arXiv subjects

Jiashuo Zhang

Publications and source records attributed to Jiashuo Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Generating the wide sequence of Diffuse Galaxies with de Broglie waves of Dark Matter

Extensive Euclid satellite imaging at low surface brightness has revealed that most nearby galaxies are diffuse-looking spheroids, where the stellar radius increases monotonically over three decades in luminosity. We argue this Diffuse Galaxy sequence results from internal stellar diffusion by Wave Dark Matter ($\psi$DM), as wave energy is transferred to star orbits over time. In particular, the soliton random motion scatters central stars onto radial orbits that become enhanced with each passage through the centre, slowly "puffing up" the stellar profile. Heating is greater within massive galaxies as $\psi$DM fluctuations are stronger and more frequent, reproducing the Diffuse Galaxy sequence and also accounts for the rising velocity dispersion along the sequence, from Ultra-Faint to Dwarf Spheroidal and Ultra Diffuse galaxies, favouring a light boson, $m_\psi=2.88^{+0.14}_{-0.13}\times10^{-22}$eV. Winding back this diffusion, we predict the stellar content of Diffuse Galaxies, including globular clusters, formed near the centre, as anticipated by $\psi$DM simulations, where gas cools efficiently within the dense soliton. This predicted $\psi$DM evolution from compact beginnings towards diffuse-looking spheroidal galaxies today can now be fully charted from JWST to Euclid.

astro-ph.GA↗

arXiv2026

QoS-Aware RACH Preamble Slicing via Quota-Projected Branching Deep Reinforcement Learning

Quality-of-service (QoS)-aware random access requires adaptive allocation of a finite random access channel (RACH) preamble budget across heterogeneous traffic and access procedures. This paper proposes QP-BD3QN-RACH, a quota-projected branching deep reinforcement learning controller for mixed two-step (2RA) and four-step (4RA) contention-based random access. Four action branches correspond to the delay-sensitive and delay-tolerant 2RA/4RA preamble pools. A branching dueling Double DQN selects pool-specific multipliers, and deterministic quota projection converts them to nonnegative integer allocations that preserve the preamble budget. With five actions per branch, the controller represents 625 pre-projection branch-action tuples using 20 branch-action outputs. Evaluation covers five arrival loads, cross-method comparison under nominal seed 42, six-seed sensitivity of QP-BD3QN-RACH, and targeted ablations. Across the five-load grid, its mean direction-aligned differences relative to four comparators are positive: 5.74 to 8.21 percentage points for success/collision, 1.23 to 1.92 percentage points for fallback, 0.35 to 0.68 percentage points for blocking, and 0.128 to 0.456 decision intervals for successful-access delay. Load-wise results exhibit metric-dependent tradeoffs, particularly under intermediate and overload conditions.

cs.NI↗

arXiv2026

Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards

Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while systematic research on reinforcement learning (RL) post-training and automated evaluation of feedback quality remains limited. We propose RLAES, a unified LLM framework that jointly optimizes essay scoring and feedback generation through RL. To make feedback quality measurable, interpretable, and usable for training, we introduce Rubric-based Feedback Evaluation (RFE), an essay-grounded feedback evaluation framework comprising 166 fine-grained binary rubric items and an LLM-as-judge. Building on RFE, we propose Adaptive Gated Feedback Optimization (AGFO), which activates rubric-based feedback rewards on demand during RL, reducing evaluation overhead while improving feedback quality. We also propose Adjacent Contrastive Reasoning (ACR) to improve ordinal score calibration by explicitly contrasting adjacent score levels. Experimental results show that the RFE framework captures essay-feedback consistency, exhibits strong pairwise discriminative power, and closely aligns with expert preferences. On the ASAP benchmark, RLAES-AGFO achieves the best scoring performance among LLM-based methods (QWK = 0.803), while maintaining feedback quality comparable to GPT-5.5 and avoiding the feedback degradation observed under score-only RL. Code and datasets are publicly available at https://github.com/hellomuyi/RLAES.

cs.CL↗

arXiv2026

Reliable Tests of Faint-end UV Luminosity Functions in Strong Lensing Fields

Dark matter comprises ~85% of the entire mass of the Universe, but the fundamental nature of its constituent particles remains elusive. In this thesis, I test for two competitive dark matter models: the conventional heavy particle paradigm, and dark matter being ultralight bosons of mass $\sim 10^{-22}$eV ($\psi$DM). More specifically, I test for the faint-end turnover induced by $\psi$DM models, exploiting the strong lensing power by massive galaxy clusters to probe intrinsically fainter magnitudes. A key challenge for such an analysis would be contamination by low-z galaxies sharing similar observed SEDs as high-z galaxies. As I will demonstrate, such a contamination issue is generally severe and may wash out the faint-end turnover signatures. I also show that $\sim 50\%$ of the purported $3.5\leq z\leq 5.5$ galaxies within existing photometric redshift catalogs constructed for Hubble Frontier Fields (HFF) are in fact low-z interlopers. Luckily, individual mitigation of interlopers can be achieved with the combination of deep HST and JWST observations. For fields without supplementary data, machine learning methods will be shown useful in preserving the mitigating power. Cleaner $3.5\leq z\leq 5.5$ and $6\leq z\leq 10$ samples are derived for a more reliable test in strong lensing field of MACS J0416, with which I found no evidence for faint-end turnovers, leading to a constraint on the $\psi$DM mass of $>2.97\times10^{-22}$eV at 95\% confidence. This constraint will also be interpreted in an scheme where dark matter is composed of multiple particle copies, where I argue the derived mass bound is likely on an effective de Broglie scale governing the collective behavior of the entire $\psi$DM budget under gravitational equilibrium established.

astro-ph.CO↗

arXiv2025

Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography

Low-dose chest computed tomography (LDCT) captures pulmonary and cardiac structures in a single scan, enabling joint assessment of lung and cardiovascular health. Existing approaches typically model these domains independently and do not explicitly represent their physiological interactions. We propose an Explainable Cross-Disease Reasoning Framework for cardiovascular risk assessment from LDCT. The framework follows a constrained clinical-information pathway: it extracts pulmonary findings, grounds cross-organ mechanisms in medical knowledge, and produces a cardiovascular prediction with a natural-language rationale. It combines four components: a frozen lung-risk prior, a pulmonary perception module, an agentic reasoning module, and a cardiac subvolume feature extractor. Their outputs are fused to integrate localized cardiac evidence with mechanism-level pulmonary context. On the National Lung Screening Trial cohort, the framework achieves an AUC of 0.919 for CVD screening and up to 0.838 for CVD mortality prediction, outperforming cardiac-specific, single-disease, and foundation-model baselines. Targeted controls indicate that the gains are not explained by additional thoracic visual features alone, fixed rule propagation, or a single reasoning backend. The proposed framework thus provides an auditable approach to cross-disease cardiovascular risk assessment from LDCT.

cs.CV↗

arXiv2025

Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context

Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare. However, the reported strong average-case performance of these models do not necessarily reflect their actual utility when used in heterogeneous clinical settings, potentially masking weaker performance in medically significant scenarios. In this work we use clinical context to provide a more holistic evaluation of models for CXR diagnosis. In particular, we use discharge summaries, recorded prior to each CXR, to derive a ``pre-CXR'' probability of each CXR label, as a proxy for existing contextual knowledge available to clinicians when interpreting CXRs. We use this measure to probe model performance along two dimensions: First, using a stratified analysis, we show that models tend to have lower performance (as measured by AUROC and other metrics) among individuals with higher pre-CXR probability. Second, by controlling for pre-CXR probability via matching and re-weighting, we demonstrate that performance degrades when the correlation is broken between prior context and the current CXR label, suggesting that model performance is highly sensitive to the underlying distribution of clinical context. Specifically, cases with high pre-test probabilities present a fundamentally more difficult visual classification task, highlighting a gap in clinical utility when models are applied to high-risk cohorts.

cs.LG↗

arXiv2025

Lensed stars in galaxy-galaxy strong lensing -- a JWST prediction for the Cosmic Horseshoe

We explore for the first time the possibility of detecting lensed star transients in galaxy-galaxy strong lensing systems upon repeated, deep imaging using the {\it James-Webb Space Telescope} ({\it JWST}). Our calculation predicts that the extremely high recent star formation rate of $\sim 140\,M_{\odot}\textrm{yr}^{-1}$ over the last 50 Myr (not accounting for image multiplicity) in the ``Cosmic Horseshoe'' lensed system ($z = 2.381$) generates many young, bright stars, of which their large abundance is expected to lead to a detection rate of $\sim 60$ transients per pointing in {\it JWST} observations with a $5\sigma$ limiting magnitude of $\sim 29\,m_{AB}$. With the high expected detection rate and little room for uncertainty for the lens model compared with cluster lenses, our result suggests that the Cosmic Horseshoe could be an excellent tool to test the nature of dark matter based on the spatial distribution of transients, and can be used to constrain axion mass if dark matter is constituted of ultra-light axions. We also argue that the large distance modulus of $\sim46.5\,$mag at $z \approx 2.4$ can act as a filter to screen out less massive stars as transients and allow one to better constrain the high-mass end of the stellar initial mass function based on the transient detection rate. Follow-up {\it JWST} observations of the Cosmic Horseshoe would allow one to better probe the nature of dark matter and the star formation properties, such as the initial mass function at the cosmic noon, via lensed star transients.

astro-ph.CO↗

arXiv2025

When Large Language Models Meet UAV Projects: An Empirical Study from Developers' Perspective

In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in our daily lives and have attracted significant research interest in software engineering. At the same time, large language models (LLMs) have made notable advancements in language understanding, reasoning, and generation, making LLM applications in UAVs a promising research direction. However, existing studies have largely remained in preliminary exploration with a limited understanding of real-world practice, which causes an academia-industry gap and hinders the application of LLMs in UAVs. To address this, we conducted the first empirical study to investigate how LLMs support UAVs. To characterize common tasks and application scenarios of real-world UAV-LLM practices, we conducted a large-scale empirical study involving 997 research papers and 1,509 GitHub projects. The results classified nine common tasks (e.g., Natural Language Command Parsing) in four UAV workflows (e.g., Information Input) undertaken by LLMs in real-world UAV projects and revealed a large difference in the task distribution of research efforts and industry practices. To gain deeper insight into these differences and understand developers' perspectives on the application of LLMs in UAVs, we conducted a survey of practitioners, receiving 52 valid responses from 15 countries. The results revealed that while 40.4% of developers have attempted to apply LLMs to UAV tasks, 59.6% still face challenges integrating their UAV projects with advanced LLM capabilities. Their feedback attributes these challenges to five factors, including technological maturity, performance, safety, cost, and others, and provides practical implications for researchers and developers in conducting UAV-LLM practices.

cs.SE↗

arXiv2025

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges

The convergence of Web3 technologies and AI agents represents a rapidly evolving frontier poised to reshape decentralized ecosystems. This paper presents the first and most comprehensive analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms. Through an analysis of 133 existing projects, we first develop a taxonomy and systematically map the current market landscape (RQ1), identifying distinct patterns in project distribution and capitalization. Building upon these findings, we further investigate four key integrations: (1) the role of AI agents in participating in and optimizing decentralized finance (RQ2); (2) their contribution to enhancing Web3 governance mechanisms (RQ3); (3) their capacity to strengthen Web3 security via intelligent vulnerability detection and automated smart contract auditing (RQ4); and (4) the establishment of robust reliability frameworks for AI agent operations leveraging Web3's inherent trust infrastructure (RQ5). By synthesizing these dimensions, we identify key integration patterns, highlight foundational challenges related to scalability, security, and ethics, and outline critical considerations for future research toward building robust, intelligent, and trustworthy decentralized systems with effective AI agent interactions.

cs.CY↗

arXiv2025

KLAN: Kuaishou Landing-page Adaptive Navigator

Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the user and the platform: (1) Stage I: page navigation, navigating users to a specific page and (2) Stage II: in-page interaction, where users engage with customized content within the specific page. While the majority of research has been focusing on the sequential recommendation task that improves users' feedback in Stage II, there has been little investigation on how to achieve better page navigation in Stage I. To fill this gap, we formally define the task of Personalized Landing Page Modeling (PLPM) into the field of recommender systems: Given a user upon app entry, the goal of PLPM is to proactively select the most suitable landing page from a set of candidates (e.g., functional tabs, content channels, or aggregation pages) to optimize the short-term PDR metric and the long-term user engagement and satisfaction metrics, while adhering to industrial constraints. Additionally, we propose KLAN (Kuaishou Landing-page Adaptive Navigator), a hierarchical solution framework designed to provide personalized landing pages under the formulation of PLPM. KLAN comprises three key components: (1) KLAN-ISP captures inter-day static page preference; (2) KLAN-IIT captures intra-day dynamic interest transitions and (3) KLAN-AM adaptively integrates both components for optimal navigation decisions. Extensive online experiments conducted on the Kuaishou platform demonstrate the effectiveness of KLAN, obtaining +0.205% and +0.192% improvements on in Daily Active Users (DAU) and user Lifetime (LT). Our KLAN is ultimately deployed on the online platform at full traffic, serving hundreds of millions of users. To promote further research in this important area, we will release our dataset and code upon paper acceptance.

cs.IR↗

arXiv2025

Magnification bias reveals severe contamination in Hubble Frontier Field photo-z catalogs

Gravitational lensing by massive galaxy clusters enables faint distant galaxies to be more abundantly detected than in blank fields, thereby allowing one to construct galaxy luminosity functions (LFs) to an unprecedented depth at high redshifts. Intriguingly, photometric redshift catalogs (e.g. Shipley et al. (2018)) constructed from the Hubble Frontier Fields survey display an excess of z$\gtrsim$4 galaxies in the cluster lensing fields and are not seen in accompanying blank parallel fields. The observed excess, while maybe a gift of gravitational lensing, could also be from misidentified low-z contaminants having similar spectral energy distributions as high-z galaxies. In the latter case, the contaminants may result in nonphysical turn-ups in UV LFs and/or wash out faint end turnovers predicted by contender cosmological models to $\Lambda$CDM. Here, we employ the concept of magnification bias to perform the first statistical estimation of contamination levels in HFF lensing field photometric redshift catalogs. To our great worry, while we were able to reproduce a lower-z lensed sample, it was found $\sim56\%$ of $3.5 < z_{phot} < 5.5$ samples are likely low-z contaminants! Widely adopted Lyman Break Galaxy-like selection rules in literature may give a 'cleaner' sample magnification bias-wise but we warn readers the resulting sample would also be less complete. Individual mitigation of the contaminants is arguably the best way for the investigation of faint high-z Universe, and this may be made possible with JWST observations.

astro-ph.CO↗

arXiv2025

Generalized Tree Edit Distance (GTED): A Faithful Evaluation Metric for Statement Autoformalization

Statement autoformalization, the automated translation of statements from natural language into formal languages, has become a subject of extensive research, yet the development of robust automated evaluation metrics remains limited. Existing evaluation methods often lack semantic understanding, face challenges with high computational costs, and are constrained by the current progress of automated theorem proving. To address these issues, we propose GTED (Generalized Tree Edit Distance), a novel evaluation framework that first standardizes formal statements and converts them into operator trees, then determines the semantic similarity using the eponymous GTED metric. Across the miniF2F and ProofNet benchmarks, GTED consistently ranks as a top-performing metric, achieving the highest accuracy and Kappa on miniF2F and the joint-highest accuracy on ProofNet. This strong overall performance provides the community with a computationally lightweight and more faithful metric for automated evaluation. The code and experimental results are available at https://github.com/XiaoyangLiu-sjtu/GTED.

cs.LG↗

arXiv2025

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction

High-quality smart contract vulnerability datasets are critical for evaluating security tools and advancing smart contract security research. Two major limitations of current manual dataset construction are (1) labor-intensive and error-prone annotation processes limiting the scale, quality, and evolution of the dataset, and (2) absence of standardized classification rules results in inconsistent vulnerability categories and labeling results across different datasets. To address these limitations, we present FORGE, the first automated approach for constructing smart contract vulnerability datasets. FORGE leverages an LLM-driven pipeline to extract high-quality vulnerabilities from real-world audit reports and classify them according to the CWE, the most widely recognized classification in software security. FORGE employs a divide-and-conquer strategy to extract structured and self-contained vulnerability information from these reports. Additionally, it uses a tree-of-thoughts technique to classify the vulnerability information into the hierarchical CWE classification. To evaluate FORGE's effectiveness, we run FORGE on 6,454 real-world audit reports and generate a dataset comprising 81,390 solidity files and 27,497 vulnerability findings across 296 CWE categories. Manual assessment of the dataset demonstrates high extraction precision and classification consistency with human experts (precision of 95.6% and inter-rater agreement k-$\alpha$ of 0.87). We further validate the practicality of our dataset by benchmarking 13 existing security tools on our dataset. The results reveal the significant limitations in current detection capabilities. Furthermore, by analyzing the severity-frequency distribution patterns through a unified CWE perspective in our dataset, we highlight inconsistency between current smart contract research focus and priorities identified from real-world vulnerabilities...

cs.CR↗

arXiv2025

Constraining the z $\sim$ 1 Initial Mass Function with {\it HST} and {\it JWST} Lensed Stars in MACS J0416.1-2403

Our understanding of galaxy properties and evolution is contingent on knowing the initial mass function (IMF), and yet to date, the IMF is constrained only to local galaxies. Individual stars are now becoming routinely detected at cosmological distances, where luminous stars such as supergiants in background galaxies strongly lensed by galaxy clusters are temporarily further magnified by huge factors (up to $10^{4}$) by intracluster stars, thus being detected as transients. The detection rate of these events depends on the abundance of luminous stars in the background galaxy and is thus sensitive to the IMF and the star-formation history (SFH), especially for the blue supergiants detected as transients in the rest-frame ultraviolet/optical filters. As a proof of concept, we use simple SFH and IMF models constrained by spectral energy distributions (SEDs) to see how well we can predict the {\it HST} and {\it JWST} transient detection rate in a lensed arc dubbed ``Spock'' ($z = 1.0054$). We find that demanding a simultaneous fit of the SED and the transient detection rate places constraints on the IMF, independent of the assumed simple SFH model. We conclude our likelihood analysis indicates that the data definitively prefers the ``Spock'' galaxy to have a Salpeter IMF ($\alpha = 2.35$) rather than a Top-heavy IMF ($\alpha = 1$) -- which is thought to be the case in the early universe -- with no clear excess of supergiants above the standard IMF.

astro-ph.CO↗

arXiv2025

Multi-copy Axion Transfer Function and Observational Implications of Effective de Broglie Scales

Ultra-light axions are viable fuzzy/wave-like dark matter ($\psi $DM) candidates generically predicted by the String Axiverse paradigm with multiple particle copies, whereas most of the discussions/constraints on $\psi $DM from astronomical observations to date are based on the assumption of a single particle copy. Here, we aim to complete this gap by exploring the generic multi-axion scenario motivated in the String Axiverse context, and investigate its astronomical implications in both the linear and nonlinear regimes. In the linear regime, with linear density perturbation analysis, we provide a simplified prescription for obtaining multi-copy axion transfer functions and also identify an "equivalence" among all axion copies owing to the mutual coupling to the gravitational potential. As a result of this 'equivalence', we argue the suppression to LSS is governed by an effective mass $m_{eff}^{-2}=\sum_i w_i m_i^{-2}$, with $\{ w_i\}$ being fractional contributions of different copies to the full cosmic dark matter density. In non-linear regime within galaxy halos, we show that similar notions of effective mass, with expressions provided, to govern the collective wave interference and hence determine the net stellar heating rates and the substructure-induced spread of JWST transients near critical curves. Distinctive to the multi-copy scenario, the effective mass within galaxy halos is generically anticipated to be radially decreasing following the stronger concentration of heavier copies to the galactic center. Such a spatial variation leads to radially increasing spreading scales for micro-lensed transients at different radial positions, a signature that may be tested with future JWST lensing observations.

astro-ph.CO↗

arXiv2025

ATLAS: Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of Data

Autoformalization, the automatic translation of mathematical content from natural language into machine-verifiable formal languages, has seen significant progress driven by advances in large language models (LLMs). Nonetheless, a primary barrier to further improvements is the limited availability of parallel corpora that map informal mathematical text to its formal counterpart. To address this limitation, we propose ATLAS (Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of Data), a novel data generation framework designed to produce large-scale, high-quality parallel corpora of theorem statements. Distinct from prior approaches, ATLAS begins with a concept repository, accelerates the improvement of the student model through expert iteration combined with knowledge distillation, and introduces two novel augmentation strategies that exploit the structural characteristics of formal languages. Running the proposed ATLAS framework for 10 iterations, we construct an undergraduate-level dataset of 117k theorem statements and develop the ATLAS Translator by fine-tuning Llama3.1-8B-Instruct with LoRA. This model establishes a new state of the art, demonstrating statistically significant improvements over both the Herald Translator and the Kimina-Autoformalizer across all benchmarks (p<0.05, two-sided t-test). Furthermore, we demonstrate that the full-parameter fine-tuning of a stronger base model on the ATLAS dataset leads to superior performance. The datasets, model, and code are available at https://github.com/XiaoyangLiu-sjtu/ATLAS.

cs.CL↗

arXiv2025

Explaining JWST counts with galaxy formation models

A distinct power-law break is apparent m_AB approximately 21 in the deep Near-Infrared PEARLS-JWST galaxy counts. The break becomes more pronounced at longer wavelengths, with the counts slope flattening smoothly with apparent magnitude in the shortest band used at 0.9 microns, trending towards an increasingly broken slope by the longest wavelength passband of JWST NIRCam, 4.4 microns. This behaviour is remarkably well predicted by the GALFORM semi-analytical model of galaxy formation. We use the model to diagnose the origin of this behaviour. We find that the features that are responsible for the break are: 1) the inherent break in the luminosity function; 2) the change in the volume element with redshift and 3) the redshift-dependent nature of the k-correction. We study the contribution to these effects by early and late-type galaxies, using as a proxy for morphology the bulge-to-total stellar mass ratio. We find that the way in which ellipticals populate the bright end of the luminosity function while spirals dominate the faint end is preserved in the galaxy number counts, with a characteristic stellar mass at the break of approximately 10^10 M_sun. We also find that the shape of the number counts is mainly driven by galaxies with relatively low redshift (z < 2) for the PEARLS observational limit of m_AB < 28. We give a comprehensive description of why the galaxy number counts in the near-infrared PEARLS-JWST observation look the way they do and which population of galaxies is dominant at each apparent magnitude.

astro-ph.GA↗

arXiv2024

Discovery of Two New Eruptions of the Ultrashort Recurrence Time Nova M31N 2017-01e

We report the recent discovery of two new eruptions of the recurrent nova M31N 2017-01e in the Andromeda galaxy. The latest eruption, M31N 2024-08c, reached $R=17.8$ on 2024 August 06.85 UT, $\sim2$ months earlier than predicted. In addition to this recent eruption, a search of archival PTF data has revealed a previously unreported eruption on 2014 June 18.46 UT that reached a peak brightness of $R\sim17.9$ approximately a day later. The addition of these two eruption timings has allowed us to update the mean recurrence time of the nova. We find $\langle T_\mathrm{rec} \rangle = 924.0\pm7.0$ days ($2.53\pm0.02$ yr), which is slightly shorter than our previous determination. Thus, M31N 2017-01e remains the nova with the second shortest recurrence time known, with only M31N 2008-12a being shorter. We also present a low-resolution spectrum of the likely quiescent counterpart of the nova, a $\sim20.5$ mag evolved B star displaying an $\sim14.3$ d photometric modulation.

astro-ph.SR↗