🚀 Master Data Science & Programming!
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🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
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🔖 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.
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🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
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🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
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🧑🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
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😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
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💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9
🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
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🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://news.1rj.ru/str/DataScienceN
📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
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🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
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⭐️ Research Papers
Professional Academic Writing & Simulation Services
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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!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://news.1rj.ru/str/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://news.1rj.ru/str/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://news.1rj.ru/str/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://news.1rj.ru/str/DataScienceQ
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://news.1rj.ru/str/datasets1
The first channel in Telegram that offers free Udemy coupons
https://news.1rj.ru/str/DataScienceC
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://news.1rj.ru/str/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://news.1rj.ru/str/DataScienceV
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://news.1rj.ru/str/DataAnalyticsX
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://news.1rj.ru/str/Python53
Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY
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✨Puzzle Curriculum GRPO for Vision-Centric Reasoning
📝 Summary:
Puzzle Curriculum GRPO PC-GRPO improves VLM visual reasoning without annotations. It uses self-supervised puzzle environments for verifiable rewards and a difficulty-aware curriculum to enhance consistency and accuracy.
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14944
• PDF: https://arxiv.org/pdf/2512.14944
• Project Page: https://pcgrpo.github.io/
==================================
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#VLM #VisualReasoning #SelfSupervisedLearning #ComputerVision #AI
📝 Summary:
Puzzle Curriculum GRPO PC-GRPO improves VLM visual reasoning without annotations. It uses self-supervised puzzle environments for verifiable rewards and a difficulty-aware curriculum to enhance consistency and accuracy.
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14944
• PDF: https://arxiv.org/pdf/2512.14944
• Project Page: https://pcgrpo.github.io/
==================================
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#VLM #VisualReasoning #SelfSupervisedLearning #ComputerVision #AI
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✨FrontierCS: Evolving Challenges for Evolving Intelligence
📝 Summary:
FrontierCS is a new benchmark for evaluating models on 156 open-ended computer science problems with unknown optimal solutions. Models must implement executable programs for tasks like NP-hard algorithmic and research problems. Empirical results show models lag human experts and over-optimize for...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15699
• PDF: https://arxiv.org/pdf/2512.15699
• Github: https://github.com/FrontierCS/Frontier-CS
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
FrontierCS is a new benchmark for evaluating models on 156 open-ended computer science problems with unknown optimal solutions. Models must implement executable programs for tasks like NP-hard algorithmic and research problems. Empirical results show models lag human experts and over-optimize for...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15699
• PDF: https://arxiv.org/pdf/2512.15699
• Github: https://github.com/FrontierCS/Frontier-CS
==================================
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✨SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations
📝 Summary:
SonicMoE optimizes Mixture of Experts models by reducing activation memory through minimal caching, overlapping IO with computation, and using token rounding to minimize padding waste. This achieves a 45 percent memory reduction and significantly improves compute throughput, enabling faster MoE t...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14080
• PDF: https://arxiv.org/pdf/2512.14080
==================================
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📝 Summary:
SonicMoE optimizes Mixture of Experts models by reducing activation memory through minimal caching, overlapping IO with computation, and using token rounding to minimize padding waste. This achieves a 45 percent memory reduction and significantly improves compute throughput, enabling faster MoE t...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14080
• PDF: https://arxiv.org/pdf/2512.14080
==================================
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✨Kling-Omni Technical Report
📝 Summary:
Kling-Omni is a versatile generative framework that synthesizes high-quality videos from multimodal inputs. It unifies video generation, editing, and reasoning tasks, supporting diverse inputs to create cinematic content. This system represents a pivotal step toward multimodal world simulators.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16776
• PDF: https://arxiv.org/pdf/2512.16776
==================================
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📝 Summary:
Kling-Omni is a versatile generative framework that synthesizes high-quality videos from multimodal inputs. It unifies video generation, editing, and reasoning tasks, supporting diverse inputs to create cinematic content. This system represents a pivotal step toward multimodal world simulators.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16776
• PDF: https://arxiv.org/pdf/2512.16776
==================================
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✨LLaDA2.0: Scaling Up Diffusion Language Models to 100B
📝 Summary:
LLaDA2.0 converts auto-regressive models into discrete diffusion large language models using a block-level training scheme, improving efficiency and performance at large scales. AI-generated summary T...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15745
• PDF: https://arxiv.org/pdf/2512.15745
==================================
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📝 Summary:
LLaDA2.0 converts auto-regressive models into discrete diffusion large language models using a block-level training scheme, improving efficiency and performance at large scales. AI-generated summary T...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15745
• PDF: https://arxiv.org/pdf/2512.15745
==================================
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✨DeContext as Defense: Safe Image Editing in Diffusion Transformers
📝 Summary:
DeContext defends against unauthorized in-context image editing by weakening cross-attention pathways in multimodal attention layers, preserving visual quality while blocking unwanted modifications. A...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16625
• PDF: https://arxiv.org/pdf/2512.16625
• Project Page: https://linghuiishen.github.io/decontext_project_page/
• Github: https://github.com/LinghuiiShen/DeContext
==================================
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📝 Summary:
DeContext defends against unauthorized in-context image editing by weakening cross-attention pathways in multimodal attention layers, preserving visual quality while blocking unwanted modifications. A...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16625
• PDF: https://arxiv.org/pdf/2512.16625
• Project Page: https://linghuiishen.github.io/decontext_project_page/
• Github: https://github.com/LinghuiiShen/DeContext
==================================
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✨Adaptation of Agentic AI
📝 Summary:
This paper presents a framework for agent and tool adaptation in agentic AI systems, clarifying design strategies and identifying open challenges for improving AI capabilities. AI-generated summary Cu...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16301
• PDF: https://arxiv.org/pdf/2512.16301
• Github: https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI
==================================
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📝 Summary:
This paper presents a framework for agent and tool adaptation in agentic AI systems, clarifying design strategies and identifying open challenges for improving AI capabilities. AI-generated summary Cu...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16301
• PDF: https://arxiv.org/pdf/2512.16301
• Github: https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI
==================================
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✨TabReX : Tabular Referenceless eXplainable Evaluation
📝 Summary:
TabReX is a reference-less framework using graph-based reasoning to evaluate the quality of tables generated by LLMs, offering structural and factual fidelity scores. AI-generated summary Evaluating t...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15907
• PDF: https://arxiv.org/pdf/2512.15907
• Project Page: https://coral-lab-asu.github.io/TabReX/
• Github: https://github.com/CoRAL-ASU/TabReX
==================================
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📝 Summary:
TabReX is a reference-less framework using graph-based reasoning to evaluate the quality of tables generated by LLMs, offering structural and factual fidelity scores. AI-generated summary Evaluating t...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15907
• PDF: https://arxiv.org/pdf/2512.15907
• Project Page: https://coral-lab-asu.github.io/TabReX/
• Github: https://github.com/CoRAL-ASU/TabReX
==================================
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✨Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model
📝 Summary:
Seedance 1.5 pro, a dual-branch Diffusion Transformer model, achieves high-quality audio-visual synchronization and generation through cross-modal integration, post-training optimizations, and an acce...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13507
• PDF: https://arxiv.org/pdf/2512.13507
• Project Page: https://seed.bytedance.com/seedance1_5_pro
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Seedance 1.5 pro, a dual-branch Diffusion Transformer model, achieves high-quality audio-visual synchronization and generation through cross-modal integration, post-training optimizations, and an acce...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13507
• PDF: https://arxiv.org/pdf/2512.13507
• Project Page: https://seed.bytedance.com/seedance1_5_pro
==================================
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✨Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation
📝 Summary:
A panoramic metric depth foundation model using DINOv3-Large and a three-stage pseudo-label pipeline achieves robust performance across diverse real-world scenes. AI-generated summary In this work, we...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16913
• PDF: https://arxiv.org/pdf/2512.16913
• Github: https://insta360-research-team.github.io/DAP
==================================
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📝 Summary:
A panoramic metric depth foundation model using DINOv3-Large and a three-stage pseudo-label pipeline achieves robust performance across diverse real-world scenes. AI-generated summary In this work, we...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16913
• PDF: https://arxiv.org/pdf/2512.16913
• Github: https://insta360-research-team.github.io/DAP
==================================
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✨Next-Embedding Prediction Makes Strong Vision Learners
📝 Summary:
Generative pretraining using next embedding prediction outperforms traditional self-supervised methods in visual learning tasks, achieving high accuracy on ImageNet and effective transfer to semantic ...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16922
• PDF: https://arxiv.org/pdf/2512.16922
🔹 Models citing this paper:
• https://huggingface.co/SixAILab/nepa-base-patch14-224-sft
• https://huggingface.co/SixAILab/nepa-large-patch14-224
• https://huggingface.co/SixAILab/nepa-base-patch14-224
==================================
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📝 Summary:
Generative pretraining using next embedding prediction outperforms traditional self-supervised methods in visual learning tasks, achieving high accuracy on ImageNet and effective transfer to semantic ...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16922
• PDF: https://arxiv.org/pdf/2512.16922
🔹 Models citing this paper:
• https://huggingface.co/SixAILab/nepa-base-patch14-224-sft
• https://huggingface.co/SixAILab/nepa-large-patch14-224
• https://huggingface.co/SixAILab/nepa-base-patch14-224
==================================
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✨Alchemist: Unlocking Efficiency in Text-to-Image Model Training via Meta-Gradient Data Selection
📝 Summary:
Alchemist, a meta-gradient-based framework, automatically selects high-quality subsets from large-scale text-image datasets to improve visual quality and training efficiency in Text-to-Image models. A...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16905
• PDF: https://arxiv.org/pdf/2512.16905
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Alchemist, a meta-gradient-based framework, automatically selects high-quality subsets from large-scale text-image datasets to improve visual quality and training efficiency in Text-to-Image models. A...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16905
• PDF: https://arxiv.org/pdf/2512.16905
==================================
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✨StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priors
📝 Summary:
StereoPilot, a feed-forward model leveraging a learnable domain switcher and cycle consistency loss, synthesizes high-quality stereo video directly without depth maps, outperforming existing methods i...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16915
• PDF: https://arxiv.org/pdf/2512.16915
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
StereoPilot, a feed-forward model leveraging a learnable domain switcher and cycle consistency loss, synthesizes high-quality stereo video directly without depth maps, outperforming existing methods i...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16915
• PDF: https://arxiv.org/pdf/2512.16915
==================================
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✨Exploration v.s. Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward
📝 Summary:
Reinforcement learning with verifiable rewards improves LLM reasoning through spurious rewards and entropy minimization, despite seemingly paradoxical effects, by reducing clipping bias and policy ent...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16912
• PDF: https://arxiv.org/pdf/2512.16912
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Reinforcement learning with verifiable rewards improves LLM reasoning through spurious rewards and entropy minimization, despite seemingly paradoxical effects, by reducing clipping bias and policy ent...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16912
• PDF: https://arxiv.org/pdf/2512.16912
==================================
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✨Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification
📝 Summary:
AuditDM, an automated framework using reinforcement learning, identifies and rectifies failure modes in multimodal LLMs by generating challenging examples, leading to improved performance across bench...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16921
• PDF: https://arxiv.org/pdf/2512.16921
• Project Page: https://auditdm.github.io/
• Github: https://auditdm.github.io/
==================================
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📝 Summary:
AuditDM, an automated framework using reinforcement learning, identifies and rectifies failure modes in multimodal LLMs by generating challenging examples, leading to improved performance across bench...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16921
• PDF: https://arxiv.org/pdf/2512.16921
• Project Page: https://auditdm.github.io/
• Github: https://auditdm.github.io/
==================================
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✨EmoCaliber: Advancing Reliable Visual Emotion Comprehension via Confidence Verbalization and Calibration
📝 Summary:
EmoCaliber, a confidence-aware Multimodal Large Language Model, enhances Visual Emotion Comprehension by verbalizing confidence in emotion predictions, leading to improved reliability and accuracy. AI...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15528
• PDF: https://arxiv.org/pdf/2512.15528
• Github: https://github.com/wdqqdw/EmoCaliber
==================================
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📝 Summary:
EmoCaliber, a confidence-aware Multimodal Large Language Model, enhances Visual Emotion Comprehension by verbalizing confidence in emotion predictions, leading to improved reliability and accuracy. AI...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15528
• PDF: https://arxiv.org/pdf/2512.15528
• Github: https://github.com/wdqqdw/EmoCaliber
==================================
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✨Generative Refocusing: Flexible Defocus Control from a Single Image
📝 Summary:
Generative Refocusing uses DeblurNet and BokehNet for high-quality single-image refocusing. Its semi-supervised training with real bokeh images and EXIF metadata enables controllable bokeh and text-guided adjustments, outperforming current methods.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16923
• PDF: https://arxiv.org/pdf/2512.16923
• Project Page: https://generative-refocusing.github.io/
• Github: https://github.com/rayray9999/Genfocus
==================================
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📝 Summary:
Generative Refocusing uses DeblurNet and BokehNet for high-quality single-image refocusing. Its semi-supervised training with real bokeh images and EXIF metadata enables controllable bokeh and text-guided adjustments, outperforming current methods.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16923
• PDF: https://arxiv.org/pdf/2512.16923
• Project Page: https://generative-refocusing.github.io/
• Github: https://github.com/rayray9999/Genfocus
==================================
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✨RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing
📝 Summary:
RePlan, a plan-then-execute framework, enhances instruction-based image editing by combining a vision-language planner with a diffusion editor, achieving superior performance in complex and intricate ...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16864
• PDF: https://arxiv.org/pdf/2512.16864
• Project Page: https://replan-iv-edit.github.io/
• Github: https://github.com/dvlab-research/RePlan
==================================
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📝 Summary:
RePlan, a plan-then-execute framework, enhances instruction-based image editing by combining a vision-language planner with a diffusion editor, achieving superior performance in complex and intricate ...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16864
• PDF: https://arxiv.org/pdf/2512.16864
• Project Page: https://replan-iv-edit.github.io/
• Github: https://github.com/dvlab-research/RePlan
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✨AdaTooler-V: Adaptive Tool-Use for Images and Videos
📝 Summary:
AdaTooler-V, a multimodal large language model, adaptively uses vision tools based on reinforcement learning, improving performance and reducing unnecessary tool invocations in visual reasoning tasks....
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16918
• PDF: https://arxiv.org/pdf/2512.16918
• Github: https://github.com/CYWang735/AdaTooler-V
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📝 Summary:
AdaTooler-V, a multimodal large language model, adaptively uses vision tools based on reinforcement learning, improving performance and reducing unnecessary tool invocations in visual reasoning tasks....
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16918
• PDF: https://arxiv.org/pdf/2512.16918
• Github: https://github.com/CYWang735/AdaTooler-V
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✨N3D-VLM: Native 3D Grounding Enables Accurate Spatial Reasoning in Vision-Language Models
📝 Summary:
N3D-VLM integrates native 3D perception and reasoning in vision-language models, enabling precise 3D localization and spatial understanding with a large-scale dataset. AI-generated summary While curre...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16561
• PDF: https://arxiv.org/pdf/2512.16561
• Github: https://github.com/W-Ted/N3D-VLM
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
N3D-VLM integrates native 3D perception and reasoning in vision-language models, enabling precise 3D localization and spatial understanding with a large-scale dataset. AI-generated summary While curre...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16561
• PDF: https://arxiv.org/pdf/2512.16561
• Github: https://github.com/W-Ted/N3D-VLM
==================================
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