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Xiangyu Wang

Publications and source records attributed to Xiangyu Wang.

At least 37 records · Page 2Linked to original sources
arXiv2026

The Pulse Beneath the Job Title: Monthly Readings of Requirements and Tasks from 750 Million Chinese Job Ads

How do we define an occupation? By its job title? An accountant at a small trading company keeps the books; at a listed firm the same title demands a certified-accountant licence, and the week goes to the reports that regulators and the board read. Same title, different bar, different work. What defines an occupation is who it lets in and what it asks them to do. In a rapidly changing labor market, tracking those requirements and tasks is how to take the market's pulse. Yet no instrument reads both at the speed they change. Official occupational directories like O*NET report one national average per occupation, updated every few years. Job postings are timely but unstructured. Research built on them works from job titles plus proprietary skill keywords, which blur what is asked of a candidate into what a candidate is asked to do. The blur matters, because rising requirements and changing tasks are different events with different causes. We separate them. From 752.6 million job ads posted on China's five leading recruitment platforms between 2022 and 2026, we extract the phrases employers write, unify those that name the same thing, and validate the mapping from text back to entry. By doing so we construct two catalogs, 20,721 requirements a candidate must meet and 44,479 tasks the hire will do. With the entries standardized, we annotate them further. Each task, for example, carries a score for how far a language model could absorb it. Matched back onto every ad, the catalogs read the market month by month. Two examples show what the layer beneath the job title buys. First, the occupational registry records one accountant where the ads record a staircase, the junior certificate at the bottom of the wage range and the intermediate one at the top. Second, counting occupations says the work most exposed to language models is disappearing, and counting tasks says far less of it is.

econ.GN↗
arXiv2026

Test-Time Scaling for Scientific Equation Discovery

Test-time scaling (TTS) improves language model reasoning by allocating additional test-time compute, but prior work mainly studies closed-ended tasks such as math and coding. We study TTS for automated equation discovery, an open-ended setting where models search over candidate equations and rely on observed datapoints for feedback. We formulate LLM-driven equation discovery as an iterative search process that unifies Best-of-N, sequential refinement, tree search, and evolution-style methods under a common compute-allocation view. To isolate allocation effects from prompt engineering and other heuristics, we compare minimal parallel controllers under fixed budgets. On LLM-SRBench equation-discovery tasks, we find that search width is the dominant allocation parameter: the best width in our sweep generally increases with the compute budget, while the population--branching split and controller choice matter less. Appropriate width selection also improves wall-clock efficiency by increasing parallelism. These results suggest that, given an informative verifier, controlling exploration and exploitation is central to scaling LLM-based equation discovery.

cs.CL↗
arXiv2026

Janus: Disaggregating Attention and Experts for Scalable MoE Inference

Serving large Mixture-of-Experts (MoE) models is challenging because of their large memory footprints, heterogeneous resource demands, and highly dynamic inference workloads. Most existing MoE inference systems deploy the entire model as a monolithic unit, forcing attention and MoE layers to share the same resource configuration despite their different scaling behaviors and resource bottlenecks. Such coarse-grained provisioning leads to resource inefficiency and suboptimal performance. We present JANUS, a scalable and resource-efficient MoE inference system built around three key principles. First, JANUS disaggregates attention and MoE layers onto separate GPU worker pools, enabling independent resource provisioning for the two layer types, and uses an adaptive two-phase communication mechanism for low-latency data exchange. Second, because MoE-layer execution is often memory-bound and highly sensitive to activated-expert imbalance, JANUS introduces a lightweight, microsecond-scale activation scheduler that balances per-layer activated experts across MoE instances to reduce inference latency. Third, JANUS employs a fine-grained, SLO-aware resource scaling scheme that jointly selects attention resources, MoE resources, and expert placement to minimize GPU cost under token-level SLOs. Evaluation shows that JANUS improves per-GPU throughput by up to 4.7x over state-of-the-art MoE inference baselines while satisfying token-level latency SLOs.

cs.DC↗
arXiv2026

X-rays breaking out of pre-explosion ejecta mark a supernova's first light

Massive stars die as core-collapse supernovae, whose optical light emerges days after the implosion. Theory predicts that the initial collapse-driven shock, upon breaking through the star and dense circumstellar medium, emits a brief thermal flash of soft X-rays and ultraviolet. Yet these elusive first signals have remained largely undetected, owing to limited wide-field soft X-ray monitoring. Here we report the discovery of a soft X-ray flash, EP260321a, followed days later by a broad-lined supernova from an envelope-stripped progenitor. Its X-ray spectrum, best modeled with blackbody, establishes it as the long-sought archetypal shock breakout. The burst's duration and energetics place the breakout at a radius of 300 solar radii, tracing a dense surrounding shell and revealing abrupt mass ejection within the final month before collapse.

astro-ph.HE↗
arXiv2026

PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback

Effective Automated Essay Scoring (AES) are expected to support both reliable assessment and actionable instructional feedback. However, existing approaches often treat scoring and feedback as separate components: neural scoring models provide limited interpretability, while Large Language Model (LLM)-based feedback is typically insensitive to learners proficiency levels. To address this fragmentation, this work proposes PsyScore, a psychometrically-aware framework that integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation. PsyScore comprises three key modules: a Trait-Adaptive Neural IRT Scorer that incorporates the Graded Partial Credit Model (GPCM) into a neural architecture, enabling the precise estimation of student ability while maintaining psychometric interpretability, a ZPD-Scaffolded Feedback Generator, which conditions multi-agent feedback strategies on the diagnosed ability parameter to adapt instructional focus across different proficiency levels, and a Multi-Perspective Feedback Evaluation Strategy that assesses feedback quality via pairwise preference judgements and student revision simulations. Experiments on the ASAP++ dataset demonstrate that PsyScore achieves competitive scoring performance while providing more pedagogically aligned feedback.

cs.CL↗
arXiv2026

Scaling Learning-based AEB with Massive Unlabeled Data

This paper studies how to scale learning-based automatic emergency braking (AEB) with massive unlabeled fleet data under production constraints. Our approach is based on meta-feedback semi-supervised learning (MF-SSL), where a teacher generates pseudo labels for unlabeled driving data and is updated using a small labeled anchor set as safety-critical feedback. In production, anchor ambiguity and labeled-unlabeled mismatch can amplify systematic pseudo-label errors, leading to spurious triggers. We propose a stabilized MF-SSL framework with (i) Noise-Aware Decoupling, which removes ambiguity-prone anchors from the teacher's supervised update path, and (ii) kinematics-gated pseudo-labeling with a teacher conflict penalty to suppress mismatch-induced risk hallucinations on unlabeled data while maintaining broad coverage. Extensive experiments show consistent gains as unlabeled data scale from 1M to 1B windows, improving safety while keeping comfort stable. The 1B-trained student model is deployed to hundreds of thousands of vehicles and validated over \$10^9$ km of driving, achieving a positive-to-false activation ratio exceeding 100:1 and a 35% improvement in accident-free driving mileage over a production rule-only baseline.

cs.LG↗
arXiv2026

TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation

Vision-Language-Action (VLA) models have become a powerful framework for robotic manipulation, and recent studies have introduced tactile or force feedback into VLAs to address contact-rich tasks. However, these models are typically deployed as offline policies. When contact conditions shift from the training distribution, the policy cannot perform online adaptation, leading to problems such as inappropriate contact forces and inefficient retries. Therefore, we propose TORL-VLA, a tactile-guided online reinforcement learning framework that couples tactile feedback with policy refinement for contact-rich manipulation. Our method introduces a tactile-derived wrench-aware VLA to predict reference actions and future wrench sequences, while a lightweight online RL module is used to refine the reference actions. To stabilize learning from mixed exploratory policy-generated and human-intervention data, we introduce an intervention-censored critic that prevents post-intervention success from being wrongly credited to policy-generated actions preceding intervention. Real-robot experiments on long-horizon contact-rich tasks, including latch manipulation, coffee-cup placement, and egg handling, show that TORL-VLA improves success rates at both subtask and full-task levels, as well as time-bounded execution efficiency over strong baselines. Project page: https://torl-vla.github.io/

cs.RO↗
arXiv2026

GRB 250424A: A Case Study of Energy Injection with Multiwavelength Observations

We present a comprehensive multiwavelength analysis of the long-duration gamma-ray burst (GRB) 250424A. Our dataset spans from the prompt gamma-ray emission to late-time optical monitoring, including spectra obtained with the Keck 10\,m telescope. We find that the afterglow light curves display a prominent, simultaneous shallow decay phase in both X-ray and optical bands, followed by an achromatic transition to a standard decay regime. The broadband spectral energy distributions are well-modeled by a single power-law function, indicating a common synchrotron origin for the emission across frequencies. We interpret the afterglow evolution within the framework of a relativistic forward shock refreshed by continuous energy injection. This scenario successfully reproduces the observed temporal and spectral behavior, yielding an isotropic equivalent kinetic energy of $E_{\rm K,iso} \approx 5.5 \times 10^{52}$ erg and an injection index of $q\approx 0.34$ in a constant-density circumburst environment. The shallow decay phase is consistent with sustained energy injection lasting $\sim$ 9 ks. Despite the relatively low redshift, late-time optical observations reveal no distinct supernova component; however, our derived upper limits do not strictly rule out the presence of a typical GRB-associated supernova.

astro-ph.HE↗
arXiv2026

Broadened Lensing Rings of Compact Boson Stars: Enhanced Imprint of Accretion Flow in Images and Visibilities

In this work, we systematically study the gravitational lensing properties and observational signatures of compact boson stars. Unlike black holes, the photon effective potential of a compact boson star develops a nearly flat region, whose width increases with the compactness of the star. This flat structure significantly broadens the range of impact parameters that can produce large-angle deflections, leading to noticeably wider lensing rings of all orders. Photons constituting these rings traverse more complex paths, rendering the resulting images more sensitive to the spatial distribution of the accretion flow. Ray tracing results show that, compared to black hole models, the image topology and visibility amplitudes of compact boson stars exhibit a stronger dependence on the accretion flow structure. These results highlight qualitative differences in the observational properties of compact boson stars and black holes.

astro-ph.HE↗
arXiv2026

Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression

The deployment of Large Language Models is constrained by the memory and bandwidth demands of static weights and dynamic Key-Value cache. SVD-based compression provides a hardware-friendly solution to reduce these costs. However, existing methods suffer from two key limitations: some are suboptimal in reconstruction error, while others are theoretically optimal but practically inefficient. In this paper, we propose Swift-SVD, an activation-aware, closed-form compression framework that simultaneously guarantees theoretical optimum, practical efficiency and numerical stability. Swift-SVD incrementally aggregates covariance of output activations given a batch of inputs and performs a single eigenvalue decomposition after aggregation, enabling training-free, fast, and optimal layer-wise low-rank approximation. We employ effective rank to analyze local layer-wise compressibility and design a dynamic rank allocation strategy that jointly accounts for local reconstruction loss and end-to-end layer importance. Extensive experiments across six LLMs and eight datasets demonstrate that Swift-SVD outperforms state-of-the-art baselines, achieving optimal compression accuracy while delivering 3-70X speedups in end-to-end compression time. Our code is available at https://github.com/hiahei/Swift-SVD.

cs.CL↗
arXiv2026

DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction

Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems. This task presents a modeling challenge: click signals are abundant and exhibit short temporal horizons, whereas conversion signals are inherently sparse, long-delayed, and frequently unattributed. Despite these statistical disparities, both signal types must inform models that operate within strict serving-latency constraints. Prior pre-training approaches address this heterogeneity with a single, undifferentiated encoder applied uniformly across both data streams. We propose DUET (Dual User Embedding Transformers), a framework that explicitly partitions user behavioral data into two domain-coherent streams -- clicks and conversions -- and pre-trains dedicated transformer encoders with architectures tailored to each stream's statistical characteristics: multi-layer self-attention for the dense click stream and interleaved cross- and self-attention for the sparse conversion stream. The resulting complementary embeddings are jointly consumed by a downstream ranker without exceeding serving-latency budgets. Evaluation demonstrates up to 0.38% normalized entropy (NE) reduction relative to the strongest baseline, and A/B test shows consistent improvements in OCVR prediction accuracy.

cs.LG↗
arXiv2026

The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning

Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlying structure. We show that this assumption introduces systematic bias: it obscures geometry-dependent performance variations and can lead to misleading conclusions about model generalization. In this work, we identify intrinsic geometry as a key latent factor governing model effectiveness. We demonstrate that conventional aggregated metrics mask critical performance trade-offs that only become visible when datasets are stratified by their geometric properties. To address this issue, we introduce a curvature-stratified evaluation framework that partitions datasets into positive, negative, and near-zero curvature regimes. Our benchmark evaluates 18 representative models including Graph Convolutional Networks (GCNs), Graph Foundation Models (GFMs), and tabular learning methods across 14 datasets. We find that model rankings are highly stable within each curvature regime but shift significantly across regimes, indicating that performance is fundamentally geometry-dependent rather than universally transferable. Notably, we identify regimes where GFMs offer diminishing returns compared to geometry-aligned GNNs. Based on these findings, we propose a geometry-aware evaluation protocol that yields more reliable and interpretable comparisons than standard aggregated benchmarks. We release all code, curvature-stratified dataset splits, and evaluation tools to support reproducible and rigorous assessment of future relational learning methods. Code and datasets are provided in our project homepage: https://sirbabbage.github.io/CurvBench_HOME/.

cs.LG↗
arXiv2026

Think Like a Pilot: Fine-Grained Long-Horizon UAV Navigation

Language-guided UAV agents must execute long-horizon semantic instructions while producing smooth, physically feasible continuous flight commands, yet existing Vision-Language Navigation (VLN) benchmarks typically use discrete or coarse actions and existing UAV Vision-Language-Action (VLA) tasks focus on short, atomic maneuvers. To address this gap in UAV task settings, we introduce \textbf{FLIGHT}, a \textbf{F}ine-grained \textbf{L}ong-horizon \textbf{I}nstruction-\textbf{G}uided benchmark for \textbf{H}ybrid UAV navigation and reasoning \textbf{T}asks, which combines multi-stage instructions with dense 6-DoF trajectory annotations across two dataset splits: Fine-grained VLN and Long-horizon Flow. To endow the UAV agent with the capability of real-time in-flight reasoning over task execution status and mission planning, while simultaneously accommodating high-frequency, real-time precise control, we further propose \textbf{FLIGHT VLA}, an asynchronous architecture that decouples a low-frequency Streaming Pilot Vision-Language Model (VLM) for task-state reasoning from a high-frequency diffusion action model for continuous control, supervised by explicit \textbf{Pilot Reasoning} texts that summarize the current flight state and anticipate the next subgoal. In closed-loop evaluation, FLIGHT VLA consistently surpasses representative VLN and VLA baselines on our FLIGHT benchmarks, achieving stronger multi-stage completion, subgoal adherence, and terminal control. Its trained Streaming Pilot Reasoning VLM further improves UAV video reasoning, validating the effectiveness of our design.

cs.RO↗
arXiv2026

Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems

Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditional approaches mitigate this via model-specific knowledge distillation from source domains to a target domain. Inspired by the transformative success of synthetic data generation in large language models (LLMs), we introduce Synthetic Cross-domain Augmentation and Learning for Recommendation (SCALR), a framework that generates synthetic user-item interaction events for a target recommendation domain by leveraging observed events from a source domain. SCALR decomposes cross-domain learning into two modular stages. First, it translates observed user events in source domains by framing event generation as estimating the likelihood that a user would interact with a target-domain item, conditioned on their observed interactions in a source domain. Second, downstream models train on these synthetic events as cross-domain learning objectives, where the synthetic events augment the target domain's training data in a model-agnostic manner. Our approach yields statistically significant improvements in online A/B tests on an industrial recommendation platform. To the best of our knowledge, this is among the first works to explicitly frame cross-domain event transfer as synthetic data generation for recommendation systems.

cs.IR↗
arXiv2026

SuiChat-CN: Benchmarking Contextual Suicide Risk Assessment in Chinese Group Chats

Suicide is a critical global public health challenge, causing approximately 720,000 deaths each year and calling for timely, effective prevention strategies. Existing computational studies primarily focus on post-based social media platforms such as Twitter and Weibo, leaving instant messaging environments such as Telegram underexplored. Yet group chats pose distinct challenges: messages are short, fragmented, multi-party, and often rely on implicit or culturally specific expressions, making isolated post-level analysis insufficient. We introduce SuiChat-CN, a Chinese group-chat benchmark for contextual suicide risk assessment. We collect public Telegram group-chat data, construct coherent conversational segments through signal-word extraction and bidirectional context expansion, and annotate user risk levels with an expert-validated, LLM-assisted paradigm. SuiChat-CN contains 13,312 contextual segments from 1,406 users, covering 258,228 raw chat messages. Extensive experiments with PLMs and more than 40 LLMs demonstrate that contextual information is essential for reliable risk assessment, while fine-tuning and partial-context evaluation further reveal the challenges of early detection in multi-party conversations. Due to ethical and sensitivity concerns, the dataset is not publicly released but will be shared with accredited mental health and suicide-prevention research institutions upon reasonable request.

cs.AI↗
arXiv2026

Bayesian Analysis of Massive Boson Star Models for Sagittarius A* Using Near-Infrared Astrometry Data

Assuming that the compact source at the Galactic center, Sagittarius A*, is a massive boson star, we fit the near-infrared flare astrometry data. We consider 12 discrete boson star configurations and model the flare as a hotspot on a circular equatorial orbit. The analysis is performed in a Bayesian framework using nested sampling, yielding the marginal posterior distributions of all parameters as well as the Bayesian evidence for each model. For comparison, the same procedure is applied to a Schwarzschild black hole. The resulting Bayesian evidence values differ only marginally between the boson star and black hole cases, and the well-determined mass of Sgr~A* (${\sim}4.296\times 10^6\,M_\odot$) falls within the 68\% highest density interval in every configuration. We conclude that, under current near-infrared astrometric constraints and within the considered parameter ranges, a massive boson star and a Schwarzschild black hole remain statistically indistinguishable as the compact object at the Galactic center.

astro-ph.HE↗
arXiv2026

Overview of Ground-based Wide-Angle Cameras array

As one of the key ground-based facilities of the Chinese-French SVOM mission, the main scientific objectives of the Ground-based Wide Angle Camera array (GWAC) are to detect prompt optical emission of gamma-ray bursts or other short duration astronomical transients on a second-scale temporal resolution. GWAC is located at Xinglong observatory, China, and consists of 10 mounts and 40 cameras, providing a joint field of view of about 3600 square degrees.The detection ability is 16 magnitude in 10 seconds of exposure time in the visual band under the condition of the new moon phase. Here, we give an overview of GWAC and introduce the science motivation of the project, as well as the performance of the hardware and the software. The observation strategies and the data processing are briefly presented. The early sciences in the last 5 years since the first light are summarized.

astro-ph.IM↗
arXiv2026

Testing solitonic boson star interpretations of Sagittarius A* with near-infrared flare astrometry

We use GRAVITY near-infrared (NIR) flare astrometry to test whether Sagittarius A* could be a solitonic boson star. We consider five spherically symmetric solitonic boson-star models with different effective radii, together with the Schwarzschild black hole. Treating the flares as hot spots on circular equatorial orbits, we analyze their centroid motions and images in these spacetimes and use them for parameter fitting. We perform the fitting using both $χ^2$ analysis and Markov Chain Monte Carlo (MCMC) methods, which yield consistent results: the inferred masses of boson-star models are systematically larger than the established value of $4.3\times10^6M_\odot$. Notably, more diffusive boson stars exhibit imaging properties closer to those of a black hole, leading to mass estimates that are correspondingly closer to the established value. Overall, our results place stringent constraints on solitonic boson star interpretations of Sagittarius A*, although do not completely rule them out.

astro-ph.HE↗