π Master Data Science & Programming!
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!
π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
π Machine Learning
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.
https://t.me/DataScienceM
π§ Code With Python
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.
https://t.me/DataScience4
π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
πΎ Kaggle Data Hub
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
π§βπ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
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π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
π Python Arab| Ψ¨Ψ§ΩΨ«ΩΩ ΨΉΨ±Ψ¨Ω
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
π Data Science Jupyter Notebooks
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.
https://t.me/DataScienceN
πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
π§ Learn Python Hub
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
βοΈ Research Papers
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Admin: @HusseinSheikho
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!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
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.
https://t.me/DataScienceM
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.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
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.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
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Admin: @HusseinSheikho
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β¨InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion
π Summary:
InsertAnywhere is a framework for realistic video object insertion. It uses 4D aware mask generation for geometric consistency and an extended diffusion model for appearance-faithful synthesis, outperforming existing methods.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17504
β’ PDF: https://arxiv.org/pdf/2512.17504
β’ Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
β’ Github: https://github.com/myyzzzoooo/InsertAnywhere
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI
π Summary:
InsertAnywhere is a framework for realistic video object insertion. It uses 4D aware mask generation for geometric consistency and an extended diffusion model for appearance-faithful synthesis, outperforming existing methods.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17504
β’ PDF: https://arxiv.org/pdf/2512.17504
β’ Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
β’ Github: https://github.com/myyzzzoooo/InsertAnywhere
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI
β€1
β¨MAI-UI Technical Report: Real-World Centric Foundation GUI Agents
π Summary:
MAI-UI introduces a family of foundation GUI agents tackling real-world deployment challenges. It uses a self-evolving data pipeline, device-cloud collaboration, and online RL to set new state-of-the-art in GUI grounding and mobile navigation, significantly boosting performance and privacy.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22047
β’ PDF: https://arxiv.org/pdf/2512.22047
==================================
For more data science resources:
β https://t.me/DataScienceT
#GUIAgents #AI #ReinforcementLearning #MobileTech #HCI
π Summary:
MAI-UI introduces a family of foundation GUI agents tackling real-world deployment challenges. It uses a self-evolving data pipeline, device-cloud collaboration, and online RL to set new state-of-the-art in GUI grounding and mobile navigation, significantly boosting performance and privacy.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22047
β’ PDF: https://arxiv.org/pdf/2512.22047
==================================
For more data science resources:
β https://t.me/DataScienceT
#GUIAgents #AI #ReinforcementLearning #MobileTech #HCI
β€2
β¨See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning
π Summary:
Bi-directional Perceptual Shaping BiPS improves vision-language models by using question-conditioned masked views to shape perception during training. It employs two constraints to ensure complete coverage of relevant pixels and enforce fine-grained visual reliance, preventing text-only shortcuts...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22120
β’ PDF: https://arxiv.org/pdf/2512.22120
β’ Github: https://github.com/zss02/BiPS
==================================
For more data science resources:
β https://t.me/DataScienceT
#MultimodalAI #VisionLanguageModels #MachineLearning #AIResearch #DeepLearning
π Summary:
Bi-directional Perceptual Shaping BiPS improves vision-language models by using question-conditioned masked views to shape perception during training. It employs two constraints to ensure complete coverage of relevant pixels and enforce fine-grained visual reliance, preventing text-only shortcuts...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22120
β’ PDF: https://arxiv.org/pdf/2512.22120
β’ Github: https://github.com/zss02/BiPS
==================================
For more data science resources:
β https://t.me/DataScienceT
#MultimodalAI #VisionLanguageModels #MachineLearning #AIResearch #DeepLearning
β€1
β¨ProEdit: Inversion-based Editing From Prompts Done Right
π Summary:
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22118
β’ PDF: https://arxiv.org/pdf/2512.22118
β’ Project Page: https://isee-laboratory.github.io/ProEdit/
β’ Github: https://isee-laboratory.github.io/ProEdit
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22118
β’ PDF: https://arxiv.org/pdf/2512.22118
β’ Project Page: https://isee-laboratory.github.io/ProEdit/
β’ Github: https://isee-laboratory.github.io/ProEdit
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨SVBench: Evaluation of Video Generation Models on Social Reasoning
π Summary:
Recent text-to-video generation models exhibit remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they remain fundamentally limited in their ability to generate soci...
πΉ Publication Date: Published on Dec 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21507
β’ PDF: https://arxiv.org/pdf/2512.21507
β’ Github: https://github.com/Gloria2tt/SVBench-Evaluation
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Recent text-to-video generation models exhibit remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they remain fundamentally limited in their ability to generate soci...
πΉ Publication Date: Published on Dec 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21507
β’ PDF: https://arxiv.org/pdf/2512.21507
β’ Github: https://github.com/Gloria2tt/SVBench-Evaluation
==================================
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β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨SWE-RM: Execution-free Feedback For Software Engineering Agents
π Summary:
This paper introduces SWE-RM, a robust, execution-free reward model for software engineering agents. It overcomes limitations of execution-based feedback, improving coding agent performance in both test-time scaling and reinforcement learning. SWE-RM achieves new state-of-the-art results for open...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21919
β’ PDF: https://arxiv.org/pdf/2512.21919
==================================
For more data science resources:
β https://t.me/DataScienceT
#SoftwareEngineering #AI #ReinforcementLearning #CodingAgents #RewardModels
π Summary:
This paper introduces SWE-RM, a robust, execution-free reward model for software engineering agents. It overcomes limitations of execution-based feedback, improving coding agent performance in both test-time scaling and reinforcement learning. SWE-RM achieves new state-of-the-art results for open...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21919
β’ PDF: https://arxiv.org/pdf/2512.21919
==================================
For more data science resources:
β https://t.me/DataScienceT
#SoftwareEngineering #AI #ReinforcementLearning #CodingAgents #RewardModels
β€1
β¨Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding
π Summary:
MiA-RAG enhances RAG systems with global context awareness, inspired by human understanding. It uses hierarchical summarization to build a 'mindscape,' improving long-context retrieval and generation for better evidence-based understanding.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17220
β’ PDF: https://arxiv.org/pdf/2512.17220
πΉ Models citing this paper:
β’ https://huggingface.co/MindscapeRAG/MiA-Emb-8B
β’ https://huggingface.co/MindscapeRAG/MiA-Emb-4B
β’ https://huggingface.co/MindscapeRAG/MiA-Emb-0.6B
==================================
For more data science resources:
β https://t.me/DataScienceT
#RAG #LLM #NLP #GenerativeAI #ContextUnderstanding
π Summary:
MiA-RAG enhances RAG systems with global context awareness, inspired by human understanding. It uses hierarchical summarization to build a 'mindscape,' improving long-context retrieval and generation for better evidence-based understanding.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17220
β’ PDF: https://arxiv.org/pdf/2512.17220
πΉ Models citing this paper:
β’ https://huggingface.co/MindscapeRAG/MiA-Emb-8B
β’ https://huggingface.co/MindscapeRAG/MiA-Emb-4B
β’ https://huggingface.co/MindscapeRAG/MiA-Emb-0.6B
==================================
For more data science resources:
β https://t.me/DataScienceT
#RAG #LLM #NLP #GenerativeAI #ContextUnderstanding
β€1
β¨TimeBill: Time-Budgeted Inference for Large Language Models
π Summary:
TimeBill is a framework for LLMs in time-critical systems. It predicts execution time and adaptively adjusts KV cache eviction to balance inference efficiency and response performance within given time budgets, improving task completion rates.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21859
β’ PDF: https://arxiv.org/pdf/2512.21859
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AI #RealTimeAI #InferenceOptimization #DeepLearning
π Summary:
TimeBill is a framework for LLMs in time-critical systems. It predicts execution time and adaptively adjusts KV cache eviction to balance inference efficiency and response performance within given time budgets, improving task completion rates.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21859
β’ PDF: https://arxiv.org/pdf/2512.21859
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AI #RealTimeAI #InferenceOptimization #DeepLearning
β€1
β¨UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture
π Summary:
UniPercept-Bench provides a unified framework and datasets for perceptual image understanding aesthetics, quality, structure, texture. The UniPercept model, trained with DAPT and T-ARL, outperforms MLLMs, generalizes across VR and VQA, and acts as a text-to-image reward model.
πΉ Publication Date: Published on Dec 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21675
β’ PDF: https://arxiv.org/pdf/2512.21675
β’ Project Page: https://thunderbolt215.github.io/Unipercept-project/
β’ Github: https://github.com/thunderbolt215/UniPercept
πΉ Models citing this paper:
β’ https://huggingface.co/Thunderbolt215215/UniPercept
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Thunderbolt215215/UniPercept-Bench
==================================
For more data science resources:
β https://t.me/DataScienceT
#ImageUnderstanding #ComputerVision #AIResearch #PerceptualAI #DeepLearning
π Summary:
UniPercept-Bench provides a unified framework and datasets for perceptual image understanding aesthetics, quality, structure, texture. The UniPercept model, trained with DAPT and T-ARL, outperforms MLLMs, generalizes across VR and VQA, and acts as a text-to-image reward model.
πΉ Publication Date: Published on Dec 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21675
β’ PDF: https://arxiv.org/pdf/2512.21675
β’ Project Page: https://thunderbolt215.github.io/Unipercept-project/
β’ Github: https://github.com/thunderbolt215/UniPercept
πΉ Models citing this paper:
β’ https://huggingface.co/Thunderbolt215215/UniPercept
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Thunderbolt215215/UniPercept-Bench
==================================
For more data science resources:
β https://t.me/DataScienceT
#ImageUnderstanding #ComputerVision #AIResearch #PerceptualAI #DeepLearning
arXiv.org
UniPercept: Towards Unified Perceptual-Level Image Understanding...
Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks such as visual grounding, segmentation, and captioning. However, their ability to perceive...
β€1
β¨Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding
π Summary:
Omni-Weather is a new multimodal foundation model that unifies weather generation and understanding in a single architecture. It uses shared self-attention and a Chain-of-Thought dataset for interpretable, high-quality outputs, achieving state-of-the-art performance.
πΉ Publication Date: Published on Dec 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21643
β’ PDF: https://arxiv.org/pdf/2512.21643
==================================
For more data science resources:
β https://t.me/DataScienceT
#WeatherGeneration #FoundationModels #MultimodalAI #AIResearch #DeepLearning
π Summary:
Omni-Weather is a new multimodal foundation model that unifies weather generation and understanding in a single architecture. It uses shared self-attention and a Chain-of-Thought dataset for interpretable, high-quality outputs, achieving state-of-the-art performance.
πΉ Publication Date: Published on Dec 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21643
β’ PDF: https://arxiv.org/pdf/2512.21643
==================================
For more data science resources:
β https://t.me/DataScienceT
#WeatherGeneration #FoundationModels #MultimodalAI #AIResearch #DeepLearning
β€1
β¨SlideTailor: Personalized Presentation Slide Generation for Scientific Papers
π Summary:
SlideTailor generates personalized presentation slides for scientific papers by learning user preferences implicitly from example pairs and visual templates. It uses a chain-of-speech mechanism to align content with oral narration.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20292
β’ PDF: https://arxiv.org/pdf/2512.20292
β’ Project Page: https://github.com/nusnlp/SlideTailor
β’ Github: https://github.com/nusnlp/SlideTailor
==================================
For more data science resources:
β https://t.me/DataScienceT
#SlideGeneration #ScientificCommunication #AI #NLP #ResearchTools
π Summary:
SlideTailor generates personalized presentation slides for scientific papers by learning user preferences implicitly from example pairs and visual templates. It uses a chain-of-speech mechanism to align content with oral narration.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20292
β’ PDF: https://arxiv.org/pdf/2512.20292
β’ Project Page: https://github.com/nusnlp/SlideTailor
β’ Github: https://github.com/nusnlp/SlideTailor
==================================
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β https://t.me/DataScienceT
#SlideGeneration #ScientificCommunication #AI #NLP #ResearchTools
β€2
β¨Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
π Summary:
Expert-Router Coupling ERC loss aligns MoE router decisions with expert capabilities. It uses proxy tokens and activation constraints to ensure experts specialize, improving performance and computational efficiency. ERC also allows tracking expert specialization during training.
πΉ Publication Date: Published on Dec 29
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23447
β’ PDF: https://arxiv.org/pdf/2512.23447
==================================
For more data science resources:
β https://t.me/DataScienceT
#MixtureOfExperts #DeepLearning #MachineLearning #AI #NeuralNetworks
π Summary:
Expert-Router Coupling ERC loss aligns MoE router decisions with expert capabilities. It uses proxy tokens and activation constraints to ensure experts specialize, improving performance and computational efficiency. ERC also allows tracking expert specialization during training.
πΉ Publication Date: Published on Dec 29
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23447
β’ PDF: https://arxiv.org/pdf/2512.23447
==================================
For more data science resources:
β https://t.me/DataScienceT
#MixtureOfExperts #DeepLearning #MachineLearning #AI #NeuralNetworks
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β¨LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation
π Summary:
LiveTalk enables real-time multimodal interactive video generation from text, image, and audio by improving on-policy diffusion distillation. It reduces inference latency by 20x while maintaining quality, allowing seamless human-AI interaction.
πΉ Publication Date: Published on Dec 29
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23576
β’ PDF: https://arxiv.org/pdf/2512.23576
β’ Github: https://github.com/GAIR-NLP/LiveTalk
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #AI #DiffusionModels #RealTimeAI #MultimodalAI
π Summary:
LiveTalk enables real-time multimodal interactive video generation from text, image, and audio by improving on-policy diffusion distillation. It reduces inference latency by 20x while maintaining quality, allowing seamless human-AI interaction.
πΉ Publication Date: Published on Dec 29
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23576
β’ PDF: https://arxiv.org/pdf/2512.23576
β’ Github: https://github.com/GAIR-NLP/LiveTalk
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #AI #DiffusionModels #RealTimeAI #MultimodalAI
β¨SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents
π Summary:
SmartSnap introduces proactive, in-situ self-verification for autonomous agents, moving away from passive, post-hoc task verification. Self-Verifying Agents complete tasks and curate minimal snapshot evidence to prove accomplishment, boosting scalability and performance for LLM-driven agents in G...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://huggingface.co/collections/yolay/smartsnap
β’ PDF: https://arxiv.org/pdf/2512.22322
β’ Project Page: https://huggingface.co/collections/yolay/smartsnap
πΉ Models citing this paper:
β’ https://huggingface.co/yolay/SmartSnap-LLaMA3.1-8B
β’ https://huggingface.co/yolay/SmartSnap-Qwen2.5-7B
β’ https://huggingface.co/yolay/SmartSnap-Qwen3-8B
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/yolay/SmartSnap-FT
β’ https://huggingface.co/datasets/yolay/SmartSnap-RL
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #LLM #AutonomousAgents #AgentVerification #AIResearch
π Summary:
SmartSnap introduces proactive, in-situ self-verification for autonomous agents, moving away from passive, post-hoc task verification. Self-Verifying Agents complete tasks and curate minimal snapshot evidence to prove accomplishment, boosting scalability and performance for LLM-driven agents in G...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://huggingface.co/collections/yolay/smartsnap
β’ PDF: https://arxiv.org/pdf/2512.22322
β’ Project Page: https://huggingface.co/collections/yolay/smartsnap
πΉ Models citing this paper:
β’ https://huggingface.co/yolay/SmartSnap-LLaMA3.1-8B
β’ https://huggingface.co/yolay/SmartSnap-Qwen2.5-7B
β’ https://huggingface.co/yolay/SmartSnap-Qwen3-8B
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/yolay/SmartSnap-FT
β’ https://huggingface.co/datasets/yolay/SmartSnap-RL
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #LLM #AutonomousAgents #AgentVerification #AIResearch
huggingface.co
SmartSnap - a yolay Collection
Data and Checkpoints of "SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents" [arxiv.org/abs/2512.22322]
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β¨Yume-1.5: A Text-Controlled Interactive World Generation Model
π Summary:
Yume-1.5 is a novel framework that generates realistic, interactive, and continuous worlds from a single image or text prompt. It overcomes prior limitations in real-time performance and text control by using unified context compression, streaming acceleration, and text-controlled world events.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22096
β’ PDF: https://arxiv.org/pdf/2512.22096
β’ Project Page: https://stdstu12.github.io/YUME-Project/
β’ Github: https://github.com/stdstu12/YUME
πΉ Models citing this paper:
β’ https://huggingface.co/stdstu123/Yume-5B-720P
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #GenerativeAI #WorldGeneration #ComputerGraphics #DeepLearning
π Summary:
Yume-1.5 is a novel framework that generates realistic, interactive, and continuous worlds from a single image or text prompt. It overcomes prior limitations in real-time performance and text control by using unified context compression, streaming acceleration, and text-controlled world events.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22096
β’ PDF: https://arxiv.org/pdf/2512.22096
β’ Project Page: https://stdstu12.github.io/YUME-Project/
β’ Github: https://github.com/stdstu12/YUME
πΉ Models citing this paper:
β’ https://huggingface.co/stdstu123/Yume-5B-720P
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #GenerativeAI #WorldGeneration #ComputerGraphics #DeepLearning
β¨Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation
π Summary:
Transparent objects are hard for perception. This work observes video diffusion models can synthesize transparent phenomena, so they repurpose one. Their DKT model, trained on a new dataset, achieves zero-shot SOTA for depth and normal estimation of transparent objects, proving diffusion knows tr...
πΉ Publication Date: Published on Dec 29
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23705
β’ PDF: https://arxiv.org/pdf/2512.23705
β’ Project Page: https://daniellli.github.io/projects/DKT/
β’ Github: https://github.com/Daniellli/DKT
==================================
For more data science resources:
β https://t.me/DataScienceT
#ComputerVision #DiffusionModels #DepthEstimation #TransparentObjects #AIResearch
π Summary:
Transparent objects are hard for perception. This work observes video diffusion models can synthesize transparent phenomena, so they repurpose one. Their DKT model, trained on a new dataset, achieves zero-shot SOTA for depth and normal estimation of transparent objects, proving diffusion knows tr...
πΉ Publication Date: Published on Dec 29
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23705
β’ PDF: https://arxiv.org/pdf/2512.23705
β’ Project Page: https://daniellli.github.io/projects/DKT/
β’ Github: https://github.com/Daniellli/DKT
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β https://t.me/DataScienceT
#ComputerVision #DiffusionModels #DepthEstimation #TransparentObjects #AIResearch
β¨SpotEdit: Selective Region Editing in Diffusion Transformers
π Summary:
SpotEdit is a training-free framework for selective image editing in diffusion transformers. It avoids reprocessing stable regions by reusing their features, combining them with edited areas. This reduces computation and preserves unchanged regions, enhancing efficiency and precision.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22323
β’ PDF: https://arxiv.org/pdf/2512.22323
β’ Project Page: https://biangbiang0321.github.io/SpotEdit.github.io
β’ Github: https://biangbiang0321.github.io/SpotEdit.github.io
==================================
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β https://t.me/DataScienceT
#ImageEditing #DiffusionModels #ComputerVision #AIResearch #DeepLearning
π Summary:
SpotEdit is a training-free framework for selective image editing in diffusion transformers. It avoids reprocessing stable regions by reusing their features, combining them with edited areas. This reduces computation and preserves unchanged regions, enhancing efficiency and precision.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22323
β’ PDF: https://arxiv.org/pdf/2512.22323
β’ Project Page: https://biangbiang0321.github.io/SpotEdit.github.io
β’ Github: https://biangbiang0321.github.io/SpotEdit.github.io
==================================
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β https://t.me/DataScienceT
#ImageEditing #DiffusionModels #ComputerVision #AIResearch #DeepLearning
β¨Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone
π Summary:
Dream-VL and Dream-VLA are diffusion-based vision-language and vision-language-action models. They achieve state-of-the-art performance in visual planning and robotic control, surpassing autoregressive baselines via their diffusion backbone's superior action generation.
πΉ Publication Date: Published on Dec 27
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22615
β’ PDF: https://arxiv.org/pdf/2512.22615
β’ Project Page: https://hkunlp.github.io/blog/2025/dream-vlx/
β’ Github: https://github.com/DreamLM/Dream-VLX
==================================
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β https://t.me/DataScienceT
#VisionLanguageModels #DiffusionModels #Robotics #AI #ComputerVision
π Summary:
Dream-VL and Dream-VLA are diffusion-based vision-language and vision-language-action models. They achieve state-of-the-art performance in visual planning and robotic control, surpassing autoregressive baselines via their diffusion backbone's superior action generation.
πΉ Publication Date: Published on Dec 27
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22615
β’ PDF: https://arxiv.org/pdf/2512.22615
β’ Project Page: https://hkunlp.github.io/blog/2025/dream-vlx/
β’ Github: https://github.com/DreamLM/Dream-VLX
==================================
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β https://t.me/DataScienceT
#VisionLanguageModels #DiffusionModels #Robotics #AI #ComputerVision