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Meng Zhang

Publications and source records attributed to Meng Zhang.

At least 19 recordsLinked to original sources
arXiv2026

Identifying Chemically Coherent Stellar Families in the Galactic High-α Disk with Low-Resolution Spectroscopy

Identification of chemical substructures in the Galactic high-α disk provides valuable insight into its formation history. In this work, we demonstrate the capability of low-resolution spectroscopy for this purpose by applying DBSCAN clustering to 87,275 red giants in the high-α disk using 18 elemental abundances derived from LAMOST low-resolution spectra (R~1800). We assess the effectiveness of this approach by comparing the clustering results from the low-resolution data with those obtained by applying the same procedure to high-resolution survey abundances and to mock catalogs. These comparisons suggest that, despite the relatively large uncertainties in individual elemental abundances derived from low-resolution spectra, the astrophysical coherence among different elements provides sufficient information to identify chemically coherent families that may share similar enrichment histories without necessarily sharing a common birth environment. After scanning the parameter space of the DBSCAN procedure and merging the results from different parameter combinations, we identify 133 candidate chemical families containing 4,642 unique stars (5.3% of the high-α disk sample). These families exhibit diverse multi-element abundance patterns, with particularly prominent differences in neutron-capture elements at nearly identical [Fe/H] values. These families therefore offer new constraints on the diversity of chemical enrichment pathways and on the assembly history of the high-α disk.

astro-ph.GA↗
arXiv2026

The Milky Way's Early Disk Was Built in Bursts. II. The Multi-component Age-Metallicity Structure of the High-$α$ Disk

Whether the high-$α$ disk experienced a continuous or episodic star-formation history remains an open question. Here we investigate the age--metallicity distribution of the high-$α$ disk using approximately 145,000 Gaia DR3 subgiants with $[\mathrm{Fe/H}]>-1$, 6.5~kpc $\lesssim R\lesssim9.5$~kpc, and $|Z|\lesssim1.5$~kpc. We adopt a forward-modelling framework that accounts for individual age and metallicity uncertainties and corrects for sample selection effects. The reconstructed distribution reveals two components: a broad, dominant population peaking at $\sim10.6$ Gyr and $[\mathrm{Fe/H}]\simeq-0.38$, and an older, more metal-poor population peaking at $\sim12.4$ Gyr and $[\mathrm{Fe/H}]\simeq-0.79$. These features suggest an episodic history of star formation and chemical enrichment in the high-$α$ disk, qualitatively consistent with cosmological simulations in which recurrent gas accretion drives variations in the star formation rate. The age of the older component is close to the $\sim12.3$~Gyr epoch inferred for the proposed Low-energy--Kraken--Heracles accretion event, suggesting a possible connection between this early interaction and enhanced disk star formation. There is tentative evidence that the dominant population is possibly composed of two sub-components with age distributions peaking at $\sim11.1$ Gyr and $\sim10.0$ Gyr, respectively. Together, these results favour a picture in which the high-$α$ disk was built through recurrent episodes of enhanced star formation rather than a single temporally smooth phase, even for the regime of ${\rm [Fe/H]}>-1$.

astro-ph.GA↗
arXiv2026

Star formation history of Draco revealed by DESI DR1: suppressed early star formation efficiency for Milky Way dwarf galaxies

Dwarf galaxies serve as fossils for understanding the early star formation history of the local Universe. In this work, we use stellar abundances for individual stars derived from the first data release of the Dark Energy Spectroscopic Instrument (DESI DR1) to investigate the star formation history of the dwarf spheroidal galaxy Draco. We simultaneously fit the stellar metallicity distribution function (MDF) and the [Mg/Fe]-[Fe/H] trend using the galactic chemical evolution (GCE) model flexCE. Our best-fit models imply a low star formation efficiency (SFE) of $0.003$ to $0.007\,\mathrm{Gyr}^{-1}$, which is about ten times lower than values reported in the literature, but consistent with recent SFE estimates for ultra-faint dwarf galaxies. By combining recent literature measurements, we find that the SFEs of Milky Way dwarf galaxies scale with stellar mass as $\mathrm{SFE}\propto M_\star^{0.24}$. A low star formation efficiency is associated with a strong outflow efficiency; we estimate a large wind mass-loading factor $η_{\mathrm{wind}}$ of approximately 172-260. This is well in line with the observed outflow-stellar mass relation and with the Feedback in Realistic Environment (FIRE) cosmological simulations. Our results demonstrate the power of large-scale, low-to-medium resolution stellar spectroscopic surveys to deliver robust constraints on the early formation history of galaxies in the local Universe.

astro-ph.GA↗
arXiv2026

The Milky Way's Early Disk Was Built in Bursts. I. Evidence from Low-angular-momentum, High-$α$ Stars Scattered Out of the Protodisk by Merger Events

Stars born in the high-$α$ disk can later acquire low angular momentum through dynamical scattering during early merger events. Their age--metallicity distribution thus provides a fossil record of the history of both star formation and dynamical perturbations in the early Milky Way disk. Here we identify a sample of 2,078 such stars from more than two million Gaia DR3 subgiants with robust determinations of ages, kinematics, and abundances. From this sample we reconstruct their intrinsic age--metallicity distribution with a forward modeling approach. Our results confirm that these scattered stars are older and more metal-poor than the bulk of the high-$α$ disk on average. In particular, the joint age--metallicity distribution reveals a robust two-component feature: a broad, dominant component with a peak age of $\sim$11.5~Gyr and ${\rm [Fe/H]}\simeq-0.59$, and an older and more metal-poor component with a peak age of $\sim$12.5~Gyr and ${\rm [Fe/H]}\simeq-0.86$. The presence of this two-component structure suggests that the high-$α$ disk experienced episodic phases of star formation and chemical enrichment. The age distribution of the younger Splash component, compared with that of the high-$α$ disk, provides evidence for a substantial dynamical perturbation at a lookback time of 10.8--11.5~Gyr, possibly associated with an early passage of Gaia--Sausage--Enceladus (GSE). The older Splash component, with a peak age of $\sim$12.5~Gyr, may preserve signatures of an earlier interaction, possibly associated with the proposed Low-energy--Kraken--Heracles (LKH) event.

astro-ph.GA↗
arXiv2026

ZStar: A unified toolkit for polarization, Born effective charges, dielectric and piezoelectric responses, and infrared and Raman spectra

Born effective charge (BEC) tensors link atomic displacements to polarization, thereby connecting lattice vibrations with dielectric response and infrared activity. We present ZStar, an open-source Python toolkit that automates calculations of polarization, BEC, phonons, static and frequency-dependent dielectric response, and infrared and Raman spectra. ZStar supports bulk crystals, two-dimensional materials, one-dimensional structures, and molecules through an integrated workflow for input preparation, execution, and response analysis. Berry-phase polarization and real-space dipoles describe responses along periodic and open directions, respectively. Its unified, symmetry-adapted finite-displacement framework jointly determines BEC and zone-center force constants from the same calculations, improving computational efficiency while preserving numerical accuracy. Strain-response calculations further provide proper piezoelectric coefficients and their internal-relaxation contributions for bulk crystals. In the ABACUS+PYATB route, electronic Hamiltonian data are reused to obtain Raman response without additional self-consistent calculations. Native response implementations from VASP and CP2K are also integrated. Matched infrared/Raman benchmarks against the implemented separate workflows achieve speedups of up to 8.4. An accompanying agent skill provides an agent-friendly interface for workflow integration. ZStar therefore offers an efficient and automated route for exploring polarization, BEC, dielectric and piezoelectric responses, and vibrational spectra across different material dimensionalities.

cond-mat.mtrl-sci↗
arXiv2026

Causal adaptive intensity estimation for non-stationary point processes: a Kalman-kernel duality framework

Estimating the instantaneous conditional intensity of a non-stationary point process from a single realization of discrete event timestamps is a fundamental problem in computational neuroscience, quantitative finance, and network monitoring. Traditional adaptive kernel methods achieve variable bandwidths through Abramson's square-root law, yet existing implementations require non-causal access to the complete event record and incur $\mathcal{O}(N^2)$ or at best $\mathcal{O}(N \log N)$ computational cost. Causal alternatives based on reciprocal inter-arrival times, meanwhile, suffer from systematic divergence under Poisson and bursty arrival dynamics due to Jensen's inequality. This paper addresses both limitations by establishing a formal duality between steady-state Kalman filtering and adaptive exponential kernel smoothing. An endogenous state-space observer is constructed in which Poisson counting statistics dictate the measurement noise ($R \propto λ$) and geometric diffusion governs the process volatility ($Q \propto λ^2$). Under these physically motivated axioms, the algebraic Riccati equation autonomously yields an effective smoothing bandwidth scaling as $τ\propto λ^{-1/2}$, giving a causal, recursive, $\mathcal{O}(1)$ per-step realization of square-root adaptive bandwidth scaling. A second-order constant-velocity extension produces a $λ^{-1/4}$ scaling that reduces approximation bias, and a probabilistic dormant-state gating mechanism suppresses phantom intensity leakage during structural zeros. Synthetic benchmarks together with neural and financial data show that the observer adapts across orders of magnitude in intensity with an empirical per-step cost of approximately 30\,$μ$s.

stat.ME↗
arXiv2026

LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts

Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, suboptimal behavior, and failures: imitating every recorded action can reinforce poor behavior, while filtering out failures discards evidence of physical interactions and their consequences. We introduce LoopWAM, a closed-loop world-action model that learns directly from this full quality spectrum, using rollouts from both itself and other deployed policies. Source-dependent noise sampling unifies behavior learning and action-conditioned world modeling, while quality steering and quality-weighted supervision retain full video supervision across qualities and regulate action imitation. Video and action tokens interact throughout parallel denoising, allowing learned consequences to inform action generation directly. On RoboChallenge Table30-V2, three deployment-and-training rounds raise success from 23.67% to 43.33%, a 19.7-percentage-point gain, with a final score of 56.80. LoopWAM also achieves 94.5% average success on RoboTwin 2.0 and 49.5% overall success on RoboCasa365 without additional embodied pretraining, obtaining the best overall results among the compared methods across all three benchmarks. On challenging shirt folding task, two rounds of deployment-driven improvement after single-task post-training raise success from 13.20% to 55.56%. These results demonstrate closed-loop improvement from mixed-quality experience under both multi-task learning and single-task post-training. Project page: https://loop-wam.github.io/

cs.RO↗
arXiv2026

On IDA-PBC with Maximum Energy Shapeability

Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) is a well-established stabilization technique for affine nonlinear systems. However, its application is generally hindered by the requirement of solving a set of partial differential equations (PDEs), i.e., the so-called matching equation. This paper explores the notion of maximum energy shapeability which describes the scenario that the homogeneous part of the matching equation admits $m$ independent solutions with $m$ the dimension of the control input. We show that the maximum energy shapeability enables a systematic procedure for the IDA-PBC design by transforming the matching equation into a set of new PDEs. Specifically, the transformed matching equation admits an explicit solution in the single input case. Sufficient conditions for maximum energy shapeability are also provided. It is shown that some existing constructive IDA-PBC designs actually implicitly exploit the maximum energy shapeability. The proposed procedure for the IDA-PBC design is demonstrated with two practical examples.

math.OC↗
arXiv2026

Illusory Pattern Perception Drives Spurious Inference in Large Language Models

Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tendency, often described as "connecting the dots" where none exist, can result in systematic reasoning errors. This paper investigates whether Large Language Models (LLMs) exhibit such perceptual tendencies, which can lead to systematic errors in downstream applications. To our knowledge, this work presents the first systematic study of illusory pattern perception in LLMs, adapting classic psychological paradigms to three tasks with direct empirical comparison to human behaviors. We find that LLMs frequently exhibit stronger illusory pattern perception than humans. In particular, models tend to over-associate frequent positive attributes with majority groups or large organizations, and show increased tendencies to construct causal narratives from ambiguous events. To uncover the mechanism behind these behaviors, we develop a feature interpretability framework based on Sparse Autoencoders (SAEs) to analyze internal representations. Our results reveal that holistic frequency perception and analytic cognitive orientation are linked to the emergence of illusory perceptions. These findings highlight a previously underexplored cognitive-like illusion that may affect the reliability of LLM reasoning. Code available at https://github.com/NusIoraPrivacy/illusory.

cs.AI↗
arXiv2026

Learning When and How to Intervene: A Hindsight-Distilled Sentinel for Coding Agents

Coding agents solve repository-level tasks through sequences of actions, where a single erroneous action can misdirect subsequent decisions and increase recovery costs. Existing approaches use execution feedback for recovery or specialized checks to block errors, but deciding before execution whether intervention will benefit eventual task completion remains challenging. To address this challenge, we propose HiSentinel, a hindsight-distillation framework that trains lightweight 0.6B and 1.7B sentinels to select pre-execution interventions aimed at improving task completion rather than correcting every imperfect action. A privileged teacher uses recorded execution outcomes as evidence for intervention judgments, which are distilled into a causal student that receives only the pre-action context and proposed action. Beyond identifying whether and when to intervene, the sentinel must also provide actionable feedback that helps the coding agent recover or obtain necessary human input. To support these capabilities, we introduce SWE-Intervene, an action-level dataset constructed from software-engineering trajectories that annotates whether an action should be allowed, autonomously redirected, or paused for human assistance, together with corresponding intervention feedback. Across SWE-bench Verified Mini and Ask or Assume, HiSentinel consistently improves task completion across Sentinel scales and coding-agent families, with gains of up to 14% and 10%, respectively, while maintaining competitive token consumption. These results demonstrate that lightweight pre-execution intervention can effectively prevent error propagation and improve the reliability of autonomous coding agents.

cs.SE↗
arXiv2026

Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision

High-resolution digital elevation models (DEMs) support Earth observation applications, but paired training references are often available only at coarser output resolutions. Reconstructing finer terrain grids therefore requires both effective transfer beyond the supervised scale and control of dense-query computation. To address this problem, SCOPE learns a continuous terrain representation from coarser-resolution pairs. It predicts a latent coefficient field on the low-resolution grid and reuses local Fourier residual functions through basis evaluation and geometry-guided ensemble fusion. This separates high-dimensional coefficient prediction from output-grid construction. Experiments on geographically distributed land--ocean samples assess supervised reconstruction, unseen-scale inference, cross-domain generalization, and theoretical computation. SCOPE leads the compared methods across six metrics in the main supervised-scale evaluation. At an unseen factor three times the training factor, land reconstruction reduces RMSE and MAE by approximately 12\% relative to bicubic interpolation, with errors close to target-scale fine-tuning. Ninefold output density increases counted multiply--accumulate operations by only about 2\%. Frozen-model validation on held-out external marine regions reduces RMSE relative to the DEM-specific implicit baseline EBCF-CDEM by approximately 19\% under self-downsampling and 2\% with cross-product inputs, while also yielding lower RMSE than LIIF-MS in both settings. These results demonstrate the value of reusable coefficient fields for accurate reconstruction beyond the supervised resolution with low incremental arithmetic cost.

cs.CV↗
arXiv2026

SFPF: Spatio-Frequency Polarization Fingerprint for Anomalous Wireless Device Detection

Periodic inspection of deployed wireless devices is necessary because unauthorized hardware replacement may preserve communication functions, credentials, and logical identity, making anomalous devices difficult to detect. Such inspections are conducted under controlled measurement conditions to verify that each device remains consistent with its enrolled hardware state. Conventional radio-frequency fingerprint (RFF) may provide insufficient separation when replacement hardware closely resembles legitimate hardware, while a polarization fingerprint (PF) constructed at one observation direction may miss spatially nonuniform polarization changes. This paper proposes the spatio-frequency polarization fingerprint (SFPF), which jointly represents complex polarization responses over multiple frequencies and observation directions; conventional PF is its fixed-direction slice. We derive SFPF formation from hardware-dependent modal excitation, directional far-field radiation, and polarization projection. A first-order sensitivity analysis shows that the response to the same hardware change varies with both frequency and direction, motivating joint spatio-frequency acquisition. Electromagnetic simulations confirm the nonuniform spatio-frequency sensitivity and show that, under the same observation budget, SFPF improves normalized distance, Fisher score, and the inter-/intra-class ratio over PF by 17.7%, 45.8%, and 11.3%, respectively. Experiments show that SFPF consistently outperforms RFF and PF over 0--20~dB. At 15--20~dB, SFPF achieves anomalous-device F1 scores of 87.3--90.4% and AUROC values of 85.4--95.5%.

cs.CR↗
arXiv2026

FreqSpaNet: Frequency and Spatial Learning of SFPF for Physical Layer Hardware Integrity Detection

Unauthorized hardware replacement can preserve a wireless device's logical identity while altering its physical implementation, posing a challenge to hardware integrity verification. Spatio-frequency polarization fingerprints (SFPFs) capture device-dependent responses across multiple frequencies and directions, but their frequency and spatial dimensions exhibit different structural dependencies. We propose FreqSpaNet, an SFPF representation learning network for open set hardware anomaly detection. A frequency branch captures local variations among neighboring frequencies, while a geometry-aware spatial branch models directional relationships using angular information. The two representations are combined through adaptive fusion, and complementary pretraining further captures shared information while preserving the distinct characteristics of the frequency and spatial representations. Experiments show that FreqSpaNet achieves a mean AUROC of 96.31\%, 9.05 points above the baseline. Results under seven hardware replacement scenarios further verify the effectiveness of FreqSpaNet.

cs.LG↗
arXiv2026

Constraining the Delay Time Distribution of the r- and s-Process from Stellar Ages at Solar Metallicity

We present a new approach to constraining the delay time distribution (DTD) governing r- and s-process element production. Rather than using metallicity as a proxy for time, we work directly with stellar ages at solar metallicity, where chemical equilibrium models are most applicable. We analyze two complementary samples: more than $10,000$ red clump (RC) thin disk stars at solar metallicity from LAMOST, and solar twins with high-precision abundances; both have well-determined stellar ages $τ$. We fit chemical-equilibrium models with power-law DTDs to the evolution of [Eu/Mg]$(τ)$ and [Ba/Mg]$(τ)$ in each sample, taking europium as a canonical r-process element and barium as an s-process element, each referenced to magnesium as a near-instantaneous, CCSNe-dominated element. To validate the method, we first apply it to [Fe/Mg]$(τ)$ and recover that $\approx\!70\%$ of Fe comes from a delayed channel with a declining DTD $\propto t^{-1.2}$, in agreement with well-established results for iron production in thermonuclear supernovae. It also shows that about $60\%$ of Eu and Ba forming today originate from strongly-delayed processes, and the inferred delayed-production rates of both Eu and Ba rise with delay time, opposite to that of thermonuclear supernovae. These results provide direct temporal constraints on r- and s-process enrichment, complementary to metallicity-based chemical-evolution studies. Because we model abundance referenced to Mg, and the same trend is recovered across stars of different guiding-center radii, this appears unaffected by radial migration and chemical evolution varying with Galactocentric radius. If confirmed, it points to delayed neutron-capture production beyond NS--NS mergers.

astro-ph.GA↗
arXiv2026

$^{139}$La nuclear quadrupole resonance studies of pressurized La$_4$Ni$_3$O$_{10}$

Density-wave (DW) orders are considered as competing orders to unconventional superconductivity and are commonly seen in a variety of superconductors including but not limited to the recently discovered Ruddlesden-Popper-phase nickelates. By utilizing $^{139}$La nuclear quadrupole resonance, we systematically investigate into the nature of DW orders and their evolution under pressure in La$_4$Ni$_3$O$_{10}$. Spin and charge DW orders are found to be intertwined in this material, which is in stark contrast to those in La$_3$Ni$_2$O$_7$. Short-range DW orders are observed near 150 K, well above the development of long-range DW orders at around 139 K. Upon applying a hydrostatic pressure of 2.3 GPa, the transition temperatures of the short-range and long-range orders decrease at rates of 1 K/GPa and 10 K/GPa, respectively. Our results thus affirm that both spin density wave and charge density wave as competing orders with the superconducting state in La$_4$Ni$_3$O$_{10}$, and provide new insights into the interplay between DW orders and unconventional superconductivity.

cond-mat.supr-con↗
arXiv2026

Switch Attention: Towards Dynamic and Fine-grained Hybrid Transformers

The attention mechanism has been the core component in modern transformer architectures. However, the computation of standard full attention scales quadratically with the sequence length, serving as a major bottleneck in long-context language modeling. Sliding window attention restricts the context length for better efficiency at the cost of narrower receptive fields. While existing efforts attempt to take the benefits from both sides by building hybrid models, they often resort to static, heuristically designed alternating patterns that limit efficient allocation of computation in various scenarios. In this paper, we propose Switch Attention (SwiAttn), a novel hybrid transformer that enables dynamic and fine-grained routing between full attention and sliding window attention. For each token at each transformer layer, SwiAttn dynamically routes the computation to either a full-attention branch for global information aggregation or a sliding-window branch for efficient local pattern matching. An adaptive regularization objective is designed to encourage the model towards efficiency. Moreover, we adopt continual pretraining to optimize the model, transferring the full attention architecture to the hybrid one. Extensive experiments are conducted on twenty-three benchmark datasets across both regular (4K) and long (32K) context lengths, demonstrating the effectiveness of the proposed method.

cs.CL↗
arXiv2026

Tilted $p$-wave magnet candidate CeNiAsO

The unexpectedly small ordered moments of CeNiAsO, a candidate for correlated $p$-wave magnet, have posed a serious challenge to the precise determination of its magnetic structure, hindering the understanding of its fundamental properties. By leveraging the high sensitivity to local internal fields, our $^{75}$As nuclear quadrupole / magnetic resonance experiments reveal a commensurate antiferromagnetic order with a small out-of-plane moment $m_z\approx0.05$ $μ_{\mathrm{B}}$. This tilted magnetic configuration not only rotates the spin polarization axis away from the crystallographic $\mathbf{c}$-axis, but also enhances the non-relativistic spin splitting. We refer to this rare paradigm as a \textit{tilted $p$-wave magnet}.

cond-mat.str-el↗
arXiv2026

Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform

Implantable neural interfaces require low-power real-time signal processing to remain within strict thermal and bandwidth constraints, motivating lightweight feature extraction methods for on-chip spike sorting. This work presents the Walsh-Hadamard Transform (WHT) as a hardware-efficient feature extraction method for neural spike classification. WHT can be implemented using only adders, subtractors, and registers without coefficient memory. WHT performance is compared against the Compressed Hadamard Transform (CHT) and Principal Component Analysis (PCA), improving mean F1-scores from 55-60% to 70-75% on difficult high-noise datasets and from 90-95% to 95-99% on all other simulated datasets. In addition to improved classification performance, WHT demonstrates greater robustness to noise, downsampling, reduced training size, and distance metric selection, maintaining standard deviations typically below 5%, while CHT and PCA reach up to 10% under high-noise conditions.

eess.SP↗