π 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!
π° 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
π 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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π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
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π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ
πΎ Kaggle Data Hub
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1
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π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
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π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
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The largest Arabic-speaking group for Python developers to share knowledge and help.
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π Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.iss.one/DataScienceN
πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
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Master Python with step-by-step courses β from basics to advanced projects and practical applications.
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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://t.iss.one/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://t.iss.one/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.iss.one/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1
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Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
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An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
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Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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https://t.iss.one/DataAnalyticsX
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β€1
β¨Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents
π Summary:
Memory-T1 is an RL framework improving temporal reasoning in long dialogues by selecting relevant sessions. It uses rewards for accuracy, evidence, and temporal consistency to achieve state-of-the-art performance on Time-Dialog and robustness to extensive histories.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20092
β’ PDF: https://arxiv.org/pdf/2512.20092
β’ Github: https://github.com/Elvin-Yiming-Du/Memory-T1/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#ReinforcementLearning #TemporalReasoning #NLP #DialogueSystems #AI
π Summary:
Memory-T1 is an RL framework improving temporal reasoning in long dialogues by selecting relevant sessions. It uses rewards for accuracy, evidence, and temporal consistency to achieve state-of-the-art performance on Time-Dialog and robustness to extensive histories.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20092
β’ PDF: https://arxiv.org/pdf/2512.20092
β’ Github: https://github.com/Elvin-Yiming-Du/Memory-T1/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#ReinforcementLearning #TemporalReasoning #NLP #DialogueSystems #AI
β€1
β¨Learning to Refocus with Video Diffusion Models
π Summary:
A novel method enables realistic post-capture refocusing from a single defocused image. It uses video diffusion models to generate a focal stack for interactive focus adjustment. This approach outperforms existing methods, improving photography focus-editing.
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19823
β’ PDF: https://arxiv.org/pdf/2512.19823
β’ Project Page: https://learn2refocus.github.io/
β’ Github: https://github.com/tedlasai/learn2refocus
πΉ Models citing this paper:
β’ https://huggingface.co/tedlasai/learn2refocus
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VideoDiffusionModels #ComputationalPhotography #ImageRefocusing #DeepLearning #ComputerVision
π Summary:
A novel method enables realistic post-capture refocusing from a single defocused image. It uses video diffusion models to generate a focal stack for interactive focus adjustment. This approach outperforms existing methods, improving photography focus-editing.
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19823
β’ PDF: https://arxiv.org/pdf/2512.19823
β’ Project Page: https://learn2refocus.github.io/
β’ Github: https://github.com/tedlasai/learn2refocus
πΉ Models citing this paper:
β’ https://huggingface.co/tedlasai/learn2refocus
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VideoDiffusionModels #ComputationalPhotography #ImageRefocusing #DeepLearning #ComputerVision
β€3
β¨T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation
π Summary:
T2AV-Compass introduces a unified benchmark for text-to-audio-video generation evaluation. It features 500 diverse prompts and a dual-level framework. Evaluations reveal current T2AV models struggle significantly with realism and cross-modal consistency.
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21094
β’ PDF: https://arxiv.org/pdf/2512.21094
β’ Project Page: https://nju-link.github.io/T2AV-Compass/
β’ Github: https://github.com/NJU-LINK/T2AV-Compass/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#TextToAudioVideo #MultimodalAI #AIEvaluation #GenerativeAI #AIResearch
π Summary:
T2AV-Compass introduces a unified benchmark for text-to-audio-video generation evaluation. It features 500 diverse prompts and a dual-level framework. Evaluations reveal current T2AV models struggle significantly with realism and cross-modal consistency.
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21094
β’ PDF: https://arxiv.org/pdf/2512.21094
β’ Project Page: https://nju-link.github.io/T2AV-Compass/
β’ Github: https://github.com/NJU-LINK/T2AV-Compass/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#TextToAudioVideo #MultimodalAI #AIEvaluation #GenerativeAI #AIResearch
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β¨Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models
π Summary:
DSR Suite improves vision language models weak dynamic spatial reasoning. It creates 4D training data from videos using an automated pipeline and integrates geometric priors via a Geometry Selection Module. This significantly enhances VLM dynamic spatial reasoning capability while maintaining gen...
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20557
β’ PDF: https://arxiv.org/pdf/2512.20557
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VisionLanguageModels #SpatialReasoning #4D #ComputerVision #AIResearch
π Summary:
DSR Suite improves vision language models weak dynamic spatial reasoning. It creates 4D training data from videos using an automated pipeline and integrates geometric priors via a Geometry Selection Module. This significantly enhances VLM dynamic spatial reasoning capability while maintaining gen...
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20557
β’ PDF: https://arxiv.org/pdf/2512.20557
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VisionLanguageModels #SpatialReasoning #4D #ComputerVision #AIResearch
β¨NVIDIA Nemotron 3: Efficient and Open Intelligence
π Summary:
NVIDIA introduces Nemotron 3, a family of models with strong agentic, reasoning, and conversational capabilities. They feature a hybrid Mamba-Transformer MoE architecture for high throughput and long context, plus advanced post-training for tool use. The models will be openly released.
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20856
β’ PDF: https://arxiv.org/pdf/2512.20856
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #LLM #DeepLearning #NVIDIA #OpenSource
π Summary:
NVIDIA introduces Nemotron 3, a family of models with strong agentic, reasoning, and conversational capabilities. They feature a hybrid Mamba-Transformer MoE architecture for high throughput and long context, plus advanced post-training for tool use. The models will be openly released.
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20856
β’ PDF: https://arxiv.org/pdf/2512.20856
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #LLM #DeepLearning #NVIDIA #OpenSource
β¨Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
π Summary:
We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 trillion new unique t...
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20848
β’ PDF: https://arxiv.org/pdf/2512.20848
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 trillion new unique t...
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20848
β’ PDF: https://arxiv.org/pdf/2512.20848
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Streaming Video Instruction Tuning
π Summary:
We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that focus narrowly on question answering or captioning,...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21334
β’ PDF: https://arxiv.org/pdf/2512.21334
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that focus narrowly on question answering or captioning,...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21334
β’ PDF: https://arxiv.org/pdf/2512.21334
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics
π Summary:
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ranking system that ef...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21010
β’ PDF: https://arxiv.org/pdf/2512.21010
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ranking system that ef...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21010
β’ PDF: https://arxiv.org/pdf/2512.21010
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times
π Summary:
TurboDiffusion significantly accelerates video generation by 100-200x while maintaining quality. It achieves this speedup through attention acceleration, step distillation, and W8A8 quantization. Experiments confirm the substantial speedup on a single GPU.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16093
β’ PDF: https://jt-zhang.github.io/files/TurboDiffusion_Technical_Report.pdf
β’ Project Page: https://github.com/thu-ml/TurboDiffusion
β’ Github: https://github.com/thu-ml/TurboDiffusion
πΉ Models citing this paper:
β’ https://huggingface.co/TurboDiffusion/TurboWan2.2-I2V-A14B-720P
β’ https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P
β’ https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-14B-720P
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
TurboDiffusion significantly accelerates video generation by 100-200x while maintaining quality. It achieves this speedup through attention acceleration, step distillation, and W8A8 quantization. Experiments confirm the substantial speedup on a single GPU.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16093
β’ PDF: https://jt-zhang.github.io/files/TurboDiffusion_Technical_Report.pdf
β’ Project Page: https://github.com/thu-ml/TurboDiffusion
β’ Github: https://github.com/thu-ml/TurboDiffusion
πΉ Models citing this paper:
β’ https://huggingface.co/TurboDiffusion/TurboWan2.2-I2V-A14B-720P
β’ https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P
β’ https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-14B-720P
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€2
β¨HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming
π Summary:
High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To a...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21338
β’ PDF: https://arxiv.org/pdf/2512.21338
β’ Project Page: https://haonanqiu.com/projects/HiStream.html
β’ Github: https://github.com/arthur-qiu/HiStream
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To a...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21338
β’ PDF: https://arxiv.org/pdf/2512.21338
β’ Project Page: https://haonanqiu.com/projects/HiStream.html
β’ Github: https://github.com/arthur-qiu/HiStream
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models
π Summary:
VLMs exhibit a significant popularity bias, performing better on famous items via memorization rather than general understanding. We introduce YearGuessr, a large multi-modal dataset and benchmark, confirming VLMs struggle with unrecognized subjects.
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21337
β’ PDF: https://arxiv.org/pdf/2512.21337
β’ Project Page: https://sytwu.github.io/BeyondMemo/
β’ Github: https://sytwu.github.io/BeyondMemo/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
VLMs exhibit a significant popularity bias, performing better on famous items via memorization rather than general understanding. We introduce YearGuessr, a large multi-modal dataset and benchmark, confirming VLMs struggle with unrecognized subjects.
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21337
β’ PDF: https://arxiv.org/pdf/2512.21337
β’ Project Page: https://sytwu.github.io/BeyondMemo/
β’ Github: https://sytwu.github.io/BeyondMemo/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨Learning from Next-Frame Prediction: Autoregressive Video Modeling Encodes Effective Representations
π Summary:
Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregressive (AR) generative models like GPT have revolutioniz...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21004
β’ PDF: https://arxiv.org/pdf/2512.21004
β’ Github: https://github.com/Singularity0104/NExT-Vid
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregressive (AR) generative models like GPT have revolutioniz...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21004
β’ PDF: https://arxiv.org/pdf/2512.21004
β’ Github: https://github.com/Singularity0104/NExT-Vid
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior
π Summary:
Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokenization, its role in LM performance and behavior is po...
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20757
β’ PDF: https://arxiv.org/pdf/2512.20757
β’ Github: https://github.com/r-three/Tokenizers
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokenization, its role in LM performance and behavior is po...
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20757
β’ PDF: https://arxiv.org/pdf/2512.20757
β’ Github: https://github.com/r-three/Tokenizers
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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β¨DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation
π Summary:
DreaMontage is a framework for generating seamless, expressive, long-duration one-shot videos from diverse inputs. It integrates an intermediate-conditioning DiT, a tailored DPO for smoothness, and a segment-wise auto-regressive inference strategy for long sequences.
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21252
β’ PDF: https://arxiv.org/pdf/2512.21252
β’ Project Page: https://dreamontage.github.io/DreaMontage/
β’ Github: https://dreamontage.github.io/DreaMontage/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
DreaMontage is a framework for generating seamless, expressive, long-duration one-shot videos from diverse inputs. It integrates an intermediate-conditioning DiT, a tailored DPO for smoothness, and a segment-wise auto-regressive inference strategy for long sequences.
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21252
β’ PDF: https://arxiv.org/pdf/2512.21252
β’ Project Page: https://dreamontage.github.io/DreaMontage/
β’ Github: https://dreamontage.github.io/DreaMontage/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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β¨Multi-hop Reasoning via Early Knowledge Alignment
π Summary:
Early Knowledge Alignment EKA improves iterative RAG by aligning LLMs with relevant knowledge before planning. This enhances retrieval, reduces errors, and boosts performance and efficiency.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20144
β’ PDF: https://arxiv.org/pdf/2512.20144
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MultiHopReasoning #LLM #RAG #KnowledgeAlignment #AI
π Summary:
Early Knowledge Alignment EKA improves iterative RAG by aligning LLMs with relevant knowledge before planning. This enhances retrieval, reduces errors, and boosts performance and efficiency.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20144
β’ PDF: https://arxiv.org/pdf/2512.20144
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MultiHopReasoning #LLM #RAG #KnowledgeAlignment #AI
β¨SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios
π Summary:
SWE-EVO is a new benchmark for AI coding agents that evaluates them on long-horizon, multi-step software evolution tasks across many files. It reveals a significant gap in current models abilities, with even top models achieving only 21 percent resolution. This highlights their struggle with sust...
πΉ Publication Date: Published on Dec 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.18470
β’ PDF: https://arxiv.org/pdf/2512.18470
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Fsoft-AIC/SWE-EVO
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AICoding #SoftwareEvolution #Benchmarking #LLMs #AIResearch
π Summary:
SWE-EVO is a new benchmark for AI coding agents that evaluates them on long-horizon, multi-step software evolution tasks across many files. It reveals a significant gap in current models abilities, with even top models achieving only 21 percent resolution. This highlights their struggle with sust...
πΉ Publication Date: Published on Dec 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.18470
β’ PDF: https://arxiv.org/pdf/2512.18470
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Fsoft-AIC/SWE-EVO
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AICoding #SoftwareEvolution #Benchmarking #LLMs #AIResearch
β€2
π 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!
π° 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
π 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
π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.iss.one/DataScience4
π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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An active community group for discussing data challenges and networking with peers.
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Professional Academic Writing & Simulation Services
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Admin: @HusseinSheikho
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!
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https://t.iss.one/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
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Admin: @HusseinSheikho
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ML Engineers: NVIDIA has released a guide for beginners on fine-tuning LLMs using Unsloth.
The guide covers:
- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more
Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/
π https://t.iss.one/DataScienceT
The guide covers:
- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more
Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/
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β¨Latent Implicit Visual Reasoning
π Summary:
Large Multimodal Models struggle with visual reasoning due to their text-centric nature and limitations of prior methods. This paper introduces a task-agnostic mechanism for LMMs to discover and use visual reasoning tokens without explicit supervision. The approach achieves state-of-the-art resul...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21218
β’ PDF: https://arxiv.org/pdf/2512.21218
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LMMs #VisualReasoning #AI #ComputerVision #DeepLearning
π Summary:
Large Multimodal Models struggle with visual reasoning due to their text-centric nature and limitations of prior methods. This paper introduces a task-agnostic mechanism for LMMs to discover and use visual reasoning tokens without explicit supervision. The approach achieves state-of-the-art resul...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21218
β’ PDF: https://arxiv.org/pdf/2512.21218
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LMMs #VisualReasoning #AI #ComputerVision #DeepLearning
β€1