✨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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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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✨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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📝 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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✨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
==================================
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📝 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
==================================
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✨See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models
📝 Summary:
AV-SpeakerBench is a new benchmark assessing speaker-centric audiovisual reasoning in MLLMs. It features 3,212 expert-curated questions focused on precise speech understanding. Gemini models outperform open-source systems, particularly in audiovisual fusion capabilities.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02231
• PDF: https://arxiv.org/pdf/2512.02231
• Project Page: https://plnguyen2908.github.io/AV-SpeakerBench-project-page/
• Github: https://github.com/plnguyen2908/AV-SpeakerBench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/plnguyen2908/AV-SpeakerBench
==================================
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📝 Summary:
AV-SpeakerBench is a new benchmark assessing speaker-centric audiovisual reasoning in MLLMs. It features 3,212 expert-curated questions focused on precise speech understanding. Gemini models outperform open-source systems, particularly in audiovisual fusion capabilities.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02231
• PDF: https://arxiv.org/pdf/2512.02231
• Project Page: https://plnguyen2908.github.io/AV-SpeakerBench-project-page/
• Github: https://github.com/plnguyen2908/AV-SpeakerBench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/plnguyen2908/AV-SpeakerBench
==================================
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✨Arbitrage: Efficient Reasoning via Advantage-Aware Speculation
📝 Summary:
Arbitrage is a speculative decoding framework for LLMs that dynamically routes generation. It uses a router to predict when a target model will provide a better reasoning step, preventing wasted compute from regenerating rejected steps. This approach reduces inference latency by up to two times w...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05033
• PDF: https://arxiv.org/pdf/2512.05033
• Project Page: https://www.monishwaran.com/arbitrage.html
• Github: https://github.com/SqueezeAILab/Arbitrage
==================================
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📝 Summary:
Arbitrage is a speculative decoding framework for LLMs that dynamically routes generation. It uses a router to predict when a target model will provide a better reasoning step, preventing wasted compute from regenerating rejected steps. This approach reduces inference latency by up to two times w...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05033
• PDF: https://arxiv.org/pdf/2512.05033
• Project Page: https://www.monishwaran.com/arbitrage.html
• Github: https://github.com/SqueezeAILab/Arbitrage
==================================
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✨COREA: Coarse-to-Fine 3D Representation Alignment Between Relightable 3D Gaussians and SDF via Bidirectional 3D-to-3D Supervision
📝 Summary:
COREA unifies 3D Gaussians and SDF for accurate geometry and relighting. It uses a coarse-to-fine bidirectional 3D-to-3D alignment, learning geometry directly in 3D to overcome prior limitations. This improves novel-view synthesis, mesh reconstruction, and physically-based rendering.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07107
• PDF: https://arxiv.org/pdf/2512.07107
• Project Page: https://cau-vilab.github.io/COREA/
• Github: https://github.com/CAU-VILab/COREA-arXiv
==================================
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📝 Summary:
COREA unifies 3D Gaussians and SDF for accurate geometry and relighting. It uses a coarse-to-fine bidirectional 3D-to-3D alignment, learning geometry directly in 3D to overcome prior limitations. This improves novel-view synthesis, mesh reconstruction, and physically-based rendering.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07107
• PDF: https://arxiv.org/pdf/2512.07107
• Project Page: https://cau-vilab.github.io/COREA/
• Github: https://github.com/CAU-VILab/COREA-arXiv
==================================
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✨SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images
📝 Summary:
A preliminary exploration of using SAM 3 for remote sensing open-vocabulary semantic segmentation demonstrates promising results through a mask fusion strategy and presence score filtering. AI-generat...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08730
• PDF: https://arxiv.org/pdf/2512.08730
• Github: https://github.com/earth-insights/SegEarth-OV-3
==================================
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📝 Summary:
A preliminary exploration of using SAM 3 for remote sensing open-vocabulary semantic segmentation demonstrates promising results through a mask fusion strategy and presence score filtering. AI-generat...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08730
• PDF: https://arxiv.org/pdf/2512.08730
• Github: https://github.com/earth-insights/SegEarth-OV-3
==================================
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✨UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving
📝 Summary:
A unified framework combines vision-language models and video generation to improve autonomous driving in complex scenarios by enhancing reasoning, trajectory planning, and video generation. AI-genera...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09864
• PDF: https://arxiv.org/pdf/2512.09864
==================================
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📝 Summary:
A unified framework combines vision-language models and video generation to improve autonomous driving in complex scenarios by enhancing reasoning, trajectory planning, and video generation. AI-genera...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09864
• PDF: https://arxiv.org/pdf/2512.09864
==================================
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