Orionnnn/hub-stats huggingface dataset:
Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion
Authors: name: Yueming Pan, name: Ruoyu Feng, name: Qi Dai, name: Yuqi Wang, name: Wenfeng Lin, name: Mingyu Guo, name: Chong Luo, name: Nanning Zheng
Published: 2025-12-04 15:57:27

Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. This indicates the preceding semantics potentially benefit texture generation by providing a semantic anchor. Recent advances have integrated semantic priors from pretrained visual encoders to further enhance LDMs, yet they still denoise semantic and VAE-encoded texture synchronously, neglecting such ordering. Observing these, we propose Semantic-First Diffusion (SFD), a latent diffusion paradigm that explicitly prioritizes semantic formation. SFD first constructs composite latents by combining a compact semantic latent, which is extracted from a pretrained visual encoder via a dedicated Semantic VAE, with the texture latent. The core of SFD is to denoise the semantic and texture latents asynchronously using separate noise schedules: semantics precede textures by a temporal offset, providing clearer high-level guidance for texture refinement and enabling natural coarse-to-fine generation. On ImageNet 256x256 with guidance, SFD achieves FID 1.06 (LightningDiT-XL) and FID 1.04 (1.0B LightningDiT-XXL), while achieving up to 100x faster convergence than the original DiT. SFD also improves existing methods like ReDi and VA-VAE, demonstrating the effectiveness of asynchronous, semantics-led modeling. Project page and code: https://yuemingpan.github.io/SFD.github.io/.
4DLangVGGT: 4D Language-Visual Geometry Grounded Transformer
Authors: name: Xianfeng Wu, name: Yajing Bai, name: Minghan Li, name: Xianzu Wu, name: Xueqi Zhao, name: Zhongyuan Lai, name: Wenyu Liu, name: Xinggang Wang
Published: 2025-12-04 18:15:27

Constructing 4D language fields is crucial for embodied AI, augmented/virtual reality, and 4D scene understanding, as they provide enriched semantic representations of dynamic environments and enable open-vocabulary querying in complex scenarios. However, existing approaches to 4D semantic field construction primarily rely on scene-specific Gaussian splatting, which requires per-scene optimization, exhibits limited generalization, and is difficult to scale to real-world applications. To address these limitations, we propose 4DLangVGGT, the first Transformer-based feed-forward unified framework for 4D language grounding, that jointly integrates geometric perception and language alignment within a single architecture. 4DLangVGGT has two key components: the 4D Visual Geometry Transformer, StreamVGGT, which captures spatio-temporal geometric representations of dynamic scenes; and the Semantic Bridging Decoder (SBD), which projects geometry-aware features into a language-aligned semantic space, thereby enhancing semantic interpretability while preserving structural fidelity. Unlike prior methods that depend on costly per-scene optimization, 4DLangVGGT can be jointly trained across multiple dynamic scenes and directly applied during inference, achieving both deployment efficiency and strong generalization. This design significantly improves the practicality of large-scale deployment and establishes a new paradigm for open-vocabulary 4D scene understanding. Experiments on HyperNeRF and Neu3D datasets demonstrate that our approach not only generalizes effectively but also achieves state-of-the-art performance, achieving up to 2% gains under per-scene training and 1% improvements under multi-scene training. Our code released in https://github.com/hustvl/4DLangVGGT
Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting
Authors: name: Hao-Jen Chien, name: Yi-Chuan Huang, name: Chung-Ho Wu, name: Wei-Lun Chao, name: Yu-Lun Liu
Published: 2025-12-04 18:59:53

Synthesizing high-fidelity frozen 3D scenes from monocular Mannequin-Challenge (MC) videos is a unique problem distinct from standard dynamic scene reconstruction. Instead of focusing on modeling motion, our goal is to create a frozen scene while strategically preserving subtle dynamics to enable user-controlled instant selection. To achieve this, we introduce a novel application of dynamic Gaussian splatting: the scene is modeled dynamically, which retains nearby temporal variation, and a static scene is rendered by fixing the model's time parameter. However, under this usage, monocular capture with sparse temporal supervision introduces artifacts like ghosting and blur for Gaussians that become unobserved or occluded at weakly supervised timestamps. We propose Splannequin, an architecture-agnostic regularization that detects two states of Gaussian primitives, hidden and defective, and applies temporal anchoring. Under predominantly forward camera motion, hidden states are anchored to their recent well-observed past states, while defective states are anchored to future states with stronger supervision. Our method integrates into existing dynamic Gaussian pipelines via simple loss terms, requires no architectural changes, and adds zero inference overhead. This results in markedly improved visual quality, enabling high-fidelity, user-selectable frozen-time renderings, validated by a 96% user preference. Project page: https://chien90190.github.io/splannequin/
SeeNav-Agent: Enhancing Vision-Language Navigation with Visual Prompt and Step-Level Policy Optimization
Authors: name: Zhengcheng Wang, name: Zichuan Lin, name: Yijun Yang, name: Haobo Fu, name: Deheng Ye
Published: 2025-12-02 10:40:46

Existing Vision-Language Navigation (VLN) agents based on Large Vision-Language Models (LVLMs) often suffer from perception errors, reasoning errors, and planning errors, which significantly hinder their navigation performance. To address these limitations, a novel VLN agent framework, named SeeNav-Agent, is proposed in this work. First, to reduce perception hallucinations of the visual module of the VLN agent, a dual-view Visual Prompt (VP) technique is introduced in the input space, which can also improve the agent's understanding of current spatial states. Subsequently, a novel step-level Reinforcement Fine-Tuning (RFT) method, Step Reward Group Policy Optimization (SRGPO), is designed for the post-training of VLN agents. In SRGPO, we first define verifiable process rewards for the navigation task, and then perform efficient step-level advantage estimation by randomly grouping different navigation steps. SRGPO provides dense reward signals for the reinforcement learning process of the VLN agent and enhances its planning capability. Experimental results on the EmbodiedBench Navigation benchmark indicate that by introducing the zero-shot VP module, the GPT-4.1 achieves a navigation success rate of 86.7%, surpassing the current best LVLM by approximately 20 percentage points (pp). Through post-training based on SRGPO, the Qwen2.5-VL-3B model reaches a navigation success rate of 72.3%, outperforming the best existing LVLM model by 5.6 pp. Moreover, compared to RFT algorithms such as GRPO and GiGPO, the proposed SRGPO demonstrates significant improvements in training stability, convergence efficiency, and generalization capability.
SIMA 2: A Generalist Embodied Agent for Virtual Worlds
Authors: name: SIMA team, name: Adrian Bolton, name: Alexander Lerchner, name: Alexandra Cordell, name: Alexandre Moufarek, name: Andrew Bolt, name: Andrew Lampinen, name: Anna Mitenkova, name: Arne Olav Hallingstad, name: Bojan Vujatovic, name: Bonnie Li, name: Cong Lu, name: Daan Wierstra, name: Daniel P. Sawyer, name: Daniel Slater, name: David Reichert, name: Davide Vercelli, name: Demis Hassabis, name: Drew A. Hudson, name: Duncan Williams, name: Ed Hirst, name: Fabio Pardo, name: Felix Hill, name: Frederic Besse, name: Hannah Openshaw, name: Harris Chan, name: Hubert Soyer, name: Jane X. Wang, name: Jeff Clune, name: John Agapiou, name: John Reid, name: Joseph Marino, name: Junkyung Kim, name: Karol Gregor, name: Kaustubh Sridhar, name: Kay McKinney, name: Laura Kampis, name: Lei M. Zhang, name: Loic Matthey, name: Luyu Wang, name: Maria Abi Raad, name: Maria Loks-Thompson, name: Martin Engelcke, name: Matija Kecman, name: Matthew Jackson, name: Maxime Gazeau, name: Ollie Purkiss, name: Oscar Knagg, name: Peter Stys, name: Piermaria Mendolicchio, name: Raia Hadsell, name: Rosemary Ke, name: Ryan Faulkner, name: Sarah Chakera, name: Satinder Singh Baveja, name: Shane Legg, name: Sheleem Kashem, name: Tayfun Terzi, name: Thomas Keck, name: Tim Harley, name: Tim Scholtes, name: Tyson Roberts, name: Volodymyr Mnih, name: Yulan Liu, name: Zhengdong Wang, name: Zoubin Ghahramani
Published: 2025-12-04 13:46:11

We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a significant step toward active, goal-directed interaction within an embodied environment. Unlike prior work (e.g., SIMA 1) limited to simple language commands, SIMA 2 acts as an interactive partner, capable of reasoning about high-level goals, conversing with the user, and handling complex instructions given through language and images. Across a diverse portfolio of games, SIMA 2 substantially closes the gap with human performance and demonstrates robust generalization to previously unseen environments, all while retaining the base model's core reasoning capabilities. Furthermore, we demonstrate a capacity for open-ended self-improvement: by leveraging Gemini to generate tasks and provide rewards, SIMA 2 can autonomously learn new skills from scratch in a new environment. This work validates a path toward creating versatile and continuously learning agents for both virtual and, eventually, physical worlds.
TV2TV: A Unified Framework for Interleaved Language and Video Generation
Authors: name: Xiaochuang Han, name: Youssef Emad, name: Melissa Hall, name: John Nguyen, name: Karthik Padthe, name: Liam Robbins, name: Amir Bar, name: Delong Chen, name: Michal Drozdzal, name: Maha Elbayad, name: Yushi Hu, name: Shang-Wen Li, name: Sreya Dutta Roy, name: Jakob Verbeek, name: XuDong Wang, name: Marjan Ghazvininejad, name: Luke Zettlemoyer, name: Emily Dinan
Published: 2025-12-04 18:59:09

Video generation models are rapidly advancing, but can still struggle with complex video outputs that require significant semantic branching or repeated high-level reasoning about what should happen next. In this paper, we introduce a new class of omni video-text models that integrate ideas from recent LM reasoning advances to address this challenge. More specifically, we present TV2TV, a unified generative modeling framework which decomposes video generation into an interleaved text and video generation process. TV2TV jointly learns language modeling (next-token prediction) and video flow matching (next-frame prediction) using a Mixture-of-Transformers (MoT) architecture. At inference time, TV2TV decides when to alternate between generating text and video frames, allowing the model to "think in words" about subsequent content before ``acting in pixels'' to produce frames. This design offloads much of the responsibility for deciding what should happen next to the language modeling tower, enabling improved visual quality and prompt alignment of generated videos. It also enables fine-grained controllability, allowing users to modify the video generation trajectory through text interventions at any point in the process. In controlled experiments on video game data, TV2TV demonstrates substantial improvements in both visual quality and controllability. TV2TV also scales to natural videos, as we show by augmenting sports videos with interleaved natural language action descriptions using vision-language models (VLMs). Training TV2TV on this corpus yields strong visual quality and prompt alignment, showcasing the model's ability to reason about and generate complex real-world action sequences. Together, these results highlight TV2TV as a promising step toward video generation with open-ended textual reasoning and control.
On GRPO Collapse in Search-R1: The Lazy Likelihood-Displacement Death Spiral
Authors: name: Wenlong Deng, name: Yushu Li, name: Boying Gong, name: Yi Ren, name: Christos Thrampoulidis, name: Xiaoxiao Li
Published: 2025-12-03 19:41:15

Tool-integrated (TI) reinforcement learning (RL) enables large language models (LLMs) to perform multi-step reasoning by interacting with external tools such as search engines and retrievers. Group Relative Policy Optimization (GRPO), exemplified by the recent Search-R1, offers fast convergence and a value-free formulation that makes it appealing for this setting, yet consistently suffers from training collapse. We identify Lazy Likelihood Displacement (LLD), a systematic reduction or stagnation in the likelihood of both correct and incorrect responses, as the core mechanism driving this failure. LLD emerges early and triggers a self-reinforcing LLD Death Spiral, where declining likelihood leads to low-confidence responses, inflating gradients, and ultimately causing collapse. We empirically characterize this process across models on a Search-R1-style, search-integrated question answering task, revealing a consistent three-phase trajectory: early stagnation, steady decay, and accelerated collapse. To address this, we propose a lightweight likelihood-preserving regularization LLDS for GRPO that activates only when a trajectory's likelihood decreases, and regularizes only the tokens responsible. This fine-grained structure mitigates LLD with minimal interference to optimization. Across seven open-domain and multi-hop QA benchmarks, our method stabilizes training, prevents gradient explosion, and yields substantial performance improvements, including +37.8% gains on Qwen2.5-3B and +32.0% gains on Qwen2.5-7B. Our results establish LLD as a fundamental bottleneck in GRPO-based TIRL and provide a practical path toward stable, scalable training of tool-integrated LLM.
DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle
Authors: name: Fangyu Lei, name: Jinxiang Meng, name: Yiming Huang, name: Junjie Zhao, name: Yitong Zhang, name: Jianwen Luo, name: Xin Zou, name: Ruiyi Yang, name: Wenbo Shi, name: Yan Gao, name: Shizhu He, name: Zuo Wang, name: Qian Liu, name: Yang Wang, name: Ke Wang, name: Jun Zhao, name: Kang Liu
Published: 2025-12-03 23:21:28

Real-world enterprise data intelligence workflows encompass data engineering that turns raw sources into analytical-ready tables and data analysis that convert those tables into decision-oriented insights. We introduce DAComp, a benchmark of 210 tasks that mirrors these complex workflows. Data engineering (DE) tasks require repository-level engineering on industrial schemas, including designing and building multi-stage SQL pipelines from scratch and evolving existing systems under evolving requirements. Data analysis (DA) tasks pose open-ended business problems that demand strategic planning, exploratory analysis through iterative coding, interpretation of intermediate results, and the synthesis of actionable recommendations. Engineering tasks are scored through execution-based, multi-metric evaluation. Open-ended tasks are assessed by a reliable, experimentally validated LLM-judge, which is guided by hierarchical, meticulously crafted rubrics. Our experiments reveal that even state-of-the-art agents falter on DAComp. Performance on DE tasks is particularly low, with success rates under 20%, exposing a critical bottleneck in holistic pipeline orchestration, not merely code generation. Scores on DA tasks also average below 40%, highlighting profound deficiencies in open-ended reasoning and demonstrating that engineering and analysis are distinct capabilities. By clearly diagnosing these limitations, DAComp provides a rigorous and realistic testbed to drive the development of truly capable autonomous data agents for enterprise settings. Our data and code are available at https://da-comp.github.io
Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
Authors: name: Nex-AGI Team, name: Yuxuan Cai, name: Lu Chen, name: Qiaoling Chen, name: Yuyang Ding, name: Liwen Fan, name: Wenjie Fu, name: Yufei Gao, name: Honglin Guo, name: Pinxue Guo, name: Zhenhua Han, name: Zhengfu He, name: Hanglei Hu, name: Kai Hu, name: Shengjia Hua, name: Tianyu Huai, name: Baodai Huang, name: Li Ji, name: Zhen Jiang, name: Zhikai Lei, name: Bufan Li, name: Jiahang Lin, name: Lizhi Lin, name: Jinxiu Liu, name: Shichun Liu, name: Ziming Liu, name: Yuchen Ni, name: Pengfang Qian, name: Yujiong Shen, name: Qingyun Shi, name: Wentao Shu, name: Peng Sun, name: Yiran Suo, name: Tian Tang, name: Boyu Tian, name: Guoteng Wang, name: Junzhe Wang, name: Peixin Wang, name: Zhiheng Xi, name: Hang Yan, name: Jie Yang, name: Zhixiong Yang, name: Tianchu Yao, name: Guangze Ye, name: Qianxi Yu, name: Shuo Zhang, name: Xinyue Zhang, name: Yiqi Zhang, name: Jiarong Zhao, name: Miao Zheng, name: Rui Zheng, name: Enyu Zhou, name: Jiazheng Zhou, name: Maosen Zhou, name: Yuhao Zhou, name: Tao Gui, name: Yining Zheng, name: Xinchi Chen, name: Jie Zhou, name: Siyuan Feng, name: Qin Chen, name: Liang He, name: Qi Zhang, name: Xuanjing Huang, name: Xipeng Qiu
Published: 2025-12-04 16:57:02

The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incentive-driven decision making. However, this transition is significantly impeded by the lack of scalable infrastructure capable of constructing high-quality interaction signals for effective policy learning. To address this, we introduce a comprehensive method designed to systematically scale the diversity and complexity of interactive environments. Our method realizes this scaling by addressing three orthogonal dimensions: (1) Complexity: NexAU, a flexible agent framework that supports building complex agent hierarchies via simple configurations; (2) Diversity: NexA4A automatically generates diverse agent hierarchies from natural language to cover infinite domains; and (3) Fidelity: NexGAP bridges the simulation-reality gap by integrating dynamic real-world environment for grounded trajectories synthesis. We train Nex-N1 upon the diverse and complex interactive environments established by our infrastructure. Empirical results on benchmarks such as SWE-bench and tau2 demonstrate that Nex-N1 consistently outperforms SOTA open-source models and achieves competitive performance against frontier proprietary models on complex agentic tasks. We open-source the Nex ecosystem and model weights to facilitate further research.
SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
Authors: name: Wenhua Cheng, name: Weiwei Zhang, name: Heng Guo, name: Haihao Shen
Published: 2025-12-04 12:35:10

Extreme low-bit quantization is critical for efficiently deploying Large Language Models (LLMs), yet it often leads to severe performance degradation at 2-bits and even 4-bits (e.g., MXFP4). We present SignRoundV2, a post-training quantization framework that is highly effective even without mixed-precision. SignRoundV2 introduces (1) a fast sensitivity metric that combines gradient information with quantization-induced deviations to guide layer-wise bit allocation, and (2) a lightweight pre-tuning search for quantization scales to improve extremely low-bit quantization. These components allow SignRoundV2 to close the gap with full-precision models. Extensive experiments indicate that our method sustains competitive accuracy for LLMs, achieving production-grade performance with about 1 percent variance at 4-5 bits and strong results even at 2 bits. The implementation is available at https://github.com/intel/auto-round.
Light-X: Generative 4D Video Rendering with Camera and Illumination Control
Authors: name: Tianqi Liu, name: Zhaoxi Chen, name: Zihao Huang, name: Shaocong Xu, name: Saining Zhang, name: Chongjie Ye, name: Bohan Li, name: Zhiguo Cao, name: Wei Li, name: Hao Zhao, name: Ziwei Liu
Published: 2025-12-04 18:59:57

Recent advances in illumination control extend image-based methods to video, yet still facing a trade-off between lighting fidelity and temporal consistency. Moving beyond relighting, a key step toward generative modeling of real-world scenes is the joint control of camera trajectory and illumination, since visual dynamics are inherently shaped by both geometry and lighting. To this end, we present Light-X, a video generation framework that enables controllable rendering from monocular videos with both viewpoint and illumination control. 1) We propose a disentangled design that decouples geometry and lighting signals: geometry and motion are captured via dynamic point clouds projected along user-defined camera trajectories, while illumination cues are provided by a relit frame consistently projected into the same geometry. These explicit, fine-grained cues enable effective disentanglement and guide high-quality illumination. 2) To address the lack of paired multi-view and multi-illumination videos, we introduce Light-Syn, a degradation-based pipeline with inverse-mapping that synthesizes training pairs from in-the-wild monocular footage. This strategy yields a dataset covering static, dynamic, and AI-generated scenes, ensuring robust training. Extensive experiments show that Light-X outperforms baseline methods in joint camera-illumination control and surpasses prior video relighting methods under both text- and background-conditioned settings.
PSA: Pyramid Sparse Attention for Efficient Video Understanding and Generation
Authors: name: Xiaolong Li, name: Youping Gu, name: Xi Lin, name: Weijie Wang, name: Bohan Zhuang
Published: 2025-12-03 18:02:11

Attention mechanisms are the core of foundation models, but their quadratic complexity remains a critical bottleneck for scaling. This challenge has driven the development of efficient attention mechanisms, with sparsity emerging as the dominant paradigm. Current methods typically retain or discard entire key-value blocks with binary masks, resulting in substantial information loss under high sparsity. To mitigate this gap, we present Pyramid Sparse Attention (PSA), a versatile module applicable to both video understanding and generation tasks. Instead of binary masking, PSA introduces multi-level pooled KV representations, enabling finer mask granularity. Specifically, each query block dynamically allocates lower pooling levels to critical KV blocks and higher levels to less important ones, creating an informative interpolation between full retention and complete pruning. This design, analogous to fixed-point quantization and classical feature pyramid networks in computer vision, effectively mitigates information loss while preserving computational efficiency under a low compute budget. It works with a native, hardware-friendly kernel that leverages decoupled block-tile design to ensure efficient execution. Across video understanding and generation benchmarks, PSA preserves contextual information and visual fidelity, consistently outperforming or achieving comparable performance over existing sparse attention baselines with superior efficiency-quality trade-offs. Our code and model weights are publicly available at: http://ziplab.co/PSA