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🔥 Vision Pretraining for Dense Spatial Perception

💡 The paper addresses the problem of dense spatial perception in visual systems, which is crucial for physical intelligence and embodied artificial intelligence applications. Current visual foundation models prioritize semantic invariance over detailed spatial understanding, resulting in limited ability to recover structured and metric representations from pixel observations. To overcome this, the authors propose a new approach to vision pretraining that focuses on boundary modeling, which is motivated by the idea that boundaries and shape discontinuities provide essential cues for perceiving geometric properties.

The proposed method, called masked boundary modeling, is a self-supervised paradigm that dynamically learns sub-pixel boundary representations. This is achieved by discovering boundary-bearing tokens and using them as masked targets to facilitate dense visual token learning. The authors scale this framework and develop a model called LingBot-Vision, which is evaluated on a diverse set of downstream vision tasks.

The results show that LingBot-Vision outperforms a strong baseline, DINOv3, and drives significant improvements in depth completion and estimation. Specifically, it enables the progression from LingBot-Depth 1.0 to LingBot-Depth 2.0, resulting in enhanced depth estimation, a key component of embodied artificial intelligence. The findings demonstrate that boundary modeling is a scalable pretraining principle for learning spatially structured visual representations, going beyond simple line segments and offering a new approach to vision pretraining. Overall, the paper contributes a novel approach to vision pretraining that prioritizes dense spatial perception and boundary modeling, with significant implications for embodied artificial intelligence applications.

📅 Published on Jul 6

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.05247
• PDF: https://arxiv.org/pdf/2607.05247
• Project Page: https://technology.robbyant.com/lingbot-vision

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

#VisionPretraining #DenseSpatialPerception #BoundaryModeling #EmbodiedArtificialIntelligence #PhysicalIntelligence

🔥 ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes

💡 The paper presents ResearchStudio-Idea, a skill suite designed to support effective research ideation by combining literature search, novelty checking, and pattern-guided generation. The goal is to help researchers develop well-grounded research proposals by identifying meaningful bottlenecks, differentiating from existing solutions, and evaluating risks. The suite consists of three main components: Paper-Search, a multi-source literature search skill, Scoop-Check, a prior-art collision checker, and IdeaSpark, an end-to-end skill that composes evidence grounding, pattern-guided generation, and idea-card rendering into one workflow.

IdeaSpark is constructed from a corpus of 1947 machine learning conference papers collected from ICLR, ICML, and NeurIPS between 2021 and 2025. Analysis of these papers reveals 31 recurring ideation sub-patterns, which are consolidated into 15 reusable ideation patterns. Each pattern is operationalized as a structured card containing research context, bottleneck types, differentiation strategies, supporting precedents, and common failure modes.

Given a research problem and an evidence bundle, IdeaSpark evaluates evidence readiness, reconstructs the surrounding research context, identifies unresolved bottlenecks, selects relevant patterns, instantiates one candidate direction, retrieves potentially conflicting prior work, and performs outcome-informed auditing. This workflow transforms reusable ideation patterns into traceable research proposals.

The results show that IdeaSpark consistently produces stronger research proposals than no-skill and generic-skill baselines while maintaining competitive novelty. The evaluation is based on blind automated-judge assessments, which demonstrate the effectiveness of ResearchStudio-Idea in supporting research ideation. Overall, the paper contributes a reusable skill suite that can help researchers develop well-grounded research proposals by leveraging evidence-grounded research ideation and pattern-guided generation.

📅 Published on Jul 5

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.04439
• PDF: https://arxiv.org/pdf/2607.04439
• Project Page: https://aka.ms/ResearchStudio

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

#ResearchIdeationTools #MachineLearningForResearch #LiteratureSearchMethods #NoveltyDetectionAlgorithms #EvidenceBasedResearchMethods

🔥 GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation

💡 The paper addresses the challenge of evaluating robotic policies, which is a critical bottleneck in the development of embodied robot foundation models. Unlike large language models, robotic policies require real-world rollouts that are slow and costly, limited by hardware and human supervision. To overcome this, the authors propose the use of world models as surrogate policy evaluators. However, the key properties that make a world model reliable for policy assessment are not well understood.

To study this, the authors introduce WMBench, a benchmark constructed from real-robot teleoperation data and matched policy rollouts covering diverse manipulation tasks. They use WMBench to analyze seven video world models, four action representation schemes, and over 324,000 simulated policy rollouts paired with real robot executions. The analysis is further enriched with large-scale community submissions, curated synthetic trajectories, and a large dataset of training videos.

The experiments reveal three core insights: first, evaluator quality is dominated by long-horizon, action-faithful rollout consistency rather than short-term visual realism; second, pretraining gains come not only from data scale but also from balancing general world knowledge with robot-specific controllability; and third, architectural choices such as action encoding, memory design, and evaluator-focused post-training strongly determine alignment with real-world robot behavior.

Based on these results, the authors derive a practical design roadmap and realize it in GigaWorld-1, a world model specially optimized for policy evaluation. The authors fully release their code, models, datasets, and toolkits to advance scalable evaluation research for embodied foundation models. The contributions of the paper include a systematic study of world models for robotic policy evaluation, the introduction of WMBench as a benchmark, and the development of GigaWorld-1 as a reliable world model for policy assessment. Overall, the paper provides a comprehensive framework for evaluating robotic policies and advancing the development of embodied robot foundation models.

📅 Published on Jul 2

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.02642
• PDF: https://arxiv.org/pdf/2607.02642
• Project Page: https://open-gigaai.github.io/giga-world-1/

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

#RobotPolicyEvaluation #WorldModelsForRobots #EmbodiedAI #RobotLearning #SurrogateModeling

🔥 Multiplayer Interactive World Models with Representation Autoencoders

💡 The paper introduces a multiplayer world model that can simulate complex physics-based environments with multiple agents. The model is trained on a large dataset of gameplay from the game Rocket League, which involves fast and tightly coupled dynamics between players. The key contribution of the paper is that the model conditions on the action streams of multiple agents, allowing it to attribute changes in the scene to the correct player and stay coherent under different combinations of actions.

The model uses a latent diffusion approach with a generative objective and is trained on short clips of gameplay. Despite this, the model is able to generate stable long-horizon rollouts, with distributional quality holding steady for up to five minutes and in practice continuing for hours without collapsing. The model is also able to produce four-player matches in real time, generating 20 frames per second on a single GPU.

The paper systematically investigates the design choices behind the model, including the video codec, generative objective, and multiplayer conditioning scheme. It also characterizes how the model's behavior changes with scale, including the capabilities that emerge and the failure modes that persist. The authors develop targeted evaluations that probe the model's physical understanding, rather than just its visual appearance.

The paper's contributions include the introduction of the first multiplayer world model for highly dynamic environments, the development of a latent diffusion model that can generate stable long-horizon rollouts, and the creation of a large-scale dataset and codebase for continued research on multiplayer world models. The authors release their dataset, codebase, and a live demo to support further research in this area. Overall, the paper presents a significant advance in the development of world models that can simulate complex multiplayer environments.

📅 Published on Jul 6

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.05352
• PDF: https://arxiv.org/pdf/2607.05352
• Project Page: https://mira-wm.com/

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

#MultiAgentSystems #PhysicsBasedModeling #GameplaySimulation #RepresentationLearning #LatentDiffusionModels

🔥 PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space

💡 The paper introduces PixWorld, a unified approach for 3D scene generation and reconstruction in pixel space. The problem with current methods is that they use separate paradigms for reconstruction and generation, with reconstruction using pixel-based regression and generation using latent diffusion. Recent attempts to unify these tasks in latent space have limitations, including information loss and the need for a pretrained autoencoder.

The method presented in the paper reformulates these tasks under a unified pixel-space diffusion paradigm. PixWorld is a single model that jointly addresses 3D reconstruction and generation by supervising diffusion directly on rendered images. This approach removes the limitations of latent space methods and aligns optimization with 3D scene fidelity. The model also introduces a geometry perception loss that aligns rendered views with their ground truth in the geometry-aware feature space of a pretrained 3D foundation model, providing 3D structural supervision.

The results show that PixWorld consistently outperforms prior latent-space generation methods and matches state-of-the-art reconstruction methods. This demonstrates the superiority of a unified pixel-space approach for 3D scene generation and reconstruction. Overall, the paper presents a novel approach that overcomes the limitations of current methods and achieves superior results, making it a significant contribution to the field of 3D scene generation and reconstruction.

📅 Published on Jul 6

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.05373
• PDF: https://arxiv.org/pdf/2607.05373
• Project Page: https://sensengao.github.io/PixWorld/

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

#3DSceneGeneration #PixelSpaceDiffusion #UnifiedReconstruction #LatentDiffusionModels #ComputerVisionApplications

🔥 OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers

💡 The paper OmniOpt presents a unified framework for selecting optimizers in large-scale model training, addressing the problem of choosing the best optimizer from over one hundred available methods. The authors argue that optimizer selection has become a critical system-level design decision that depends on various factors such as compute, memory, tuning budget, and task diversity. However, the current landscape of optimizers is fragmented, making it difficult to compare and choose the most suitable one.

To address this issue, the authors propose OmniOpt, a unified survey and benchmark cookbook of optimizers that rests on four coupled components. First, they introduce a five-stage meta-pipeline that treats every optimizer update as a structured transformation, showing that most methods engage only one or two of these stages. Second, they use norm-constrained linear minimization oracles to unify different optimizers. Third, they develop a dual-dimension taxonomy that assigns each method to a mechanism family and records the measurable training objectives it aims to improve. Fourth, they instantiate the full taxonomy in a unified cross-domain benchmark that spans representative optimizers, model scales, and training regimes.

The OmniOpt framework provides a systematic analysis of optimizer families and their trade-offs across different training objectives and model scales. The authors evaluate the performance of various optimizers on tasks such as language model pretraining and image classification, laying out their trade-offs and supplying the research community with an operational coordinate system for selecting optimizers under explicit mechanism and objective assumptions. The results of the paper chart a direction for the future development of the optimizer community, providing a unified framework for optimizer selection and benchmarking. Overall, the paper contributes to the development of a more systematic and principled approach to optimizer selection, which is essential for large-scale model training.

📅 Published on Jul 4

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.04033
• PDF: https://arxiv.org/pdf/2607.04033
• Project Page: https://openraiser.github.io/OmniOpt/

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

#OptimizerSelection #LargeScaleModelTraining #ModernOptimizers #BenchmarkingMethods #GeometryOfOptimization