Sepp Hochreiter, who invented LSTM 30+ year ago, gave a keynote talk at Neurips 2024 and introduced xLSTM (Extended Long Short-Term Memory).

I designed this Excel exercise to help you understand how xLSTM works.

More: https://www.byhand.ai/p/xlstm

🤖🧠 Qwen3-VL-8B-Instruct — The Next Generation of Vision-Language Intelligence by Qwen

🗓️ 27 Oct 2025
📚 AI News & Trends

In the rapidly evolving landscape of multimodal AI, Qwen3-VL-8B-Instruct stands out as a groundbreaking leap forward. Developed by Qwen, this model represents the most advanced vision-language (VL) system in the Qwen series to date. As artificial intelligence continues to bridge the gap between text and vision, Qwen3-VL-8B-Instruct emerges as a powerful engine capable of comprehending ...

#Qwen3VL #VisionLanguageAI #MultimodalAI #AISystems #QwenSeries #NextGenAI

🔹 Title: Redefining Retrieval Evaluation in the Era of LLMs

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21440
• PDF: https://arxiv.org/pdf/2510.21440
• Github: https://github.com/GiovanniTRA/UDCG

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🤖🧠 LangExtract by Google: Transforming Unstructured Text into Structured Data with LLM Precision

🗓️ 27 Oct 2025
📚 AI News & Trends

In the world of data-driven decision-making, one of the biggest challenges lies in extracting meaningful insights from unstructured text — documents, reports, emails or articles that lack consistent structure. Manually organizing this information is both time-consuming and prone to errors. Enter LangExtract, an advanced Python library by Google that leverages Large Language Models (LLMs) like ...

#LangExtract #LLM #StructuredData #UnstructuredText #PythonLibrary #GoogleAI

🤖🧠 AI Projects : A Comprehensive Showcase of Machine Learning, Deep Learning and Generative AI

🗓️ 27 Oct 2025
📚 AI News & Trends

Artificial Intelligence (AI) is transforming industries across the globe, driving innovation through automation, data-driven insights and intelligent decision-making. Whether it’s predicting house prices, detecting diseases or building conversational chatbots, AI is at the core of modern digital solutions. The AI Project Gallery by Hema Kalyan Murapaka is an exceptional GitHub repository that curates a wide ...

#AI #MachineLearning #DeepLearning #GenerativeAI #ArtificialIntelligence #GitHub

🤖🧠 Reinforcement Learning for Large Language Models: A Complete Guide from Foundations to Frontiers Arun Shankar, AI Engineer at Google

🗓️ 27 Oct 2025
📚 AI News & Trends

Artificial Intelligence is evolving rapidly and at the center of this evolution is Reinforcement Learning (RL), the science of teaching machines to make better decisions through experience and feedback. In “Reinforcement Learning for Large Language Models: A Complete Guide from Foundations to Frontiers”, Arun Shankar, an Applied AI Engineer at Google presents one of the ...

#ReinforcementLearning #LargeLanguageModels #ArtificialIntelligence #MachineLearning #AIEngineer #Google

🔹 Title: VITA-E: Natural Embodied Interaction with Concurrent Seeing, Hearing, Speaking, and Acting

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21817
• PDF: https://arxiv.org/pdf/2510.21817
• Project Page: https://lxysl.github.io/VITA-E/
• Github: https://github.com/Tencent/VITA/tree/VITA-E

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: Language Server CLI Empowers Language Agents with Process Rewards

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.22907
• PDF: https://arxiv.org/pdf/2510.22907
• Github: https://yifanzhang-pro.github.io/lanser-cli

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23451
• PDF: https://arxiv.org/pdf/2510.23451
• Github: https://github.com/HongbangYuan/OmniReward

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: MARS-M: When Variance Reduction Meets Matrices

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21800
• PDF: https://arxiv.org/pdf/2510.21800
• Project Page: https://github.com/AGI-Arena/MARS/tree/main/MARS_M
• Github: https://github.com/AGI-Arena/MARS/tree/main/MARS_M

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23607
• PDF: https://arxiv.org/pdf/2510.23607
• Project Page: https://pointcept.github.io/Concerto/
• Github: https://github.com/Pointcept/Pointcept

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: FARMER: Flow AutoRegressive Transformer over Pixels

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23588
• PDF: https://arxiv.org/pdf/2510.23588

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction

🔹 Publication Date: Published on Oct 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.22706
• PDF: https://arxiv.org/pdf/2510.22706
• Github: https://github.com/lifuguan/IGGT_official

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: E^2Rank: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker

🔹 Publication Date: Published on Oct 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.22733
• PDF: https://arxiv.org/pdf/2510.22733
• Project Page: https://alibaba-nlp.github.io/E2Rank/
• Github: https://alibaba-nlp.github.io/E2Rank

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: PixelRefer: A Unified Framework for Spatio-Temporal Object Referring with Arbitrary Granularity

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23603
• PDF: https://arxiv.org/pdf/2510.23603
• Project Page: https://circleradon.github.io/PixelRefer/
• Github: https://github.com/alibaba-damo-academy/PixelRefer

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: LimRank: Less is More for Reasoning-Intensive Information Reranking

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23544
• PDF: https://arxiv.org/pdf/2510.23544

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: RobotArena infty: Scalable Robot Benchmarking via Real-to-Sim Translation

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23571
• PDF: https://arxiv.org/pdf/2510.23571

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: Distilled Decoding 2: One-step Sampling of Image Auto-regressive Models with Conditional Score Distillation

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21003
• PDF: https://arxiv.org/pdf/2510.21003
• Github: https://imagination-research.github.io/distilled-decoding/

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: PRISM-Bench: A Benchmark of Puzzle-Based Visual Tasks with CoT Error Detection

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23594
• PDF: https://arxiv.org/pdf/2510.23594
• Github: https://github.com/JornyWan/PRISM-Bench

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: ReCode: Unify Plan and Action for Universal Granularity Control

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23564
• PDF: https://arxiv.org/pdf/2510.23564

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT

🔹 Title: Knocking-Heads Attention

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23052
• PDF: https://arxiv.org/pdf/2510.23052

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://t.me/DataScienceT