ML Research Hub – Telegram
ML Research Hub
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

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🤖🧠 CALM: Revolutionizing Large Language Models with Continuous Autoregressive Learning

🗓️ 23 Nov 2025
📚 AI News & Trends

Large Language Models (LLMs) such as GPT, Claude and Gemini have dramatically transformed artificial intelligence. From generating natural text to assisting in code and research, these models rely on one fundamental process: autoregressive generation predicting text one token at a time. However, this sequential nature poses a critical efficiency bottleneck. Generating text token by token ...

#CALM #ContinuousAutoregressiveLearning #LargeLanguageModels #AutoregressiveGeneration #AIEfficiency #AIInnovation
🤖🧠 Agent-o-rama: The End-to-End Platform Transforming LLM Agent Development

🗓️ 23 Nov 2025
📚 AI News & Trends

As large language models (LLMs) become more capable, developers are increasingly using them to build intelligent AI agents that can perform reasoning, automation and decision-making tasks. However, building and managing these agents at scale is far from simple. Challenges such as monitoring model behavior, debugging reasoning paths, testing reliability and tracking performance metrics can make ...

#AgentoRama #LLMAgents #EndToEndPlatform #AIIntelligence #ModelMonitoring #AIDevelopment
🤖🧠 DeepEyesV2: The Next Leap Toward Agentic Multimodal Intelligence

🗓️ 23 Nov 2025
📚 AI News & Trends

The evolution of artificial intelligence has reached a stage where models are no longer limited to understanding text or images independently. The emergence of multimodal AI systems capable of processing and reasoning across multiple types of data has transformed how machines interpret the world. Yet, most existing multimodal models remain passive observers, unable to act ...

#DeepEyesV2 #AgenticMultimodalIntelligence #MultimodalAI #AIEvolution #ActiveReasoning #AIAction
🤖🧠 Reducing Hallucinations in Vision-Language Models: A Step Forward with VisAlign

🗓️ 24 Nov 2025
📚 AI News & Trends

As artificial intelligence continues to evolve, Large Vision-Language Models (LVLMs) have revolutionized how machines understand and describe the world. These models combine visual perception with natural language understanding to perform tasks such as image captioning, visual question answering and multimodal reasoning. Despite their success, a major problem persists – hallucination. This issue occurs when a ...

#VisAlign #ReducingHallucinations #VisionLanguageModels #LVLMs #MultimodalAI #AISafety
🤖🧠 LEANN: The Bright Future of Lightweight, Private, and Scalable Vector Databases

🗓️ 24 Nov 2025
📚 AI News & Trends

In the rapidly expanding world of artificial intelligence, data storage and retrieval efficiency have become major bottlenecks for scalable AI systems. The growth of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) has further intensified the demand for fast, private and space-efficient vector databases. Traditional systems like FAISS or Milvus while powerful, are resource-heavy and ...

#LEANN #LightweightVectorDatabases #PrivateAI #ScalableAI #RAG #AIDataStorage
🤖🧠 Omnilingual ASR: Meta’s Breakthrough in Multilingual Speech Recognition for 1600+ Languages

🗓️ 24 Nov 2025
📚 AI News & Trends

In an increasingly connected world, speech technology plays a vital role in bridging communication gaps across languages and cultures. Yet, despite rapid progress in Automatic Speech Recognition (ASR), most commercial systems still cater to only a few dozen major languages. Billions of people who speak lesser-known or low-resource languages remain excluded from the benefits of ...

#OmnilingualASR #MultilingualSpeechRecognition #MetaAI #LowResourceLanguages #SpeechTechnology #GlobalCommunication
🤖🧠 Whisper by OpenAI: The Revolution in Multilingual Speech Recognition

🗓️ 25 Nov 2025
📚 AI News & Trends

Speech recognition has evolved rapidly over the past decade, transforming the way we interact with technology. From voice assistants to trannoscription services and real-time translation tools, the ability of machines to understand human speech has redefined accessibility, communication and automation. However, one of the major challenges that persisted for years was achieving robust, multilingual and ...

#Whisper #MultilingualSpeechRecognition #OpenAI #SpeechRecognition #AIAccessibility #VoiceTechnology
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
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A Survey of Large Language Models in Medicine: Principles, Applications, and Challenges

📝 Summary:
This survey comprehensively explores large language models LLMs in medicine. It covers their principles, applications, challenges, and offers guidance for their effective construction and use in clinical practice.

🔹 Publication Date: Published on Nov 9, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2311.05112
• PDF: https://arxiv.org/pdf/2311.05112
• Github: https://github.com/ai-in-health/medllmspracticalguide

Datasets citing this paper:
https://huggingface.co/datasets/BAAI/SurveyScope

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For more data science resources:
https://news.1rj.ru/str/DataScienceT

#LLM #AIinMedicine #HealthcareAI #MedicalAI #DigitalHealth
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EditThinker: Unlocking Iterative Reasoning for Any Image Editor

📝 Summary:
EditThinker proposes a deliberative framework for image editing, simulating human iterative critique and refinement of instructions. It uses an MLLM as a reasoning engine to enhance instruction-following capability. This significantly improves the performance of any image editor.

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05965
• PDF: https://arxiv.org/pdf/2512.05965
• Project Page: https://appletea233.github.io/think-while-edit/

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#ImageEditing #MLLM #AI #Reasoning #ComputerVision
ReVSeg: Incentivizing the Reasoning Chain for Video Segmentation with Reinforcement Learning

📝 Summary:
ReVSeg enhances video object segmentation. It uses sequential reasoning within pretrained vision language models, optimized by reinforcement learning. This achieves state-of-the-art results and provides interpretable reasoning.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02835
• PDF: https://arxiv.org/pdf/2512.02835
• Project Page: https://clementine24.github.io/ReVSeg/
• Github: https://github.com/Clementine24/ReVSeg

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#VideoSegmentation #ReinforcementLearning #VisionLanguageModels #ComputerVision #DeepLearning
ProPhy: Progressive Physical Alignment for Dynamic World Simulation

📝 Summary:
ProPhy is a two-stage framework that enhances video generation by explicitly incorporating physics-aware conditioning and anisotropic generation. It uses a Mixture-of-Physics-Experts mechanism to extract fine-grained physical priors, improving physical consistency and realism in dynamic world sim...

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05564
• PDF: https://arxiv.org/pdf/2512.05564

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#VideoGeneration #PhysicsAI #DynamicSimulation #DeepLearning #ComputerVision
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World Models That Know When They Don't Know: Controllable Video Generation with Calibrated Uncertainty

📝 Summary:
C3 is an uncertainty quantification method for training controllable video models that provides dense confidence estimation and out-of-distribution detection, addressing hallucination issues. AI-gener...

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05927
• PDF: https://arxiv.org/pdf/2512.05927
• Project Page: https://c-cubed-uq.github.io/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research
Self-Improving VLM Judges Without Human Annotations

📝 Summary:
A framework for self-training a Vision-Language Model (VLM) judge using self-synthesized data improves judge accuracy on VL-RewardBench, surpassing larger models in several dimensions. AI-generated su...

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05145
• PDF: https://arxiv.org/pdf/2512.05145

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

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#AI #DataScience #MachineLearning #HuggingFace #Research
Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning

📝 Summary:
This paper introduces Entropy Ratio Clipping ERC to stabilize reinforcement learning. ERC uses the entropy ratio between policies as a global metric, imposing constraints to address distributional shifts overlooked by PPO-Clip. Experiments show consistent performance improvements.

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05591
• PDF: https://arxiv.org/pdf/2512.05591

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

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#ReinforcementLearning #MachineLearning #DeepLearning #AI #ERC
Joint 3D Geometry Reconstruction and Motion Generation for 4D Synthesis from a Single Image

📝 Summary:
MoRe4D generates high-quality 4D scenes from a single image by jointly performing motion generation and geometric reconstruction. It uses a diffusion-based 4D Scene Trajectory Generator and depth-guided motion normalization for consistent dynamic details.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05044
• PDF: https://arxiv.org/pdf/2512.05044
• Project Page: https://ivg-yanranzhang.github.io/MoRe4D/
• Github: https://github.com/Zhangyr2022/MoRe4D

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

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#4DSynthesis #3DReconstruction #MotionGeneration #ComputerVision #GenerativeAI
COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence

📝 Summary:
COOPER is a unified MLLM that integrates depth and segmentation modalities to enhance spatial intelligence. It uses adaptive interleaved reasoning, improving spatial reasoning by 6.91%. Learning to generate auxiliary modalities also strengthens spatial understanding, boosting distance and size es...

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04563
• PDF: https://arxiv.org/pdf/2512.04563
• Github: https://github.com/zhangzef/COOPER

🔹 Models citing this paper:
https://huggingface.co/Starrrrrry/COOPER-AMG
https://huggingface.co/Starrrrrry/COOPER

Datasets citing this paper:
https://huggingface.co/datasets/Starrrrrry/COOPER_Train_Set

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

For more data science resources:
https://news.1rj.ru/str/DataScienceT

#MLLM #SpatialIntelligence #ComputerVision #AI #DeepLearning
From Imitation to Discrimination: Toward A Generalized Curriculum Advantage Mechanism Enhancing Cross-Domain Reasoning Tasks

📝 Summary:
CAPO, a curriculum advantage policy optimization, enhances reinforcement learning for large language models by strategically introducing positive and negative advantage signals, improving reasoning ca...

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02580
• PDF: https://arxiv.org/pdf/2512.02580

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

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#AI #DataScience #MachineLearning #HuggingFace #Research
AI & Human Co-Improvement for Safer Co-Superintelligence

📝 Summary:
The focus should be on collaborative co-improvement between humans and AI systems to achieve safer and accelerated AI research and development. AI-generated summary Self-improvement is a goal currentl...

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05356
• PDF: https://arxiv.org/pdf/2512.05356

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

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#AI #DataScience #MachineLearning #HuggingFace #Research
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SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling

📝 Summary:
SpaceControl enables explicit spatial control of 3D generation using various geometric inputs, outperforming existing methods in geometric faithfulness while maintaining visual quality. AI-generated s...

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05343
• PDF: https://arxiv.org/pdf/2512.05343

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

For more data science resources:
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#AI #DataScience #MachineLearning #HuggingFace #Research