ML Research Hub – Telegram
ML Research Hub
32.8K subscribers
4.41K photos
272 videos
23 files
4.77K links
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

Admin: @HusseinSheikho || @Hussein_Sheikho
Download Telegram
🔹 Title: World-To-Image: Grounding Text-to-Image Generation with Agent-Driven World Knowledge

🔹 Publication Date: Published on Oct 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.04201
• PDF: https://arxiv.org/pdf/2510.04201
• Github: https://github.com/mhson-kyle/World-To-Image

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model

🔹 Publication Date: Published on Oct 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.11496
• PDF: https://arxiv.org/pdf/2510.11496

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: A Tale of LLMs and Induced Small Proxies: Scalable Agents for Knowledge Mining

🔹 Publication Date: Published on Oct 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.01427
• PDF: https://arxiv.org/pdf/2510.01427
• Github: https://github.com/LongfeiYun17/falconer

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: Multimodal Policy Internalization for Conversational Agents

🔹 Publication Date: Published on Oct 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.09474
• PDF: https://arxiv.org/pdf/2510.09474
• Project Page: https://mikewangwzhl.github.io/TriMPI/
• Github: https://mikewangwzhl.github.io/TriMPI/

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections

🔹 Publication Date: Published on Oct 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.09023
• PDF: https://arxiv.org/pdf/2510.09023

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: MultiCOIN: Multi-Modal COntrollable Video INbetweening

🔹 Publication Date: Published on Oct 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.08561
• PDF: https://arxiv.org/pdf/2510.08561

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning

🔹 Publication Date: Published on Oct 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.07731
• PDF: https://arxiv.org/pdf/2510.07731
• Github: https://github.com/skylarkie/oMeBench

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: VLM-Guided Adaptive Negative Prompting for Creative Generation

🔹 Publication Date: Published on Oct 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.10715
• PDF: https://arxiv.org/pdf/2510.10715
• Github: https://shelley-golan.github.io/VLM-Guided-Creative-Generation/

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🤖🧠 Thinking with Camera 2.0: A Powerful Multimodal Model for Camera-Centric Understanding and Generation

🗓️ 14 Oct 2025
📚 AI News & Trends

In the rapidly evolving field of multimodal AI, bridging gaps between vision, language and geometry is one of the frontier challenges. Traditional vision-language models excel at describing what is in an image “a cat on a sofa” “a red car on the road” but struggle to reason about how the image was captured: the camera’s ...

#MultimodalAI #CameraCentricUnderstanding #VisionLanguageModels #AIResearch #ComputerVision #GenerativeModels
🤖🧠 Granite-Speech-3.3-8B: IBM’s Next-Gen Speech-Language Model for Enterprise AI

🗓️ 14 Oct 2025
📚 AI News & Trends

In the fast-growing field of speech and language AI, IBM continues to make strides with its Granite model family , a suite of open enterprise-grade AI models that combine accuracy, safety and efficiency. The latest addition to this ecosystem, Granite-Speech-3.3-8B marks a significant milestone in automatic speech recognition (ASR) and speech translation (AST) technology. Released ...

#SpeechAI #LanguageModel #EnterpriseAI #ASR #SpeechTranslation #GraniteModel
🤖🧠 LLaMAX2 by Nanjing University, HKU, CMU & Shanghai AI Lab: A Breakthrough in Translation-Enhanced Reasoning Models

🗓️ 14 Oct 2025
📚 AI News & Trends

The world of large language models (LLMs) has evolved rapidly, producing advanced systems capable of reasoning, problem-solving, and creative text generation. However, a persistent challenge has been balancing translation quality with reasoning ability. Most translation-enhanced models excel in linguistic diversity but falter in logical reasoning or coding tasks. Addressing this crucial gap, the research paper ...

#LLaMAX2 #TranslationEnhanced #ReasoningModels #LargeLanguageModels #NanjingUniversity #HKU
🤖🧠 Diffusion Transformers with Representation Autoencoders (RAE): The Next Leap in Generative AI

🗓️ 14 Oct 2025
📚 AI News & Trends

Diffusion Transformers (DiTs) have revolutionized image and video generation enabling stunningly realistic outputs in systems like Stable Diffusion and Imagen. However, despite innovations in transformer architectures and training methods, one crucial element of the diffusion pipeline has remained largely stagnant- the autoencoder that defines the latent space. Most current diffusion models still depend on Variational ...

#DiffusionTransformers #RAE #GenerativeAI #StableDiffusion #Imagen #LatentSpace
🔹 Title: FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.12747
• PDF: https://arxiv.org/pdf/2510.12747
• Project Page: https://zhuang2002.github.io/FlashVSR/
• Github: https://github.com/OpenImagingLab/FlashVSR

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.12635
• PDF: https://arxiv.org/pdf/2510.12635

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: Advancing End-to-End Pixel Space Generative Modeling via Self-supervised Pre-training

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.12586
• PDF: https://arxiv.org/pdf/2510.12586
• Github: https://github.com/AMAP-ML/EPG

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens

🔹 Publication Date: Published on Oct 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.11919
• PDF: https://arxiv.org/pdf/2510.11919
• Github: https://github.com/ArmelRandy/llm-reasoning-mt

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.12801
• PDF: https://arxiv.org/pdf/2510.12801

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: Detect Anything via Next Point Prediction

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.12798
• PDF: https://arxiv.org/pdf/2510.12798
• Project Page: https://rex-omni.github.io/
• Github: https://github.com/IDEA-Research/Rex-Omni

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: UniFusion: Vision-Language Model as Unified Encoder in Image Generation

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.12789
• PDF: https://arxiv.org/pdf/2510.12789
• Project Page: https://thekevinli.github.io/unifusion/

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translation

🔹 Publication Date: Published on Oct 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.09116
• PDF: https://arxiv.org/pdf/2510.09116
• Github: https://github.com/WHUNextGen/DITING

🔹 Datasets citing this paper:
https://huggingface.co/datasets/NextGenWhu/DITING

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
https://news.1rj.ru/str/DataScienceT
🔹 Title: Scaling Language-Centric Omnimodal Representation Learning

🔹 Publication Date: Published on Oct 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.11693
• PDF: https://arxiv.org/pdf/2510.11693
• Project Page: https://huggingface.co/LCO-Embedding
• Github: https://github.com/LCO-Embedding/LCO-Embedding

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

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