✨From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models
📝 Summary:
A benchmark framework evaluates Vision-Language Models in understanding microscopic spatial relationships, showing potential but highlighting the need for domain-specific knowledge integration. AI-gen...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10867
• PDF: https://arxiv.org/pdf/2512.10867
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A benchmark framework evaluates Vision-Language Models in understanding microscopic spatial relationships, showing potential but highlighting the need for domain-specific knowledge integration. AI-gen...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10867
• PDF: https://arxiv.org/pdf/2512.10867
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨MoRel: Long-Range Flicker-Free 4D Motion Modeling via Anchor Relay-based Bidirectional Blending with Hierarchical Densification
📝 Summary:
MoRel is a 4D Gaussian Splatting framework for long-range dynamic videos. It uses Anchor Relay-based Bidirectional Blending and Hierarchical Densification to achieve temporally consistent, flicker-free reconstruction with efficient memory use.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09270
• PDF: https://arxiv.org/pdf/2512.09270
• Project Page: https://cmlab-korea.github.io/MoRel/
• Github: https://github.com/CMLab-Korea/MoRel-arXiv
==================================
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#GaussianSplatting #4DMotionModeling #ComputerVision #DeepLearning #NeuralRendering
📝 Summary:
MoRel is a 4D Gaussian Splatting framework for long-range dynamic videos. It uses Anchor Relay-based Bidirectional Blending and Hierarchical Densification to achieve temporally consistent, flicker-free reconstruction with efficient memory use.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09270
• PDF: https://arxiv.org/pdf/2512.09270
• Project Page: https://cmlab-korea.github.io/MoRel/
• Github: https://github.com/CMLab-Korea/MoRel-arXiv
==================================
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#GaussianSplatting #4DMotionModeling #ComputerVision #DeepLearning #NeuralRendering
✨MOA: Multi-Objective Alignment for Role-Playing Agents
📝 Summary:
MOA is a reinforcement-learning framework for role-playing agents that uses multi-objective optimization and thought-augmented rollout. It simultaneously improves multiple skills like domain knowledge and linguistic style, addressing limitations of prior methods. MOA outperforms strong baselines,...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09756
• PDF: https://arxiv.org/pdf/2512.09756
==================================
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#AI #ReinforcementLearning #MultiObjectiveOptimization #RolePlayingAgents #MachineLearning
📝 Summary:
MOA is a reinforcement-learning framework for role-playing agents that uses multi-objective optimization and thought-augmented rollout. It simultaneously improves multiple skills like domain knowledge and linguistic style, addressing limitations of prior methods. MOA outperforms strong baselines,...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09756
• PDF: https://arxiv.org/pdf/2512.09756
==================================
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#AI #ReinforcementLearning #MultiObjectiveOptimization #RolePlayingAgents #MachineLearning
❤1
✨Thinking with Images via Self-Calling Agent
📝 Summary:
sCoT is a novel visual reasoning paradigm that reformulates interleaved multimodal CoT as a language-only CoT with self-calling subagents. It improves reasoning performance and efficiency by avoiding explicit multimodal interleaving and using group-relative policy optimization.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08511
• PDF: https://arxiv.org/pdf/2512.08511
• Github: https://github.com/YWenxi/think-with-images-through-self-calling
==================================
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#VisualReasoning #MultimodalAI #LLMs #AIagents #AIResearch
📝 Summary:
sCoT is a novel visual reasoning paradigm that reformulates interleaved multimodal CoT as a language-only CoT with self-calling subagents. It improves reasoning performance and efficiency by avoiding explicit multimodal interleaving and using group-relative policy optimization.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08511
• PDF: https://arxiv.org/pdf/2512.08511
• Github: https://github.com/YWenxi/think-with-images-through-self-calling
==================================
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#VisualReasoning #MultimodalAI #LLMs #AIagents #AIResearch
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✨T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground
📝 Summary:
T-pro 2.0 is an open-weight Russian LLM for hybrid reasoning and efficient inference. It uses a Cyrillic-dense tokenizer and EAGLE speculative decoding for low latency. The project releases model weights and benchmarks to foster reproducible research.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10430
• PDF: https://arxiv.org/pdf/2512.10430
==================================
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#LLM #AI #NaturalLanguageProcessing #HybridReasoning #EfficientInference
📝 Summary:
T-pro 2.0 is an open-weight Russian LLM for hybrid reasoning and efficient inference. It uses a Cyrillic-dense tokenizer and EAGLE speculative decoding for low latency. The project releases model weights and benchmarks to foster reproducible research.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10430
• PDF: https://arxiv.org/pdf/2512.10430
==================================
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#LLM #AI #NaturalLanguageProcessing #HybridReasoning #EfficientInference
✨ReViSE: Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning
📝 Summary:
The ReViSE framework enables reason-informed video editing by addressing the disconnect between models reasoning and editing capabilities. It uses a self-reflective learning mechanism with an internal VLM to provide intrinsic feedback. This significantly enhances editing accuracy and visual fidel...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09924
• PDF: https://arxiv.org/pdf/2512.09924
• Github: https://github.com/Liuxinyv/ReViSE
==================================
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#VideoEditing #AI #MachineLearning #VLM #SelfReflectiveLearning
📝 Summary:
The ReViSE framework enables reason-informed video editing by addressing the disconnect between models reasoning and editing capabilities. It uses a self-reflective learning mechanism with an internal VLM to provide intrinsic feedback. This significantly enhances editing accuracy and visual fidel...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09924
• PDF: https://arxiv.org/pdf/2512.09924
• Github: https://github.com/Liuxinyv/ReViSE
==================================
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#VideoEditing #AI #MachineLearning #VLM #SelfReflectiveLearning
❤1
✨StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space
📝 Summary:
StereoSpace generates stereo images from monocular input using viewpoint-conditioned diffusion, avoiding explicit depth or warping. It leverages a canonical rectified space for sharp parallax and robust results on complex scenes. This establishes a scalable, depth-free stereo synthesis solution.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10959
• PDF: https://arxiv.org/pdf/2512.10959
• Project Page: https://huggingface.co/spaces/prs-eth/stereospace_web
• Github: https://github.com/prs-eth/stereospace
🔹 Models citing this paper:
• https://huggingface.co/prs-eth/stereospace-v1-0
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prs-eth/stereospace_web
==================================
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#StereoVision #DiffusionModels #ComputerVision #DeepLearning #ImageSynthesis
📝 Summary:
StereoSpace generates stereo images from monocular input using viewpoint-conditioned diffusion, avoiding explicit depth or warping. It leverages a canonical rectified space for sharp parallax and robust results on complex scenes. This establishes a scalable, depth-free stereo synthesis solution.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10959
• PDF: https://arxiv.org/pdf/2512.10959
• Project Page: https://huggingface.co/spaces/prs-eth/stereospace_web
• Github: https://github.com/prs-eth/stereospace
🔹 Models citing this paper:
• https://huggingface.co/prs-eth/stereospace-v1-0
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prs-eth/stereospace_web
==================================
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#StereoVision #DiffusionModels #ComputerVision #DeepLearning #ImageSynthesis
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✨X-Humanoid: Robotize Human Videos to Generate Humanoid Videos at Scale
📝 Summary:
X-Humanoid generates large-scale humanoid video datasets from human videos to boost embodied AI. It uses generative video editing, finetuned on synthetic data, to translate human actions into full-body humanoid motions, generating over 3.6M robotized frames. This method outperforms existing solut...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04537
• PDF: https://arxiv.org/pdf/2512.04537
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#XHumanoid #EmbodiedAI #Robotics #GenerativeAI #ComputerVision
📝 Summary:
X-Humanoid generates large-scale humanoid video datasets from human videos to boost embodied AI. It uses generative video editing, finetuned on synthetic data, to translate human actions into full-body humanoid motions, generating over 3.6M robotized frames. This method outperforms existing solut...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04537
• PDF: https://arxiv.org/pdf/2512.04537
==================================
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#XHumanoid #EmbodiedAI #Robotics #GenerativeAI #ComputerVision
❤1
✨BEAVER: An Efficient Deterministic LLM Verifier
📝 Summary:
BEAVER is the first practical framework providing deterministic, sound probability bounds for verifying LLM output constraints. It achieves 6-8 times tighter bounds and identifies more high-risk instances than baseline methods, enabling precise risk assessment for LLMs.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05439
• PDF: https://arxiv.org/pdf/2512.05439
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLM #AI #LLMVerification #MachineLearning #AISafety
📝 Summary:
BEAVER is the first practical framework providing deterministic, sound probability bounds for verifying LLM output constraints. It achieves 6-8 times tighter bounds and identifies more high-risk instances than baseline methods, enabling precise risk assessment for LLMs.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05439
• PDF: https://arxiv.org/pdf/2512.05439
==================================
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#LLM #AI #LLMVerification #MachineLearning #AISafety
❤1
✨VibeVoice Technical Report
📝 Summary:
VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion. It introduces a highly efficient continuous speech tokenizer, achieving 80x better compression than Encodec while maintaining fidelity. This enables superior generation of up to 90 minutes of speech for four speakers.
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19205
• PDF: https://arxiv.org/pdf/2508.19205
• Project Page: https://microsoft.github.io/VibeVoice/
• Github: https://huggingface.co/collections/microsoft/vibevoice
🔹 Models citing this paper:
• https://huggingface.co/microsoft/VibeVoice-1.5B
• https://huggingface.co/microsoft/VibeVoice-Realtime-0.5B
• https://huggingface.co/aoi-ot/VibeVoice-Large
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ChaitanyaChandra/VibeVoice
• https://huggingface.co/spaces/lths/VibeVoice-Demo
• https://huggingface.co/spaces/anycoderapps/VibeVoice-Realtime-0.5B
==================================
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#SpeechSynthesis #AI #DiffusionModels #GenerativeAI #AudioTech
📝 Summary:
VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion. It introduces a highly efficient continuous speech tokenizer, achieving 80x better compression than Encodec while maintaining fidelity. This enables superior generation of up to 90 minutes of speech for four speakers.
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19205
• PDF: https://arxiv.org/pdf/2508.19205
• Project Page: https://microsoft.github.io/VibeVoice/
• Github: https://huggingface.co/collections/microsoft/vibevoice
🔹 Models citing this paper:
• https://huggingface.co/microsoft/VibeVoice-1.5B
• https://huggingface.co/microsoft/VibeVoice-Realtime-0.5B
• https://huggingface.co/aoi-ot/VibeVoice-Large
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ChaitanyaChandra/VibeVoice
• https://huggingface.co/spaces/lths/VibeVoice-Demo
• https://huggingface.co/spaces/anycoderapps/VibeVoice-Realtime-0.5B
==================================
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arXiv.org
VibeVoice Technical Report
This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing next-token diffusion, which is a unified method for modeling continuous...
❤1
✨Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization
📝 Summary:
Omni-Attribute is an open-vocabulary image attribute encoder that learns disentangled, attribute-specific representations. This enables precise visual concept personalization and compositional generation, outperforming entangled holistic embeddings via novel data and dual-objective training.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10955
• PDF: https://arxiv.org/pdf/2512.10955
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Omni-Attribute is an open-vocabulary image attribute encoder that learns disentangled, attribute-specific representations. This enables precise visual concept personalization and compositional generation, outperforming entangled holistic embeddings via novel data and dual-objective training.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10955
• PDF: https://arxiv.org/pdf/2512.10955
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
📝 Summary:
The Well is a 15TB dataset collection of 16 diverse physics simulations designed to benchmark machine learning models. It addresses the need for varied data across domains like fluid dynamics and biological systems, offering a unified PyTorch interface.
🔹 Publication Date: Published on Nov 30, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.00568
• PDF: https://arxiv.org/pdf/2412.00568
• Github: https://github.com/PolymathicAI/the_well
✨ Datasets citing this paper:
• https://huggingface.co/datasets/polymathic-ai/gray_scott_reaction_diffusion
• https://huggingface.co/datasets/polymathic-ai/rayleigh_benard
• https://huggingface.co/datasets/polymathic-ai/post_neutron_star_merger
✨ Spaces citing this paper:
• https://huggingface.co/spaces/polymathic-ai/TheWell
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
The Well is a 15TB dataset collection of 16 diverse physics simulations designed to benchmark machine learning models. It addresses the need for varied data across domains like fluid dynamics and biological systems, offering a unified PyTorch interface.
🔹 Publication Date: Published on Nov 30, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.00568
• PDF: https://arxiv.org/pdf/2412.00568
• Github: https://github.com/PolymathicAI/the_well
✨ Datasets citing this paper:
• https://huggingface.co/datasets/polymathic-ai/gray_scott_reaction_diffusion
• https://huggingface.co/datasets/polymathic-ai/rayleigh_benard
• https://huggingface.co/datasets/polymathic-ai/post_neutron_star_merger
✨ Spaces citing this paper:
• https://huggingface.co/spaces/polymathic-ai/TheWell
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
The Well: a Large-Scale Collection of Diverse Physics Simulations...
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of...
✨DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance
📝 Summary:
DuetSVG generates both image and SVG tokens end-to-end, improving SVG quality with a test-time scaling strategy. AI-generated summary Recent vision-language model ( VLM )-based approaches have achieve...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10894
• PDF: https://arxiv.org/pdf/2512.10894
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DuetSVG generates both image and SVG tokens end-to-end, improving SVG quality with a test-time scaling strategy. AI-generated summary Recent vision-language model ( VLM )-based approaches have achieve...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10894
• PDF: https://arxiv.org/pdf/2512.10894
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
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✨DragMesh: Interactive 3D Generation Made Easy
📝 Summary:
DragMesh is a real-time interactive 3D framework decoupling kinematic reasoning from motion generation. It uses a DQ-VAE and FiLM conditioning to achieve plausible, generative articulation on novel objects without retraining.
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06424
• PDF: https://arxiv.org/pdf/2512.06424
• Project Page: https://aigeeksgroup.github.io/DragMesh/
• Github: https://github.com/AIGeeksGroup/DragMesh
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DragMesh is a real-time interactive 3D framework decoupling kinematic reasoning from motion generation. It uses a DQ-VAE and FiLM conditioning to achieve plausible, generative articulation on novel objects without retraining.
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06424
• PDF: https://arxiv.org/pdf/2512.06424
• Project Page: https://aigeeksgroup.github.io/DragMesh/
• Github: https://github.com/AIGeeksGroup/DragMesh
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research