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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
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Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
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Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
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Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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β¨BlurDM: A Blur Diffusion Model for Image Deblurring
π Summary:
BlurDM integrates blur formation into diffusion models for deblurring. It uses a dual forward process of diffusing noise and blur, then simultaneously denoises and deblurs to recover sharp images. This significantly enhances existing deblurring methods.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.03979
β’ PDF: https://arxiv.org/pdf/2512.03979
==================================
For more data science resources:
β https://t.me/DataScienceT
#ImageDeblurring #DiffusionModels #ComputerVision #DeepLearning #AI
π Summary:
BlurDM integrates blur formation into diffusion models for deblurring. It uses a dual forward process of diffusing noise and blur, then simultaneously denoises and deblurs to recover sharp images. This significantly enhances existing deblurring methods.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.03979
β’ PDF: https://arxiv.org/pdf/2512.03979
==================================
For more data science resources:
β https://t.me/DataScienceT
#ImageDeblurring #DiffusionModels #ComputerVision #DeepLearning #AI
β¨AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition
π Summary:
AdaptVision is an efficient VLM that adaptively acquires visual tokens through a coarse-to-fine approach, using a bounding box tool. Trained with reinforcement learning to balance accuracy and efficiency, it achieves superior VQA performance using fewer visual tokens.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.03794
β’ PDF: https://arxiv.org/pdf/2512.03794
β’ Project Page: https://adaptvision.github.io/
β’ Github: https://github.com/AdaptVision/AdaptVision
πΉ Models citing this paper:
β’ https://huggingface.co/AdaptVision/AdaptVision-7B
==================================
For more data science resources:
β https://t.me/DataScienceT
#VisionLanguageModels #ReinforcementLearning #ComputerVision #AIResearch #EfficientAI
π Summary:
AdaptVision is an efficient VLM that adaptively acquires visual tokens through a coarse-to-fine approach, using a bounding box tool. Trained with reinforcement learning to balance accuracy and efficiency, it achieves superior VQA performance using fewer visual tokens.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.03794
β’ PDF: https://arxiv.org/pdf/2512.03794
β’ Project Page: https://adaptvision.github.io/
β’ Github: https://github.com/AdaptVision/AdaptVision
πΉ Models citing this paper:
β’ https://huggingface.co/AdaptVision/AdaptVision-7B
==================================
For more data science resources:
β https://t.me/DataScienceT
#VisionLanguageModels #ReinforcementLearning #ComputerVision #AIResearch #EfficientAI
β¨AutoNeural: Co-Designing Vision-Language Models for NPU Inference
π Summary:
AutoNeural is an NPU-native VLM co-designed for efficient edge inference. It uses a MobileNetV5-style vision backbone for stable integer quantization and a hybrid SSM-Transformer language backbone. This design reduces quantization errors and latency, improving real-time performance on edge devices.
πΉ Publication Date: Published on Dec 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.02924
β’ PDF: https://arxiv.org/pdf/2512.02924
πΉ Models citing this paper:
β’ https://huggingface.co/NexaAI/AutoNeural
==================================
For more data science resources:
β https://t.me/DataScienceT
#AutoNeural #VisionLanguageModels #EdgeAI #AIHardware #EfficientAI
π Summary:
AutoNeural is an NPU-native VLM co-designed for efficient edge inference. It uses a MobileNetV5-style vision backbone for stable integer quantization and a hybrid SSM-Transformer language backbone. This design reduces quantization errors and latency, improving real-time performance on edge devices.
πΉ Publication Date: Published on Dec 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.02924
β’ PDF: https://arxiv.org/pdf/2512.02924
πΉ Models citing this paper:
β’ https://huggingface.co/NexaAI/AutoNeural
==================================
For more data science resources:
β https://t.me/DataScienceT
#AutoNeural #VisionLanguageModels #EdgeAI #AIHardware #EfficientAI
β¨PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design
π Summary:
PosterCopilot enhances professional graphic design by training LMMs with a three-stage strategy for geometrically accurate and aesthetically superior layouts. This framework enables controllable, iterative, layer-specific editing, improving on existing automated design methods.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04082
β’ PDF: https://arxiv.org/pdf/2512.04082
β’ Project Page: https://postercopilot.github.io/
β’ Github: https://github.com/JiazheWei/PosterCopilot
==================================
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β https://t.me/DataScienceT
#GraphicDesign #AI #ComputationalDesign #LayoutDesign #DesignAutomation
π Summary:
PosterCopilot enhances professional graphic design by training LMMs with a three-stage strategy for geometrically accurate and aesthetically superior layouts. This framework enables controllable, iterative, layer-specific editing, improving on existing automated design methods.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04082
β’ PDF: https://arxiv.org/pdf/2512.04082
β’ Project Page: https://postercopilot.github.io/
β’ Github: https://github.com/JiazheWei/PosterCopilot
==================================
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β https://t.me/DataScienceT
#GraphicDesign #AI #ComputationalDesign #LayoutDesign #DesignAutomation
β€1
β¨Divide, then Ground: Adapting Frame Selection to Query Types for Long-Form Video Understanding
π Summary:
Large Multimodal Models struggle with long video understanding due to context limits. The DIG framework adapts frame selection to query types, using efficient uniform sampling for global queries and specialized selection for localized ones. This approach significantly improves LMM performance on ...
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04000
β’ PDF: https://arxiv.org/pdf/2512.04000
β’ Project Page: https://github.com/Jialuo-Li/DIG
β’ Github: https://github.com/Jialuo-Li/DIG
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoUnderstanding #LMMs #MultimodalAI #DeepLearning #ComputerVision
π Summary:
Large Multimodal Models struggle with long video understanding due to context limits. The DIG framework adapts frame selection to query types, using efficient uniform sampling for global queries and specialized selection for localized ones. This approach significantly improves LMM performance on ...
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04000
β’ PDF: https://arxiv.org/pdf/2512.04000
β’ Project Page: https://github.com/Jialuo-Li/DIG
β’ Github: https://github.com/Jialuo-Li/DIG
==================================
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β https://t.me/DataScienceT
#VideoUnderstanding #LMMs #MultimodalAI #DeepLearning #ComputerVision
β€1
β¨PSA: Pyramid Sparse Attention for Efficient Video Understanding and Generation
π Summary:
Pyramid Sparse Attention PSA introduces multi-level pooled key-value representations to overcome information loss in traditional sparse attention. It dynamically retains critical information, improving efficiency and performance for video understanding and generation tasks.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04025
β’ PDF: https://arxiv.org/pdf/2512.04025
β’ Project Page: https://ziplab.co/PSA/
β’ Github: https://github.com/ziplab/Pyramid-Sparse-Attention
==================================
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β https://t.me/DataScienceT
#SparseAttention #VideoUnderstanding #VideoGeneration #DeepLearning #ComputerVision
π Summary:
Pyramid Sparse Attention PSA introduces multi-level pooled key-value representations to overcome information loss in traditional sparse attention. It dynamically retains critical information, improving efficiency and performance for video understanding and generation tasks.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04025
β’ PDF: https://arxiv.org/pdf/2512.04025
β’ Project Page: https://ziplab.co/PSA/
β’ Github: https://github.com/ziplab/Pyramid-Sparse-Attention
==================================
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β https://t.me/DataScienceT
#SparseAttention #VideoUnderstanding #VideoGeneration #DeepLearning #ComputerVision
β¨4DLangVGGT: 4D Language-Visual Geometry Grounded Transformer
π Summary:
4DLangVGGT is a new Transformer framework for 4D scene understanding. It integrates geometry and language to enable scalable, open-vocabulary semantic fields, improving generalization and efficiency over prior methods.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.05060
β’ PDF: https://arxiv.org/pdf/2512.05060
β’ Github: https://hustvl.github.io/4DLangVGGT/
==================================
For more data science resources:
β https://t.me/DataScienceT
#4DSceneUnderstanding #Transformer #ComputerVision #DeepLearning #AI
π Summary:
4DLangVGGT is a new Transformer framework for 4D scene understanding. It integrates geometry and language to enable scalable, open-vocabulary semantic fields, improving generalization and efficiency over prior methods.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.05060
β’ PDF: https://arxiv.org/pdf/2512.05060
β’ Github: https://hustvl.github.io/4DLangVGGT/
==================================
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β https://t.me/DataScienceT
#4DSceneUnderstanding #Transformer #ComputerVision #DeepLearning #AI
β¨SIMA 2: A Generalist Embodied Agent for Virtual Worlds
π Summary:
SIMA 2 is a Gemini-based embodied agent for 3D virtual worlds. It reasons about goals, handles complex instructions, and autonomously learns new skills. This closes the gap with human performance and validates continuous learning agents.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04797
β’ PDF: https://arxiv.org/pdf/2512.04797
==================================
For more data science resources:
β https://t.me/DataScienceT
#EmbodiedAI #AI #VirtualWorlds #ReinforcementLearning #AIagents
π Summary:
SIMA 2 is a Gemini-based embodied agent for 3D virtual worlds. It reasons about goals, handles complex instructions, and autonomously learns new skills. This closes the gap with human performance and validates continuous learning agents.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04797
β’ PDF: https://arxiv.org/pdf/2512.04797
==================================
For more data science resources:
β https://t.me/DataScienceT
#EmbodiedAI #AI #VirtualWorlds #ReinforcementLearning #AIagents
β¨Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation
π Summary:
Reward Forcing improves streaming video generation by using EMA-Sink to update context tokens, preventing static initial frames. It also introduces Rewarded Distribution Matching Distillation to prioritize dynamic content, enhancing motion quality and achieving state-of-the-art performance.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04678
β’ PDF: https://arxiv.org/pdf/2512.04678
β’ Project Page: https://reward-forcing.github.io/
β’ Github: https://reward-forcing.github.io/
πΉ Models citing this paper:
β’ https://huggingface.co/JaydenLu666/Reward-Forcing-T2V-1.3B
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #DeepLearning #ComputerVision #AIResearch
π Summary:
Reward Forcing improves streaming video generation by using EMA-Sink to update context tokens, preventing static initial frames. It also introduces Rewarded Distribution Matching Distillation to prioritize dynamic content, enhancing motion quality and achieving state-of-the-art performance.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04678
β’ PDF: https://arxiv.org/pdf/2512.04678
β’ Project Page: https://reward-forcing.github.io/
β’ Github: https://reward-forcing.github.io/
πΉ Models citing this paper:
β’ https://huggingface.co/JaydenLu666/Reward-Forcing-T2V-1.3B
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #DeepLearning #ComputerVision #AIResearch
β¨SeeNav-Agent: Enhancing Vision-Language Navigation with Visual Prompt and Step-Level Policy Optimization
π Summary:
SeeNav-Agent improves Vision-Language Navigation with dual-view visual prompts, reducing perception errors and enhancing spatial understanding. It also uses SRGPO, a step-level reinforcement fine-tuning method, to boost planning and achieve higher success rates for VLN agents.
πΉ Publication Date: Published on Dec 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.02631
β’ PDF: https://arxiv.org/pdf/2512.02631
β’ Github: https://github.com/WzcTHU/SeeNav-Agent
==================================
For more data science resources:
β https://t.me/DataScienceT
#VisionLanguageNavigation #AI #ReinforcementLearning #ComputerVision #DeepLearning
π Summary:
SeeNav-Agent improves Vision-Language Navigation with dual-view visual prompts, reducing perception errors and enhancing spatial understanding. It also uses SRGPO, a step-level reinforcement fine-tuning method, to boost planning and achieve higher success rates for VLN agents.
πΉ Publication Date: Published on Dec 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.02631
β’ PDF: https://arxiv.org/pdf/2512.02631
β’ Github: https://github.com/WzcTHU/SeeNav-Agent
==================================
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β https://t.me/DataScienceT
#VisionLanguageNavigation #AI #ReinforcementLearning #ComputerVision #DeepLearning
β¨Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting
π Summary:
Splannequin improves frozen 3D scenes from monocular videos by fixing artifacts in dynamic Gaussian splatting. It uses temporal anchoring for hidden or defective Gaussians to resolve ghosting and blur from sparse supervision. This boosts visual quality for high-fidelity, user-selectable frozen-ti...
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.05113
β’ PDF: https://arxiv.org/pdf/2512.05113
β’ Project Page: https://chien90190.github.io/splannequin/
==================================
For more data science resources:
β https://t.me/DataScienceT
#ComputerVision #3DReconstruction #GaussianSplatting #NeuralRendering #DeepLearning
π Summary:
Splannequin improves frozen 3D scenes from monocular videos by fixing artifacts in dynamic Gaussian splatting. It uses temporal anchoring for hidden or defective Gaussians to resolve ghosting and blur from sparse supervision. This boosts visual quality for high-fidelity, user-selectable frozen-ti...
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.05113
β’ PDF: https://arxiv.org/pdf/2512.05113
β’ Project Page: https://chien90190.github.io/splannequin/
==================================
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β https://t.me/DataScienceT
#ComputerVision #3DReconstruction #GaussianSplatting #NeuralRendering #DeepLearning
β¨Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
π Summary:
Training autonomous LLM agents requires scalable, high-quality interactive environments. The Nex ecosystem provides NexAU for complexity, NexA4A for diversity, and NexGAP for fidelity in environment construction. Nex-N1, trained using this infrastructure, outperforms SOTA models on agentic tasks.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04987
β’ PDF: https://arxiv.org/pdf/2512.04987
β’ Github: https://github.com/nex-agi/Nex-N1
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLMAgents #LargeLanguageModels #AI #AISimulation #AIResearch
π Summary:
Training autonomous LLM agents requires scalable, high-quality interactive environments. The Nex ecosystem provides NexAU for complexity, NexA4A for diversity, and NexGAP for fidelity in environment construction. Nex-N1, trained using this infrastructure, outperforms SOTA models on agentic tasks.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04987
β’ PDF: https://arxiv.org/pdf/2512.04987
β’ Github: https://github.com/nex-agi/Nex-N1
==================================
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β https://t.me/DataScienceT
#LLMAgents #LargeLanguageModels #AI #AISimulation #AIResearch
β¨Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion
π Summary:
Semantic-First Diffusion SFD asynchronously denoises semantic and texture latents for image generation. This method prioritizes semantic formation, providing clearer guidance for texture refinement. SFD significantly improves convergence speed by up to 100x and enhances image quality.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04926
β’ PDF: https://arxiv.org/pdf/2512.04926
==================================
For more data science resources:
β https://t.me/DataScienceT
#DiffusionModels #ImageGeneration #SemanticAI #GenerativeAI #DeepLearning
π Summary:
Semantic-First Diffusion SFD asynchronously denoises semantic and texture latents for image generation. This method prioritizes semantic formation, providing clearer guidance for texture refinement. SFD significantly improves convergence speed by up to 100x and enhances image quality.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04926
β’ PDF: https://arxiv.org/pdf/2512.04926
==================================
For more data science resources:
β https://t.me/DataScienceT
#DiffusionModels #ImageGeneration #SemanticAI #GenerativeAI #DeepLearning
β¨SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
π Summary:
SignRoundV2 is a post-training quantization framework for LLMs. It uses a sensitivity metric for bit allocation and pre-tuning for scales to achieve competitive accuracy even at 2-bit quantization, closing the gap with full-precision models.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04746
β’ PDF: https://arxiv.org/pdf/2512.04746
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLMs #Quantization #DeepLearning #AI #MachineLearning
π Summary:
SignRoundV2 is a post-training quantization framework for LLMs. It uses a sensitivity metric for bit allocation and pre-tuning for scales to achieve competitive accuracy even at 2-bit quantization, closing the gap with full-precision models.
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04746
β’ PDF: https://arxiv.org/pdf/2512.04746
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLMs #Quantization #DeepLearning #AI #MachineLearning
β¨TV2TV: A Unified Framework for Interleaved Language and Video Generation
π Summary:
TV2TV is a unified framework for interleaved language and video generation, using a Mixture-of-Transformers. It learns to 'think in words' before 'acting in pixels,' enhancing visual quality, controllability, and prompt alignment. The model shows strong performance on video game and natural video...
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.05103
β’ PDF: https://arxiv.org/pdf/2512.05103
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #MultimodalAI #Transformers #AI
π Summary:
TV2TV is a unified framework for interleaved language and video generation, using a Mixture-of-Transformers. It learns to 'think in words' before 'acting in pixels,' enhancing visual quality, controllability, and prompt alignment. The model shows strong performance on video game and natural video...
πΉ Publication Date: Published on Dec 4
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.05103
β’ PDF: https://arxiv.org/pdf/2512.05103
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #MultimodalAI #Transformers #AI
β¨DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle
π Summary:
DAComp is a benchmark with 210 tasks for data engineering and analysis workflows. It reveals significant deficiencies in state-of-the-art agents, with success rates under 20% for engineering and below 40% for analysis, highlighting critical gaps.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04324
β’ PDF: https://arxiv.org/pdf/2512.04324
β’ Project Page: https://da-comp.github.io/
==================================
For more data science resources:
β https://t.me/DataScienceT
#DataAgents #Benchmarking #DataEngineering #DataAnalysis #AIResearch
π Summary:
DAComp is a benchmark with 210 tasks for data engineering and analysis workflows. It reveals significant deficiencies in state-of-the-art agents, with success rates under 20% for engineering and below 40% for analysis, highlighting critical gaps.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04324
β’ PDF: https://arxiv.org/pdf/2512.04324
β’ Project Page: https://da-comp.github.io/
==================================
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β https://t.me/DataScienceT
#DataAgents #Benchmarking #DataEngineering #DataAnalysis #AIResearch
β¨On GRPO Collapse in Search-R1: The Lazy Likelihood-Displacement Death Spiral
π Summary:
GRPO in tool-integrated RL collapses due to Lazy Likelihood Displacement LLD, a systematic drop in response likelihoods. LLDS regularization addresses this by preserving likelihoods, stabilizing training, preventing gradient explosion, and substantially improving performance.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04220
β’ PDF: https://arxiv.org/pdf/2512.04220
==================================
For more data science resources:
β https://t.me/DataScienceT
#ReinforcementLearning #MachineLearning #AI #DeepLearning #AIResearch
π Summary:
GRPO in tool-integrated RL collapses due to Lazy Likelihood Displacement LLD, a systematic drop in response likelihoods. LLDS regularization addresses this by preserving likelihoods, stabilizing training, preventing gradient explosion, and substantially improving performance.
πΉ Publication Date: Published on Dec 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.04220
β’ PDF: https://arxiv.org/pdf/2512.04220
==================================
For more data science resources:
β https://t.me/DataScienceT
#ReinforcementLearning #MachineLearning #AI #DeepLearning #AIResearch
β€1
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β¨Stable Video Infinity: Infinite-Length Video Generation with Error Recycling
π Summary:
Stable Video Infinity SVI generates infinite-length videos with high consistency and controllable stories. It introduces Error-Recycling Fine-Tuning, teaching the Diffusion Transformer to correct its self-generated errors and address the training-test discrepancy.
πΉ Publication Date: Published on Oct 10
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.09212
β’ PDF: https://arxiv.org/pdf/2510.09212
β’ Project Page: https://stable-video-infinity.github.io/homepage/
β’ Github: https://github.com/vita-epfl/Stable-Video-Infinity
πΉ Models citing this paper:
β’ https://huggingface.co/vita-video-gen/svi-model
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/vita-video-gen/svi-benchmark
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #AI #DiffusionModels #DeepLearning #ComputerVision
π Summary:
Stable Video Infinity SVI generates infinite-length videos with high consistency and controllable stories. It introduces Error-Recycling Fine-Tuning, teaching the Diffusion Transformer to correct its self-generated errors and address the training-test discrepancy.
πΉ Publication Date: Published on Oct 10
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.09212
β’ PDF: https://arxiv.org/pdf/2510.09212
β’ Project Page: https://stable-video-infinity.github.io/homepage/
β’ Github: https://github.com/vita-epfl/Stable-Video-Infinity
πΉ Models citing this paper:
β’ https://huggingface.co/vita-video-gen/svi-model
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/vita-video-gen/svi-benchmark
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #AI #DiffusionModels #DeepLearning #ComputerVision
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β¨PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing
π Summary:
PaperDebugger is an in-editor, multi-agent academic writing assistant that integrates large language models directly into LaTeX environments. It allows deep interaction with document state and revision history for enhanced writing, review, and editing workflows.
πΉ Publication Date: Published on Dec 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.02589
β’ PDF: https://arxiv.org/pdf/2512.02589
β’ Project Page: https://www.paperdebugger.com/
β’ Github: https://github.com/PaperDebugger/PaperDebugger
==================================
For more data science resources:
β https://t.me/DataScienceT
#AcademicWriting #LLM #MultiAgentSystems #ResearchTools #AI
π Summary:
PaperDebugger is an in-editor, multi-agent academic writing assistant that integrates large language models directly into LaTeX environments. It allows deep interaction with document state and revision history for enhanced writing, review, and editing workflows.
πΉ Publication Date: Published on Dec 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.02589
β’ PDF: https://arxiv.org/pdf/2512.02589
β’ Project Page: https://www.paperdebugger.com/
β’ Github: https://github.com/PaperDebugger/PaperDebugger
==================================
For more data science resources:
β https://t.me/DataScienceT
#AcademicWriting #LLM #MultiAgentSystems #ResearchTools #AI