🔥 SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

💡 The paper introduces SmolVLA, a compact and efficient vision-language-action model designed for affordable and efficient robotics. The problem with existing vision-language-action models is that they are typically very large, with billions of parameters, which leads to high training costs and limited deployability on consumer-grade hardware. These models also rely on academic and industrial datasets, overlooking community-collected data from affordable robotic platforms.

To address this issue, the authors developed SmolVLA, a small and efficient model that can be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. SmolVLA is designed to be community-driven, using community-collected data from affordable robotic platforms, which reduces both training and inference costs while retaining competitive performance.

The method used to achieve this involves adapting vision-language models into vision-language-action models, but with a much smaller size. The authors also introduced an asynchronous inference stack that decouples perception and action prediction from action execution, allowing for higher control rates with chunked action generation.

The results show that SmolVLA achieves performance comparable to vision-language-action models that are 10 times larger, despite its compact size. The model is evaluated on a range of simulated and real-world robotic benchmarks, and the authors release all code, pretrained models, and training data. Overall, SmolVLA provides a more efficient and affordable solution for robotics, making it possible to deploy vision-language-action models on consumer-grade hardware.

📅 Published on Jun 2, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2506.01844
• PDF: https://arxiv.org/pdf/2506.01844
• Project Page: https://huggingface.co/blog/smolvla

🤖 Models citing this paper:
• https://huggingface.co/lerobot/smolvla_base
• https://huggingface.co/HuggingFaceVLA/smolvla_libero
• https://huggingface.co/jadechoghari/smolvla_metaworld

📊 Datasets citing this paper:
• https://huggingface.co/datasets/lerobot/community_dataset_v3
• https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v1
• https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v2

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/HuggingFaceVLA/libero-vla-leaderboard
• https://huggingface.co/spaces/lerobot/robot-learning-tutorial
• https://huggingface.co/spaces/arpitg1304/lerobot_scripts_simplified

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

#VisionLanguageAction #EfficientRobotics #CompactNeuralNetworks #RoboticsForAll #AffordableAI

🔥 Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

💡 The paper presents Xiaomi-Robotics-1, a foundational vision-language-action model that can follow diverse language instructions to perform a wide range of mobile manipulation tasks in unseen environments. The model is trained using a two-stage training recipe consisting of pre-training and post-training. During pre-training, the model is trained on over 100,000 hours of real-world manipulation trajectories collected via UM devices, and a scalable auto-labeling pipeline is developed to annotate trajectory clips with natural language descriptions of scene state transitions. This provides rich and precise conditioning for action learning.

During post-training, the model is fine-tuned to align with robot embodiment and imperative instructions that humans naturally use to prompt robots. The experiments demonstrate strong scaling behavior, with the model consistently improving with increased data scales and model sizes during pre-training. This scaling behavior directly transfers to post-training, where stronger pre-training models yield better out-of-the-box real-robot performance in unseen environments.

The results show that Xiaomi-Robotics-1 serves as a strong robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency. Across multiple simulation benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods, establishing a new state-of-the-art with a 57.6% success rate on RoboCasa 365, surpassing the previous best of 46.6%. Additionally, it achieves an average score of 20.07 on RoboDojo, significantly outperforming the prior state-of-the-art. The code and model checkpoints will be released.

Overall, the paper contributes to the development of a robust and scalable vision-language-action model that can be applied to a wide range of robotic tasks, and demonstrates the effectiveness of the proposed two-stage training recipe and auto-labeling pipeline in achieving state-of-the-art performance.

📅 Published on Jul 16

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.15330
• PDF: https://arxiv.org/pdf/2607.15330
• Project Page: https://robotics.xiaomi.com/xiaomi-robotics-1.html

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

#VisionLanguageModels #MobileManipulation #RoboticsLearning #RealWorldTrajectories #LanguageActionInterfaces

🔥 RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM

💡 The paper introduces RAGU, a multi-step graph retrieval-augmented generation engine that enhances large language models with structured knowledge. Existing systems construct knowledge graphs in a single extraction pass, producing noisy entities and brittle retrieval. RAGU addresses this by separating extraction from consolidation, using a two-stage typed extraction process, DBSCAN-backed deduplication, LLM summarization, and Leiden community detection.

A key insight is that the skills an in-pipeline LLM needs, such as comprehension, extraction, and reasoning over context, are language skills that grow only weakly with model size, unlike factual world knowledge. Therefore, the authors train MENO-LITE-0.1, a 7B model optimized for language skills, which outperforms Qwen 2.5-32B on knowledge-graph construction and matches it on English GraphRAG tasks.

The results show that RAGU retrieves the most complete context at every factoid level, with evidence recall up to 0.84, and overtakes HippoRAG 2 on synthesis tasks. On multi-hop factoid QA, the apparent HippoRAG 2 advantage is shown to be largely an answer-format artifact. RAGU is installable via pip install graphragu, runs on a single GPU, and is released under MIT. The source code and MENO-LITE-0.1 model are publicly available. Overall, RAGU provides a more effective and efficient approach to graph retrieval-augmented generation, with significant improvements in knowledge-graph construction and question answering tasks.

📅 Published on Jul 13

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.11683
• PDF: https://arxiv.org/pdf/2607.11683

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

#GraphRetrievalAugmentedGeneration #DomainAdaptedLLM #MultiStepGraphEngine #KnowledgeGraphConstruction #RetrievalAugmentedGenerationModels

🔥 RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources

💡 The paper presents RESOURCE2SKILL, a framework that extracts executable skills for software agents from human-created multimodal resources such as tutorial videos, repositories, articles, and reference artifacts. Existing skill libraries are mostly hand-written, text-centric, or derived from agent traces, leaving multimodal human resources largely underused. RESOURCE2SKILL addresses this issue by distilling these resources into executable skills, which are organized as a hierarchical multimodal Skill Wiki. Each entry in the wiki combines structured text, code, visual examples, metadata, and provenance, preserving complementary signals from different sources.

The framework allows agents to retrieve and compose relevant skills from the wiki at inference time. When coverage is insufficient, the same construction operator can acquire new skills online. The authors evaluate RESOURCE2SKILL across seven practical authoring domains and demonstrate that it improves the average overall score by 11.9 percentage points over no-skill agents and outperforms strong harness baselines in 26 of 28 main-aggregate model-domain cells.

The results confirm the value of the multimodal skill format, hierarchical organization, source diversity, selection strategy, and online acquisition. The paper's contributions include a novel framework for extracting executable skills from multimodal resources, a hierarchical multimodal Skill Wiki, and a method for online skill acquisition. Overall, RESOURCE2SKILL provides a new approach to creating skill libraries for software agents, leveraging the wealth of information available in human-created multimodal resources.

📅 Published on Jul 16

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.29538
• PDF: https://arxiv.org/pdf/2606.29538
• Project Page: https://aka.ms/Resources2Skill

📊 Datasets citing this paper:
• https://huggingface.co/datasets/microsoft/RESOURCE2SKILL

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

#MultimodalLearning #ExecutableAgentSkills #HumanCentricAI #SkillExtraction #MultimodalKnowledgeRepresentation

🔥 xHC: Expanded Hyper-Connections

💡 The paper introduces xHC, a new method for expanding hyper-connections in transformers, which allows for more efficient and effective scaling of residual streams. The existing hyper-connection methods, such as HC and mHC, have limitations that prevent them from scaling beyond a certain point, resulting in diminishing performance gains and increasing training costs. The authors identify two bottlenecks that limit the scaling of mHC: insufficient write-back information for an expanding number of streams and residual-mixing generation costs that scale cubically with the number of streams.

To address these bottlenecks, the authors propose xHC, which combines temporal feature augmentation for richer write-back information with a sparse residual-stream architecture that updates only a subset of the streams while retaining dense access to the full residual state. The xHC method achieves meaningful expansion beyond the previous limit of N=4, resulting in strong and consistent downstream improvements across 18B and 28B MoE models.

The results show that xHC delivers significant improvements over mHC, with an average downstream score improvement of 4.0 points on an 18B MoE model, while adding only modest training FLOPs over the vanilla baseline. Scaling-law experiments demonstrate that the vanilla and mHC methods require 1.50 and 1.19 times the compute of xHC, respectively, to reach the same loss. To address the memory traffic requirements of large-N training, the authors introduce xHC-Flash, which reduces the per-sublayer memory traffic from 73.5C to 40C, comparable to the 34C required by mHC at N=4, while retaining the gains of full xHC. Overall, xHC and xHC-Flash make large-N residual-stream expansion effective and practical for large language model pre-training.

📅 Published on Jul 16

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.14530
• PDF: https://arxiv.org/pdf/2607.14530

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

#TransformerArchitecture #HyperConnectionMethods #ResidualStreamScaling #EfficientNeuralNetworks #ExpandedHyperConnections

🔥 HuggingFace's Transformers: State-of-the-art Natural Language Processing

💡 The paper discusses the Transformers library, an open source collection of state of the art Transformer architectures and pretrained models for natural language processing tasks. The library aims to make recent advances in natural language processing accessible to the wider machine learning community. The problem addressed is the difficulty in utilizing recent advances in model architecture and pretraining for natural language processing tasks. The method used is the creation of a unified API that provides access to a range of carefully engineered state of the art Transformer architectures, along with a curated collection of pretrained models. The library is designed to be extensible for researchers, simple for practitioners, and fast and robust for industrial deployments. The results are a library that provides a simple and unified way to access and utilize state of the art natural language processing models, making it easier for researchers and practitioners to build and deploy effective natural language processing systems. The library is available for use and contribution by the community, with the goal of driving further advances in natural language processing.

📅 Published on Oct 9, 2019

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/1910.03771
• PDF: https://arxiv.org/pdf/1910.03771
• Project Page: https://huggingface.co

🤖 Models citing this paper:
• https://huggingface.co/PJMixers-Images/Florence-2-base-Castollux-v0.5
• https://huggingface.co/Ian332/Helper_Bob
• https://huggingface.co/PJMixers-Images/Florence-2-base-Castollux-v0.2

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/dpratapa/bio-seq-lm-explorer
• https://huggingface.co/spaces/itchybeetle3/img_caption_generation
• https://huggingface.co/spaces/PJMixers-Images/Florence-2-base-Castollux-v0.5

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

#NaturalLanguageProcessing #TransformerArchitectures #PretrainedModels #StateOfTheArtAI #MachineLearningLibrary

🔥 TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training

💡 The paper presents TorchUMM, a unified codebase for evaluating and analyzing multimodal models that can understand, generate, and edit across visual and textual modalities. The development of unified multimodal models has been hindered by the diversity of model architectures and training paradigms, making it challenging to compare and analyze different models. To address this issue, TorchUMM provides a comprehensive framework that supports a wide range of models and tasks, including multimodal understanding, generation, and editing. The codebase includes a broad spectrum of models and integrates both established and novel datasets to evaluate perception, reasoning, compositionality, and instruction-following abilities. By providing a unified interface and standardized evaluation protocols, TorchUMM enables fair and reproducible comparisons across heterogeneous models, facilitating the development of more capable unified multimodal systems. The codebase is designed to foster deeper insights into the strengths and limitations of different models, and its availability is expected to accelerate the development of unified multimodal models. The TorchUMM codebase is available for use, allowing researchers to evaluate and analyze their models in a standardized and reproducible manner. Overall, the paper contributes to the development of unified multimodal models by providing a unified framework for evaluation, analysis, and post-training, which can help to advance the field of multimodal modeling.

📅 Published on Apr 12

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2604.10784
• PDF: https://arxiv.org/pdf/2604.10784
• Project Page: https://aifrontierlab.github.io/TorchUMM/

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

#MultimodalModeling #UnifiedCodebase #MultimodalUnderstanding #VisualTextualModalities #MultimodalGeneration

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🔥 HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enchancement

💡 The paper proposes a framework called HOMIE for human-object centric video personalization, which is a core task in subject-driven video generation. Existing methods suffer from two key limitations: they struggle to balance high subject fidelity and accurate interaction patterns between humans and diverse objects, and they lack mechanisms to understand latent correspondence in intra-subject references. To address these challenges, HOMIE tackles both inter- and intra-subject input settings in a unified manner. The framework proposes a better integration strategy to extract knowledge of reference-level relationships without compromising the controllability of text encoders or incurring costly re-alignment. Specifically, it introduces global multimodal guidance within self-attention to better align semantic features derived from large language models with tokens from variational autoencoders. Additionally, it proposes modality-reference embedding to differentiate tokens from large language model features and variational autoencoder tokens and associate intra-subject reference image tokens. Extensive experiments validate that the method achieves state-of-the-art performance across various human-object centric video personalization tasks. The results demonstrate the effectiveness of HOMIE in addressing the limitations of existing methods and providing a unified framework for video personalization.

📅 Published on Jul 20

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.18217
• PDF: https://arxiv.org/pdf/2607.18217
• Project Page: https://yiyangcai.github.io/homie-page.github.io/

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

#HumanCentricVideoPersonalization #MultimodalIntelligentEnhancement #SubjectDrivenVideoGeneration #HumanObjectInteraction #VideoPersonalizationFrameworks

🔥 TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs

💡 The paper introduces TimeLens2, a generalist video temporal grounding model that can predict variable-cardinality sets of evidence intervals across video lengths, domains, query forms, and viewpoints. Existing training strategies are misaligned with this set-valued task, often relying on brittle one-pass annotation or reinforcement learning that fails to distinguish non-overlapping predictions or requires fragile segment matching.

TimeLens2 treats temporal evidence as an interval set throughout supervision and optimization. It constructs reliable multi-span supervision through caption-derived proposals, independent localization, cross-agent consensus, semantic verification, and boundary refinement. The temporal Wasserstein reward computes exact one-dimensional distances between uniform distributions over merged interval supports, providing dense, matching-free feedback under unequal cardinalities and equivalent fragmentation. Temporal IoU complements it with precise overlap feedback.

The results show that TimeLens2 outperforms all size-matched baselines on every benchmark, while the 4B and 8B variants achieve state-of-the-art performance, surpassing open-source models with up to 397B parameters. The 2B, 4B, and 8B variants improve over their Qwen3-VL backbones by 14.2, 13.0, and 18.1 mIoU points, respectively. Overall, TimeLens2 demonstrates significant advancements in video temporal grounding, offering a robust and effective solution for this complex task.

📅 Published on Jul 19

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.17423
• PDF: https://arxiv.org/pdf/2607.17423
• Project Page: https://mcg-nju.github.io/TimeLens2/

🤖 Models citing this paper:
• https://huggingface.co/MCG-NJU/TimeLens2-8B
• https://huggingface.co/MCG-NJU/TimeLens2-4B
• https://huggingface.co/MCG-NJU/TimeLens2-2B

📊 Datasets citing this paper:
• https://huggingface.co/datasets/MCG-NJU/TimeLens2-93K

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/hugging-apps/timelens2-8b-temporal-grounding
• https://huggingface.co/spaces/hugging-apps/timelens2-2b-temporal-grounding

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

#VideoTemporalGrounding #MultimodalLLMs #TemporalEvidenceLocalization #VideoAnalysisModels #GeneralistAIModels

🔥 Apple-π: Benchmarking Thinking with Video Towards Law-Grounded Physical Intelligence

💡 The paper introduces Apple-PI, a benchmark for evaluating video models based on their ability to reason about physical laws. Current video models are often evaluated only on their output, without considering whether they arrive at that output through a faithful, law-grounded reasoning process. Apple-PI addresses this limitation by explicitly anchoring video model evaluation in physical laws.

The benchmark consists of three components: Orchard, a dataset of 400 videos covering ten canonical tasks in classical mechanics, which separates single-law tasks for confounder-free diagnosis from multi-law tasks for probing generalization; Benchmark Protocol, a three-stage protocol based on scientific reasoning, including Perception, Formulation, and Deduction, which uses chain-of-frames prompting on infographic-annotated first frames to treat the generated video as the model's visible reasoning trace; and Evaluation Suite, a hybrid evaluation suite that combines machine learning-based subjective scoring with physics-law-grounded objective measures.

The authors used Apple-PI to benchmark 11 video models and found that current models remain far from reliable law-grounded world simulators, with the best video models scoring only 0.473. The stage-, pillar-, and source-resolved analyses further exposed a Perception-to-Formulation-to-Deduction bottleneck, weak multi-law state transfer, and a persistent Sim-to-Real gap.

The paper positions Apple-PI as a diagnostic foundation for guiding future video models towards world models with law-grounded physical intelligence. The benchmark provides a comprehensive evaluation framework for video models, enabling researchers to identify areas for improvement and develop more robust and physically informed models.

📅 Published on Jul 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.16401
• PDF: https://arxiv.org/pdf/2607.16401
• Project Page: https://21yrm.github.io/Apple-PI-homepage

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/yaorunmao/apple-pi-leaderboard

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

#VideoModelEvaluation #PhysicalIntelligence #LawGroundedReasoning #ClassicalMechanics #BenchmarkingAI