✨ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
📝 Summary:
ThreadWeaver, a framework for adaptive parallel reasoning, achieves accuracy comparable to sequential models while reducing inference latency through parallel trajectory generation, trie-based trainin...
🔹 Publication Date: Published on Nov 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07843
• PDF: https://arxiv.org/pdf/2512.07843
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
ThreadWeaver, a framework for adaptive parallel reasoning, achieves accuracy comparable to sequential models while reducing inference latency through parallel trajectory generation, trie-based trainin...
🔹 Publication Date: Published on Nov 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07843
• PDF: https://arxiv.org/pdf/2512.07843
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Modular Neural Image Signal Processing
📝 Summary:
A modular neural ISP framework provides high rendering accuracy, scalability, and flexibility for diverse photo-editing operations with competitive results. AI-generated summary This paper presents a ...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08564
• PDF: https://arxiv.org/pdf/2512.08564
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A modular neural ISP framework provides high rendering accuracy, scalability, and flexibility for diverse photo-editing operations with competitive results. AI-generated summary This paper presents a ...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08564
• PDF: https://arxiv.org/pdf/2512.08564
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
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✨Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation
📝 Summary:
DualVLN is a dual-system model for vision-language navigation. It integrates a VLM global planner with a fast local policy for smooth actions, enabling robust real-time control and long-horizon planning in dynamic environments.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08186
• PDF: https://arxiv.org/pdf/2512.08186
• Project Page: https://internrobotics.github.io/internvla-n1-dualvln.github.io/
• Github: https://github.com/InternRobotics/InternNav
🔹 Models citing this paper:
• https://huggingface.co/InternRobotics/InternVLA-N1-System2
• https://huggingface.co/InternRobotics/InternVLA-N1-w-NavDP
• https://huggingface.co/InternRobotics/InternVLA-N1-DualVLN
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DualVLN is a dual-system model for vision-language navigation. It integrates a VLM global planner with a fast local policy for smooth actions, enabling robust real-time control and long-horizon planning in dynamic environments.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08186
• PDF: https://arxiv.org/pdf/2512.08186
• Project Page: https://internrobotics.github.io/internvla-n1-dualvln.github.io/
• Github: https://github.com/InternRobotics/InternNav
🔹 Models citing this paper:
• https://huggingface.co/InternRobotics/InternVLA-N1-System2
• https://huggingface.co/InternRobotics/InternVLA-N1-w-NavDP
• https://huggingface.co/InternRobotics/InternVLA-N1-DualVLN
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs
📝 Summary:
This paper introduces a principled method to adapt autoregressive LLMs into block-wise diffusion models, enabling efficient parallel generation. This adaptation retains pretrained knowledge, achieving state-of-the-art performance for 7B diffusion LLMs, and avoids expensive training from scratch.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06776
• PDF: https://arxiv.org/pdf/2512.06776
==================================
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#LLM #DiffusionModels #AI #ParallelGeneration #MachineLearning
📝 Summary:
This paper introduces a principled method to adapt autoregressive LLMs into block-wise diffusion models, enabling efficient parallel generation. This adaptation retains pretrained knowledge, achieving state-of-the-art performance for 7B diffusion LLMs, and avoids expensive training from scratch.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06776
• PDF: https://arxiv.org/pdf/2512.06776
==================================
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#LLM #DiffusionModels #AI #ParallelGeneration #MachineLearning
✨Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs
📝 Summary:
New benchmarks reveal MLLMs struggle with cross-modal inconsistency, failing to reason consistently across image, text, and mixed modalities with the same information. Visual characteristics like color and resolution significantly impact performance, even when text recognition is perfect. This hi...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08923
• PDF: https://arxiv.org/pdf/2512.08923
==================================
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#MLLMs #CrossModalAI #AIResearch #ComputerVision #NLP
📝 Summary:
New benchmarks reveal MLLMs struggle with cross-modal inconsistency, failing to reason consistently across image, text, and mixed modalities with the same information. Visual characteristics like color and resolution significantly impact performance, even when text recognition is perfect. This hi...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08923
• PDF: https://arxiv.org/pdf/2512.08923
==================================
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#MLLMs #CrossModalAI #AIResearch #ComputerVision #NLP
✨Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
📝 Summary:
A Deep Operator Network predicts unsteady flow velocity fields over complex geometries with up to 1000X speedup over traditional simulations. It accurately captures near-term transients but shows error accumulation in fine-scale wakes.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04434
• PDF: https://arxiv.org/pdf/2512.04434
• Github: https://github.com/baskargroup/TimeDependent-DeepONet
==================================
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#DeepLearning #FluidDynamics #AI #CFD #MachineLearning
📝 Summary:
A Deep Operator Network predicts unsteady flow velocity fields over complex geometries with up to 1000X speedup over traditional simulations. It accurately captures near-term transients but shows error accumulation in fine-scale wakes.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04434
• PDF: https://arxiv.org/pdf/2512.04434
• Github: https://github.com/baskargroup/TimeDependent-DeepONet
==================================
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#DeepLearning #FluidDynamics #AI #CFD #MachineLearning
✨MIND-V: Hierarchical Video Generation for Long-Horizon Robotic Manipulation with RL-based Physical Alignment
📝 Summary:
MIND-V generates long-horizon, physically plausible robotic manipulation videos. This hierarchical framework uses semantic reasoning and an RL-based physical alignment strategy to synthesize robust, coherent actions, addressing data scarcity.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06628
• PDF: https://arxiv.org/pdf/2512.06628
• Project Page: https://github.com/Richard-Zhang-AI/MIND-V
• Github: https://github.com/Richard-Zhang-AI/MIND-V
==================================
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#Robotics #VideoGeneration #ReinforcementLearning #AI #MachineLearning
📝 Summary:
MIND-V generates long-horizon, physically plausible robotic manipulation videos. This hierarchical framework uses semantic reasoning and an RL-based physical alignment strategy to synthesize robust, coherent actions, addressing data scarcity.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06628
• PDF: https://arxiv.org/pdf/2512.06628
• Project Page: https://github.com/Richard-Zhang-AI/MIND-V
• Github: https://github.com/Richard-Zhang-AI/MIND-V
==================================
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#Robotics #VideoGeneration #ReinforcementLearning #AI #MachineLearning
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✨OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory
📝 Summary:
OneStory generates coherent multi-shot videos by modeling global cross-shot context. It uses a Frame Selection module and an Adaptive Conditioner for next-shot generation, leveraging pretrained models and a new dataset. This achieves state-of-the-art narrative coherence for long-form video storyt...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07802
• PDF: https://arxiv.org/pdf/2512.07802
• Project Page: https://zhaochongan.github.io/projects/OneStory/
==================================
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#VideoGeneration #AI #DeepLearning #ComputerVision #GenerativeAI
📝 Summary:
OneStory generates coherent multi-shot videos by modeling global cross-shot context. It uses a Frame Selection module and an Adaptive Conditioner for next-shot generation, leveraging pretrained models and a new dataset. This achieves state-of-the-art narrative coherence for long-form video storyt...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07802
• PDF: https://arxiv.org/pdf/2512.07802
• Project Page: https://zhaochongan.github.io/projects/OneStory/
==================================
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#VideoGeneration #AI #DeepLearning #ComputerVision #GenerativeAI
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✨LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning
📝 Summary:
LYNX is an early-exit mechanism for large reasoning models that prevents overthinking. It uses hidden-state awareness and reasoning cues for confidence-controlled stopping, significantly reducing tokens by 40-70% while maintaining or improving accuracy across diverse tasks with a single trained p...
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05325
• PDF: https://arxiv.org/pdf/2512.05325
• Github: https://github.com/farukakgul/LYNX
🔹 Models citing this paper:
• https://huggingface.co/farukakgul/LYNX
==================================
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#LLM #AI #MachineLearning #EarlyExit #Efficiency
📝 Summary:
LYNX is an early-exit mechanism for large reasoning models that prevents overthinking. It uses hidden-state awareness and reasoning cues for confidence-controlled stopping, significantly reducing tokens by 40-70% while maintaining or improving accuracy across diverse tasks with a single trained p...
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05325
• PDF: https://arxiv.org/pdf/2512.05325
• Github: https://github.com/farukakgul/LYNX
🔹 Models citing this paper:
• https://huggingface.co/farukakgul/LYNX
==================================
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#LLM #AI #MachineLearning #EarlyExit #Efficiency
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✨Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform
📝 Summary:
Visionary is a web-native platform for real-time 3D Gaussian Splatting and mesh rendering. It uses WebGPU and per-frame ONNX inference for dynamic content and generative models, offering efficient, browser-based deployment and support for various neural rendering methods.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08478
• PDF: https://arxiv.org/pdf/2512.08478
• Project Page: https://visionary-laboratory.github.io/visionary/
• Github: https://visionary-laboratory.github.io/visionary/
==================================
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#GaussianSplatting #WebGPU #NeuralRendering #3DGraphics #GenerativeAI
📝 Summary:
Visionary is a web-native platform for real-time 3D Gaussian Splatting and mesh rendering. It uses WebGPU and per-frame ONNX inference for dynamic content and generative models, offering efficient, browser-based deployment and support for various neural rendering methods.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08478
• PDF: https://arxiv.org/pdf/2512.08478
• Project Page: https://visionary-laboratory.github.io/visionary/
• Github: https://visionary-laboratory.github.io/visionary/
==================================
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#GaussianSplatting #WebGPU #NeuralRendering #3DGraphics #GenerativeAI
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✨SUCCESS-GS: Survey of Compactness and Compression for Efficient Static and Dynamic Gaussian Splatting
📝 Summary:
This survey overviews efficient 3D and 4D Gaussian Splatting. It categorizes parameter and restructuring compression methods to reduce memory and computation while maintaining reconstruction quality. It also covers current limitations and future research.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07197
• PDF: https://arxiv.org/pdf/2512.07197
• Project Page: https://cmlab-korea.github.io/Awesome-Efficient-GS/
• Github: https://cmlab-korea.github.io/Awesome-Efficient-GS/
==================================
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#GaussianSplatting #3DVision #ComputerGraphics #DeepLearning #Efficiency
📝 Summary:
This survey overviews efficient 3D and 4D Gaussian Splatting. It categorizes parameter and restructuring compression methods to reduce memory and computation while maintaining reconstruction quality. It also covers current limitations and future research.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07197
• PDF: https://arxiv.org/pdf/2512.07197
• Project Page: https://cmlab-korea.github.io/Awesome-Efficient-GS/
• Github: https://cmlab-korea.github.io/Awesome-Efficient-GS/
==================================
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#GaussianSplatting #3DVision #ComputerGraphics #DeepLearning #Efficiency
✨Boosting Unsupervised Video Instance Segmentation with Automatic Quality-Guided Self-Training
📝 Summary:
AutoQ-VIS is an unsupervised Video Instance Segmentation framework that bridges the synthetic-to-real domain gap. It uses quality-guided self-training with automatic quality assessment for progressive adaptation. This method achieves state-of-the-art results without requiring human annotations.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06864
• PDF: https://arxiv.org/pdf/2512.06864
• Github: https://github.com/wcbup/AutoQ-VIS/
==================================
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#VideoInstanceSegmentation #UnsupervisedLearning #ComputerVision #MachineLearning #DeepLearning
📝 Summary:
AutoQ-VIS is an unsupervised Video Instance Segmentation framework that bridges the synthetic-to-real domain gap. It uses quality-guided self-training with automatic quality assessment for progressive adaptation. This method achieves state-of-the-art results without requiring human annotations.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06864
• PDF: https://arxiv.org/pdf/2512.06864
• Github: https://github.com/wcbup/AutoQ-VIS/
==================================
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#VideoInstanceSegmentation #UnsupervisedLearning #ComputerVision #MachineLearning #DeepLearning
✨Efficiently Reconstructing Dynamic Scenes One D4RT at a Time
📝 Summary:
D4RT is a transformer-based model that efficiently reconstructs 4D scenes from videos. It uses a novel querying mechanism to infer depth and motion by flexibly probing 3D space-time points, outperforming previous methods.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08924
• PDF: https://arxiv.org/pdf/2512.08924
==================================
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#4DReconstruction #ComputerVision #Transformers #DynamicScenes #DeepLearning
📝 Summary:
D4RT is a transformer-based model that efficiently reconstructs 4D scenes from videos. It uses a novel querying mechanism to infer depth and motion by flexibly probing 3D space-time points, outperforming previous methods.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08924
• PDF: https://arxiv.org/pdf/2512.08924
==================================
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#4DReconstruction #ComputerVision #Transformers #DynamicScenes #DeepLearning
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✨UI-TARS: Pioneering Automated GUI Interaction with Native Agents
📝 Summary:
UI-TARS is a native GUI agent that uses screenshots for human-like interaction. It achieves state-of-the-art performance, outperforming commercial models, across various GUI benchmarks. This is due to enhanced perception, unified action modeling, system-2 reasoning, and iterative training.
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.12326
• PDF: https://arxiv.org/pdf/2501.12326
• Github: https://github.com/bytedance/UI-TARS
🔹 Models citing this paper:
• https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B
• https://huggingface.co/ByteDance-Seed/UI-TARS-7B-DPO
• https://huggingface.co/ByteDance-Seed/UI-TARS-7B-SFT
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Hcompany/WebClick
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prithivMLmods/CUA-GUI-Operator
• https://huggingface.co/spaces/bytedance-research/UI-TARS
• https://huggingface.co/spaces/Aheader/gui_test_app
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
UI-TARS is a native GUI agent that uses screenshots for human-like interaction. It achieves state-of-the-art performance, outperforming commercial models, across various GUI benchmarks. This is due to enhanced perception, unified action modeling, system-2 reasoning, and iterative training.
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.12326
• PDF: https://arxiv.org/pdf/2501.12326
• Github: https://github.com/bytedance/UI-TARS
🔹 Models citing this paper:
• https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B
• https://huggingface.co/ByteDance-Seed/UI-TARS-7B-DPO
• https://huggingface.co/ByteDance-Seed/UI-TARS-7B-SFT
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Hcompany/WebClick
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prithivMLmods/CUA-GUI-Operator
• https://huggingface.co/spaces/bytedance-research/UI-TARS
• https://huggingface.co/spaces/Aheader/gui_test_app
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
UI-TARS: Pioneering Automated GUI Interaction with Native Agents
This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). Unlike prevailing...
✨Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images
📝 Summary:
Two novel deep learning architectures, SAETCN and SAS-Net, achieve high accuracy in classifying and segmenting brain tumors from MRI scans. AI-generated summary Brain tumors pose a significant threat ...
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06531
• PDF: https://arxiv.org/pdf/2512.06531
• Github: https://github.com/arghadip2002/SAETCN-and-SASNET-Architectures
✨ Spaces citing this paper:
• https://huggingface.co/spaces/arghadip2002/NeuroGuard-Web-Application
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Two novel deep learning architectures, SAETCN and SAS-Net, achieve high accuracy in classifying and segmenting brain tumors from MRI scans. AI-generated summary Brain tumors pose a significant threat ...
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06531
• PDF: https://arxiv.org/pdf/2512.06531
• Github: https://github.com/arghadip2002/SAETCN-and-SASNET-Architectures
✨ Spaces citing this paper:
• https://huggingface.co/spaces/arghadip2002/NeuroGuard-Web-Application
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
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❤1
✨SAM-Body4D: Training-Free 4D Human Body Mesh Recovery from Videos
📝 Summary:
SAM-Body4D is a training-free framework for 3D human mesh recovery from videos. It enhances temporal consistency and occlusion robustness by generating and refining identity-consistent masklets, guiding existing methods to produce stable full-body mesh trajectories without retraining.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08406
• PDF: https://arxiv.org/pdf/2512.08406
• Github: https://github.com/gaomingqi/sam-body4d
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
SAM-Body4D is a training-free framework for 3D human mesh recovery from videos. It enhances temporal consistency and occlusion robustness by generating and refining identity-consistent masklets, guiding existing methods to produce stable full-body mesh trajectories without retraining.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08406
• PDF: https://arxiv.org/pdf/2512.08406
• Github: https://github.com/gaomingqi/sam-body4d
==================================
For more data science resources:
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨MemLoRA: Distilling Expert Adapters for On-Device Memory Systems
📝 Summary:
MemLoRA and MemLoRA-V enable efficient on-device memory-augmented AI by equipping small language and vision-language models with specialized, distilled memory adapters. This allows accurate local memory operations and native visual understanding, outperforming larger baselines in text and visual ...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04763
• PDF: https://arxiv.org/pdf/2512.04763
==================================
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#OnDeviceAI #LLMs #VLMs #AIAdapters #MemoryAugmentedAI
📝 Summary:
MemLoRA and MemLoRA-V enable efficient on-device memory-augmented AI by equipping small language and vision-language models with specialized, distilled memory adapters. This allows accurate local memory operations and native visual understanding, outperforming larger baselines in text and visual ...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04763
• PDF: https://arxiv.org/pdf/2512.04763
==================================
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#OnDeviceAI #LLMs #VLMs #AIAdapters #MemoryAugmentedAI
❤1
✨Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise in Infinite, Real-Time Terrain Generation
📝 Summary:
Terrain Diffusion uses diffusion models and a novel algorithm called InfiniteDiffusion to generate realistic, seamless, and boundless procedural worlds with constant-time random access. AI-generated s...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08309
• PDF: https://arxiv.org/pdf/2512.08309
• Project Page: https://xandergos.github.io/terrain-diffusion/
• Github: https://github.com/xandergos/terrain-diffusion
==================================
For more data science resources:
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Terrain Diffusion uses diffusion models and a novel algorithm called InfiniteDiffusion to generate realistic, seamless, and boundless procedural worlds with constant-time random access. AI-generated s...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08309
• PDF: https://arxiv.org/pdf/2512.08309
• Project Page: https://xandergos.github.io/terrain-diffusion/
• Github: https://github.com/xandergos/terrain-diffusion
==================================
For more data science resources:
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#AI #DataScience #MachineLearning #HuggingFace #Research