🔥 Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence

💡 The paper introduces LingBot-Video, a video pretraining framework designed for embodied intelligence applications, such as robot control. The current video generative models are not suitable for these applications due to their focus on content creation, prioritizing visual fidelity and creativity over computational efficiency and physical realism. To address this issue, the authors propose a framework that combines a Mixture-of-Experts architecture with specialized data augmentation and a multi-dimensional reward system.

The Mixture-of-Experts architecture is used instead of a dense framework to achieve a better balance between modeling capacity and inference efficiency. The authors also construct a data profiling engine that augments standard internet videos with robot-oriented footage, including manipulation, navigation, and egocentric perspectives, to equip the base model with an understanding of actions and world dynamics.

The training process involves a multi-dimensional reward system that enforces physical rationality and task completion, in addition to standard criteria such as aesthetics and motion consistency. The authors evaluate the performance and efficiency of LingBot-Video and validate its effectiveness as a video foundation model.

The main contributions of the paper are the introduction of LingBot-Video, a large-scale, open-source Mixture-of-Experts video foundation model, and the development of a framework that bridges digital creativity and physical actuation. The authors provide a pioneering effort to address the domain mismatch between video generative models and embodied intelligence applications, and their work has the potential to improve the performance of robots in various tasks.

📅 Published on Jul 8

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.07675
• PDF: https://arxiv.org/pdf/2607.07675
• Project Page: https://technology.robbyant.com/lingbot-video

📊 Datasets citing this paper:
• https://huggingface.co/datasets/cy0307/awesome-egocentric-atlas

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📢 By: https://xn--r1a.website/PaperNexus

#MixtureOfExpertsArchitecture #EmbodiedIntelligence #VideoPretraining #RobotControlSystems #MixtureOfExpertsModels

🔥 RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies

💡 The paper introduces RoboDojo, a unified sim-and-real benchmark for evaluating generalist robot manipulation policies. The existing benchmarks have limitations as they rely on simple, short-horizon, or skill-narrow tasks with limited capability coverage and are often conducted only in simulation or only in the real world. Simulation enables scalable feedback but misses physical deployment challenges, while real-world evaluation is costly, time-consuming, and difficult to reproduce.

RoboDojo addresses these limitations by providing a comprehensive evaluation framework that includes 42 simulation tasks and 18 real-world tasks covering diverse and complementary manipulation capabilities. The simulation benchmark evaluates five dimensions: generalization, memory, precision, long-horizon execution, and open-vocabulary instruction following, while the real-world benchmark exposes policies to challenging physical-world deployment conditions.

The RoboDojo framework supports scalable evaluation through heterogeneous parallel simulation in Isaac Sim and provides RoboDojo-RealEval, a reproducible real-world evaluation system with remote cloud access, standardized hardware, scene reset, evaluation protocol, and deployment interface. The paper also integrates 30 policies into XPolicyLab and evaluates them on RoboDojo, establishing a public leaderboard and systematic analysis of current policy performance.

The contributions of the paper are the introduction of a unified sim-and-real benchmark for evaluating generalist robot manipulation policies, a comprehensive evaluation framework that covers diverse manipulation capabilities, and a scalable and reproducible evaluation system. The paper provides a systematic analysis of current policy performance and establishes a public leaderboard, which can facilitate the development and evaluation of generalist robot manipulation policies.

📅 Published on Jul 7

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.04434
• PDF: https://arxiv.org/pdf/2607.04434
• Project Page: https://robodojo-benchmark.com/

📊 Datasets citing this paper:
• https://huggingface.co/datasets/cy0307/awesome-egocentric-atlas

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📢 By: https://xn--r1a.website/PaperNexus

#RobotManipulationPolicies #SimToRealRobotics #RobotLearningBenchmarks #GeneralistRobotics #RoboticManipulationTasks

🔥 AlayaWorld: Long-Horizon and Playable Video World Generation

💡 The paper presents AlayaWorld, an open source framework for creating interactive generative worlds that can be used in real time. Traditionally, video game worlds are built using labor intensive production pipelines, which are costly to develop, difficult to customize, and expensive to modify after deployment. Recent advances in video world models offer a new approach, where models can synthesize future observations conditioned on the current world state and user interactions, allowing playable worlds to be generated online.

The AlayaWorld framework addresses this problem by providing a modular architecture that enables real time user interaction and supports diverse actions. The framework is trained on both gameplay recordings and real world videos, which allows it to capture diverse visual appearances and physical dynamics. This enables the creation of interactive applications beyond gaming, including embodied intelligence.

The AlayaWorld framework provides a full stack solution, including data preparation, model architecture, model training, inference acceleration, and deployment. The framework is modular and extensible, allowing users to easily customize and extend it. The authors also release reproducible pipelines, reference implementations, evaluation tools, and comprehensive documentation, making it easier for others to use and build upon the framework.

The contributions of the paper include the development of the AlayaWorld framework, which enables open ended real time interaction and allows users to freely navigate and perform diverse actions. The framework also provides a practical foundation for future research and real time applications of generative world models. Overall, the paper presents a significant contribution to the field of video world generation, enabling the creation of interactive and dynamic worlds that can be used in a variety of applications.

📅 Published on Jul 7

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.06291
• PDF: https://arxiv.org/pdf/2607.06291
• Project Page: https://alaya-lab.github.io/AlayaWorld/

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📢 By: https://xn--r1a.website/PaperNexus

#GenerativeWorlds #VideoWorldGeneration #InteractiveWorldModels #RealTimeWorldSynthesis #PlayableWorldCreation

🔥 Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation

💡 The paper introduces a benchmark for evaluating the ability of artificial intelligence systems to reason about and generate scientific ideas based on their lineage. The authors propose that scientific ideas can be represented as genetic-like objects, called Idea Genome objects, which capture the inheritance structure of ideas. The benchmark, called IdeaGene-Bench, consists of a large dataset of scientific papers and proposals, represented as Idea Genome objects, and includes records of how these objects evolve over time.

The benchmark supports two types of evaluations: IG-Exam, which tests a system's ability to reason about the lineage of ideas, and IG-Arena, which evaluates a system's ability to generate new ideas that are coherent with a given lineage. The IG-Exam evaluation includes 42 task types and 1029 instances, and tests a system's ability to perform tasks such as tracing the inheritance of ideas, reasoning about evolutionary relationships, and verifying the lineage of a proposal.

The IG-Arena evaluation uses a lineage-conditioned Population-Evolution Score to assess a system's ability to generate new ideas that are meaningful and valuable. The score takes into account whether a proposal inherits the right Idea Genome objects, varies meaningfully from nearby work, and offers selection value for future research.

The authors conducted experiments on 14 language model-based systems and found that the strongest system achieved only 27.3% exact accuracy on lineage reasoning tasks. The results also showed that providing structured lineage context did not uniformly improve the performance of all systems, but rather reshuffled the rankings of the systems.

The paper's contributions include the introduction of the IdeaGene-Bench benchmark, the Idea Genome framework for representing scientific ideas, and the evaluation of language model-based systems on lineage reasoning and idea generation tasks. The results highlight the challenges of developing systems that can reason about and generate scientific ideas based on their lineage, and demonstrate the need for further research in this area.

📅 Published on Jul 9

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.08758
• PDF: https://arxiv.org/pdf/2607.08758
• Project Page: https://visionxlab.github.io/IdeasHaveGenomes/

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📢 By: https://xn--r1a.website/PaperNexus

#IdeaGeneration #ScientificLineage #ArtificialIntelligenceInScience #IdeaGenome #LineageReasoning

🔥 Advancing Open-source World Models

💡 The paper presents LingBot-World, an open-source world simulator that offers high-fidelity dynamics, long-term memory capabilities, and real-time interactivity across diverse environments. The simulator is capable of generating realistic and scientifically accurate environments, as well as cartoon styles and other settings. It achieves a high level of fidelity and robustness in its dynamics, allowing it to maintain contextual consistency over time, which is referred to as long-term memory. This means that the simulator can preserve the context of a scene over a period of minutes, rather than just seconds. Additionally, LingBot-World supports real-time interactivity, with a latency of under 1 second when producing 16 frames per second. The release of LingBot-World as an open-source model aims to bridge the gap between open-source and closed-source technologies, and is expected to have practical applications in areas such as content creation, gaming, and robot learning. The authors provide public access to the code and model, allowing the community to build upon and extend the capabilities of LingBot-World. Overall, the paper contributes to the advancement of open-source world models, providing a powerful tool for researchers and developers to explore and create complex simulated environments.

📅 Published on Jan 28

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2601.20540
• PDF: https://arxiv.org/pdf/2601.20540
• Project Page: https://technology.robbyant.com/lingbot-world

🤖 Models citing this paper:
• https://huggingface.co/robbyant/lingbot-world-base-cam
• https://huggingface.co/robbyant/lingbot-world-base-act-preview
• https://huggingface.co/robbyant/lingbot-world-fast

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/naeem123goplay/Real-World
• https://huggingface.co/spaces/iCoderX/Lingbot-World
• https://huggingface.co/spaces/ftdgfxxfg66/Real-World

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📢 By: https://xn--r1a.website/PaperNexus

#OpenSourceWorldModels #WorldSimulators #LongTermMemoryInAI #RealTimeInteractivity #HighFidelityDynamics

🔥 Vidu S1: A Real-Time Interactive Video Generation Model

💡 The paper introduces Vidu S1, a real-time interactive video generation model that enables voice-controlled digital character animation with high frame rates on consumer hardware. The model addresses the problem of generating high-quality, real-time video content that can be controlled by users through voice instructions. To achieve this, the authors employ TurboDiffusion and TurboServe, which allow Vidu S1 to produce 540p videos at up to 42 frames per second on regular consumer GPUs. The model supports infinite-length video generation without visual distortion and allows users to upload custom images and choose different voice tones for personalized experiences. The results show that Vidu S1 achieves the best performance across all test metrics while meeting real-time inference requirements, demonstrating its effectiveness in generating high-quality, interactive video content. Overall, the paper presents a significant contribution to the field of video generation, enabling real-time, voice-controlled, and personalized video experiences.

📅 Published on Jul 3

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.03118
• PDF: https://arxiv.org/pdf/2607.03118
• Project Page: https://vidu.com/vidu-stream

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📢 By: https://xn--r1a.website/PaperNexus

#RealTimeVideoGeneration #InteractiveVideoModel #VoiceControlledAnimation #DigitalCharacterAnimation #TurboDiffusionTechnology

🔥 CommonForms: A Large, Diverse Dataset for Form Field Detection

💡 This paper introduces CommonForms, a large and diverse dataset for form field detection, which involves identifying the location and type of form fields such as text input, choice buttons, and signatures in images of pages. The dataset was constructed by filtering 8 million documents from Common Crawl to arrive at a final dataset of 55,000 documents with over 450,000 pages, containing a diverse mixture of languages and domains. The dataset is unique in that one third of the pages are non-English and no single domain makes up more than 25 percent of the dataset.

The paper also presents a family of form field detectors, FFDNet-Small and FFDNet-Large, which achieve high average precision on the CommonForms test set. The models were trained at a relatively low cost of less than 500 dollars. The results show that high-resolution inputs are crucial for high-quality form field detection and that the cleaning process improves data efficiency.

The paper makes several key contributions, including the release of the first large-scale dataset for form field detection and the first open-source models. The dataset, models, and code will be made available to the public. The qualitative analysis shows that the models outperform a popular commercially available PDF reader and can predict checkboxes in addition to text and signature fields, making this a significant advancement in the field of form field detection. Overall, the paper provides a valuable resource for researchers and developers working on form field detection and related tasks.

📅 Published on Sep 20, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2509.16506
• PDF: https://arxiv.org/pdf/2509.16506

🤖 Models citing this paper:
• https://huggingface.co/jbarrow/FFDNet-L
• https://huggingface.co/jbarrow/FFDNet-L-cpu
• https://huggingface.co/jbarrow/FFDNet-S

📊 Datasets citing this paper:
• https://huggingface.co/datasets/Voxel51/commonforms_val_subset
• https://huggingface.co/datasets/jbarrow/CommonForms
• https://huggingface.co/datasets/WEwoCram/CommonForms

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/jbarrow/CommonForms
• https://huggingface.co/spaces/Voxel51/2025-ai-timeline
• https://huggingface.co/spaces/PSynx/widget-detector-demo

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📢 By: https://xn--r1a.website/PaperNexus

#FormFieldDetection #DocumentImageAnalysis #FormFieldRecognition #DatasetConstruction #DocumentUnderstanding

🔥 Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

💡 The paper introduces Hunyuan3D 2.0, a large-scale 3D synthesis system for generating high-resolution textured 3D assets. The system consists of two main components: Hunyuan3D-DiT, a shape generation model, and Hunyuan3D-Paint, a texture synthesis model. The shape generation model uses a scalable flow-based diffusion transformer to create geometry that aligns with a given condition image. The texture synthesis model uses strong geometric and diffusion priors to produce high-resolution and vibrant texture maps for generated or hand-crafted meshes.

The authors also introduce Hunyuan3D-Studio, a user-friendly production platform that simplifies the re-creation process of 3D assets, allowing both professional and amateur users to manipulate or animate their meshes efficiently. The system is evaluated and compared to previous state-of-the-art models, showing that Hunyuan3D 2.0 outperforms them in terms of geometry details, condition alignment, and texture quality.

The main contributions of the paper are the development of a scalable and efficient 3D synthesis system, the introduction of a user-friendly production platform, and the public release of the code and pre-trained weights of the models. The system aims to fill the gaps in the open-source 3D community for large-scale foundation generative models, providing a valuable resource for researchers and developers. Overall, the paper presents a significant advancement in the field of 3D synthesis, enabling the generation of high-quality textured 3D assets with improved geometry and texture details.

📅 Published on Jan 21, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2501.12202
• PDF: https://arxiv.org/pdf/2501.12202
• Project Page: https://huggingface.co/alluriaiprojects

🤖 Models citing this paper:
• https://huggingface.co/tencent/Hunyuan3D-2
• https://huggingface.co/tencent/Hunyuan3D-2.1
• https://huggingface.co/tencent/Hunyuan3D-2mv

📊 Datasets citing this paper:
• https://huggingface.co/datasets/tencent/HY3D-Bench

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/tencent/Hunyuan3D-2
• https://huggingface.co/spaces/frogleo/Image-to-3D
• https://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Image-to-3D

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📢 By: https://xn--r1a.website/PaperNexus

#DiffusionModels #3DAssetGeneration #Textured3DModeling #GeometrySynthesis #3DSynthesisSystems

🔥 OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

💡 The paper proposes a method called On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators, or OPSD-V, which aims to improve the quality of videos generated by few-step autoregressive video diffusion models. The problem with existing models is that they can produce long videos with low latency, but the quality of the video degrades over time due to error accumulation and weakened motion dynamics.

To address this issue, OPSD-V introduces real long-video data as temporal context during training, providing dense trajectory-level supervision to improve visual quality and motion dynamics. The method works by having a student model follow the exact inference-time rollout, generating each chunk of the video conditioned on its own previously generated cache. In parallel, a teacher model is evaluated at the same denoising states, but uses a cleaner temporal cache that can be replaced by real-video context. This provides corrective targets under on-policy cache dynamics, without changing the inference mechanism.

The results show that OPSD-V consistently improves the visual quality, motion dynamics, and VBenchLong scores of the generated videos. The method is applied to representative few-step autoregressive video models, including Self-Forcing and LongLive, and the experiments demonstrate significant improvements. A user study with 10 participants also shows that OPSD-V is preferred over the base models in 66 percent of overall-preference judgments, and 82.5 percent excluding ties. Overall, the paper contributes a novel method for improving the quality of videos generated by few-step autoregressive video diffusion models, without altering the inference mechanism.

📅 Published on Jul 9

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.08766
• PDF: https://arxiv.org/pdf/2607.08766
• Project Page: https://meigen-ai.github.io/OPSD-V/

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📢 By: https://xn--r1a.website/PaperNexus

#AutoregressiveVideoGeneration #VideoDiffusionModels #SelfDistillationTechniques #FewStepVideoGeneration #PostTrainingOptimization

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