjustable jump length. Based on this encoding architecture, the original file information can always be decoded and recovered under any strand loss ratio, with fidelity degrading smoothly as damage increases. We demonstrate that original files can be effectively recovered even with 10% strand loss, and machine learning datasets stored under these conditions retain their classification performance. Experiments further confirmed that PJ successfully decodes image files after extreme environmental disturbance using accelerated aging and high-intensity X-ray irradiation. By eliminating reliance on prior error probabilities, PJ establishes a general framework for robust, archival DNA storage capable of withstanding the rigorous conditions of real-world preservation.

cs.IT↗

arXiv2026

Bridging Expert Reasoning and LLM Detection: A Knowledge-Driven Framework for Malicious Packages

Open-source ecosystems such as NPM and PyPI are increasingly targeted by supply chain attacks, yet existing detection methods either depend on fragile handcrafted rules or data-driven features that fail to capture evolving attack semantics. We present IntelGuard, a retrieval-augmented generation (RAG) based framework that integrates expert analytical reasoning into automated malicious package detection. IntelGuard constructs a structured knowledge base from over 8,000 threat intelligence reports, linking malicious code snippets with behavioral descriptions and expert reasoning. When analyzing new packages, it retrieves semantically similar malicious examples and applies LLM-guided reasoning to assess whether code behaviors align with intended functionality. Experiments on 4,027 real-world packages show that IntelGuard achieves 99% accuracy and a 0.50% false positive rate, while maintaining 96.5% accuracy on obfuscated code. Deployed on PyPI.org, it discovered 54 previously unreported malicious packages, demonstrating interpretable and robust detection guided by expert knowledge.

cs.SE↗

arXiv2026

Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model

Recent advances in multimodal models have demonstrated impressive capabilities in unified image generation and editing. However, many prominent open-source models prioritize scaling model parameters over optimizing training strategies, limiting their efficiency and performance. In this work, we present UniPic2-SD3.5M-Kontext, a 2B-parameter DiT model based on SD3.5-Medium, which achieves state-of-the-art image generation and editing while extending seamlessly into a unified multimodal framework. Our approach begins with architectural modifications to SD3.5-Medium and large-scale pre-training on high-quality data, enabling joint text-to-image generation and editing capabilities. To enhance instruction following and editing consistency, we propose a novel Progressive Dual-Task Reinforcement strategy (PDTR), which effectively strengthens both tasks in a staged manner. We empirically validate that the reinforcement phases for different tasks are mutually beneficial and do not induce negative interference. After pre-training and reinforcement strategies, UniPic2-SD3.5M-Kontext demonstrates stronger image generation and editing capabilities than models with significantly larger generation parameters-including BAGEL (7B) and Flux-Kontext (12B). Furthermore, following the MetaQuery, we connect the UniPic2-SD3.5M-Kontext and Qwen2.5-VL-7B via a connector and perform joint training to launch a unified multimodal model UniPic2-Metaquery. UniPic2-Metaquery integrates understanding, generation, and editing, achieving top-tier performance across diverse tasks with a simple and scalable training paradigm. This consistently validates the effectiveness and generalizability of our proposed training paradigm, which we formalize as Skywork UniPic 2.0.

cs.CV↗

arXiv2026

Skywork UniPic 3.0: Unified Multi-Image Composition via Sequence Modeling

The recent surge in popularity of Nano-Banana and Seedream 4.0 underscores the community's strong interest in multi-image composition tasks. Compared to single-image editing, multi-image composition presents significantly greater challenges in terms of consistency and quality, yet existing models have not disclosed specific methodological details for achieving high-quality fusion. Through statistical analysis, we identify Human-Object Interaction (HOI) as the most sought-after category by the community. We therefore systematically analyze and implement a state-of-the-art solution for multi-image composition with a primary focus on HOI-centric tasks. We present Skywork UniPic 3.0, a unified multimodal framework that integrates single-image editing and multi-image composition. Our model supports an arbitrary (1~6) number and resolution of input images, as well as arbitrary output resolutions (within a total pixel budget of 1024x1024). To address the challenges of multi-image composition, we design a comprehensive data collection, filtering, and synthesis pipeline, achieving strong performance with only 700K high-quality training samples. Furthermore, we introduce a novel training paradigm that formulates multi-image composition as a sequence-modeling problem, transforming conditional generation into unified sequence synthesis. To accelerate inference, we integrate trajectory mapping and distribution matching into the post-training stage, enabling the model to produce high-fidelity samples in just 8 steps and achieve a 12.5x speedup over standard synthesis sampling. Skywork UniPic 3.0 achieves state-of-the-art performance on single-image editing benchmark and surpasses both Nano-Banana and Seedream 4.0 on multi-image composition benchmark, thereby validating the effectiveness of our data pipeline and training paradigm. Code, models and dataset are publicly available.

cs.CV↗