Orionnnn/hub-stats huggingface dataset:
Step-Audio-R1 Technical Report
Authors: name: Fei Tian, name: Xiangyu Tony Zhang, name: Yuxin Zhang, name: Haoyang Zhang, name: Yuxin Li, name: Daijiao Liu, name: Yayue Deng, name: Donghang Wu, name: Jun Chen, name: Liang Zhao, name: Chengyuan Yao, name: Hexin Liu, name: Eng Siong Chng, name: Xuerui Yang, name: Xiangyu Zhang, name: Daxin Jiang, name: Gang Yu
Published: 2025-11-19 20:12:50

Recent advances in reasoning models have demonstrated remarkable success in text and vision domains through extended chain-of-thought deliberation. However, a perplexing phenomenon persists in audio language models: they consistently perform better with minimal or no reasoning, raising a fundamental question - can audio intelligence truly benefit from deliberate thinking? We introduce Step-Audio-R1, the first audio reasoning model that successfully unlocks reasoning capabilities in the audio domain. Through our proposed Modality-Grounded Reasoning Distillation (MGRD) framework, Step-Audio-R1 learns to generate audio-relevant reasoning chains that genuinely ground themselves in acoustic features rather than hallucinating disconnected deliberations. Our model exhibits strong audio reasoning capabilities, surpassing Gemini 2.5 Pro and achieving performance comparable to the state-of-the-art Gemini 3 Pro across comprehensive audio understanding and reasoning benchmarks spanning speech, environmental sounds, and music. These results demonstrate that reasoning is a transferable capability across modalities when appropriately anchored, transforming extended deliberation from a liability into a powerful asset for audio intelligence. By establishing the first successful audio reasoning model, Step-Audio-R1 opens new pathways toward building truly multimodal reasoning systems that think deeply across all sensory modalities.
First Frame Is the Place to Go for Video Content Customization
Authors: name: Jingxi Chen, name: Zongxia Li, name: Zhichao Liu, name: Guangyao Shi, name: Xiyang Wu, name: Fuxiao Liu, name: Cornelia Fermuller, name: Brandon Y. Feng, name: Yiannis Aloimonos
Published: 2025-11-19 18:56:50

What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation. In this work, we reveal a fundamentally different perspective: video models implicitly treat the first frame as a conceptual memory buffer that stores visual entities for later reuse during generation. Leveraging this insight, we show that it's possible to achieve robust and generalized video content customization in diverse scenarios, using only 20-50 training examples without architectural changes or large-scale finetuning. This unveils a powerful, overlooked capability of video generation models for reference-based video customization.
SAM2S: Segment Anything in Surgical Videos via Semantic Long-term Tracking
Authors: name: Haofeng Liu, name: Ziyue Wang, name: Sudhanshu Mishra, name: Mingqi Gao, name: Guanyi Qin, name: Chang Han Low, name: Alex Y. W. Kong, name: Yueming Jin
Published: 2025-11-20 18:18:49

Surgical video segmentation is crucial for computer-assisted surgery, enabling precise localization and tracking of instruments and tissues. Interactive Video Object Segmentation (iVOS) models such as Segment Anything Model 2 (SAM2) provide prompt-based flexibility beyond methods with predefined categories, but face challenges in surgical scenarios due to the domain gap and limited long-term tracking. To address these limitations, we construct SA-SV, the largest surgical iVOS benchmark with instance-level spatio-temporal annotations (masklets) spanning eight procedure types (61k frames, 1.6k masklets), enabling comprehensive development and evaluation for long-term tracking and zero-shot generalization. Building on SA-SV, we propose SAM2S, a foundation model enhancing SAM2 for Surgical iVOS through: (1) DiveMem, a trainable diverse memory mechanism for robust long-term tracking; (2) temporal semantic learning for instrument understanding; and (3) ambiguity-resilient learning to mitigate annotation inconsistencies across multi-source datasets. Extensive experiments demonstrate that fine-tuning on SA-SV enables substantial performance gains, with SAM2 improving by 12.99 average J\&F over vanilla SAM2. SAM2S further advances performance to 80.42 average J\&F, surpassing vanilla and fine-tuned SAM2 by 17.10 and 4.11 points respectively, while maintaining 68 FPS real-time inference and strong zero-shot generalization. Code and dataset will be released at https://jinlab-imvr.github.io/SAM2S.
TimeViper: A Hybrid Mamba-Transformer Vision-Language Model for Efficient Long Video Understanding
Authors: name: Boshen Xu, name: Zihan Xiao, name: Jiaze Li, name: Jianzhong Ju, name: Zhenbo Luo, name: Jian Luan, name: Qin Jin
Published: 2025-11-20 17:48:21

We introduce TimeViper, a hybrid vision-language model designed to tackle challenges of long video understanding. Processing long videos demands both an efficient model architecture and an effective mechanism for handling extended temporal contexts. To this end, TimeViper adopts a hybrid Mamba-Transformer backbone that combines the efficiency of state-space models with the expressivity of attention mechanisms. Through this hybrid design, we reveal the vision-to-text information aggregation phenomenon, where information progressively flows from vision tokens to text tokens across increasing LLM depth, resulting in severe vision token redundancy. Motivated by this observation, we propose TransV, a token information transfer module that transfers and compresses vision tokens into instruction tokens while maintaining multimodal understanding capabilities. This design enables TimeViper to process hour-long videos exceeding 10,000 frames. Extensive experiments across multiple benchmarks demonstrate that TimeViper competes with state-of-the-art models while extending frame numbers. We further analyze attention behaviors of both Mamba and Transformer layers, offering new insights into hybrid model interpretability. This work represents an initial step towards developing, interpreting, and compressing hybrid Mamba-Transformer architectures.
Video-as-Answer: Predict and Generate Next Video Event with Joint-GRPO
Authors: name: Junhao Cheng, name: Liang Hou, name: Xin Tao, name: Jing Liao
Published: 2025-11-20 18:59:44

While language models have become impactful in many real-world applications, video generation remains largely confined to entertainment. Motivated by video's inherent capacity to demonstrate physical-world information that is difficult to convey through language alone (e.g., imagine teaching someone to tie a tie using only text), we identify an underutilized opportunity to extend video as a new answer modality for Next-Event Prediction (NEP), formalized as Video-Next-Event Prediction (VNEP). While the established NEP task takes a video with a procedural or predictive question as input to predict the next event in text, VNEP requires dynamic video responses. This shift from telling to showing unlocks more intuitive and customized answers for procedural learning and creative exploration. However, this task remains challenging for existing models, as it demands an understanding of multimodal input, instruction-conditioned reasoning, and the generation of video with visual and semantic consistency. To address this, we introduce VANS, a model that leverages reinforcement learning to align a Vision-Language Model (VLM) with a Video Diffusion Model (VDM) for VNEP. The core of VANS is our proposed Joint-GRPO that orchestrates the VLM and VDM to function as a unit. Driven by a shared reward on their respective output, it optimizes the VLM to produce captions that are both accurate and friendly to visualize, while guiding the VDM to generate videos that are faithful to these captions and the input visual context. To enable this learning, we craft VANS-Data-100K, a dedicated dataset for the VNEP task. Experiments on procedural and predictive benchmarks demonstrate that VANS achieves state-of-the-art performance in both video event prediction and visualization. Codes are released in https://github.com/KlingTeam/VANS.
MiMo-Embodied: X-Embodied Foundation Model Technical Report
Authors: name: Xiaoshuai Hao, name: Lei Zhou, name: Zhijian Huang, name: Zhiwen Hou, name: Yingbo Tang, name: Lingfeng Zhang, name: Guang Li, name: Zheng Lu, name: Shuhuai Ren, name: Xianhui Meng, name: Yuchen Zhang, name: Jing Wu, name: Jinghui Lu, name: Chenxu Dang, name: Jiayi Guan, name: Jianhua Wu, name: Zhiyi Hou, name: Hanbing Li, name: Shumeng Xia, name: Mingliang Zhou, name: Yinan Zheng, name: Zihao Yue, name: Shuhao Gu, name: Hao Tian, name: Yuannan Shen, name: Jianwei Cui, name: Wen Zhang, name: Shaoqing Xu, name: Bing Wang, name: Haiyang Sun, name: Zeyu Zhu, name: Yuncheng Jiang, name: Zibin Guo, name: Chuhong Gong, name: Chaofan Zhang, name: Wenbo Ding, name: Kun Ma, name: Guang Chen, name: Rui Cai, name: Diyun Xiang, name: Heng Qu, name: Fuli Luo, name: Hangjun Ye, name: Long Chen
Published: 2025-11-20 16:34:55

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.
V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models
Authors: name: Yang Luo, name: Xuanlei Zhao, name: Baijiong Lin, name: Lingting Zhu, name: Liyao Tang, name: Yuqi Liu, name: Ying-Cong Chen, name: Shengju Qian, name: Xin Wang, name: Yang You
Published: 2025-11-20 18:59:42

Recent progress in generative video models, such as Veo-3, has shown surprising zero-shot reasoning abilities, creating a growing need for systematic and reliable evaluation. We introduce V-ReasonBench, a benchmark designed to assess video reasoning across four key dimensions: structured problem-solving, spatial cognition, pattern-based inference, and physical dynamics. The benchmark is built from both synthetic and real-world image sequences and provides a diverse set of answer-verifiable tasks that are reproducible, scalable, and unambiguous. Evaluations of six state-of-the-art video models reveal clear dimension-wise differences, with strong variation in structured, spatial, pattern-based, and physical reasoning. We further compare video models with strong image models, analyze common hallucination behaviors, and study how video duration affects Chain-of-Frames reasoning. Overall, V-ReasonBench offers a unified and reproducible framework for measuring video reasoning and aims to support the development of models with more reliable, human-aligned reasoning skills.
PartUV: Part-Based UV Unwrapping of 3D Meshes
Authors: name: Zhaoning Wang, name: Xinyue Wei, name: Ruoxi Shi, name: Xiaoshuai Zhang, name: Hao Su, name: Minghua Liu
Published: 2025-11-20 18:58:39

UV unwrapping flattens 3D surfaces to 2D with minimal distortion, often requiring the complex surface to be decomposed into multiple charts. Although extensively studied, existing UV unwrapping methods frequently struggle with AI-generated meshes, which are typically noisy, bumpy, and poorly conditioned. These methods often produce highly fragmented charts and suboptimal boundaries, introducing artifacts and hindering downstream tasks. We introduce PartUV, a part-based UV unwrapping pipeline that generates significantly fewer, part-aligned charts while maintaining low distortion. Built on top of a recent learning-based part decomposition method PartField, PartUV combines high-level semantic part decomposition with novel geometric heuristics in a top-down recursive framework. It ensures each chart's distortion remains below a user-specified threshold while minimizing the total number of charts. The pipeline integrates and extends parameterization and packing algorithms, incorporates dedicated handling of non-manifold and degenerate meshes, and is extensively parallelized for efficiency. Evaluated across four diverse datasets, including man-made, CAD, AI-generated, and Common Shapes, PartUV outperforms existing tools and recent neural methods in chart count and seam length, achieves comparable distortion, exhibits high success rates on challenging meshes, and enables new applications like part-specific multi-tiles packing. Our project page is at https://www.zhaoningwang.com/PartUV.
SAM 3D: 3Dfy Anything in Images
Authors: name: SAM 3D Team, name: Xingyu Chen, name: Fu-Jen Chu, name: Pierre Gleize, name: Kevin J Liang, name: Alexander Sax, name: Hao Tang, name: Weiyao Wang, name: Michelle Guo, name: Thibaut Hardin, name: Xiang Li, name: Aohan Lin, name: Jiawei Liu, name: Ziqi Ma, name: Anushka Sagar, name: Bowen Song, name: Xiaodong Wang, name: Jianing Yang, name: Bowen Zhang, name: Piotr Dollár, name: Georgia Gkioxari, name: Matt Feiszli, name: Jitendra Malik
Published: 2025-11-20 18:31:46

We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. SAM 3D excels in natural images, where occlusion and scene clutter are common and visual recognition cues from context play a larger role. We achieve this with a human- and model-in-the-loop pipeline for annotating object shape, texture, and pose, providing visually grounded 3D reconstruction data at unprecedented scale. We learn from this data in a modern, multi-stage training framework that combines synthetic pretraining with real-world alignment, breaking the 3D "data barrier". We obtain significant gains over recent work, with at least a 5:1 win rate in human preference tests on real-world objects and scenes. We will release our code and model weights, an online demo, and a new challenging benchmark for in-the-wild 3D object reconstruction.
Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs
Authors: name: Ali Taghibakhshi, name: Sharath Turuvekere Sreenivas, name: Saurav Muralidharan, name: Ruisi Cai, name: Marcin Chochowski, name: Ameya Sunil Mahabaleshwarkar, name: Yoshi Suhara, name: Oluwatobi Olabiyi, name: Daniel Korzekwa, name: Mostofa Patwary, name: Mohammad Shoeybi, name: Jan Kautz, name: Bryan Catanzaro, name: Ashwath Aithal, name: Nima Tajbakhsh, name: Pavlo Molchanov
Published: 2025-11-20 18:59:21

Training a family of large language models targeting multiple scales and deployment objectives is prohibitively expensive, requiring separate training runs for each different size. Recent work on model compression through pruning and knowledge distillation has reduced this cost; however, this process still incurs hundreds of billions of tokens worth of training cost per compressed model. In this paper, we present Nemotron Elastic, a framework for building reasoning-oriented LLMs, including hybrid Mamba-Attention architectures, that embed multiple nested submodels within a single parent model, each optimized for different deployment configurations and budgets. Each of these submodels shares weights with the parent model and can be extracted zero-shot during deployment without additional training or fine-tuning. We enable this functionality through an end-to-end trained router, tightly coupled to a two-stage training curriculum designed specifically for reasoning models. We additionally introduce group-aware SSM elastification that preserves Mamba's structural constraints, heterogeneous MLP elastification, normalized MSE-based layer importance for improved depth selection, and knowledge distillation enabling simultaneous multi-budget optimization. We apply Nemotron Elastic to the Nemotron Nano V2 12B model, simultaneously producing a 9B and a 6B model using only 110B training tokens; this results in over 360x cost reduction compared to training model families from scratch, and around 7x compared to SoTA compression techniques. Each of the nested models performs on par or better than the SoTA in accuracy. Moreover, unlike other compression methods, the nested capability of our approach allows having a many-in-one reasoning model that has constant deployment memory against the number of models in the family.
RoMa v2: Harder Better Faster Denser Feature Matching
Authors: name: Johan Edstedt, name: David Nordström, name: Yushan Zhang, name: Georg Bökman, name: Jonathan Astermark, name: Viktor Larsson, name: Anders Heyden, name: Fredrik Kahl, name: Mårten Wadenbäck, name: Michael Felsberg
Published: 2025-11-19 18:59:38

Dense feature matching aims to estimate all correspondences between two images of a 3D scene and has recently been established as the gold-standard due to its high accuracy and robustness. However, existing dense matchers still fail or perform poorly for many hard real-world scenarios, and high-precision models are often slow, limiting their applicability. In this paper, we attack these weaknesses on a wide front through a series of systematic improvements that together yield a significantly better model. In particular, we construct a novel matching architecture and loss, which, combined with a curated diverse training distribution, enables our model to solve many complex matching tasks. We further make training faster through a decoupled two-stage matching-then-refinement pipeline, and at the same time, significantly reduce refinement memory usage through a custom CUDA kernel. Finally, we leverage the recent DINOv3 foundation model along with multiple other insights to make the model more robust and unbiased. In our extensive set of experiments we show that the resulting novel matcher sets a new state-of-the-art, being significantly more accurate than its predecessors. Code is available at https://github.com/Parskatt/romav2
What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
Authors: name: Alexis Audran-Reiss, name: Jordi Armengol Estapé, name: Karen Hambardzumyan, name: Amar Budhiraja, name: Martin Josifoski, name: Edan Toledo, name: Rishi Hazra, name: Despoina Magka, name: Michael Shvartsman, name: Parth Pathak, name: Justine T Kao, name: Lucia Cipolina-Kun, name: Bhavul Gauri, name: Jean-Christophe Gagnon-Audet, name: Emanuel Tewolde, name: Jenny Zhang, name: Taco Cohen, name: Yossi Adi, name: Tatiana Shavrina, name: Yoram Bachrach
Published: 2025-11-19 16:32:18

AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is still in its infancy, and the key factors driving the success or failure of agent trajectories are not fully understood. We examine the role that ideation diversity plays in agent performance. First, we analyse agent trajectories on MLE-bench, a well-known benchmark to evaluate AI research agents, across different models and agent scaffolds. Our analysis reveals that different models and agent scaffolds yield varying degrees of ideation diversity, and that higher-performing agents tend to have increased ideation diversity. Further, we run a controlled experiment where we modify the degree of ideation diversity, demonstrating that higher ideation diversity results in stronger performance. Finally, we strengthen our results by examining additional evaluation metrics beyond the standard medal-based scoring of MLE-bench, showing that our findings still hold across other agent performance metrics.
Mixture of States: Routing Token-Level Dynamics for Multimodal Generation
Authors: name: Haozhe Liu, name: Ding Liu, name: Mingchen Zhuge, name: Zijian Zhou, name: Tian Xie, name: Sen He, name: Yukang Yang, name: Shuming Liu, name: Yuren Cong, name: Jiadong Guo, name: Hongyu Xu, name: Ke Xu, name: Kam-Woh Ng, name: Juan C. Pérez, name: Juan-Manuel~Pérez-Rúa, name: Tao Xiang, name: Wei Liu, name: Shikun Liu, name: Jürgen Schmidhuber
Published: 2025-11-15 13:24:57

We introduce MoS (Mixture of States), a novel fusion paradigm for multimodal diffusion models that merges modalities using flexible, state-based interactions. The core of MoS is a learnable, token-wise router that creates denoising timestep- and input-dependent interactions between modalities' hidden states, precisely aligning token-level features with the diffusion trajectory. This router sparsely selects the top-k hidden states and is trained with an ε-greedy strategy, efficiently selecting contextual features with minimal learnable parameters and negligible computational overhead. We validate our design with text-to-image generation (MoS-Image) and editing (MoS-Editing), which achieve state-of-the-art results. With only 3B to 5B parameters, our models match or surpass counterparts up to 4times larger. These findings establish MoS as a flexible and compute-efficient paradigm for scaling multimodal diffusion models.
Medal S: Spatio-Textual Prompt Model for Medical Segmentation
Authors: name: Pengcheng Shi, name: Jiawei Chen, name: Jiaqi Liu, name: Xinglin Zhang, name: Tao Chen, name: Lei Li
Published: 2025-11-17 05:44:19

We introduce Medal S, a medical segmentation foundation model that supports native-resolution spatial and textual prompts within an end-to-end trainable framework. Unlike text-only methods lacking spatial awareness, Medal S achieves channel-wise alignment between volumetric prompts and text embeddings, mitigating inaccuracies from resolution mismatches. By preserving full 3D context, it efficiently processes multiple native-resolution masks in parallel, enhancing multi-class segmentation performance. A lightweight 3D convolutional module enables precise voxel-space refinement guided by both prompt types, supporting up to 243 classes across CT, MRI, PET, ultrasound, and microscopy modalities in the BiomedSegFM dataset. Medal S offers two prompting modes: a text-only mode, where model predictions serve as spatial prompts for self-refinement without human input, and a hybrid mode, incorporating manual annotations for enhanced flexibility. For 24-class segmentation, parallel spatial prompting reduces inference time by more than 90% compared to sequential prompting. We propose dynamic resampling to address target-patch ratio imbalance, extending SAT and nnU-Net for data augmentation. Furthermore, we develop optimized text preprocessing, a two-stage inference strategy, and post-processing techniques to improve memory efficiency, precision, and inference speed. On the five-modality average on the validation set, Medal S outperforms SAT with a DSC of 75.44 (vs. 69.83), NSD of 77.34 (vs. 71.06), F1 of 38.24 (vs. 24.88), and DSC TP of 65.46 (vs. 46.97). Medal S achieves excellent performance by harmonizing spatial precision with semantic textual guidance, demonstrating superior efficiency and accuracy in multi-class medical segmentation tasks compared to sequential prompt-based approaches. Medal S will be publicly available at https://github.com/yinghemedical/Medal-S.