arXiv subjects

Yuxi Lu

Publications and source records attributed to Yuxi Lu.

At least 19 recordsLinked to original sources

arXiv2026

MARCUS: Missing-Aware Region Representation with Contextual Urban Signals for Rent Prediction

Multimodal urban data has expanded the applications of urban region representation learning, such as functional zone identification and real estate appraisal, but also introduces challenges caused by data incompleteness. Existing studies usually handle missing data through imputation, treating missingness as noise while ignoring its potential semantic value. To address this issue, we propose MARCUS, a missing-aware region representation model that treats missingness as a contextual urban signal. MARCUS models missingness in three stages: Intra Learning jointly encodes observed features and missing patterns, Inter Learning estimates modality reliability to guide cross-modal interaction, and Fusion uses missing-aware and time-aware gating to generate the final region embedding. We apply MARCUS to rent prediction, a task with long-term trends and seasonal fluctuations, using real-world datasets from Sydney and New York. Experimental results show that MARCUS achieves state-of-the-art performance, reducing MAE by 51.35% on Sydney and 12.62% on New York compared with the best baselines. Additional experiments, including an imputation-based ablation study and randomized additional-missingness analysis, further demonstrate the effectiveness of the proposed method.

cs.LG↗

arXiv2026

Consensusability of Continuous-Time Multi-Agent Systems With Unbounded Heterogeneous Constant Delays: A Signed Laplacian Perspective

The consensus of continuous-time multi-agent systems with unbounded and heterogeneous constant delays is investigated by combining frequency-domain analysis and algebraic graph theory. Several types of signed Laplacians are constructed to characterize consensusability under delays. The core results are established based on the defined delay-embedded signed Laplacian, where a small-delay link creates a cooperative interaction and a possibly unbounded large-delay link creates an antagonistic interaction between the agents. The dividing line between small and large delays is given by $\tau_{ij}=\pi/2\lambda_{\max}(L_0)$, where $\lambda_{\max}(L_0)$ refers to the maximum eigenvalue of the conventional graph Laplacian. It is proved that the consensusability is preserved if the delay-embedded signed Laplacian is positive semi-definite with a simple zero eigenvalue. Moreover, we derived some consensus conditions in terms of the extended effective resistance which measures the overall coupling between two sets of agents. The obtained results provide new insights into the mechanism of delayed consensus from the interplay between the small-delay-induced cooperativeness and large-delay-induced antagonism in the underlying network topology.

math.OC↗

arXiv2026

Benchmarking and Reasoning Distillation of Large Language Models for Feedback Controller Design in Complex Dynamical Systems

Although remarkable capabilities have been demonstrated by Large Language Models (LLMs) across scientific domains, feedback controller design remains underexplored. Existing benchmarks focus mainly on linear single-Degree-of-Freedom (DoF) systems and large API-hosted models, leaving performance on complex controller-design tasks and feasibility for edge deployment unclear. To address these limitations, we introduce the Complex Dynamics-to-Control Benchmark for Large Language Models (CoDyControlBench), comprising 132 system configurations across five evaluation dimensions: number of DoF, system type, coupling level, damping regime, and controller type. Six state-of-the-art LLMs were evaluated over three independent runs, including three commercial models (GPT, Gemini, and Claude) and three open-source models (GLM, DeepSeek, and Qwen). GPT achieved the highest design success rate at 94.8\%, whereas Qwen showed the lowest rate at 50.0\%. Across the benchmark dimensions, DoF and controller type exhibited the largest model-averaged variations in design success, with success-rate ranges of 36.3\% and 17.6\%, respectively, exceeding those associated with system type, coupling level, and damping regime. Comparison of GPT and Qwen showed that their performance gap arose mainly from the control-design knowledge, particularly gain selection and the use of transient-limiting mechanisms. For edge deployment, a specialized 1.5B-parameter model was developed through reasoning distillation. The reasoning-distilled model outperformed the answer-distilled and base model on CoDyControlBench, maintained stable performance across 1-6 DoFs, and achieved successful traget tracking in all three physical trials on a pneumatic-artificial-muscle-driven robotic arm. These results establish a benchmark baseline and highlight the potential of lightweight, edge-deployable controller-design models.

cs.RO↗

arXiv2026

They Won't Be Giants: Missing Metal-Rich RGB Stars in Gaia Data Indicate Truncated Stellar Evolution

We investigate the population of luminous red giant branch stars as a function of metallicity using Gaia XP metallicity combined with SDSS-V, GALAH, and LAMOST. After applying uniform selection criteria and extinction corrections, we construct absolute magnitude distributions across metallicity bins spanning [Fe/H] =-1 to >0.4. We find a systematic deficit of luminous giants at high metallicity, while the red clump and lower red giant branch populations remain largely unchanged. This behavior is consistent with enhanced mass loss at high metallicity, arising from either binary interactions or single-star evolution. This trend is robust across multiple surveys and persists within volume-limited subsamples (1-4 kpc), suggesting it is not driven by distance or selection effects. Synthetic stellar populations based on PARSEC isochrones reproduce the overall magnitude distributions but do not predict a decline in luminous giants with metallicity. Tests of potential systematics, including extinction effects and metallicity scale consistency using open clusters, do not account for the observed trend. We also find no evidence that survey-to-survey differences in metallicity drive the observed result. Together, these findings suggest a metallicity-dependent reduction in the number of luminous red giants that is not captured by current models. This result may have implications for stellar evolution at high metallicity, helium white dwarf formation, and the initial mass function as well as the UV upturn in metal-rich galaxies.

astro-ph.SR↗

arXiv2026

Ensemble Kinematic Ages for 1.5 Million LAMOST Stars

We present a framework for inferring stellar ages from spectroscopic stellar parameters, calibrated with ensemble kinematics by averaging over the median vertical action, $J_z$, for stars with similar atmospheric and chemical properties, yielding self-consistent age estimates across the Hertzsprung-Russell (HR) diagram. We refer to these ages as ensemble kinematic ages as the age scale is calibrated from the average kinematics of ensembles of stars with similar stellar parameters. Individual stellar kinematics are not used in assigning ages. We validate the method against subgiant ages, achieving an accuracy of ~30%, comparable to [C/N]-based estimates. We find a clear age-$J_z$ relations that enable age inference up to ~10 Gyr for both the high- and low-$\alpha$ disks. Applying this framework to 1.5 million LAMOST stars, we derive ages for subgiants and giants with typical uncertainties of ~2 Gyr. The inferred ages agree well with literature age catalogs, with no significant systematic trends as a function of $\log g$. We also demonstrate the potential of empirical isochrones to calibrate theoretical stellar models. We identify an old (~7 Gyr) population within the low-$\alpha$ disk but draw no firm conclusions. Although ensemble kinematic ages are statistical and sensitive to selection effects, Galactic potential assumptions, and Galactic location, they provide a robust population-level tool for Galactic archaeology, complementing traditional age indicators and extending age estimates across the full HR diagram.

astro-ph.SR↗

arXiv2026

Conflict-Based Lazy Search for Fast Multi-Manipulator Planning

Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do. However, real-time planning for multiple manipulators in a cluttered workspace still poses significant challenges for planning algorithms. This article proposes a new planning algorithm called Conflict-Based Lazy Search (CBLS) for multimanipulator planning. CBLS is built on Conflict-Based Search (CBS), an efficient multiagent pathfinding (MAPF) algorithm that has shown an order of magnitude speedup over previous approaches [1], [2]. CBS addresses MAPF by solving many single-agent pathfinding (SAPF) problems. Thus, its planning time directly depends on the efficiency of the SAPF algorithm adopted. Our CBLS algorithm enhances CBS with precomputation and lazy search. First, a lazily evaluated graph with controlled sparsity is precomputed for a single manipulator. Second, we propose the Lazy Edged-based A* (LEA*) for efficient SAPF. Since edge evaluation is the computational bottleneck of manipulator planning, LEA* uses lazy search and an edge queue to reduce the number of edge evaluations. We show that LEA* is optimally vertex efficient and has improved edge efficiency compared to A*. We apply the proposed CBLS to multi-manipulator planning problems and show its superior performance by comparing it with CBS and a sampling-based algorithm, namely, RRT-Connect.

cs.RO↗

arXiv2026

Robust Optimization Under Objective Functional Uncertainty

This paper proposes a new robust optimization (RO) formulation namely the RO under objective functional uncertainty (ObRO). The ObRO adopts a min-max structure where the inner problem finds the worst-case objective function in a continuous function space to maximize the cost, and the outer problem finds the optimal decision in a Euclidean space to minimize the cost. A solution algorithm is designed to alternately generate the worst-case objective function at the current decision and the optimal decision for the current collection of objective functions. Using operator theory, we prove that this algorithm converges to the defined ``semi-global'' saddle point of the ObRO problem. In addition, we propose a numerical solver based on the piece-wise linearization (PWL) approximation of objective functions. The PWL approximate problem is proved to be numerically consistent with the original ObRO problem. The obtained results are applied to the degradation-aware battery charging scheduling in distribution networks.

math.OC↗

arXiv2026

Transferring Physical Priors into Remote Sensing Segmentation via Large Language Models

Semantic segmentation of remote sensing imagery is fundamental to Earth observation. Achieving accurate results requires integrating not only optical images but also physical variables such as the Digital Elevation Model (DEM), Synthetic Aperture Radar (SAR) and Normalized Difference Vegetation Index (NDVI). Recent foundation models (FMs) leverage pre-training to exploit these variables but still depend on spatially aligned data and costly retraining when involving new sensors. To overcome these limitations, we introduce a novel paradigm for integrating domain-specific physical priors into segmentation models. We first construct a Physical-Centric Knowledge Graph (PCKG) by prompting large language models to extract physical priors from 1,763 vocabularies, and use it to build a heterogeneous, spatial-aligned dataset, Phy-Sky-SA. Building on this foundation, we develop PriorSeg, a physics-aware residual refinement model trained with a joint visual-physical strategy that incorporates a novel physics-consistency loss. Experiments on heterogeneous settings demonstrate that PriorSeg improves segmentation accuracy and physical plausibility without retraining the FMs. Ablation studies verify the effectiveness of the Phy-Sky-SA dataset, the PCKG, and the physics-consistency loss.

cs.CV↗

arXiv2026

[C/N] Ages and Extra-Mixing for [Fe/H] <- 0.5: Insights from the LMC and SMC

The [C/N]-age relation has become a powerful tool for reconstructing the formation history of the Milky Way (MW), providing the largest age sample for field giant stars. However, at metallicities below [Fe/H] $< -0.5$, stellar surfaces are altered by a poorly understood process known as extra mixing, which modifies [C/N] in a mass- and metallicity-dependent manner. This effect complicates the application of the traditional [C/N]-age relation in metal-poor regimes. Within the MW, constraining the mass dependence of extra mixing is particularly challenging because stars at [Fe/H] $< -0.5$ are predominantly old and therefore low-mass, leading to strong degeneracies between mass and metallicity. In this work, we explore the potential of the Magellanic Clouds (MCs) to disentangle these effects and constrain extra mixing as a function of age and metallicity. By comparing empirical corrections calibrated in the MW with predictions from thermohaline mixing models, we isolate the mass dependence of extra mixing in the MCs down to [Fe/H] $\sim-0.7$. We find that the empirical calibration performs well for lower-mass stars ($< 1.25$ $M_{\odot}$), while theoretical models successfully reproduce the observed mass dependence down to $\sim$ 1.25 $M_{\odot}$. We further present the first observational evidence that extra mixing becomes ineffective above $\sim$ 1.8 $M_{\odot}$ at [Fe/H] $\sim -0.7$. Our results demonstrate the feasibility of deriving [C/N]-based ages for individual stars in external galaxies. Future observations targeting higher-$\log g$ or fainter stars in the MCs will provide stronger constraints on extra-mixing processes and enable the calibration of [C/N]-age relation that can be applied to low-metallicity individual stars in the MW or external galaxies.

astro-ph.SR↗

arXiv2025

Stellar Birth Radii in the LMC: Insights into Chemodynamics, Radial Migration, and Star Formation Across the Disk

The LMC and SMC are interacting dwarf galaxies that offer a valuable testbed for studying the effects of galactic mergers. We investigate the chemodynamic history of the LMC in the context of its interaction with the SMC by inferring stellar birth radii, first validated on a hydrodynamical simulation tailored to reproduce their interaction history. Using inferred birth radii and stellar ages, we identify signatures of dynamical and chemical evolution across the LMC disk. We find that the LMC's metallicity gradient steepened around 5, 3, and 1 Gyr ago, coinciding with enhanced star formation (SF) episodes. These events exhibit distinct spatial patterns -- initially concentrated in the inner disk at 5 Gyr, expanding outward by 3 Gyr, and becoming widespread with renewed central activity at 1 Gyr -- likely reflecting changes in spin alignment between the interacting disks if the enhancements of SF tracks the pericenter passages of the SMC to the LMC. The inferred radial migration strength of the LMC shows notable enhancements at 0.5, 2, and 5 Gyr. The most $\alpha$-enriched stars form 2-3 Gyr ago at birth radii of 2-4 kpc, the only epoch when star formation is broadly distributed across the disk. Finally, unlike the Milky Way, the LMC lacks a clear [$\alpha$/M]-[Fe/H] bimodality. This is likely due to its more centrally concentrated star formation during these periods, compared to the MW's more extended outer-disk star formation enhancements. These findings place strong constraints on the LMC's assembly history and its interaction with the SMC.

astro-ph.GA↗

arXiv2025

Spectroscopic Follow-up of Young High-$\alpha$ Dwarf Star Candidates: Still Likely Genuinely Young

The question of whether genuinely young high-$\alpha$ stars exist has been discussed for over a decade since their discovery from asteroseismology of giant stars as it is challenging to break the degeneracy between the binary interaction and the genuinely young scenarios. Young high-$\alpha$ stars are hard to explain with traditional chemical evolution model as the high-$\alpha$ disk is typically associated with the early epoch of star formation in the Milky Way. Combined with recent advances of gyrochronology, and that $^7$Li can serve as an unambiguous indicator for identifying merger products in dwarfs thanks to its low burning temperature, we identified young high-$\alpha$ dwarf candidate stars through their fast rotation in a previous study. In this paper, we performed high-resolution spectroscopic follow-up of these candidates using Potsdam Echelle Polarimetric and Spectroscopic Instrument (PEPSI), and confirm 3 additional stars that are most likely genuinely young. Together with the star from the earlier paper, we find three out of four of them center around [Fe/H]=-0.5 dex, are ~5 Gyr old, and have a similar amount of elevated Li (~0.5 dex) and Al (~0.1 dex) compared to stars with matching $\log g$, $T_{\rm eff}$, Mg, and Fe within observational uncertainties, hinting at their common formation pathway.

astro-ph.GA↗

arXiv2025

Remote Sensing-Oriented World Model

World models have shown potential in artificial intelligence by predicting and reasoning about world states beyond direct observations. However, existing approaches are predominantly evaluated in synthetic environments or constrained scene settings, limiting their validation in real-world contexts with broad spatial coverage and complex semantics. Meanwhile, remote sensing applications urgently require spatial reasoning capabilities for disaster response and urban planning. This paper bridges these gaps by introducing the first framework for world modeling in remote sensing. We formulate remote sensing world modeling as direction-conditioned spatial extrapolation, where models generate semantically consistent adjacent image tiles given a central observation and directional instruction. To enable rigorous evaluation, we develop RSWISE (Remote Sensing World-Image Spatial Evaluation), a benchmark containing 1,600 evaluation tasks across four scenarios: general, flood, urban, and rural. RSWISE combines visual fidelity assessment with instruction compliance evaluation using GPT-4o as a semantic judge, ensuring models genuinely perform spatial reasoning rather than simple replication. Afterwards, we present RemoteBAGEL, a unified multimodal model fine-tuned on remote sensing data for spatial extrapolation tasks. Extensive experiments demonstrate that RemoteBAGEL consistently outperforms state-of-the-art baselines on RSWISE.

cs.LG↗

arXiv2025

Data-driven solar forecasting enables near-optimal economic decisions

Solar energy adoption is critical to achieving net-zero emissions. However, it remains difficult for many industrial and commercial actors to decide on whether they should adopt distributed solar-battery systems, which is largely due to the unavailability of fast, low-cost, and high-resolution irradiance forecasts. Here, we present SunCastNet, a lightweight data-driven forecasting system that provides 0.05$^\circ$, 10-minute resolution predictions of surface solar radiation downwards (SSRD) up to 7 days ahead. SunCastNet, coupled with reinforcement learning (RL) for battery scheduling, reduces operational regret by 76--93\% compared to robust decision making (RDM). In 25-year investment backtests, it enables up to five of ten high-emitting industrial sectors per region to cross the commercial viability threshold of 12\% Internal Rate of Return (IRR). These results show that high-resolution, long-horizon solar forecasts can directly translate into measurable economic gains, supporting near-optimal energy operations and accelerating renewable deployment.

physics.geo-ph↗

arXiv2025

FastAvatar: Towards Unified and Fast 3D Avatar Reconstruction with Large Gaussian Reconstruction Transformers

Despite significant progress in 3D avatar reconstruction, it still faces challenges such as high time complexity, sensitivity to data quality, and low data utilization. We propose FastAvatar, a feedforward 3D avatar framework capable of flexibly leveraging diverse daily recordings (e.g., a single image, multi-view observations, or monocular video) to reconstruct a high-quality 3D Gaussian Splatting (3DGS) model within seconds, using only a single unified model. The core of FastAvatar is a Large Gaussian Reconstruction Transformer (LGRT) featuring three key designs: First, a 3DGS transformer aggregating multi-frame cues while injecting initial 3D prompt to predict the corresponding registered canonical 3DGS representations; Second, multi-granular guidance encoding (camera pose, expression coefficient, head pose) mitigating animation-induced misalignment for variable-length inputs; Third, incremental Gaussian aggregation via landmark tracking and sliced fusion losses. Integrating these features, FastAvatar enables incremental reconstruction, i.e., improving quality with more observations without wasting input data as in previous works. This yields a quality-speed-tunable paradigm for highly usable 3D avatar modeling. Extensive experiments show that FastAvatar has a higher quality and highly competitive speed compared to existing methods.

cs.CV↗

arXiv2025

LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models

With rapid advances in code generation, reasoning, and problem-solving, Large Language Models (LLMs) are increasingly applied in robotics. Most existing work focuses on high-level tasks such as task decomposition. A few studies have explored the use of LLMs in feedback controller design; however, these efforts are restricted to overly simplified systems, fixed-structure gain tuning, and lack real-world validation. To further investigate LLMs in automatic control, this work targets a key subfield: adaptive control. Inspired by the framework of model reference adaptive control (MRAC), we propose an LLM-guided adaptive compensator framework that avoids designing controllers from scratch. Instead, the LLMs are prompted using the discrepancies between an unknown system and a reference system to design a compensator that aligns the response of the unknown system with that of the reference, thereby achieving adaptivity. Experiments evaluate five methods: LLM-guided adaptive compensator, LLM-guided adaptive controller, indirect adaptive control, learning-based adaptive control, and MRAC, on soft and humanoid robots in both simulated and real-world environments. Results show that the LLM-guided adaptive compensator outperforms traditional adaptive controllers and significantly reduces reasoning complexity compared to the LLM-guided adaptive controller. The Lyapunov-based analysis and reasoning-path inspection demonstrate that the LLM-guided adaptive compensator enables a more structured design process by transforming mathematical derivation into a reasoning task, while exhibiting strong generalizability, adaptability, and robustness. This study opens a new direction for applying LLMs in the field of automatic control, offering greater deployability and practicality compared to vision-language models.

cs.RO↗

arXiv2025

HOTA: Hierarchical Overlap-Tiling Aggregation for Large-Area 3D Flood Mapping

Floods are among the most frequent natural hazards and cause significant social and economic damage. Timely, large-scale information on flood extent and depth is essential for disaster response; however, existing products often trade spatial detail for coverage or ignore flood depth altogether. To bridge this gap, this work presents HOTA: Hierarchical Overlap-Tiling Aggregation, a plug-and-play, multi-scale inference strategy. When combined with SegFormer and a dual-constraint depth estimation module, this approach forms a complete 3D flood-mapping pipeline. HOTA applies overlapping tiles of different sizes to multispectral Sentinel-2 images only during inference, enabling the SegFormer model to capture both local features and kilometre-scale inundation without changing the network weights or retraining. The subsequent depth module is based on a digital elevation model (DEM) differencing method, which refines the 2D mask and estimates flood depth by enforcing (i) zero depth along the flood boundary and (ii) near-constant flood volume with respect to the DEM. A case study on the March 2021 Kempsey (Australia) flood shows that HOTA, when coupled with SegFormer, improves IoU from 73\% (U-Net baseline) to 84\%. The resulting 3D surface achieves a mean absolute boundary error of less than 0.5 m. These results demonstrate that HOTA can produce accurate, large-area 3D flood maps suitable for rapid disaster response.

cs.CV↗

arXiv2025

Anchoring Stellar Age Indicators: A Cross-Calibration of [C/N] and Gyrochronology Ages via the Age-Velocity-Dispersion Relation

Determining stellar ages is challenging, as it depends on other stellar parameters in a non-linear way and often relies on stellar evolution models to infer the underlying relation between these parameters and age. This complexity increases when comparing different age-dating methods, as they rely on distinct indicators and are often applicable to non-overlapping regions of the color-magnitude diagram. Moreover, many empirical calibration methods rely on pre-determined ages, often from open clusters or asteroseismology, which only cover a limited parameter space. Fortunately, the age-velocity-dispersion relation (AVR), in which the velocity dispersion increases with age, is a universal feature among stars of all evolutionary stages. In this paper, we 1) explore the parameter space in which [C/N] and gyrochronology are applicable, extending beyond the domains probed by asteroseismology and open clusters, and 2) assess whether the traditionally assumed [C/N] and gyrochronology relations yield ages on a consistent physical scale, after calibrating both using the same AVR. We find gyrochronology can be applied to all partially convective stars after they have converged onto the slow rotating sequence and before they experience weakened magnetic braking; [C/N] can be used to infer ages for all giants with metallicity > -0.8 dex and [C/N] < -0.05 dex, and can be used as an age-indicator down to [Fe/H] of -1 dex if only selecting the low-$\alpha$ disk. Lastly, ages obtained from [C/N] and gyrochronology agree within uncertainty after accounting for systematic offsets.

astro-ph.SR↗

arXiv2025

Causal Discovery of Latent Variables in Galactic Archaeology

Galactic archaeology--the study of stellar migration histories--provides insights into galaxy formation and evolution. However, establishing causal relationships between observable stellar properties and their birth conditions remains challenging, as key properties like birth radius are not directly observable. We employ Rank-based Latent Causal Discovery (RLCD) to uncover the causal structure governing the chemodynamics of a simulated Milky Way galaxy. Using only five observable properties (metallicity, age, and orbital parameters), we recover in a purely data-driven manner a causal graph containing two latent nodes that correspond to real physical properties: the birth radius and guiding radius of stars. Our study demonstrates the potential of causal discovery models in astrophysics.

astro-ph.GA↗