论文检索

Deep Learning · Graph Neural Networks

Youqing Wang, Jiahao Long, Tianxiang Zhao, Man Cao, Mengyuan Xin, Jiapu Wang, Junbin Gao, Jipeng Guo

Graph neural networks (GNNs) have been demonstrated to be powerful tools for analyzing structural graph data. However, most existing methods usually rely on fixed adjacency structures for information propagation, lacking strong adaptability to the latent semantic relationships that exist but are not explicitly connected in graph, especially in complementary high-pass and low-pass filtering views. To this end, this paper proposes a novel Dual-channel Dynamic Graph Neural Network (DCD-GNN), mainly consisting of parallel representation learning channels: a static structure-preserving channel and a dynamic adjacency-enhancing channel. The dynamic channel exploits both low-pass structural filtering and high-pass personalized detail via self-attention adjacency learning and then integrates them for comprehensive semantic modeling, while the static channel maintains structural stability. Both channels employ a multi-scale representation fusion mechanism and are finally fused into a unified and discriminative node embedding representation. Extensive experiments on various graph benchmark datasets verify the superiority of DCD-GNN in discriminative graph representation learning.

Applications · Computer Vision

Luke Miller, Yugyung Lee

Segmenting small and sparse structures in large-scale images is fundamentally constrained by pixel-level, lattice-bound computation and extreme class imbalance--dense, full-resolution inference scales poorly and forces most pipelines to rely on fixed regionization or downsampling, coupling computational cost to image resolution and attenuating boundary evidence precisely where minority structures are most informative. We introduce **SEMIR** (*Semantic Minor-Induced Representation Learning*), a representation framework that decouples inference from the native grid by learning a task-adapted inference space. **SEMIR** transforms the underlying grid graph into a compact, boundary-aligned graph minor through parameterized edge contraction, node deletion, and edge deletion, while preserving an exact lifting map from minor predictions to lattice labels. Minor construction is formalized as a few-shot optimization problem that replaces hand-tuned preprocessing with a *boundary-alignment objective*: minor parameters are learned by maximizing agreement between predicted boundary elements and class-agnostic semantic edges under a *boundary Dice criterion*, and the induced minor is annotated with scale- and rotation-robust geometric and intensity descriptors and supports efficient region-level inference via message passing on a graph neural network (GNN) with relational edge features. We benchmark **SEMIR** on three tumor segmentation datasets—**BraTS2021**, **KiTS2023**, and **LiTS2017**—where targets exhibit high structural variability and distributional uncertainty, providing a stringent testbed for *structure-adaptive inference*. **SEMIR** yields consistent improvements in *minority-structure Dice* at practical runtime, positioning *minor-induced representations* as a principled alternative to pixel-centric segmentation in challenging, high-variability visual domains.

Deep Learning · Graph Neural Networks

Jiaming Zhuo, Ziyi Ma, Kun Fu, Di Jin, Chuan Wang, Zhen Wang, Xiaochun Cao, Huazhu Fu, Liang Yang

By synergizing graph topology with the global expressive power of the attention mechanism, Graph Transformers (GTs) have emerged as a dominant architecture for node classification. However, existing models primarily focus on diverse topology injection mechanisms, specifically score-level and representation-level designs, yet lack a unified theoretical foundation to characterize how these mechanisms shape the representation propagation. To bridge this research gap, this paper unifies these designs under a common Graph Signal Denoising framework, revealing that denoising efficacy (\textit{i.e.}, representation quality) is fundamentally dictated by the block-diagonal structure of the propagation operator. To instantiate this prior efficiently, this paper introduces a novel Block-Diagonal GT architecture, named \textsc{BDFormer}, which enforces a block-diagonal constraint via spectral-regularized cross-attention on latent anchors. Specifically, by routing global interactions through these anchors, \textsc{BDFormer} imposes the spectral block-constraint directly on the anchor-level affinity. Crucially, the learned global affinity guides the pruning of local heterophilous edges, ensuring that both scales synergistically adhere to the target distribution. Extensive evaluations on benchmark datasets demonstrate the scalability and robustness of \textsc{BDFormer}.

Deep Learning · Graph Neural Networks

Kaixuan Yao, Ting Guo, Ming Li, Feilong Cao

In recent years, hypergraph contrastive learning methods have gained widespread attention due to their excellent performance in processing high-order structural data. However, traditional hypergraph learning method often assume that neighboring nodes are homogeneous, which can lead to the mixing of heterogeneous information in highly heterogeneous datasets, thereby affecting node feature representation. To address this issue, this paper proposes a heterogeneity-sensitive hypergraph contrastive learning method. In the view enhancement stage, we introduce a heterogeneity-aware mechanism that masks high-heterogeneity nodes using hyperedges as intermediaries for information filtering. This mechanism weakens the interference of heterogeneous nodes on view consistency, enabling the model to focus more on key features. In the encoding stage, a heterogeneity-sensitive hypergraph encoder is designed. It dynamically adjusts the weights of information propagation through hyperedges in two phases: ``node-to-hyperedge" and ``hyperedge-to-node". This adjustment allows hyperedges to focus on homogeneous information and feedback the aggregated homogeneous information to the respective nodes. Besides, we provide a theoretical proof that our model is capable of aggregating information based on node heterogeneity using hyperedges as intermediate structures. Extensive experimental results demonstrate that this method effectively reduces the interference of heterogeneous information and improves model performance on multiple benchmark datasets. Our code is availabl at: https://anonymous.4open.science/r/HHCL-F926

Deep Learning · Generative Models and Autoencoders

Binhao Wang, Shihao Zhao, Bo Cheng, Qiuyu Ji, YuhangMa, Liebucha Wu, Shanyuan Liu, Dawei Leng, Yuhui Yin

Recent diffusion-based approaches have made substantial progress in image layer decomposition. However, accurately decomposing complex natural images remains challenging due to difficulties in occlusion completion, robust layer disentanglement, and precise foreground boundaries. Moreover, the scarcity of high-quality multi-layer natural image datasets limits advancement. To address these challenges, we propose **RevealLayer**, a diffusion-based framework that decomposes an RGB image into multiple RGBA layers, enabling precise layer separation and reliable recovery of occluded content in natural images. RevealLayer incorporates three key components: (1) a **Region-Aware Attention** module to disentangle hidden and visible layers; (2) an **Occlusion-Guided Adapter** to leverage contextual information to enhance overlapping regions; and (3) a **composite loss** to enforce sharp alpha boundaries and suppress residual artifacts. To support training and evaluation, we introduce **RevealLayer-100K**, a high-quality multi-layer natural image constructed through a collaboration between automated algorithms and human annotation, and further establish **RevealLayerBench** for benchmarking layer decomposition in general natural scenes. Extensive experiments demonstrate that RevealLayer consistently outperforms existing approaches in layer decomposition.

Applications · Health / Medicine

Luke Nightingale, Joseph Tuersley, Scott Warchal, Andrea Cairoli, Jacob Howes, Cameron Shand, Andrew Powell, Darren Green, Amy Strange, Michael Howell

Phenotypic screening experiments produce many microscope images of cells under diverse perturbations, with biologically significant responses often subtle or difficult to identify visually. A central challenge is to extract image representations that distinguish activity from controls and group phenotypically similar perturbations. In this work we propose new adaptations of contrastive loss functions that incorporate experimental metadata as learned class vectors, and a geometrically inspired variant, called SPC, where class vectors are confined to the unit sphere and updated only by attractive terms (allowing more overlap of phenotypically similar classes). The approach is tested on two popular benchmarking datasets, BBBC021 and RxRx3-core; and we also evaluate performance on uncurated screens of HaCaT cells to gauge effectiveness in a realistic use-case scenario. We find we outperform prior methods across the three datasets and on a wide array of metrics measuring phenotype grouping, biological recall, drug-target interaction and mechanism-of-action inference. We also show we maintain this improved performance compared to models over 10x larger in parameter count, and that SPC can be used as an effective fine-tuning technique. The method is easy to implement and is well suited to settings with limited data or compute resources.

Deep Learning · Algorithms

Yun Wang, Junbin Xiao, Han Lyu, Yifan Wang, Jing Zuo, Zhanjie Zhang, Hong Huang, Dapeng Wu, Angela Yao

We introduce UCS-Bench, a dataset spanning 170+ hours of egocentric visual observations with 7K+ timestamped questions for diagnosing User-centric Continual Spatial intelligence in egocentric video streams. UCS-Bench targets a new problem that emphasizes dynamic spatial reasoning, long-term memory, and their alignment with users' real-time locations. We propose DirectMe, a framework that incrementally constructs and maintains a structured spatial memory from streaming egocentric observations. DirectMe enables robust tracking and recall of object locations, all relative to user's movement over time. By tightly coupling visual perception with memory updates and spatial reasoning, our approach supports long-horizon queries that require recalling interactions, resolving viewpoint-induced ambiguities, and adaptation to dynamic scenes. Our experiments show that DirectMe significantly improves the spatial reasoning of leading multimodal LLMs; it also surpasses many spatial-aware and long streaming video models. We hope our benchmark and solution will advance spatial intelligence research for egocentric AI assistants. Data and code will be released.

Deep Learning · Robustness

Rohit Saxena, Alessandro Suglia, Pasquale Minervini

Vision-language models (VLMs) achieve strong performance on standard, high-quality datasets, but we still don't fully understand how they perform under real-world image distortions. We present **VLM-RobustBench**, a benchmark spanning 49 augmentation types across noise, blur, weather, digital, and geometric perturbations, evaluated under graded severities (low/mid/high) and binary transforms, yielding 133 corrupted settings. We evaluate VLMs from four families (Qwen, InternVL, Molmo, Gemma) on two complementary benchmarks: MMBench (visually grounded) and MMMU-Pro (reasoning-oriented). Our results reveal that visual severity is a weak predictor of difficulty: low-severity spatial perturbations often degrade performance more than visually severe photometric corruptions. In particular, low-severity *glass_blur* reduces MMBench accuracy by about 8pp on average across models, while the largest drops arise from resampling and geometric distortions (e.g., *upsample*, *elastic\_transform*, reaching up to 34pp. Overall, our findings suggest current VLMs are *semantically strong but spatially fragile*, motivating the definition of novel robustness evaluation protocols and training regimes that emphasize resampling and geometric invariances.

Social Aspects · Privacy

Huan Wang, Jun Shen, Haoran Li, Zhenyu Yang, Jun Yan, Ousman Manjang, Yanlong Zhai, Di Wu, Guansong Pang

Federated Learning (FL) enables collaborative training of distributed clients while protecting privacy. To enhance generalization capability in FL, prototype-based FL is in the spotlight, since shared global prototypes offer semantic anchors for aligning client-specific local prototypes. However, existing methods update global prototypes at the prototype-level via averaging local prototypes or refining global anchors, which often leads to semantic drift across clients and subsequently yields a misaligned global signal. To alleviate this issue, we introduce ***hyper-prototypes***, defined by a set of learnable global class-wise prototypes to preserve underlying semantic knowledge across clients. The hyper-prototypes are optimized via gradient matching to align with class-relevant characteristics distilled directly from clients' real samples, rather than prototype-level descriptors. We further propose ***FedHPro***, a Federated Hyper-Prototype Learning framework, to leverage hyper-prototypes to promote inter-class separability via mutual-contrastive learning with client-specific margin, while encouraging intra-class uniformity through a consistency penalty. Comprehensive experiments under diverse heterogeneous scenarios confirm that 1) hyper-prototypes produce a more semantically consistent global signal, and 2) FedHPro achieves state-of-the-art performance on several benchmark datasets.

General Machine Learning · Evaluation

Ali Al-Lawati, Jason Lucas, Dongwon Lee, Suhang Wang

Benchmark datasets are critical for reproducible, reliable and discriminative evaluation of LLMs. However, recent studies reveal that many benchmark datasets are included in pretraining corpora, i.e. *contaminated*, which diminishes their value as a reliable measure of model generalization. In this position paper, we argue that benchmark datasets should be *contamination-resistant*, i.e. *unlearnable* but support *inference*. To accomplish this, we first underline the wide prevalence of benchmark dataset contamination and outline the properties of contamination-resistant datasets. Second, we highlight how the asymmetry between the inference and training pipelines in the Transformer architecture can be leveraged to support contamination-resistance. Third, we outline mathematical advancements to make these datasets interoperable across various LLM architectures. Based on the above, we call on the community to ensure the reliability of LLM benchmarking by: (i) advancing novel contamination-resistant methodologies, (ii) develop supporting methods and platforms, and (iii) adopt contamination-resistant benchmarks into existing evaluation pipelines.

Deep Learning · Foundation Models

David Schiff, Ofir Lindenbaum, Yonathan Efroni

Recent advancements in machine learning have largely been driven by foundation models (FMs) trained on large, diverse datasets, enabling them to generalize effectively to new, related tasks. However, extending this paradigm to reinforcement learning (RL), where an agent interacts with an environment to select actions, remains a significant challenge. Most existing approaches train FMs directly on sets of control tasks, but developing diverse RL environments and scaling training across them can be costly and complex. In this study, we explore a simpler alternative approach based on a classical reduction from RL to regression. We demonstrate that a foundation model pre-trained for regression tasks, when used as an in-context regression (ICR) model, can be directly applied to RL problems. Building on this insight, we introduce a gradient-free method, ICR-RL, that requires no additional training and leverages an ICR foundation model to tackle RL tasks. We evaluate our approach by applying the ICR model with the recently proposed TabPFN, which is trained on a wide range of regression tasks. Experiments conducted on the Gymnasium classic-control benchmark indicate that ICR-RL matches or outperforms state-of-the-art methods, including DQN and PPO. These results show that ICR foundation models can effectively solve RL tasks without fine-tuning, demonstrating their potential as a foundation for RL-oriented models

General Machine Learning · Everything Else

Chunxu Zhang, Weipeng Zhang, Guodong Long, Zhiheng Xue, Bo Yang

Most federated recommender systems represent each user with a single embedding learned from local interaction data, implicitly assuming that user preferences are fixed and precisely identifiable. In federated settings, however, each client observes only a limited and fragmentary view of user behavior, rendering such point estimates inherently brittle. To address this mismatch, we model user preferences as distributions rather than points, allowing multiple compatible preference representations to coexist. Rather than collapsing evidence into a single embedding, our approach preserves uncertainty and diversity in user representations, providing a richer basis for preference modeling. We instantiate this idea with a diffusion-based generative framework that produces diverse user embeddings and derives recommendation scores by aggregating predictions across them. This distributional formulation yields more stable ranking behavior and improved robustness under ambiguous feedback. Extensive experiments on federated recommendation benchmark datasets demonstrate consistent and significant improvements over baselines. Our code is available.

General Machine Learning · Transfer, Multitask and Meta-learning

Md Abdullah Al Forhad, Yuansheng Zhu Zhu., Abhinab Acharya, Xumin Liu, Qi Yu, Weishi Shi

The Mixture of Experts (MoE) framework is widely used in continual learning to mitigate catastrophic forgetting. MoEs typically combine a small inter-task shared parameter space with largely independent expert parameters. However, as the number of tasks increases, the shared space becomes a bottleneck, reintroducing forgetting, while fully independent experts require explicit task ID predictors (e.g., routers), adding complexity. In this work, we eliminate the inter-task shared parameter space and the need for a task ID predictor by enabling expert communication and allowing knowledge to be shared dynamically, akin to human collaboration. We bridge the inter-expert knowledge sharing by leveraging the open-set learning capabilities of a multimodal foundation model (e.g., CLIP), thereby providing “expert priors” that bolster each expert’s task-specific representations. Guided by these priors, experts learn calibrated inter-task posteriors. Additionally, Multivariate Gaussians over the learned posteriors promote complementary specialization among experts. We propose new evaluation benchmarks that simulate realistic continual learning scenarios, and our prior-conditioned strategy consistently outperforms existing methods across diverse settings without relying on reference datasets or replay memory.

Deep Learning · Generative Models and Autoencoders

Yufan Deng, Zilin Pan, Hongyu Zhang, Xiaojie Li, Huruoqing, Yufei Ding, Yiming Zou, Yan Zeng, Zhou Daquan

While video generation holds promise for embodied intelligence, current video models struggle with physical realism, and progress is hindered by the lack of standardized benchmarks. To address this gap, we introduce a comprehensive robotics benchmark, RBench, designed to evaluate robot-oriented video generation across five task domains and four distinct embodiments. By assessing task correctness and visual fidelity through reproducible metrics, our evaluation of 25 models reveals significant deficiencies in generating physically realistic robot behaviors. Furthermore, the benchmark achieves a 0.96 Spearman correlation with human judgment, validating its effectiveness. While RBench provides the necessary lens to identify these deficiencies, achieving physical realism requires moving beyond evaluation to address the critical shortage of high-quality training data. Driven by these insights, we introduce a refined four-stage data pipeline, resulting in RoVid-X, the largest open-source robotic dataset for video generation with 4 million annotated video clips, covering thousands of tasks and enriched with physical property annotations. Extensive experiments demonstrate that finetuning on RoVid-X yields consistent performance gains. Collectively, this synergistic ecosystem of evaluation and data establishes a robust foundation for rigorous assessment and scalable training of video models, accelerating the evolution of embodied AI toward physical intelligence. The code and video demos are available in the supplementary materials.

Deep Learning · Large Language Models

Liu Yang, Zeyu Nie, Andrew Liu, Ruomu Zou, Deniz Altınbüken, Amir Yazdanbakhsh, Quanquan Liu

The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming. This challenge is magnified for \textbf{irregular data structures} (such as sparse graphs, unbalanced trees, and non-uniform meshes) where static scheduling fails and data dependencies are unpredictable. Current Large Language Models (LLMs) often fail catastrophically on these tasks, generating code plagued by subtle race conditions, deadlocks, and sub-optimal scaling. We bridge this gap with \textbf{\sys}, a framework designed to synthesize high-performance parallel algorithms for irregular data. Our contributions include: (1) \textbf{The Parlay-Instruct Corpus}, a curated dataset of 12,000 tasks synthesized via a "Critic-Refine" pipeline that explicitly filters for theoretically optimal algorithms under the Work-Span cost model; (2) specialized \textbf{DeepSeek}, \textbf{Qwen}, and \textbf{Gemini} models fine-tuned to align probabilistic generation with the rigorous semantics of the ParlayLib intermediate representation; and (3) an \textbf{Evolutionary Coding Agent (ECA)} that solves the ``last mile'' of correctness by iteratively repairing code using feedback from compilers, race detectors, and performance profilers. On the ParEval benchmark, \sys achieves a \textbf{$106\times$ speedup} on complex irregular graph problems, significantly outperforming state-of-the-art commercial models like GPT-5.2 and Gemini 3 Pro. Furthermore, our approach surpasses expert \emph{human-written} baselines in the standard PBBSBench by \textbf{$4\times$}, demonstrating that AI-driven agents can effectively navigate the complex landscape of high-performance computing.

Deep Learning · Large Language Models

Shobhita Sundaram, John Quan, Ariel Kwiatkowski, Kartik Ahuja, Yann Ollivier, Julia Kempe

RL methods for finetuning large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigate a fundamental question: Can a pretrained LLM leverage latent knowledge to generate an automated curriculum for problems it cannot solve? We explore this with SOAR: A self-improvement framework designed to surface these pedagogical signals through meta-RL. A teacher model proposes synthetic problems for a student model, and is rewarded with its improvement on a subset of hard problems, thus grounding the curriculum in real student progress rather than proxy rewards. Our study on the hardest subsets of math benchmarks (0/128 success) reveal three core findings. First, it is possible to realize bi-level meta-RL that unlocks learning under sparse, binary rewards by sharpening a latent capacity of pretrained models to generate useful problems. Second, grounded rewards outperform intrinsic rewards used in prior LLM self-play, reliably avoiding the typical instability and diversity collapse modes. Third, the structure and well-posedness of questions are more critical for learning progress than solution correctness. Our results suggest that the ability to generate useful stepping stones does not require the preexisting ability to solve the hard problems, paving a principled path to escape reasoning plateaus without additional curated data.

Yonghao Su, Yantao Gan, Junfeng Long, Caiyang Yu, Wenhao Zheng, Xianggen Liu, Jiancheng Lv

Three-dimensional (3D) shape classification plays a central role in computer vision and computer-aided design (CAD), underpinning applications in intelligent manufacturing, automated inspection, and digital engineering. Despite recent progress with 3D CNNs and graph-based approaches, existing methods often overlook the geometric-topological regularities and symmetry principles intrinsic to CAD boundary representations (B-reps). To address this challenge, we introduce EquiCAD, a symmetry-aware learning framework that integrates equivariant representations with graph-based reasoning. By leveraging group-theoretic decomposition of curve and surface descriptors, EquiCAD enforces consistent $SO(3)/O(3)$-equivariance while preserving rich geometric details. The model further exploits hierarchical message passing to capture interactions between local features and global structure. Experimental results across multiple datasets, including SolidLetters, Parts, the Machining Feature benchmark, and our newly constructed Features dataset, demonstrate substantial improvements over prior state-of-the-art approaches, particularly on industrially relevant shapes with fine-grained attributes. These findings highlight the value of symmetry-aware modeling for robust and generalizable 3D shape analysis.

Jiachen Liu

Spectral clustering, a widely-used technique for graph-based data partitioning, faces a severe computational bottleneck due to its $O(n^{3})$ time complexity. While anchor-based approximations reduce the complexity to $O(nm^{2})$ ($m \ll n$), they often yield degenerate solutions in the absence of explicit cluster-size control. To address this limitation, we propose \textbf{SC-FAGC (Size-Constrained Fast Anchor Graph Clustering)}, a unified formulation that integrates entropy regularization and bilateral cardinality constraints within an anchor-based spectral clustering framework. Our model simultaneously promotes cohesive clusters and enforces soft lower and upper bounds on cluster sizes, thus avoiding trivial or highly unbalanced partitions. To solve the resulting non-convex optimization problem efficiently, we develop an \textbf{Iteratively Re-weighted (IRW)} optimization scheme, which sequentially linearizes the objective and solves each subproblem via a \textbf{Double-Bounded Optimal Transport (DB-OT)} solver based on the \textbf{Sinkhorn--Knopp} algorithm. This approach guarantees convergence while maintaining scalability. Extensive experiments on benchmark datasets demonstrate that SC-FAGC consistently achieves state-of-the-art performance in terms of accuracy, purity, and recall, while strictly satisfying the prescribed cluster-size constraints. Thus the proposed method offers a principled and scalable solution for large-scale graph clustering with controllable partition structure.

Deep Learning · Foundation Models

Yanbo Wang, Jiaxuan You, Chuan Shi, Muhan Zhang

Relational Databases (RDBs) are the backbone of modern business, yet they have missed the Foundation Model revolution. Unlike text or images, high-quality RDB data is private and scarce, rendering the standard approach of ``pre-training on the internet'' infeasible. Consequently, existing solutions typically rely on limited real-world datasets, requiring costly fine-tuning to achieve viable performance. To overcome this data scarcity, we introduce RDB-PFN, the first foundation model for databases trained purely on synthetic data. Drawing inspiration from Prior-Data Fitted Networks (PFNs) where synthetic data generated from Structural Causal Models (SCMs) enables reasoning on i.i.d. single tables, we construct a novel Relational Prior Generator to create an infinite stream of random, complex, and diverse database schemas from scratch. By pre-training on a large-scale curriculum of over 2 million synthetic single-table and relational tasks, RDB-PFN learns to adapt to any new database instantly via genuine In-Context Learning. Experiments demonstrate that RDB-PFN outperforms both fine-tuned Graph Foundation Models and state-of-the-art Single-Table Foundation Models on real-world benchmarks. Notably, these results are achieved using a naive model architecture, proving that a rigorously defined synthetic generator is all you need to solve relational reasoning.

Applications · Computer Vision

zhihong Chen, Xuehai Bai, Yang Shi, Chaoyou Fu, Huanyu Zhang, Haotian Wang, Xiaoyan Sun, Zhang Zhang, Liang Wang, Yuanxing Zhang 等

The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data. While existing datasets have covered basic tasks like style transfer and simple object manipulation, they often lack the systematic structure and challenging scenarios required for real-world applications. To address this bottleneck, we introduce \textbf{OpenGPT-4o-Image}, a large-scale dataset constructed using a novel methodology that combines hierarchical task taxonomy with automated data generation. Our taxonomy not only includes fundamental capabilities such as {text rendering} and {style control} but also introduces highly practical yet challenging categories like \textbf{scientific imagery} for physics/chemistry illustrations and \textbf{complex instruction editing} requiring simultaneous execution of multiple operations. Through an automated pipeline leveraging structured resource pools and GPT-4o, we generate 80k high-quality instruction-image pairs with controlled diversity, covering 11 major domains and 51 subtasks. Extensive experiments show that fine-tuning leading models on our dataset achieves significant performance gains across multiple benchmarks, with improvements of up to 18% on editing tasks (UniWorld-V1 on ImgEdit-Bench) and 13% on generation tasks (Harmon on GenEval). Our work demonstrates that systematic data construction is key to advancing multimodal AI capabilities.