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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

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ML Research Hub
✨VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos

📝 Summary:
VideoRAG introduces the first RAG framework for long videos, using a dual-channel architecture to integrate textual knowledge grounding and multi-modal context encoding. This enables unlimited-length video processing and significantly outperforms existing methods.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.01549
• PDF: https://arxiv.org/pdf/2502.01549
• Github: https://github.com/hkuds/videorag

==================================

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#VideoRAG #RAG #LongVideo #AI #MultimodalAI
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An active community group for discussing data challenges and networking with peers.
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ML Research Hub
✨A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

📝 Summary:
This survey reviews self-evolving AI agents that adapt to dynamic environments via automatic enhancement from interaction data. It proposes a unified framework and systematically reviews current techniques, addressing evaluation, safety, and ethics.

🔹 Publication Date: Published on Aug 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07407
• PDF: https://arxiv.org/pdf/2508.07407
• Project Page: https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
• Github: https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents

✨ Spaces citing this paper:
• https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents

==================================

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#SelfEvolvingAI #AIAgents #FoundationModels #LifelongLearning #ArtificialIntelligence
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ML Research Hub
🚀 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!


🔰 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.iss.one/CodeProgrammer

🔖 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.iss.one/DataScienceM

🧠 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.
https://t.iss.one/DataScience4

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😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT

💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9

🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab

🖊 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://t.iss.one/DataScienceN

📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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✨InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion

📝 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.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17504
• PDF: https://arxiv.org/pdf/2512.17504
• Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
• Github: https://github.com/myyzzzoooo/InsertAnywhere

==================================

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#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI
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ML Research Hub
✨MAI-UI Technical Report: Real-World Centric Foundation GUI Agents

📝 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.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22047
• PDF: https://arxiv.org/pdf/2512.22047

==================================

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#GUIAgents #AI #ReinforcementLearning #MobileTech #HCI
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ML Research Hub
✨See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning

📝 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...

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22120
• PDF: https://arxiv.org/pdf/2512.22120
• Github: https://github.com/zss02/BiPS

==================================

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#MultimodalAI #VisionLanguageModels #MachineLearning #AIResearch #DeepLearning
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✨ProEdit: Inversion-based Editing From Prompts Done Right

📝 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...

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22118
• PDF: https://arxiv.org/pdf/2512.22118
• Project Page: https://isee-laboratory.github.io/ProEdit/
• Github: https://isee-laboratory.github.io/ProEdit

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#AI #DataScience #MachineLearning #HuggingFace #Research
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✨SVBench: Evaluation of Video Generation Models on Social Reasoning

📝 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...

🔹 Publication Date: Published on Dec 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21507
• PDF: https://arxiv.org/pdf/2512.21507
• Github: https://github.com/Gloria2tt/SVBench-Evaluation

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#AI #DataScience #MachineLearning #HuggingFace #Research
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✨SWE-RM: Execution-free Feedback For Software Engineering Agents

📝 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...

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21919
• PDF: https://arxiv.org/pdf/2512.21919

==================================

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#SoftwareEngineering #AI #ReinforcementLearning #CodingAgents #RewardModels
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✨Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

📝 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.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17220
• PDF: https://arxiv.org/pdf/2512.17220

🔹 Models citing this paper:
• https://huggingface.co/MindscapeRAG/MiA-Emb-8B
• https://huggingface.co/MindscapeRAG/MiA-Emb-4B
• https://huggingface.co/MindscapeRAG/MiA-Emb-0.6B

==================================

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#RAG #LLM #NLP #GenerativeAI #ContextUnderstanding
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✨TimeBill: Time-Budgeted Inference for Large Language Models

📝 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.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21859
• PDF: https://arxiv.org/pdf/2512.21859

==================================

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#LLM #AI #RealTimeAI #InferenceOptimization #DeepLearning
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ML Research Hub
✨UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture

📝 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.

🔹 Publication Date: Published on Dec 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21675
• PDF: https://arxiv.org/pdf/2512.21675
• Project Page: https://thunderbolt215.github.io/Unipercept-project/
• Github: https://github.com/thunderbolt215/UniPercept

🔹 Models citing this paper:
• https://huggingface.co/Thunderbolt215215/UniPercept

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Thunderbolt215215/UniPercept-Bench

==================================

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#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...
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✨Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding

📝 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.

🔹 Publication Date: Published on Dec 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21643
• PDF: https://arxiv.org/pdf/2512.21643

==================================

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#WeatherGeneration #FoundationModels #MultimodalAI #AIResearch #DeepLearning
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✨SlideTailor: Personalized Presentation Slide Generation for Scientific Papers

📝 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.

🔹 Publication Date: Published on Dec 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20292
• PDF: https://arxiv.org/pdf/2512.20292
• Project Page: https://github.com/nusnlp/SlideTailor
• Github: https://github.com/nusnlp/SlideTailor

==================================

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#SlideGeneration #ScientificCommunication #AI #NLP #ResearchTools
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ML Research Hub
🚀 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!


🔰 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.iss.one/CodeProgrammer

🔖 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.iss.one/DataScienceM

🧠 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.
https://t.iss.one/DataScience4

🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ

💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1

🧑‍🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC

😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT

💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9

🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab

🖊 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://t.iss.one/DataScienceN

📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.iss.one/DataScienceV

📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX

🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.iss.one/Python53

⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.iss.one/DataScienceY

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