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✨Relational Visual Similarity
📝 Summary:
Vision-Language models fine-tuned on anonymized image captions can capture relational similarity between images, a capability lacking in current visual similarity metrics. AI-generated summary Humans ...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07833
• PDF: https://arxiv.org/pdf/2512.07833
• Project Page: https://thaoshibe.github.io/relsim/
• Github: https://github.com/thaoshibe/relsim
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Vision-Language models fine-tuned on anonymized image captions can capture relational similarity between images, a capability lacking in current visual similarity metrics. AI-generated summary Humans ...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07833
• PDF: https://arxiv.org/pdf/2512.07833
• Project Page: https://thaoshibe.github.io/relsim/
• Github: https://github.com/thaoshibe/relsim
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Group Representational Position Encoding
📝 Summary:
GRAPE is a unified positional encoding framework that combines multiplicative rotations and additive logit biases, extending existing methods like RoPE and ALiBi. AI-generated summary We present GRAPE...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07805
• PDF: https://model-architectures.github.io/GRAPE/GRAPE.pdf
• Github: https://model-architectures.github.io/GRAPE/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
GRAPE is a unified positional encoding framework that combines multiplicative rotations and additive logit biases, extending existing methods like RoPE and ALiBi. AI-generated summary We present GRAPE...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07805
• PDF: https://model-architectures.github.io/GRAPE/GRAPE.pdf
• Github: https://model-architectures.github.io/GRAPE/
==================================
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✨Voxify3D: Pixel Art Meets Volumetric Rendering
📝 Summary:
Voxify3D is a two-stage framework that combines 3D mesh optimization with 2D pixel art supervision to generate high-quality voxel art with semantic preservation, pixel-art aesthetics, and discrete col...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07834
• PDF: https://arxiv.org/pdf/2512.07834
• Project Page: https://yichuanh.github.io/Voxify-3D/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Voxify3D is a two-stage framework that combines 3D mesh optimization with 2D pixel art supervision to generate high-quality voxel art with semantic preservation, pixel-art aesthetics, and discrete col...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07834
• PDF: https://arxiv.org/pdf/2512.07834
• Project Page: https://yichuanh.github.io/Voxify-3D/
==================================
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✨On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
📝 Summary:
A controlled experimental framework isolates and evaluates the contributions of pre-training, mid-training, and reinforcement learning in improving language model reasoning, demonstrating the necessit...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07783
• PDF: https://arxiv.org/pdf/2512.07783
• Github: https://github.com/Interplay-LM-Reasoning/Interplay-LM-Reasoning
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A controlled experimental framework isolates and evaluates the contributions of pre-training, mid-training, and reinforcement learning in improving language model reasoning, demonstrating the necessit...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07783
• PDF: https://arxiv.org/pdf/2512.07783
• Github: https://github.com/Interplay-LM-Reasoning/Interplay-LM-Reasoning
==================================
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✨Vector Quantization using Gaussian Variational Autoencoder
📝 Summary:
Gaussian Quant (GQ) converts Gaussian VAE to VQ-VAE without training, outperforming previous VQ-VAEs and Gaussian VAE discretization methods across different architectures. AI-generated summary Vector...
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06609
• PDF: https://arxiv.org/pdf/2512.06609
• Github: https://github.com/Stability-AI/generative-models
🔹 Models citing this paper:
• https://huggingface.co/xutongda/GQModel
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Gaussian Quant (GQ) converts Gaussian VAE to VQ-VAE without training, outperforming previous VQ-VAEs and Gaussian VAE discretization methods across different architectures. AI-generated summary Vector...
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06609
• PDF: https://arxiv.org/pdf/2512.06609
• Github: https://github.com/Stability-AI/generative-models
🔹 Models citing this paper:
• https://huggingface.co/xutongda/GQModel
==================================
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✨VideoVLA: Video Generators Can Be Generalizable Robot Manipulators
📝 Summary:
VideoVLA uses a multi-modal Diffusion Transformer to predict actions and visual outcomes from language and image inputs, enabling strong generalization in robotic manipulation tasks. AI-generated summ...
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06963
• PDF: https://arxiv.org/pdf/2512.06963
• Project Page: https://videovla-nips2025.github.io/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
VideoVLA uses a multi-modal Diffusion Transformer to predict actions and visual outcomes from language and image inputs, enabling strong generalization in robotic manipulation tasks. AI-generated summ...
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06963
• PDF: https://arxiv.org/pdf/2512.06963
• Project Page: https://videovla-nips2025.github.io/
==================================
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✨Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning
📝 Summary:
NPR is a teacher-free framework enabling LLMs to perform genuine parallel reasoning. It uses self-distilled training and a new optimization algorithm. This achieves significant performance gains and speedups on reasoning benchmarks.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07461
• PDF: https://arxiv.org/pdf/2512.07461
• Github: https://bigai-nlco.github.io/Native-Parallel-Reasoner
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
NPR is a teacher-free framework enabling LLMs to perform genuine parallel reasoning. It uses self-distilled training and a new optimization algorithm. This achieves significant performance gains and speedups on reasoning benchmarks.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07461
• PDF: https://arxiv.org/pdf/2512.07461
• Github: https://bigai-nlco.github.io/Native-Parallel-Reasoner
==================================
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✨Distribution Matching Variational AutoEncoder
📝 Summary:
DMVAE explicitly aligns VAE latent distributions with arbitrary reference distributions, generalizing beyond fixed priors. This improves modeling efficiency and image synthesis fidelity, with SSL-derived distributions showing excellent balance.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07778
• PDF: https://arxiv.org/pdf/2512.07778
• Github: https://github.com/sen-ye/dmvae%7D
==================================
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#VAE #DeepLearning #GenerativeAI #ImageSynthesis #ArtificialIntelligence
📝 Summary:
DMVAE explicitly aligns VAE latent distributions with arbitrary reference distributions, generalizing beyond fixed priors. This improves modeling efficiency and image synthesis fidelity, with SSL-derived distributions showing excellent balance.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07778
• PDF: https://arxiv.org/pdf/2512.07778
• Github: https://github.com/sen-ye/dmvae%7D
==================================
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✨Multi-view Pyramid Transformer: Look Coarser to See Broader
📝 Summary:
MVP is a scalable multi-view transformer that reconstructs large 3D scenes from many images. It uses a dual hierarchy of local-to-global inter-view and fine-to-coarse intra-view processing. This achieves efficient, state-of-the-art 3D scene reconstruction quality.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07806
• PDF: https://arxiv.org/pdf/2512.07806
• Project Page: https://gynjn.github.io/MVP/
• Github: https://github.com/Gynjn/MVP
==================================
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#3DReconstruction #ComputerVision #Transformers #DeepLearning #AI
📝 Summary:
MVP is a scalable multi-view transformer that reconstructs large 3D scenes from many images. It uses a dual hierarchy of local-to-global inter-view and fine-to-coarse intra-view processing. This achieves efficient, state-of-the-art 3D scene reconstruction quality.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07806
• PDF: https://arxiv.org/pdf/2512.07806
• Project Page: https://gynjn.github.io/MVP/
• Github: https://github.com/Gynjn/MVP
==================================
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#3DReconstruction #ComputerVision #Transformers #DeepLearning #AI
✨UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation
📝 Summary:
UnityVideo is a unified framework enhancing video generation by integrating multiple modalities and training paradigms. It uses dynamic noising and a modality switcher for comprehensive world understanding. This improves video quality, consistency, and zero-shot generalization to new data.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07831
• PDF: https://arxiv.org/pdf/2512.07831
• Project Page: https://jackailab.github.io/Projects/UnityVideo/
• Github: https://github.com/dvlab-research/UnityVideo
==================================
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#VideoGeneration #MultimodalAI #GenerativeAI #DeepLearning #AIResearch
📝 Summary:
UnityVideo is a unified framework enhancing video generation by integrating multiple modalities and training paradigms. It uses dynamic noising and a modality switcher for comprehensive world understanding. This improves video quality, consistency, and zero-shot generalization to new data.
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07831
• PDF: https://arxiv.org/pdf/2512.07831
• Project Page: https://jackailab.github.io/Projects/UnityVideo/
• Github: https://github.com/dvlab-research/UnityVideo
==================================
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#VideoGeneration #MultimodalAI #GenerativeAI #DeepLearning #AIResearch
✨ReCamDriving: LiDAR-Free Camera-Controlled Novel Trajectory Video Generation
📝 Summary:
ReCamDriving generates camera-controlled novel-trajectory videos using dense 3DGS renderings and a two-stage training approach, achieving state-of-the-art results in controllability and consistency. A...
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03621
• PDF: https://arxiv.org/pdf/2512.03621
• Project Page: https://recamdriving.github.io/
• Github: https://recamdriving.github.io/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
ReCamDriving generates camera-controlled novel-trajectory videos using dense 3DGS renderings and a two-stage training approach, achieving state-of-the-art results in controllability and consistency. A...
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03621
• PDF: https://arxiv.org/pdf/2512.03621
• Project Page: https://recamdriving.github.io/
• Github: https://recamdriving.github.io/
==================================
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✨VG-Refiner: Towards Tool-Refined Referring Grounded Reasoning via Agentic Reinforcement Learning
📝 Summary:
VG-Refiner improves visual reasoning by addressing unreliable tool outputs. It uses a two-stage think-rethink mechanism and refinement reward to correct poor tool results. This significantly improves accuracy and correction ability in referring and grounding tasks.
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06373
• PDF: https://arxiv.org/pdf/2512.06373
• Github: https://github.com/VoyageWang/VG-Refiner
==================================
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#VisualReasoning #ReinforcementLearning #ComputerVision #AIResearch #MachineLearning
📝 Summary:
VG-Refiner improves visual reasoning by addressing unreliable tool outputs. It uses a two-stage think-rethink mechanism and refinement reward to correct poor tool results. This significantly improves accuracy and correction ability in referring and grounding tasks.
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06373
• PDF: https://arxiv.org/pdf/2512.06373
• Github: https://github.com/VoyageWang/VG-Refiner
==================================
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#VisualReasoning #ReinforcementLearning #ComputerVision #AIResearch #MachineLearning
✨Decouple to Generalize: Context-First Self-Evolving Learning for Data-Scarce Vision-Language Reasoning
📝 Summary:
DoGe is a framework that addresses data scarcity in vision-language models. It decouples context learning from problem solving, using a curriculum to improve reward signals and data diversity. This enhances generalization and performance.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06835
• PDF: https://arxiv.org/pdf/2512.06835
==================================
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#VisionLanguage #DataScarcity #MachineLearning #AIResearch #DeepLearning
📝 Summary:
DoGe is a framework that addresses data scarcity in vision-language models. It decouples context learning from problem solving, using a curriculum to improve reward signals and data diversity. This enhances generalization and performance.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06835
• PDF: https://arxiv.org/pdf/2512.06835
==================================
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#VisionLanguage #DataScarcity #MachineLearning #AIResearch #DeepLearning
❤1
✨GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
📝 Summary:
GLM-4.1V-Thinking is a vision-language model using a reasoning-centric training framework. It achieves state-of-the-art multimodal reasoning across various tasks like STEM and long document understanding. The model outperforms larger models and competes with closed-source systems like GPT-4o.
🔹 Publication Date: Published on Jul 1
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/glm-41v-thinking-towards-versatile-multimodal-reasoning-with-scalable-reinforcement-learning
• PDF: https://arxiv.org/pdf/2507.01006
• Github: https://github.com/THUDM/GLM-4.1V-Thinking
🔹 Models citing this paper:
• https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking
• https://huggingface.co/zai-org/GLM-4.5V
• https://huggingface.co/zai-org/GLM-4.6V-Flash
✨ Spaces citing this paper:
• https://huggingface.co/spaces/zai-org/GLM-4.1V-9B-Thinking-Demo
• https://huggingface.co/spaces/zai-org/GLM-4.1V-9B-Thinking-API-Demo
• https://huggingface.co/spaces/akhaliq/anycoder
==================================
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#GLM41VThinking #MultimodalAI #VisionLanguageModels #ReinforcementLearning #AIResearch
📝 Summary:
GLM-4.1V-Thinking is a vision-language model using a reasoning-centric training framework. It achieves state-of-the-art multimodal reasoning across various tasks like STEM and long document understanding. The model outperforms larger models and competes with closed-source systems like GPT-4o.
🔹 Publication Date: Published on Jul 1
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/glm-41v-thinking-towards-versatile-multimodal-reasoning-with-scalable-reinforcement-learning
• PDF: https://arxiv.org/pdf/2507.01006
• Github: https://github.com/THUDM/GLM-4.1V-Thinking
🔹 Models citing this paper:
• https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking
• https://huggingface.co/zai-org/GLM-4.5V
• https://huggingface.co/zai-org/GLM-4.6V-Flash
✨ Spaces citing this paper:
• https://huggingface.co/spaces/zai-org/GLM-4.1V-9B-Thinking-Demo
• https://huggingface.co/spaces/zai-org/GLM-4.1V-9B-Thinking-API-Demo
• https://huggingface.co/spaces/akhaliq/anycoder
==================================
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Arxivexplained
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning - Explained Simply
By Wenyi Hong, Wenmeng Yu, Xiaotao Gu et al.. # GLM-4.1V-Thinking: The AI That Actually Thinks Through Visual Problems
**The Problem:** Current A...
**The Problem:** Current A...
✨Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
📝 Summary:
Reinforcement Learning enhances decoding-based regression by introducing sequence-level rewards. This overcomes token-level limitations, improving precision and generalization. It establishes a robust and accurate paradigm for numerical prediction.
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06533
• PDF: https://arxiv.org/pdf/2512.06533
==================================
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#ReinforcementLearning #MachineLearning #Regression #DataScience #AI
📝 Summary:
Reinforcement Learning enhances decoding-based regression by introducing sequence-level rewards. This overcomes token-level limitations, improving precision and generalization. It establishes a robust and accurate paradigm for numerical prediction.
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06533
• PDF: https://arxiv.org/pdf/2512.06533
==================================
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✨DZ-TDPO: Non-Destructive Temporal Alignment for Mutable State Tracking in Long-Context Dialogue
📝 Summary:
DZ-TDPO addresses state inertia in long-context dialogue using dynamic KL constraints and temporal attention bias. It achieves state-of-the-art win rates and robust zero-shot generalization, resolving user intent conflicts while preserving model capabilities.
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03704
• PDF: https://arxiv.org/pdf/2512.03704
• Github: https://github.com/lyj20071013/DZ-TDPO
==================================
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#DialogueSystems #NLP #MachineLearning #StateTracking #LongContext
📝 Summary:
DZ-TDPO addresses state inertia in long-context dialogue using dynamic KL constraints and temporal attention bias. It achieves state-of-the-art win rates and robust zero-shot generalization, resolving user intent conflicts while preserving model capabilities.
🔹 Publication Date: Published on Dec 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03704
• PDF: https://arxiv.org/pdf/2512.03704
• Github: https://github.com/lyj20071013/DZ-TDPO
==================================
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#DialogueSystems #NLP #MachineLearning #StateTracking #LongContext
❤1
✨OmniSafeBench-MM: A Unified Benchmark and Toolbox for Multimodal Jailbreak Attack-Defense Evaluation
📝 Summary:
OmniSafeBench-MM is a unified toolbox for evaluating multi-modal jailbreak attacks and defenses in MLLMs. It integrates various attacks, defense strategies, and a diverse dataset to provide a comprehensive, standardized, and reproducible platform for research.
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06589
• PDF: https://arxiv.org/pdf/2512.06589
==================================
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#MLLMs #AISafety #AIsecurity #Benchmark #DeepLearning
📝 Summary:
OmniSafeBench-MM is a unified toolbox for evaluating multi-modal jailbreak attacks and defenses in MLLMs. It integrates various attacks, defense strategies, and a diverse dataset to provide a comprehensive, standardized, and reproducible platform for research.
🔹 Publication Date: Published on Dec 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06589
• PDF: https://arxiv.org/pdf/2512.06589
==================================
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#MLLMs #AISafety #AIsecurity #Benchmark #DeepLearning
❤1
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✨The SAM2-to-SAM3 Gap in the Segment Anything Model Family: Why Prompt-Based Expertise Fails in Concept-Driven Image Segmentation
📝 Summary:
This paper highlights the gap between SAM2 and SAM3. SAM2 uses spatial prompts for geometric segmentation, but SAM3 is a concept-driven multimodal model with a unified vision-language architecture. SAM3 represents a new class of foundation model for concept-driven segmentation.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06032
• PDF: https://arxiv.org/pdf/2512.06032
• Github: https://github.com/Applied-AI-Research-Lab/The-SAM2-to-SAM3-Gap-in-the-Segment-Anything-Model-Family
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#ImageSegmentation #FoundationModels #ComputerVision #MultimodalAI #AIResearch
📝 Summary:
This paper highlights the gap between SAM2 and SAM3. SAM2 uses spatial prompts for geometric segmentation, but SAM3 is a concept-driven multimodal model with a unified vision-language architecture. SAM3 represents a new class of foundation model for concept-driven segmentation.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06032
• PDF: https://arxiv.org/pdf/2512.06032
• Github: https://github.com/Applied-AI-Research-Lab/The-SAM2-to-SAM3-Gap-in-the-Segment-Anything-Model-Family
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#ImageSegmentation #FoundationModels #ComputerVision #MultimodalAI #AIResearch
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✨Small-Gain Nash: Certified Contraction to Nash Equilibria in Differentiable Games
📝 Summary:
Small-Gain Nash SGN certifies convergence in differentiable games where traditional methods fail. It constructs a custom weighted block metric making the pseudo-gradient strongly monotone, even if non-monotone in Euclidean space. This provides a structural convergence certificate with safe step-s...
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06791
• PDF: https://arxiv.org/pdf/2512.06791
• Project Page: https://huggingface.co/papers?q=projected%20Euler
• Github: https://github.com/AashVed/SmallGainNash
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Small-Gain Nash SGN certifies convergence in differentiable games where traditional methods fail. It constructs a custom weighted block metric making the pseudo-gradient strongly monotone, even if non-monotone in Euclidean space. This provides a structural convergence certificate with safe step-s...
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06791
• PDF: https://arxiv.org/pdf/2512.06791
• Project Page: https://huggingface.co/papers?q=projected%20Euler
• Github: https://github.com/AashVed/SmallGainNash
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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