✨VA-π: Variational Policy Alignment for Pixel-Aware Autoregressive Generation
📝 Summary:
VA-$\pi$ optimizes autoregressive visual generators using a pixel-space objective to improve image quality and performance without retraining tokenizers or using external rewards. AI-generated summary...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19680
• PDF: https://arxiv.org/pdf/2512.19680
• Project Page: https://lil-shake.github.io/va-pi.github.io/
• Github: https://github.com/Lil-Shake/VA-Pi
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
VA-$\pi$ optimizes autoregressive visual generators using a pixel-space objective to improve image quality and performance without retraining tokenizers or using external rewards. AI-generated summary...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19680
• PDF: https://arxiv.org/pdf/2512.19680
• Project Page: https://lil-shake.github.io/va-pi.github.io/
• Github: https://github.com/Lil-Shake/VA-Pi
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨GTR-Turbo: Merged Checkpoint is Secretly a Free Teacher for Agentic VLM Training
📝 Summary:
Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent methods densify the rewa...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13043
• PDF: https://arxiv.org/pdf/2512.13043
==================================
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✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent methods densify the rewa...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13043
• PDF: https://arxiv.org/pdf/2512.13043
==================================
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✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨How Much 3D Do Video Foundation Models Encode?
📝 Summary:
A new framework quantifies 3D understanding in Video Foundation Models VidFMs. VidFMs, trained only on video, show strong 3D awareness, often surpassing expert 3D models, providing insights for 3D AI.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19949
• PDF: https://arxiv.org/pdf/2512.19949
• Project Page: https://vidfm-3d-probe.github.io/
• Github: https://vidfm-3d-probe.github.io
==================================
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#VideoFoundationModels #3DUnderstanding #ComputerVision #AIResearch #DeepLearning
📝 Summary:
A new framework quantifies 3D understanding in Video Foundation Models VidFMs. VidFMs, trained only on video, show strong 3D awareness, often surpassing expert 3D models, providing insights for 3D AI.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19949
• PDF: https://arxiv.org/pdf/2512.19949
• Project Page: https://vidfm-3d-probe.github.io/
• Github: https://vidfm-3d-probe.github.io
==================================
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#VideoFoundationModels #3DUnderstanding #ComputerVision #AIResearch #DeepLearning
❤2
✨Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
📝 Summary:
AR models face inefficient exploration and sparse rewards in RL. Internal RL uses a higher-order model to learn temporal abstraction controllers. This enables efficient learning from sparse rewards where standard RL fails.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20605
• PDF: https://arxiv.org/pdf/2512.20605
==================================
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#ReinforcementLearning #HierarchicalRL #AutoregressiveModels #MachineLearning #ArtificialIntelligence
📝 Summary:
AR models face inefficient exploration and sparse rewards in RL. Internal RL uses a higher-order model to learn temporal abstraction controllers. This enables efficient learning from sparse rewards where standard RL fails.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20605
• PDF: https://arxiv.org/pdf/2512.20605
==================================
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#ReinforcementLearning #HierarchicalRL #AutoregressiveModels #MachineLearning #ArtificialIntelligence
❤2
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✨Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
📝 Summary:
Fast3R is a Transformer-based method for efficient and scalable multi-view 3D reconstruction. It processes many images in parallel in a single forward pass, improving speed and accuracy over pairwise approaches like DUSt3R.
🔹 Publication Date: Published on Jan 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.13928
• PDF: https://arxiv.org/pdf/2501.13928
• Github: https://github.com/naver/dust3r/pull/16
🔹 Models citing this paper:
• https://huggingface.co/jedyang97/Fast3R_ViT_Large_512
==================================
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#3DReconstruction #ComputerVision #Transformers #Fast3R #DeepLearning
📝 Summary:
Fast3R is a Transformer-based method for efficient and scalable multi-view 3D reconstruction. It processes many images in parallel in a single forward pass, improving speed and accuracy over pairwise approaches like DUSt3R.
🔹 Publication Date: Published on Jan 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.13928
• PDF: https://arxiv.org/pdf/2501.13928
• Github: https://github.com/naver/dust3r/pull/16
🔹 Models citing this paper:
• https://huggingface.co/jedyang97/Fast3R_ViT_Large_512
==================================
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#3DReconstruction #ComputerVision #Transformers #Fast3R #DeepLearning
✨SkyReels-V2: Infinite-length Film Generative Model
📝 Summary:
SkyReels-V2 is an infinite-length film generative model that addresses video generation challenges by synergizing MLLMs, reinforcement learning, and a diffusion forcing framework. It enables high-quality, long-form video synthesis with realistic motion and cinematic grammar awareness through mult...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.13074
• PDF: https://arxiv.org/pdf/2504.13074
• Github: https://github.com/skyworkai/skyreels-v2
🔹 Models citing this paper:
• https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P
• https://huggingface.co/Skywork/SkyCaptioner-V1
• https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P
✨ Spaces citing this paper:
• https://huggingface.co/spaces/fffiloni/SkyReels-V2
• https://huggingface.co/spaces/Dudu0043/SkyReels-V2
• https://huggingface.co/spaces/14eee109giet/SkyReels-V2
==================================
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#VideoGeneration #GenerativeAI #MLLM #DiffusionModels #AIResearch
📝 Summary:
SkyReels-V2 is an infinite-length film generative model that addresses video generation challenges by synergizing MLLMs, reinforcement learning, and a diffusion forcing framework. It enables high-quality, long-form video synthesis with realistic motion and cinematic grammar awareness through mult...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.13074
• PDF: https://arxiv.org/pdf/2504.13074
• Github: https://github.com/skyworkai/skyreels-v2
🔹 Models citing this paper:
• https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P
• https://huggingface.co/Skywork/SkyCaptioner-V1
• https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P
✨ Spaces citing this paper:
• https://huggingface.co/spaces/fffiloni/SkyReels-V2
• https://huggingface.co/spaces/Dudu0043/SkyReels-V2
• https://huggingface.co/spaces/14eee109giet/SkyReels-V2
==================================
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#VideoGeneration #GenerativeAI #MLLM #DiffusionModels #AIResearch
arXiv.org
SkyReels-V2: Infinite-length Film Generative Model
Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion...
❤2
✨VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
📝 Summary:
VideoRAG introduces the first RAG framework for long videos, using a dual-channel architecture to integrate textual knowledge grounding and multi-modal context encoding. This enables unlimited-length video processing and significantly outperforms existing methods.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.01549
• PDF: https://arxiv.org/pdf/2502.01549
• Github: https://github.com/hkuds/videorag
==================================
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#VideoRAG #RAG #LongVideo #AI #MultimodalAI
📝 Summary:
VideoRAG introduces the first RAG framework for long videos, using a dual-channel architecture to integrate textual knowledge grounding and multi-modal context encoding. This enables unlimited-length video processing and significantly outperforms existing methods.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.01549
• PDF: https://arxiv.org/pdf/2502.01549
• Github: https://github.com/hkuds/videorag
==================================
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❤2
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✨A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
📝 Summary:
This survey reviews self-evolving AI agents that adapt to dynamic environments via automatic enhancement from interaction data. It proposes a unified framework and systematically reviews current techniques, addressing evaluation, safety, and ethics.
🔹 Publication Date: Published on Aug 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07407
• PDF: https://arxiv.org/pdf/2508.07407
• Project Page: https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
• Github: https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents
✨ Spaces citing this paper:
• https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
==================================
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#SelfEvolvingAI #AIAgents #FoundationModels #LifelongLearning #ArtificialIntelligence
📝 Summary:
This survey reviews self-evolving AI agents that adapt to dynamic environments via automatic enhancement from interaction data. It proposes a unified framework and systematically reviews current techniques, addressing evaluation, safety, and ethics.
🔹 Publication Date: Published on Aug 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07407
• PDF: https://arxiv.org/pdf/2508.07407
• Project Page: https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
• Github: https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents
✨ Spaces citing this paper:
• https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
==================================
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✨InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion
📝 Summary:
InsertAnywhere is a framework for realistic video object insertion. It uses 4D aware mask generation for geometric consistency and an extended diffusion model for appearance-faithful synthesis, outperforming existing methods.
🔹 Publication Date: Published on Dec 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17504
• PDF: https://arxiv.org/pdf/2512.17504
• Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
• Github: https://github.com/myyzzzoooo/InsertAnywhere
==================================
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#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI
📝 Summary:
InsertAnywhere is a framework for realistic video object insertion. It uses 4D aware mask generation for geometric consistency and an extended diffusion model for appearance-faithful synthesis, outperforming existing methods.
🔹 Publication Date: Published on Dec 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17504
• PDF: https://arxiv.org/pdf/2512.17504
• Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
• Github: https://github.com/myyzzzoooo/InsertAnywhere
==================================
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#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI
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✨MAI-UI Technical Report: Real-World Centric Foundation GUI Agents
📝 Summary:
MAI-UI introduces a family of foundation GUI agents tackling real-world deployment challenges. It uses a self-evolving data pipeline, device-cloud collaboration, and online RL to set new state-of-the-art in GUI grounding and mobile navigation, significantly boosting performance and privacy.
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22047
• PDF: https://arxiv.org/pdf/2512.22047
==================================
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#GUIAgents #AI #ReinforcementLearning #MobileTech #HCI
📝 Summary:
MAI-UI introduces a family of foundation GUI agents tackling real-world deployment challenges. It uses a self-evolving data pipeline, device-cloud collaboration, and online RL to set new state-of-the-art in GUI grounding and mobile navigation, significantly boosting performance and privacy.
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22047
• PDF: https://arxiv.org/pdf/2512.22047
==================================
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#GUIAgents #AI #ReinforcementLearning #MobileTech #HCI
❤1
✨See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning
📝 Summary:
Bi-directional Perceptual Shaping BiPS improves vision-language models by using question-conditioned masked views to shape perception during training. It employs two constraints to ensure complete coverage of relevant pixels and enforce fine-grained visual reliance, preventing text-only shortcuts...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22120
• PDF: https://arxiv.org/pdf/2512.22120
• Github: https://github.com/zss02/BiPS
==================================
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#MultimodalAI #VisionLanguageModels #MachineLearning #AIResearch #DeepLearning
📝 Summary:
Bi-directional Perceptual Shaping BiPS improves vision-language models by using question-conditioned masked views to shape perception during training. It employs two constraints to ensure complete coverage of relevant pixels and enforce fine-grained visual reliance, preventing text-only shortcuts...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22120
• PDF: https://arxiv.org/pdf/2512.22120
• Github: https://github.com/zss02/BiPS
==================================
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#MultimodalAI #VisionLanguageModels #MachineLearning #AIResearch #DeepLearning
❤1
✨ProEdit: Inversion-based Editing From Prompts Done Right
📝 Summary:
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22118
• PDF: https://arxiv.org/pdf/2512.22118
• Project Page: https://isee-laboratory.github.io/ProEdit/
• Github: https://isee-laboratory.github.io/ProEdit
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22118
• PDF: https://arxiv.org/pdf/2512.22118
• Project Page: https://isee-laboratory.github.io/ProEdit/
• Github: https://isee-laboratory.github.io/ProEdit
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨SVBench: Evaluation of Video Generation Models on Social Reasoning
📝 Summary:
Recent text-to-video generation models exhibit remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they remain fundamentally limited in their ability to generate soci...
🔹 Publication Date: Published on Dec 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21507
• PDF: https://arxiv.org/pdf/2512.21507
• Github: https://github.com/Gloria2tt/SVBench-Evaluation
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Recent text-to-video generation models exhibit remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they remain fundamentally limited in their ability to generate soci...
🔹 Publication Date: Published on Dec 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21507
• PDF: https://arxiv.org/pdf/2512.21507
• Github: https://github.com/Gloria2tt/SVBench-Evaluation
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨SWE-RM: Execution-free Feedback For Software Engineering Agents
📝 Summary:
This paper introduces SWE-RM, a robust, execution-free reward model for software engineering agents. It overcomes limitations of execution-based feedback, improving coding agent performance in both test-time scaling and reinforcement learning. SWE-RM achieves new state-of-the-art results for open...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21919
• PDF: https://arxiv.org/pdf/2512.21919
==================================
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#SoftwareEngineering #AI #ReinforcementLearning #CodingAgents #RewardModels
📝 Summary:
This paper introduces SWE-RM, a robust, execution-free reward model for software engineering agents. It overcomes limitations of execution-based feedback, improving coding agent performance in both test-time scaling and reinforcement learning. SWE-RM achieves new state-of-the-art results for open...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21919
• PDF: https://arxiv.org/pdf/2512.21919
==================================
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#SoftwareEngineering #AI #ReinforcementLearning #CodingAgents #RewardModels
❤1
✨Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding
📝 Summary:
MiA-RAG enhances RAG systems with global context awareness, inspired by human understanding. It uses hierarchical summarization to build a 'mindscape,' improving long-context retrieval and generation for better evidence-based understanding.
🔹 Publication Date: Published on Dec 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17220
• PDF: https://arxiv.org/pdf/2512.17220
🔹 Models citing this paper:
• https://huggingface.co/MindscapeRAG/MiA-Emb-8B
• https://huggingface.co/MindscapeRAG/MiA-Emb-4B
• https://huggingface.co/MindscapeRAG/MiA-Emb-0.6B
==================================
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#RAG #LLM #NLP #GenerativeAI #ContextUnderstanding
📝 Summary:
MiA-RAG enhances RAG systems with global context awareness, inspired by human understanding. It uses hierarchical summarization to build a 'mindscape,' improving long-context retrieval and generation for better evidence-based understanding.
🔹 Publication Date: Published on Dec 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17220
• PDF: https://arxiv.org/pdf/2512.17220
🔹 Models citing this paper:
• https://huggingface.co/MindscapeRAG/MiA-Emb-8B
• https://huggingface.co/MindscapeRAG/MiA-Emb-4B
• https://huggingface.co/MindscapeRAG/MiA-Emb-0.6B
==================================
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#RAG #LLM #NLP #GenerativeAI #ContextUnderstanding
❤1
✨TimeBill: Time-Budgeted Inference for Large Language Models
📝 Summary:
TimeBill is a framework for LLMs in time-critical systems. It predicts execution time and adaptively adjusts KV cache eviction to balance inference efficiency and response performance within given time budgets, improving task completion rates.
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21859
• PDF: https://arxiv.org/pdf/2512.21859
==================================
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#LLM #AI #RealTimeAI #InferenceOptimization #DeepLearning
📝 Summary:
TimeBill is a framework for LLMs in time-critical systems. It predicts execution time and adaptively adjusts KV cache eviction to balance inference efficiency and response performance within given time budgets, improving task completion rates.
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21859
• PDF: https://arxiv.org/pdf/2512.21859
==================================
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#LLM #AI #RealTimeAI #InferenceOptimization #DeepLearning
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