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Keyao Wang, Shuai Liu, Hengda Shi, Lukui Shi, Haiyong Chen

RGB-Event object detection is able to capture clear and detailed features of the target while maintaining high-speed information collection. It is suitable for high dynamic or harsh environments and has become a research hotspot in recent years. The existing RGB-Event object detectors all struggle to fully utilize the fusion features of two modalities, but do not explicitly disentangle shared vs. private features to dedicated branches. To fully tap into the potential of single features, we propose a frequency-domain coherence-based Shared and Private Features Decoupling method for RGB-Event object detection, SPFD network. First, we design a FCFS module to separate shared and private features by exploring the spectral energy distribution differences between dual modalities. Then, we design a TriAdapt Encoder to process the shared and private features, selectively emphasizing texture-rich RGB features in static regions and motion-sensitive event features in dynamic regions, thereby achieving a robust balance between spatial detail and temporal awareness. Finally, a TriInject Decoder is proposed to emphasize the most discriminative modality features dynamically. Experimental results on the DSEC-Det and PKU-DAVIS-SOD datasets demonstrate that our model achieves competitive performance with state-of-the-art methods.

Heewon Park, Mugon Joe, Miru Kim, Kyungjin Im, Minhae Kwon

Federated learning (FL) in post-deployment settings must adapt to non-stationary data streams across heterogeneous clients without access to ground-truth labels. A major challenge is learning rate selection under client-specific, time-varying distribution shifts, where fixed learning rates often lead to underfitting or divergence. We propose Fed-ADE (Federated Adaptation with Distribution Shift Estimation), an unsupervised federated adaptation framework that leverages lightweight estimators of distribution dynamics. Specifically, Fed-ADE employs uncertainty dynamics estimation to capture changes in predictive uncertainty and representation dynamics estimation to detect covariate-level feature drift, combining them into a per-client, per-timestep adaptive learning rate. We provide theoretical analyses showing that our dynamics estimation approximates the underlying distribution shift and yields dynamic regret and convergence guarantees. Experiments on image and text benchmarks under diverse distribution shifts (label, covariate, and concept) demonstrate consistent improvements over strong baselines. These results highlight that distribution shift-aware adaptation enables effective and robust federated post-adaptation under real-world non-stationarity.

Xingsong Ye, Yongkun Du, JiaXin Zhang, Chen Li, Jing LYU, Zhineng Chen

Large-scale and categorical-balanced text data is essential for training effective Scene Text Recognition (STR) models, which is hard to achieve when collecting real data. Synthetic data offers a cost-effective and perfectly labeled alternative. However, its performance often lags behind, revealing a significant domain gap between real and current synthetic data. In this work, we systematically analyze mainstream rendering-based synthetic datasets and identify their key limitations: insufficient diversity in corpus, font, and layout, which restricts their realism in complex scenarios. To address these issues, we introduce UnionST, a strong data engine synthesizes text covering a union of challenging samples and better aligns with the complexity observed in the wild. We then construct UnionST-S, a large-scale synthetic dataset with improved simulations in challenging scenarios. Furthermore, we develop a self-evolution learning (SEL) framework for effective real data annotation. Experiments show that models trained on UnionST-S achieve significant improvements over existing synthetic datasets. They even surpass real-data performance in certain scenarios. Moreover, when using SEL, the trained models achieve competitive performance by only seeing 9% of real data labels. Code is available at https://github.com/YesianRohn/UnionST.

Han Han, Wei Zhai, Tiesong Zhao, Bin Li, Yang Cao, Zheng-jun Zha

Estimating the 3D motion of scene points from 2D observations, typically parameterized by optical flow and motion in depth, is a fundamental problem in computer vision. Existing learning-based methods usually rely on supervised regression from densely labeled data, but their dependence on annotations and limited use of geometric constraints restricts generalization, motivating unsupervised solutions. Unsupervised 3D motion estimation is challenging because motion along the viewing direction is unobservable, and optical flow and motion in depth are geometrically coupled, making their separation ambiguous. Event cameras capture per-pixel brightness changes asynchronously with microsecond latency, providing high temporal resolution and motion continuity. Projecting event streams along different axes reveals spatiotemporal expansion and contraction patterns that encode depth variation and geometric structure, offering rich cues for unsupervised estimation. Leveraging these properties, we propose an unsupervised event-based 3D motion estimation framework that jointly models optical flow and motion in depth. We first derive an analytical relationship to infer initial motion in depth from estimated flow and further refine it using a directional expansion modulation module that captures horizontal and vertical expansion-contraction patterns in event projections. Finally, motion in depth is incorporated into optical flow warping under a contrast maximization objective. Experiments on the CarlaEvent3D dataset show that our method achieves competitive accuracy and strong generalization, advancing unsupervised 3D motion estimation in the event domain.

Lu Wei, Yuta Nakashima, Noa Garcia

The widespread adoption of text-to-image (T2I) generation has raised concerns about privacy, bias, and copyright violations. Concept erasure techniques offer a promising solution by selectively removing undesired concepts from pre-trained models without requiring full retraining. However, these methods are often evaluated on a limited set of concepts, relying on overly simplistic and direct prompts. To test the boundaries of concept erasure techniques, and assess whether they truly remove targeted concepts from model representations, we introduce EMMA, a benchmark that evaluates five key dimensions of concept erasure over 13 metrics. EMMA goes beyond standard metrics like image quality and time efficiency, testing robustness under challenging conditions, including indirect descriptions, visually similar non-target concepts, and potential gender and ethnicity bias, providing a socially aware analysis of method behavior. Using EMMA, we analyze five concept erasure methods across five domains (objects, celebrities, art styles, NSFW, and copyright). Our results show that existing methods struggle with implicit prompts (i.e., generating the erased concept when it is indirectly referenced) and visually similar non-target concepts (i.e., failing to generate non-target concepts resembling the erased one), while some amplify gender and ethnicity bias compared to the original model. Code and prompts are available at https://github.com/lobsterlulu/EMMA.

Haowen Liu, Shaoxiong Yao, Haonan Chen, Jiawei Gao, Jiayuan Mao, Jia-Bin Huang, Yilun Du

Vision-Language Models (VLMs) exhibit remarkable common-sense and semantic reasoning capabilities. However, they lack a grounded understanding of physical dynamics. This limitation arises from training VLMs on static internet-scale visual-language data that contain no causal interactions or action-conditioned changes. Consequently, it remains challenging to leverage VLMs for fine-grained robotic manipulation tasks that require physical understanding, reasoning, and corresponding action planning. To overcome this, we present SIMPACT, a test-time, SIMulation-enabled ACTion Planning framework that equips VLMs with physical reasoning through simulation-in-the-loop world modeling, without requiring any additional training. From a single RGB-D observation, SIMPACT efficiently constructs physics simulations, enabling the VLM to propose informed actions, observe simulated rollouts, and iteratively refine its reasoning. By integrating language reasoning with physics prediction, our simulation-enabled VLM can understand contact dynamics and action outcomes in a physically grounded way. Our method demonstrates state-of-the-art performance on five challenging, real-world rigid-body and deformable manipulation tasks that require fine-grained physical reasoning, outperforming existing general-purpose robotic manipulation models. Our results demonstrate that embedding physics understanding via efficient simulation into VLM reasoning at test time offers a promising path towards generalizable embodied intelligence.

Yonghao Yu, Lang Huang, Zerun Wang, Runyi Li, Toshihiko Yamasaki

Autoregressive (AR) visual generators model images as sequences of discrete tokens and are trained with next token likelihood. This strict causality supervision optimizes each step only by its immediate next token, which diminishes global coherence and slows convergence. We ask whether foresight, training signals that originate from later tokens, can help AR visual generation. We conduct a series of controlled diagnostics along the injection level, foresight layout, and foresight source axes, unveiling a key insight: aligning foresight to AR models' internal representation on the 2D image grids improves causality modeling.We formulate this insight with Mirai (meaning "future" in Japanese), a general framework that injects future information into AR training with no architecture change and no extra inference overhead: Mirai-E uses explicit foresight from multiple future positions of unidirectional representations, whereas Mirai-I leverages implicit foresight from matched bidirectional representations.Extensive experiments show that Mirai significantly accelerates convergence and improves generation quality. For instance, Mirai can speed up LlamaGen-B's convergence by up to 10xand reduce the generation FID from 5.34 to 4.34 on the ImageNet class-condition image generation benchmark.Our study highlights that visual autoregressive models need foresight.

Xinpeng Li, Bolin Lai, Hardy Chen, Shijian Deng, Cihang Xie, Yuyin Zhou, James M. Rehg, Yapeng Tian

We introduce Omni-MMSI, a new task that requires comprehensive social interaction understanding from raw audio, vision, and speech input. The task involves perceiving identity-attributed social cues (e.g., who is speaking what) and reasoning about the social interaction (e.g., whom the speaker refers to). This task is essential for developing AI assistants that can perceive and respond to human interactions. Unlike prior studies that operate on oracle-preprocessed social cues, Omni-MMSI reflects realistic scenarios where AI assistants must perceive and reason from raw data. However, existing pipelines and multi-modal LLMs perform poorly on Omni-MMSI because they lack reliable identity attribution capabilities, which leads to inaccurate social interaction understanding. To address this challenge, we propose Omni-MMSI-R, a reference-guided pipeline that produces identity-attributed social cues with tools and conducts chain-of-thought social reasoning. To facilitate this pipeline, we construct participant-level reference pairs and curate reasoning annotations on top of the existing datasets. Experiments demonstrate that Omni-MMSI-R outperforms advanced LLMs and counterparts on Omni-MMSI. Project page: https://sampson-lee.github.io/omni-mmsi-project-page.

Tao Xie, Tao An, Feng Liu, Wensheng Jin, Zhengyu Li, Lijun Zhao, Ruifeng Li

In this work, we observe that for indoor 3D object detection, fundamental geometric cues induce homogeneous spatial responses across scenes, whereas scene-specific structure yields heterogeneous signatures. However, existing detectors lack effective mechanisms to jointly extract and exploit such dual properties, which imposes inherent limitations on detection performance. Guided by this insight, we propose H^2A^2, a homogeneity-aware and heterogeneity-aware feature perception network for unified indoor 3D object detection under cross-scene training paradigms.Technically, we introduce a structural-feature-aware kernel selection (SF-KS) method, which encompasses three core components:(i) task-aware linear modulation, a channel-wise affine transformation that strengthens scene-structural feature representation; (ii) kernel weight selection strategy that integrates an offset validity prior to suppress non-informative cross-scene transfer while utilizing a structural consistency posterior to capture scene-homogeneous cues. and (iii) task-aware channel gating that suppresses scene-irrelevant feature responses. Overall, SF-KS enables the precise optimization of homogeneous features while specializing in scene-specific heterogeneous ones. In addition, to stabilize cross-scene optimization, we further introduce norm-based gradient homogenization (NGH) algorithm, which normalizes and dynamically reweights per-task gradient norms to mitigate conflicts and promote consistent updates. Extensive experiments on diverse indoor benchmarks show that H^2A^2 delivers consistent gains over strong baselines and improves cross-scene generalization.

Wenxuan Wang, Chenglei Wang, Chengzhi Yan, Xuelin Qian, Yanning Zhang

Deep learning models remain highly vulnerable to evolving adversarial attacks. While existing continual adversarial training approaches often assume abundant adversarial data at each stage, real-world scenarios frequently involve limited data availability. This paper addresses the setting of Few-shot Continual Adversarial Training, where only a small number of adversarial examples are available per stage, presenting major challenges in achieving robust generalization and mitigating catastrophic forgetting. To tackle these challenges, we propose a novel continual adversarial training framework that incorporates three key components: (i) an Adversarial Margin loss that explicitly pushes clean samples away from decision boundaries to enhance feature discrimination; (ii) a Gaussian mixture model Prototype Replay strategy that synthesizes representative pseudo-features to preserve knowledge of past adversarial domains; and (iii) a Multi-Domain Balanced loss that guides updates to stabilize learning across diverse attack distributions. Extensive experiments on ImageNet-1K and CIFAR-100 demonstrate that our approach consistently outperforms state-of-the-art methods in both clean and robust accuracy across a variety of adversarial settings. The code will be released.

Yuechen Xie, Xiaoyan Zhang, Yicheng Shan, Zhu Hao, Rui Tang, Rong Wei, Mingli Song, Yuanyu Wan, Jie Song

Vision-Language Models (VLMs) have been increasingly applied in real-world scenarios due to their outstanding understanding and reasoning capabilities. Although VLMs have already demonstrated impressive capabilities in common visual question answering and logical reasoning, they still lack the ability to make reasonable decisions in complex real-world environments. We define this ability as spatial logical reasoning, which not only requires understanding the spatial relationships among objects in complex scenes, but also the logical dependencies between steps in multi-step tasks. To bridge this gap, we introduce Spatial Logical Question Answering (SpatiaLQA), a benchmark designed to evaluate the spatial logical reasoning capabilities of VLMs. SpatiaLQA consists of 9,605 question answer pairs derived from 241 real-world indoor scenes. We conduct extensive experiments on 41 mainstream VLMs, and the results show that even the most advanced models still struggle with spatial logical reasoning. To address this issue, we propose a method called recursive scene graph assisted reasoning, which leverages visual foundation models to progressively decompose complex scenes into task-relevant scene graphs, thereby enhancing the spatial logical reasoning ability of VLMs, outperforming all previous methods. We will release our code and dataset soon.

Chaojun Ni, Cheng Chen, Xiaofeng Wang, Zheng Zhu, Wenzhao Zheng, Boyuan Wang, Tianrun Chen, Guosheng Zhao, Haoyun Li, Zhehao Dong 等

Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts. To mitigate this issue, using a lightweight VLM has been explored, but it compromises spatiotemporal reasoning. Although some methods suggest that incorporating additional 3D inputs can help, they usually rely on large VLMs to fuse 3D and 2D inputs and still lack temporal understanding. Therefore, we propose SwiftVLA, an architecture that enhances a compact model with 4D understanding while preserving design efficiency. Specifically, our approach features a pretrained 4D visual geometry transformer with a temporal cache that incrementally extracts 4D features from 2D images. Then, to enhance the VLM's ability to exploit both 2D images and 4D features, we introduce Fusion Tokens, a set of learnable tokens trained with a future prediction objective to generate unified representations for action generation. Finally, we introduce a mask-and-reconstruct strategy that randomly masks 4D inputs to the VLM and trains the VLA to reconstruct the masked features. This self-reconstruction objective helps learn effective 4D representations, allowing the 4D branch to be dropped at inference with minimal performance loss. Extensive experiments in real and simulated environments show that SwiftVLA outperforms lightweight baselines and rivals VLAs up to 7xlarger. On edge devices, SwiftVLA achieves comparable performance while being 18xfaster than the \pi_0 and reducing the memory footprint by 12x.

Congcong Bian, Haolong Ma, Hui Li, Zhongwei Shen, Xiaoqing Luo, Xiaoning Song, Xiao-jun Wu

Spatial registration across different visual modalities is a critical but formidable step in multi-modality image fusion for real-world perception. Although several methods are proposed to address this issue, the existing registration-based fusion methods typically require extensive pre-registration operations, limiting their efficiency. To overcome these limitations, a general cross-modality registration method guided by visual priors is proposed for infrared and visible image fusion task, termed FusionRegister. Firstly, FusionRegister achieves robustness by learning cross-modality misregistration representations rather than forcing alignment of all differences, ensuring stable outputs even under challenging input conditions. Moreover, FusionRegister demonstrates strong generality by operating directly on fused results, where misregistration is explicitly represented and effectively handled, enabling seamless integration with diverse fusion methods while preserving their intrinsic properties. In addition, its efficiency is further enhanced by serving the backbone fusion method as a natural visual prior provider, which guides the registration process to focus only on mismatch regions, thereby avoiding redundant operations. Extensive experiments on three datasets demonstrate that FusionRegister not only inherits the fusion quality of state-of-the-art methods, but also delivers superior detail alignment and robustness, making it highly suitable for infrared and visible image fusion method. The code will be available at https://github.com/bociic/FusionRegister.

Fatemeh Nazarieh, Zhenhua Feng, Diptesh Kanojia, Josef Kittler, Muhammad Awais

Generating realistic and expressive audio-driven talking avatars remains a central challenge in digital human synthesis. Existing methods often depend on intermediate representations such as pose estimations for natural body motion, which restricts flexibility and adds visual distortions. Moreover, most audio-driven approaches rely on discrete emotion classifiers or text labels to regulate facial expression, reducing complex affective dynamics to coarse categories such as happy, sad, or angry. Such categorical supervision fails to capture the continuous and fine-grained speech dynamics (rhythm, energy, intensity) resulting in limited synchronization and emotionally shallow motion. To overcome these limitations, we present SyncDreamer, a unified Diffusion Transformer framework that generates identity-preserving and emotionally expressive talking avatars from only a single image, speech audio, and text prompt.We propose a visual adapter with Attention Localization Loss to maintain identity fidelity, further incorporating an audio dynamics encoder for rhythm- and emotion-aware motion, and an RL-based Cross-Modal Prompt Enhancer grounding textual cues in visual context for fine-grained motion control. Extensive experiments on portrait and full-body benchmarks demonstrate state-of-the-art performance in realism, synchronization accuracy, and semantic controllability, establishing a scalable foundation for expressive digital avatars in interactive and creative applications.

Yunlong Lin, Linqing Wang, Kunjie Lin, Zixu Lin, Kaixiong Gong, Wenbo Li, Bin Lin, Zhenxi Li, Shiyi Zhang, Yuyang Peng 等

Agent-based editing models have substantially advanced interactive experiences, processing quality, and creative flexibility. However, two critical challenges persist: (1) instruction hallucination--text-only chain-of-thought (CoT) reasoning cannot fully prevent factual errors due to inherent information bottlenecks; (2) reward hacking--dynamic policy optimization against static reward models allows agents to exploit flaws in reward functions. To address these issues, we propose JarvisEvo, a unified image editing agent that emulates an expert human designer by iteratively editing, selecting appropriate tools, evaluating results, and reflecting on its own decisions to refine outcomes. JarvisEvo offers three key advantages: (1) an interleaved multimodal chain-of-thought (iMCoT) reasoning mechanism that enhances instruction following and editing quality; (2) a synergistic editor-evaluator policy optimization (SEPO) framework that enables self-improvement without external rewards, effectively mitigating reward hacking; and (3) support for both preservative and generative editing through seamless integration of Adobe Lightroom and Qwen-Image-Edit tools. On ArtEdit-Bench, JarvisEvo outperforms Nano-Banana by an average of 18.95% on preservative editing metrics, including a substantial 44.96% improvement in pixel-level content fidelity, while maintaining competitive performance in generative editing tasks.

Yang Chen, Xieyuanli Chen, Junxiang Li, Jie Tang, Tao Wu

Robust cross-view geo-localization (CVGL) remains challenging despite the surge in recent progress. Existing methods still rely on field-of-view (FoV)-specific training paradigms, where models are optimized under a fixed FoV but collapse when tested on unseen FoVs and unknown orientations. This limitation necessitates deploying multiple models to cover diverse variations. Although studies have explored dynamic FoV training by simply randomizing FoVs, they failed to achieve robustness across diverse conditions---implicitly assuming all FoVs are equally difficult. To address this gap, we present SinGeo, a simple yet powerful framework that enables a single model to realize robust cross-view geo-localization without additional modules or explicit transformations. SinGeo employs a dual discriminative learning architecture that enhances intra-view discriminability within both ground and satellite branches, and is the first to introduce a curriculum learning strategy to achieve robust CVGL. Extensive evaluations on four benchmark datasets reveal that SinGeo sets state-of-the-art (SOTA) results under diverse conditions, and notably outperforms methods specifically trained for extreme FoVs. Beyond superior performance, SinGeo also exhibits cross-architecture transferability. Furthermore, we propose a consistency evaluation method to objectively assess model stability under varying views, providing an objective perspective for understanding and advancing robustness in future CVGL research. Codes are available at: https://github.com/Yangchen-nudt/SinGeo.

Dmitry Demidov, Muhammad Zaigham Zaheer, Zongyan Han, Omkar Thawakar, Rao Anwer

Vocabulary-free fine-grained image recognition aims to distinguish visually similar categories within a meta-class without a fixed, human-defined label set. Existing solutions for this problem remain limited by either the usage of a large and rigid list of vocabularies or by the dependency on complex pipelines with fragile heuristics where errors propagate across stages. Meanwhile, the ability of recent large multi-modal models (LMMs) equipped with explicit or implicit reasoning to comprehend visual-language data, decompose problems, retrieve latent knowledge, and self-correct suggests a more principled and effective alternative. Building on these capabilities, we propose FiNDR (Fine-grained Name Discovery via Reasoning), the first reasoning-augmented LMM-based framework for vocabulary-free fine-grained recognition. The system oper- ates in three automated steps: (i) a reasoning-enabled LMM generates descriptive candidate labels for each image; (ii) a vision-language model filters and ranks these candidates to form a coherent class set; and (iii) the verified names instantiate a lightweight multi-modal classifier used at inference time. Extensive experiments on popular fine-grained classification benchmarks demonstrate state-of-the-art per- formance under the vocabulary-free setting, with a significant relative margin of up to 18.8% over previous ap- proaches. Remarkably, the proposed method surpasses zero-shot baselines that exploit pre-defined ground-truth names, challenging the assumption that human-curated vo- cabularies define an upper bound. Ablations further confirm that advanced prompting techniques and built-in rea- soning mechanisms significantly enhance naming quality. Additionally, we show that carefully engineered prompts enable open-source LMMs to match proprietary counter- parts. These findings establish reasoning-augmented LMMs as an effective foundation for scalable, fully automated, open-world fine-grained visual recognition. The source code and relevant prompting guidelines will be released.

Jianshi Wu, Minghang Zhu, Dunqiang Liu, Wen Li, Sheng Ao, Siqi Shen, Chenglu Wen, Cheng Wang

LiDAR relocalization has attracted increasing attention as it can deliver accurate 6-DoF pose estimation in complex 3D environments. Recent learning-based regression methods offer efficient solutions by directly predicting global poses without the need for explicit map storage. However, these methods often struggle in challenging scenes due to their equal treatment of all predicted points, which is vulnerable to noise and outliers. In this paper, we propose LEADER, a robust LiDAR-based relocalization framework enhanced by a simple, yet effective geometric encoder. Specifically, a Robust Projection-based Geometric Encoder architecture which captures multi-scale geometric features is first presented to enhance descriptiveness in geometric representation. A Truncated Relative Reliability loss is then formulated to model point-wise ambiguity and mitigate the influence of unreliable predictions. Extensive experiments on the Oxford RobotCar and NCLT datasets demonstrate that LEADER outperforms state-of-the-art methods, achieving 24.1% and 73.9% relative reductions in position error over existing techniques, respectively. The source code is released on https://github.com/JiansW/LEADER.

Hyeonseong Kim, Hyun-Kurl Jang, Kuk-Jin Yoon

LiDAR semantic segmentation must remain robust under various sensor and environmental corruptions to be reliable in safety-critical applications. Existing test-time adaptation methods, including approaches based on pseudo-labels and normalization statistics, have shown promising results but can still struggle under severe distribution shifts. To complement these approaches, we propose a geometry-aware test-time training framework that leverages an auxiliary self-supervised objective. Our method is based on geometric inlier discrimination (GeoID), which injects synthetic off-manifold points into the input and trains the model to distinguish geometry-consistent inliers from synthetically displaced outliers, enabling adaptation on unlabeled test data. To further stabilize this process under real corruptions, we introduce bidirectional unreliable point filtering (BiUPF), which uses inlier scores from the source-trained model to filter out unreliable regions on both original and synthetic points, focusing updates on high-confidence samples. Experiments on two large-scale corruption benchmarks, SemanticKITTI-C and nuScenes-C, show that our method consistently outperforms strong test-time adaptation baselines and improves robustness across diverse LiDAR corruptions. Our code is available at https://github.com/hskim617/GeoID.

Shufan Li, Jiuxiang Gu, Kangning Liu, Zhe Lin, Zijun Wei, Aditya Grover, Jason Kuen

Masked Discrete Diffusion Models (MDMs) have achieved strong performance across a wide range of multimodal tasks, including image understanding, generation, and editing. However, their inference speed remains suboptimal due to the need to repeatedly process redundant masked tokens at every sampling step. In this work, we propose Sparse-LaViDa, a novel modeling framework that dynamically truncates unnecessary masked tokens at each inference step to accelerate MDM sampling. To preserve generation quality, we introduce specialized register tokens that serve as sparse representations for the truncated tokens. Furthermore, to ensure consistency between training and inference, we design a specialized attention mask that faithfully matches the truncated sampling procedure during training. Built upon the state-of-the-art unified MDM LaViDa-O, Sparse-LaViDa achieves up to a 2xspeedup across diverse tasks including text-to-image generation, image editing, and mathematical reasoning, while maintaining generation quality.