arXiv subjects

Hao Tian

Publications and source records attributed to Hao Tian.

At least 37 records · Page 2Linked to original sources

arXiv2026

GIScholarBench: Benchmarking LLM Overconfidence in GIS Research

Large language models (LLMs) are increasingly used in academic research workflows, but scholarly tasks require high factual precision and therefore expose a key weakness: overconfidence. Here, overconfidence is defined behaviorally as the tendency to produce confident, assertive, and well-formatted outputs even when the underlying knowledge is incomplete or unverifiable, rather than as a calibration gap between stated confidence and accuracy. To examine this issue, we introduce GIScholarBench, a benchmark built from 10,865 papers published in 25 core GIScience journals between 2020 and 2025. The benchmark covers three tasks with increasing cognitive complexity: metadata retrieval, literature linking, and research direction generation. We evaluate Claude Sonnet 4.5, Gemini 3, and ChatGPT 5.3 through their native web interfaces under real-world user-facing conditions. Results show consistent overconfidence across all tasks. In metadata retrieval, ChatGPT 5.3 achieves the highest accuracy, but all models still generate definitive titles and DOIs when predictions are wrong. In literature linking, Claude Sonnet 4.5 recovers the most references, but all models show a clear gap between top-ranked retrieval and longer citation lists, suggesting that references are extended beyond reliable retrieval capacity. In research direction generation, AI-generated directions show lower topic coverage, higher novel miss rates, and lower semantic diversity than real future-citing papers. These findings suggest that LLM overconfidence is task-invariant but takes different forms: factual overgeneration in retrieval, unreliable citation expansion in literature linking, and overconfidence in output completeness during research ideation.

cs.IR↗

arXiv2026

Multiple populations detection with the Chinese Space Station Survey Telescope main survey camera

Multiple stellar populations (MPs), characterized by star-to-star light-element abundance variations, are ubiquitous in globular clusters (GCs). Spectroscopy directly reveals these anomalies, while photometric studies, especially with the \textit{Hubble Space Telescope} (\textit{HST}), have been essential for tracing MP sequences in colour-magnitude diagrams (CMDs). However, the limited field of view of \textit{HST} confines most studies to cluster centres. The upcoming \textit{Chinese Space Station Survey Telescope} (CSST), with its wide field of view and UV-optical coverage, will enable systematic MP studies over entire clusters. We assess the capability of the CSST wide-field camera to detect and characterize MPs in GCs using realistic simulations. Synthetic stellar population models with different helium abundances ($ΔY$) and CNO variations were used to simulate CSST observations of GCs at distances of 9.6 and 20~kpc under different exposure times. MP detectability was evaluated using CMDs in seven CSST bands and UV-optical pseudo-colour diagrams. For a GC at 9.6~kpc, the $NUV-u$ colour is highly sensitive to $ΔY$ and CNO variations, with separations of $Δ(NUV-u)\approx0.16$ mag for red giants and up to 0.44 mag for dwarfs. MPs can be resolved when the total UV exposure exceeds $\sim1000$~s and the optical exposure exceeds $\sim300$~s. At 20~kpc, encompassing $\sim80\%$ of Galactic GCs, CSST still retains strong diagnostic power, resolving populations with $ΔY\geq0.06$ and $δ[\mathrm{N/Fe}]\geq0.64$, and separating MPs down to $i\sim19.5$ mag in clusters with large chemical spreads. The $NUV$-$u$-$g$ combination provides diagnostic performance comparable to the \textit{HST} F275W--F336W--F438W system. CSST will enable homogeneous MP surveys across the full spatial extent of star clusters in the Milky Way and nearby galaxies.

astro-ph.SR↗

arXiv2026

Stellar Density Classification and Regression for CSST Multi-color Imaging Using Deep Learning

The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the voids and $>10^5$ stars per detector in Galactic center). However, processing such heterogeneous data with a general source extraction pipeline introduces significant systematic uncertainties, standard algorithms exhibit poor accuracy in crowded fields and suffer from increased astrometric uncertainty in void regions. To mitigate these systematics, we propose a hierarchical, two-stage deep learning model for adaptive data reduction. The first stage ('classification') employs a ResNet-34 model to classify images into six discrete density categories, achieving $98.83\%$ in global accuracy. This classification acts as a critical decision gate, ensuring high calibration accuracy in the crowded fields. In the second stage ('regression'), a ResNet-50 regression model predicts the bright stars ($<23.5$ mag) in the field, which is essential for astrometric calibration, achieving a mean absolute error (MAE) of 0.0824 dex. By decoupling density characterization from source extraction, our model ensures that photometric and astrometric algorithms are optimally matched to the stellar density environment, thereby enhancing the fidelity and homogeneity of CSST as well as future large sky survey data products.

astro-ph.IM↗

arXiv2026

Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining

Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization remains constrained by the scarcity of large-scale training data spanning diverse real-world applications. Existing datasets rely heavily on costly manual annotations and are typically confined to narrow domains. To address this challenge, we propose Video2GUI, a fully automated framework that extracts grounded GUI interaction trajectories directly from unlabeled Internet videos. Video2GUI employs a coarse-to-fine filtering strategy to identify high-quality GUI tutorial videos and convert them into structured agent trajectories. Applying this pipeline to 500 million video metadata entries, we construct WildGUI, a large-scale dataset containing 12 million interaction trajectories spanning over 1,500 applications and websites. Pre-training Qwen2.5-VL and Mimo-VL on WildGUI yields consistent improvements of 5-20% across multiple GUI grounding and action benchmarks, matching or surpassing state-of-the-art performance. We will release both the WildGUI dataset and the Video2GUI pipeline to support future research of GUI agents.

cs.CL↗

arXiv2026

Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance

Improving the accuracy of photometric redshifts (photo-$z$) is essential for reliable statistical studies of cosmology and galaxy evolution. However, missing photometric bands are a common observational challenge that can significantly degrade photo-$z$ estimation accuracy. In this work, we present a systematic evaluation of data imputation methods aimed at improving photo-$z$ performance. We benchmark a range of representative machine learning (ML) and deep learning (DL) architectures, identifying k-nearest neighbors (KNN) and the attention-based SAITS model as the leading performers. These models are then applied to China Space Station Survey Telescope (CSST) mock data to assess their performance under realistic observational conditions. Our results show that KNN yields the highest accuracy under idealized missing completely at random (MCAR) conditions with complete training sets, whereas robustness tests reveal that SAITS significantly outperforms KNN when training data is incomplete or when applied to realistic mixed-mechanism scenarios. We find that domain consistency between training and testing missingness patterns is a prerequisite for optimal performance, highlighting the risks of domain shift in supervised regression tasks. Furthermore, our analysis demonstrates that while general imputation models are highly effective for MCAR and missing at random (MAR) data, they are detrimental when applied to missing not at random (MNAR) data arising from flux limits, as statistical models fail to capture the physical information inherent in these non-detections. Consequently, we advocate for more sophisticated architectures capable of disentangling stochastic missingness from physical non-detections to address these distinct mechanisms individually.

astro-ph.GA↗

arXiv2026

MiMo-Embodied: X-Embodied Foundation Model Technical Report

We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in both Autonomous Driving and Embodied AI. MiMo-Embodied sets new records across 17 embodied AI benchmarks in Task Planning, Affordance Prediction and Spatial Understanding, while also excelling in 12 autonomous driving benchmarks across Environmental Perception, Status Prediction, and Driving Planning. Across these tasks, MiMo-Embodied significantly outperforms existing open-source, closed-source, and specialized baselines. Our results indicate that through multi-stage learning, curated data construction, and CoT/RL fine-tuning, these two domains exhibit strong positive transfer and mutually reinforce one another. We provide a detailed analysis of our model design and training methodologies to facilitate further research. Code and models are available at https://github.com/XiaomiMiMo/MiMo-Embodied.

cs.RO↗

arXiv2026

GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation

Visual grounding, localizing objects from natural language descriptions, represents a critical bridge between language and vision understanding. While multimodal large language models (MLLMs) achieve impressive scores on existing benchmarks, a fundamental question remains: can MLLMs truly visually ground with human-like sophistication, or are they merely pattern-matching on simplified datasets? Current benchmarks fail to capture real-world complexity where humans effortlessly navigate intricate references and recognize when grounding is impossible. To rigorously assess MLLMs' true capabilities, we introduce GroundingME, a benchmark that systematically challenges models across four critical dimensions: (1) Discriminative: distinguishing highly similar objects, (2) Spatial: understanding complex relational descriptions, (3) Limited: handling occlusions or tiny objects, and (4) Rejection: recognizing ungroundable queries. Through careful curation combining automated generation with human verification, we create 1,005 challenging examples mirroring real-world complexity. Evaluating 25 state-of-the-art MLLMs reveals a profound capability gap: the best model achieves only 45.1% accuracy, while most score 0% on rejection tasks. We explore two strategies for improvements: (1) test-time scaling selects optimal response by thinking trajectory to improve overall performance by up to 4.5%, and (2) data-mixture training boosts rejection accuracy from 0% to 27.9%. GroundingME thus serves as both a diagnostic tool revealing current limitations in MLLMs and a roadmap toward human-level visual grounding. Project page: https://groundingme.github.io

cs.CV↗

arXiv2026

Mock Observations of Multiple Stellar Populations in Tidal Streams of Palomar 5 for the Chinese Space Station Survey Telescope

Observations show that multiple stellar populations (MPs) are ubiquitous in globular clusters. The Hubble Space Telescope (HST) has been a pivotal tool for previous photometric studies of MPs. The Chinese Space Station Survey Telescope (CSST) is a two-meter telescope scheduled for launch. One of its imaging instruments, the Survey Camera (SC), combines ultraviolet sensitivity comparable to that of HST with a significantly larger field of view, making it well-suited for conducting large-scale photometric surveys of MPs within extensive stellar stream structures. In this work, we perform mock observations of the stellar stream Palomar 5 to assess the feasibility of detecting MPs with the CSST/SC. The results indicate that the CSST/SC cannot resolve MPs in stellar streams at distances comparable to Palomar 5 ($\gtrsim 20$ kpc) with one or ten 150 s exposures. This fundamental limitation arises from the absence of the precise proper motions required to disentangle stream members. We estimate that successful resolution would require the target stream to be $\lesssim$ 8 kpc under a 150 s exposure. Furthermore, using theoretical color-magnitude diagrams, we find that the CSST/SC $g$-band provides an optimal balance between contamination rate and completeness rate for member identification in the cluster's core. However, this approach fails in the stream due to severe field star contamination. Therefore, future CSST observations of Palomar 5 and its tidal tails will employ multiple epochs across several bands to obtain the deep photometry and proper motion data for a definitive MP analysis.

astro-ph.IM↗

arXiv2026

A Robust Geometric Distortion Solution for Main Survey Camera of CSST

The advancement in sensitivity and field of view of next-generation wide-field survey telescopes requires astrometric measurements with high precision, even in the presence of significant geometric distortions. To address this challenge, we develop a Weighted Polynomial Distortion Correction in 2-Phase (WPDC-2P) method. This approach enhances stellar cross-matching, incorporates distance-based weighting into the traditional polynomial fitting, and employs a look-up table to absorb the remaining distortion residuals. Validated on simulated data from the Main Survey Camera of the \emph{Chinese Space Station Survey Telescope} (CSST), incorporating geometric distortions up to approximately $200$ pixels, the method achieves astrometric standard deviation ranging from 0.013 to 0.107 pixels (0.03 pixels for the $g$-1 detector) across all 18 detectors. Under extreme crowding conditions (e.g., globular cluster NGC 2298), the astrometric precision for the $g$-1 detector reaches 0.05-pixel level within the central region ($r_d < 4000$), despite a centroiding precision of $\sim$0.04 pixels. When applied to the Beijing-Arizona Sky Survey data, for which the standard pipeline delivers an astrometric uncertainty of $\sim$20 mas, our method reduces the positional scatter to $ σ_{Δα}=5.494$ mas (0.01 pixels) and $ σ_{Δδ}=9.981$ mas (0.02 pixels) using only a weighted 3rd-order polynomial correction. The method has been integrated into the CSST data processing pipeline and is prepared for further refinement using on-orbit calibration data.

astro-ph.IM↗

arXiv2026

Exploring the formation mechanisms of tidal structures in globular clusters of extragalactic origin

Tidal structures around globular clusters provide valuable insights into cluster evolution and the hierarchical assembly of the Milky Way. Using wide-field imaging data from the DESI Legacy Survey combined with a color-magnitude matched-filter technique, we perform a systematic analysis of extra-tidal features in 28 Galactic globular clusters of likely extragalactic origin, representing the largest homogeneous sample examined in this context to date. The clusters display diverse morphologies: 12 exhibit tidal tails, 9 show diffuse envelopes, and 7 reveal no clear extra-tidal features. Notably, we report the first detection of an extended tidal structure around the Sagittarius-associated cluster Terzan 7. To explore the underlying drivers, we compare intrinsic properties, orbital dynamics, and possible accretion associations across morphological groups. From the parameter distributions, complemented by Kolmogorov-Smirnov tests, we find that total mass, escape velocity, concentration, tidal filling factor, pericentric radius, eccentricity and radial angle in action-angle coordinates are all likely group-sensitive parameters. These results suggest that both internal cluster properties and orbital configurations play important roles in shaping extra-tidal morphologies. In addition, the cluster's accretion history shows no clear correlation with the presence of tidal features, indicating that it is not a direct driver of outer structure formation. Overall, the diversity of tidal structures is unlikely to be governed by a single factor, but instead reflects the interplay between internal dynamical evolution and the external Galactic environment. This study provides the most comprehensive constraints so far on the physical processes driving extra-tidal structures in accreted globular clusters.

astro-ph.GA↗

arXiv2026

Predicting Healthcare System Visitation Flow by Integrating Hospital Attributes and Population Socioeconomics with Human Mobility Data

Healthcare visitation patterns are influenced by a complex interplay of hospital attributes, population socioeconomics, and spatial factors. However, existing research often adopts a fragmented approach, examining these determinants in isolation. This study addresses this gap by integrating hospital capacities, occupancy rates, reputation, and popularity with population SES and spatial mobility patterns to predict visitation flows and analyze influencing factors. Utilizing four years of SafeGraph mobility data and user experience data from Google Maps Reviews, five flow prediction models, Naive Regression, Gradient Boosting, Multilayer Perceptrons (MLPs), Deep Gravity, and Heterogeneous Graph Neural Networks (HGNN),were trained and applied to simulate visitation flows in Houston, Texas, U.S. The Shapley additive explanation (SHAP) analysis and the Partial Dependence Plot (PDP) method were employed to examine the combined impacts of different factors on visitation patterns. The findings reveal that Deep Gravity outperformed other models. Hospital capacities, ICU occupancy rates, ratings, and popularity significantly influence visitation patterns, with their effects varying across different travel distances. Short-distance visits are primarily driven by convenience, whereas long-distance visits are influenced by hospital ratings. White-majority areas exhibited lower sensitivity to hospital ratings for short-distance visits, while Asian populations and those with higher education levels prioritized hospital rating in their visitation decisions. SES further influence these patterns, as areas with higher proportions of Hispanic, Black, under-18, and over-65 populations tend to have more frequent hospital visits, potentially reflecting greater healthcare needs or limited access to alternative medical services.

cs.LG↗

arXiv2026

Enhanced multi-parameter metrology in dissipative Rydberg atom time crystals

The pursuit of unprecedented sensitivity in quantum enhanced metrology has spurred interest in non-equilibrium quantum phases of matter and their symmetry breaking. In particular, criticality-enhanced metrology through time-translation symmetry breaking in many-body systems, a distinct paradigm compared to spatial symmetry breaking, is a field still in its infancy. Here, we have investigated the enhanced sensing at the boundary of a continuous time-crystal (CTC) phase in a driven Rydberg atomic gas. By mapping the full phase diagram, we identify the parameter-dependent phase boundary where the time-translation symmetry is broken. This allows us to use a single setup for measuring multiple parameters, in particular frequency and amplitude of a microwave field. By increasing the microwave field amplitude, we first observe a phase transition from a thermal phase to a CTC phase, followed by a second transition into a distinct CTC state, characterized by a different oscillation frequency. Furthermore, we reveal the precise relationship between the CTC phase boundary and the scanning rate, displaying enhanced precision beyond the Standard Quantum Limit. This work not only provides a promising paradigm rooted in the critical properties of time crystals, but also advances a method for multi-parameter sensing in non-equilibrium quantum phases.

cond-mat.quant-gas↗

arXiv2026

MiMo-V2-Flash Technical Report

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.

cs.CL↗

arXiv2025

MiMo-Audio: Audio Language Models are Few-Shot Learners

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks (MMSU, MMAU, MMAR, MMAU-Pro), spoken dialogue benchmarks (Big Bench Audio, MultiChallenge Audio) and instruct-TTS evaluations, approaching or surpassing closed-source models. Model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-Audio.

cs.CL↗

arXiv2025

An asymptotically compatible bond-based peridynamics with Gaussian kernel

In this paper, we introduce a novel bond-based peridynamic model that utilizes a Gaussian kernel function. Previous peridynamic models, when directly discretized, have exhibited a lack of asymptotically compatibility with their corresponding local elastic solutions. Additionally, these models have faced challenges in accurately computing the volume of intersecting regions between the horizon of the material point and its neighboring cells. These difficulties in numerical simulations within peridynamics have spurred numerous efforts to develop corrective numerical methods. While such corrective methods have addressed certain issues, they remain complex to formulate and computationally intensive. Instead of addressing these challenges through modified numerical discretization, this paper presents a novel approach: bond-based peridynamics with a Gaussian kernel . This model replaces the bounded kernel function commonly used in existing peridynamic models with an unbounded Gaussian kernel function. Through direct meshfree discretization, we demonstrate that the solution of the proposed model , without the need for compatibility correction or volume correction, aligns with the corresponding local elastic solution. Furthermore, we derive the relevant material parameters based on energy equivalence to an elastic continuum model. Building upon this foundation, we propose a damage model and a linearized version of the proposed peridynamic model. We validate our proposed model through simulations of 2D smooth problems, demonstrating its convergence and accuracy. Finally, we assess the applicability of our approach to realistic scenarios by predicting displacements caused by a 2D plate with pre-existing cracks and replicating high-velocity impact results from the Kalthoff-Winkler experiments.

math-ph↗

arXiv2025

AgentBay: A Hybrid Interaction Sandbox for Seamless Human-AI Intervention in Agentic Systems

The rapid advancement of Large Language Models (LLMs) is catalyzing a shift towards autonomous AI Agents capable of executing complex, multi-step tasks. However, these agents remain brittle when faced with real-world exceptions, making Human-in-the-Loop (HITL) supervision essential for mission-critical applications. In this paper, we present AgentBay, a novel sandbox service designed from the ground up for hybrid interaction. AgentBay provides secure, isolated execution environments spanning Windows, Linux, Android, Web Browsers, and Code interpreters. Its core contribution is a unified session accessible via a hybrid control interface: An AI agent can interact programmatically via mainstream interfaces (MCP, Open Source SDK), while a human operator can, at any moment, seamlessly take over full manual control. This seamless intervention is enabled by Adaptive Streaming Protocol (ASP). Unlike traditional VNC/RDP, ASP is specifically engineered for this hybrid use case, delivering an ultra-low-latency, smoother user experience that remains resilient even in weak network environments. It achieves this by dynamically blending command-based and video-based streaming, adapting its encoding strategy based on network conditions and the current controller (AI or human). Our evaluation demonstrates strong results in security, performance, and task completion rates. In a benchmark of complex tasks, the AgentBay (Agent + Human) model achieved more than 48% success rate improvement. Furthermore, our ASP protocol reduces bandwidth consumption by up to 50% compared to standard RDP, and in end-to-end latency with around 5% reduction, especially under poor network conditions. We posit that AgentBay provides a foundational primitive for building the next generation of reliable, human-supervised autonomous systems.

cs.AI↗

arXiv2025

Mock Observations for the CSST Mission: Main Surveys-the Slitless Spectroscopy Simulation

The China Space Station Telescope (CSST), slated to become China's largest space-based optical telescope in the coming decade, is designed to conduct wide-field sky surveys with high spatial resolution. Among its key observational modes, slitless spectral observation allows simultaneous imaging and spectral data acquisition over a wide field of view, offering significant advantages for astrophysical studies. Currently, the CSST is in the development phase and lacks real observational data. As a result, the development of its data processing pipeline and scientific pre-research must rely on the mock data generated through simulations. This work focuses on developing a simulation framework for the CSST slitless spectral imaging system, analyzing its spectral dispersing properties and structural design. Additionally, the detection performance of the slitless spectral system is assessed for various astrophysical targets. Simulation results demonstrate that nearly all 1st order spectra are accompanied by corresponding 0th order images, facilitating accurate source identification. Furthermore, the GI spectral band exhibits superior detection efficiency compared to the GV and GU bands, establishing it as the primary observational band for stellar and galactic studies. This work successfully develops a simulation framework for the CSST slitless spectroscopic equipment.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: Main Surveys -- the Mock Catalogue

The Chinese Space Station Survey Telescope (CSST) is a flagship space mission, designed to carry out a large-area sky survey to explore the nature of dark matter and dark energy in the Universe. The onboard multi-band imaging and slitless spectroscopic modules will enable us to obtain photometric data for billions of galaxies and stars, as well as hundreds of millions of spectroscopic measurements, advancing various scientific analyses such as galaxy clustering and weak gravitational lensing. To support the image simulations for the main survey of the CSST mission, we present a mock catalogue of stars and galaxies. For stars, the mock catalogue is generated using either Galaxia or TRILEGAL, both of which provide a range of stellar properties to meet the requirements of CSST image simulations. For galaxies, we built a mock light-cone up to redshift z~3.5 from the cosmological Nbody simulation and populated the mock galaxy catalogue from the dark mater haloes using a semi-analytical galaxy formation model. We then performed a full-sky ray-tracing simulation of weak gravitational lensing to obtain lensing shear at the position of each galaxy in the light-cone. To support both multi-band imaging and slitless spectroscopic simulations, we computed the spectral energy distribution (SED) for each galaxy based on its star formation history using a supervised deep-learning model and determined the magnitudes in each band using the CSST throughputs. Finally, the properties of our mock galaxies include positions, redshifts, stellar masses, shapes, sizes, SEDs, lensing shears and magnifications. We have validated our mock catalogue against observational data and theoretical models, with results showing good overall agreement. The catalogue provides a flexible dataset for the development of CSST image processing and can support a wide range of cosmological analyses within the CSST mission.

astro-ph.IM↗