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.
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
π§βπ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC
π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT
π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
π Python Arab| Ψ¨Ψ§ΩΨ«ΩΩ ΨΉΨ±Ψ¨Ω
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab
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.
https://t.iss.one/DataScienceV
π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX
π§ Learn Python Hub
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.iss.one/Python53
βοΈ Research Papers
Professional Academic Writing & Simulation Services
https://t.iss.one/DataScienceY
ββββββββββββββββββ
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!
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
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT
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.
https://t.iss.one/PythonArab
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
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.iss.one/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.iss.one/Python53
Professional Academic Writing & Simulation Services
https://t.iss.one/DataScienceY
ββββββββββββββββββ
Admin: @HusseinSheikho
Please open Telegram to view this post
VIEW IN TELEGRAM
β€1
β¨When Reasoning Meets Its Laws
π Summary:
The Laws of Reasoning LoRe framework defines desired reasoning for Large Reasoning Models, focusing on compute and accuracy. A benchmark, LoRe-Bench, reveals models often lack compositionality, which a finetuning method improves for better performance.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17901
β’ PDF: https://arxiv.org/pdf/2512.17901
β’ Project Page: https://lore-project.github.io/
β’ Github: https://github.com/ASTRAL-Group/LoRe
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #LargeLanguageModels #Reasoning #MachineLearning #NLP
π Summary:
The Laws of Reasoning LoRe framework defines desired reasoning for Large Reasoning Models, focusing on compute and accuracy. A benchmark, LoRe-Bench, reveals models often lack compositionality, which a finetuning method improves for better performance.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17901
β’ PDF: https://arxiv.org/pdf/2512.17901
β’ Project Page: https://lore-project.github.io/
β’ Github: https://github.com/ASTRAL-Group/LoRe
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #LargeLanguageModels #Reasoning #MachineLearning #NLP
β€1
β¨Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience
π Summary:
Seed-Prover 1.5 is a formal theorem-proving model that uses agentic reinforcement learning and an efficient scaling workflow. It achieves superior performance in solving undergraduate, graduate, and PhD-level math problems with reduced computational resources. This demonstrates the potential of l...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17260
β’ PDF: https://arxiv.org/pdf/2512.17260
β’ Github: https://github.com/ByteDance-Seed/Seed-Prover
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#TheoremProving #ReinforcementLearning #AI #Mathematics #AI4Math
π Summary:
Seed-Prover 1.5 is a formal theorem-proving model that uses agentic reinforcement learning and an efficient scaling workflow. It achieves superior performance in solving undergraduate, graduate, and PhD-level math problems with reduced computational resources. This demonstrates the potential of l...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17260
β’ PDF: https://arxiv.org/pdf/2512.17260
β’ Github: https://github.com/ByteDance-Seed/Seed-Prover
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#TheoremProving #ReinforcementLearning #AI #Mathematics #AI4Math
β€2
β¨SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositories
π Summary:
SWE-Bench++ is an automated framework generating scalable, multilingual, repository-level coding tasks from live GitHub pull requests. It overcomes manual curation limits and static datasets, offering a benchmark to evaluate and improve code generation models across 11 languages.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17419
β’ PDF: https://arxiv.org/pdf/2512.17419
β’ Project Page: https://research.turing.com/swebench
β’ Github: https://huggingface.co/papers?q=GitHub%20pull%20requests
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#SoftwareEngineering #CodeGeneration #AIBenchmarking #MachineLearning #OpenSource
π Summary:
SWE-Bench++ is an automated framework generating scalable, multilingual, repository-level coding tasks from live GitHub pull requests. It overcomes manual curation limits and static datasets, offering a benchmark to evaluate and improve code generation models across 11 languages.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17419
β’ PDF: https://arxiv.org/pdf/2512.17419
β’ Project Page: https://research.turing.com/swebench
β’ Github: https://huggingface.co/papers?q=GitHub%20pull%20requests
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#SoftwareEngineering #CodeGeneration #AIBenchmarking #MachineLearning #OpenSource
β€1
β¨4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation
π Summary:
4D-RGPT, a specialized multimodal LLM, enhances 4D perception in video inputs through Perceptual 4D Distillation and is evaluated on R4D-Bench, a new benchmark for depth-aware dynamic scenes. AI-gener...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17012
β’ PDF: https://arxiv.org/pdf/2512.17012
β’ Project Page: https://ca-joe-yang.github.io/resource/projects/4D_RGPT
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
4D-RGPT, a specialized multimodal LLM, enhances 4D perception in video inputs through Perceptual 4D Distillation and is evaluated on R4D-Bench, a new benchmark for depth-aware dynamic scenes. AI-gener...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17012
β’ PDF: https://arxiv.org/pdf/2512.17012
β’ Project Page: https://ca-joe-yang.github.io/resource/projects/4D_RGPT
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
π Summary:
A framework for Scientific General Intelligence (SGI) is presented, evaluated using SGI-Bench, and improved with Test-Time Reinforcement Learning, highlighting gaps in existing models' scientific capa...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16969
β’ PDF: https://arxiv.org/pdf/2512.16969
β’ Project Page: https://internscience.github.io/SGI-Page/
β’ Github: https://github.com/InternScience/SGI-Bench
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
A framework for Scientific General Intelligence (SGI) is presented, evaluated using SGI-Bench, and improved with Test-Time Reinforcement Learning, highlighting gaps in existing models' scientific capa...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16969
β’ PDF: https://arxiv.org/pdf/2512.16969
β’ Project Page: https://internscience.github.io/SGI-Page/
β’ Github: https://github.com/InternScience/SGI-Bench
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
Media is too big
VIEW IN TELEGRAM
β¨Animate Any Character in Any World
π Summary:
AniX extends controllable-entity models to enable diverse, user-defined character interactions in static 3D environments via natural language. It synthesizes temporally coherent videos through conditional autoregressive video generation, allowing characters to perform open-ended actions.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17796
β’ PDF: https://arxiv.org/pdf/2512.17796
β’ Project Page: https://snowflakewang.github.io/AniX/
β’ Github: https://github.com/snowflakewang/AniX
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#GenerativeAI #VideoGeneration #CharacterAnimation #NLP #3D
π Summary:
AniX extends controllable-entity models to enable diverse, user-defined character interactions in static 3D environments via natural language. It synthesizes temporally coherent videos through conditional autoregressive video generation, allowing characters to perform open-ended actions.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17796
β’ PDF: https://arxiv.org/pdf/2512.17796
β’ Project Page: https://snowflakewang.github.io/AniX/
β’ Github: https://github.com/snowflakewang/AniX
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#GenerativeAI #VideoGeneration #CharacterAnimation #NLP #3D
β€1
β¨Are We on the Right Way to Assessing LLM-as-a-Judge?
π Summary:
Sage is a human-free evaluation suite assessing LLM-as-a-Judge consistency using rational choice theory. It reveals significant reliability problems in current top LLM judges, even in difficult cases. The study suggests finetuning, explicit rubrics, and panel judging can boost consistency.
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16041
β’ PDF: https://arxiv.org/pdf/2512.16041
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LLMEvaluation #LLMReliability #AIResearch #GenAI #NLP
π Summary:
Sage is a human-free evaluation suite assessing LLM-as-a-Judge consistency using rational choice theory. It reveals significant reliability problems in current top LLM judges, even in difficult cases. The study suggests finetuning, explicit rubrics, and panel judging can boost consistency.
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16041
β’ PDF: https://arxiv.org/pdf/2512.16041
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LLMEvaluation #LLMReliability #AIResearch #GenAI #NLP
β€1
β¨Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers
π Summary:
Canon layers are lightweight architectural components that enhance language model reasoning depth and breadth by promoting horizontal information flow. They improve performance across various architectures, validated in synthetic tasks and real-world pretraining.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17351
β’ PDF: https://arxiv.org/pdf/2512.17351
β’ Project Page: https://physics.allen-zhu.com/part-4-architecture-design/part-4-1
β’ Github: https://github.com/facebookresearch/PhysicsLM4
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LanguageModels #LLM #AIArchitecture #DeepLearning #NLP
π Summary:
Canon layers are lightweight architectural components that enhance language model reasoning depth and breadth by promoting horizontal information flow. They improve performance across various architectures, validated in synthetic tasks and real-world pretraining.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17351
β’ PDF: https://arxiv.org/pdf/2512.17351
β’ Project Page: https://physics.allen-zhu.com/part-4-architecture-design/part-4-1
β’ Github: https://github.com/facebookresearch/PhysicsLM4
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LanguageModels #LLM #AIArchitecture #DeepLearning #NLP
β€1
β¨Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs
π Summary:
Turn-PPO improves multi-turn reinforcement learning for LLM agents by using a turn-level MDP for advantage estimation. This PPO variant outperforms GRPO and standard PPO, addressing limitations in long-horizon reasoning. It demonstrates effectiveness on WebShop and Sokoban datasets.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17008
β’ PDF: https://arxiv.org/pdf/2512.17008
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LLM #ReinforcementLearning #AI #MachineLearning #AgenticAI
π Summary:
Turn-PPO improves multi-turn reinforcement learning for LLM agents by using a turn-level MDP for advantage estimation. This PPO variant outperforms GRPO and standard PPO, addressing limitations in long-horizon reasoning. It demonstrates effectiveness on WebShop and Sokoban datasets.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17008
β’ PDF: https://arxiv.org/pdf/2512.17008
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LLM #ReinforcementLearning #AI #MachineLearning #AgenticAI
β€1
β¨Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding
π Summary:
A novel framework, Robust-R1, enhances multimodal large language models' robustness to visual degradations through explicit modeling, supervised fine-tuning, reward-driven alignment, and dynamic reaso...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17532
β’ PDF: https://arxiv.org/pdf/2512.17532
β’ Project Page: https://jqt.iss.one/index.html
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
A novel framework, Robust-R1, enhances multimodal large language models' robustness to visual degradations through explicit modeling, supervised fine-tuning, reward-driven alignment, and dynamic reaso...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17532
β’ PDF: https://arxiv.org/pdf/2512.17532
β’ Project Page: https://jqt.iss.one/index.html
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence
π Summary:
Proposed Egocentric2Embodiment pipeline translates human egocentric videos into structured training data for robots, enhancing their egocentric understanding and task performance. AI-generated summary...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16793
β’ PDF: https://arxiv.org/pdf/2512.16793
β’ Project Page: https://zgc-embodyai.github.io/PhysBrain/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Proposed Egocentric2Embodiment pipeline translates human egocentric videos into structured training data for robots, enhancing their egocentric understanding and task performance. AI-generated summary...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16793
β’ PDF: https://arxiv.org/pdf/2512.16793
β’ Project Page: https://zgc-embodyai.github.io/PhysBrain/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨StageVAR: Stage-Aware Acceleration for Visual Autoregressive Models
π Summary:
StageVAR accelerates visual autoregressive models by recognizing early stages are critical while later detail-refinement stages can be pruned or approximated. This plug-and-play framework achieves up to 3.4x speedup with minimal quality loss, outperforming existing methods.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16483
β’ PDF: https://arxiv.org/pdf/2512.16483
β’ Github: https://github.com/sen-mao/StageVAR
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#ComputerVision #DeepLearning #ModelAcceleration #AI #NeuralNetworks
π Summary:
StageVAR accelerates visual autoregressive models by recognizing early stages are critical while later detail-refinement stages can be pruned or approximated. This plug-and-play framework achieves up to 3.4x speedup with minimal quality loss, outperforming existing methods.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16483
β’ PDF: https://arxiv.org/pdf/2512.16483
β’ Github: https://github.com/sen-mao/StageVAR
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#ComputerVision #DeepLearning #ModelAcceleration #AI #NeuralNetworks
β€1
β¨Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing
π Summary:
This paper proposes a framework using a semantic-pixel reconstruction objective to adapt encoder features for generation. It creates a compact, semantically rich latent space, leading to state-of-the-art image reconstruction and improved text-to-image generation and editing.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17909
β’ PDF: https://arxiv.org/pdf/2512.17909
β’ Project Page: https://jshilong.github.io/PS-VAE-PAGE/
β’ Github: https://jshilong.github.io/PS-VAE-PAGE/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#TextToImage #ImageGeneration #DeepLearning #ComputerVision #AIResearch
π Summary:
This paper proposes a framework using a semantic-pixel reconstruction objective to adapt encoder features for generation. It creates a compact, semantically rich latent space, leading to state-of-the-art image reconstruction and improved text-to-image generation and editing.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17909
β’ PDF: https://arxiv.org/pdf/2512.17909
β’ Project Page: https://jshilong.github.io/PS-VAE-PAGE/
β’ Github: https://jshilong.github.io/PS-VAE-PAGE/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#TextToImage #ImageGeneration #DeepLearning #ComputerVision #AIResearch
β€1
β¨GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation
π Summary:
GroundingME is a new benchmark revealing significant visual grounding gaps in MLLMs, which often hallucinate instead of rejecting ungroundable queries. State-of-the-art models only reach 45.1% accuracy, raising safety concerns. Data-mixture training shows promise in improving their ability to rec...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17495
β’ PDF: https://arxiv.org/pdf/2512.17495
β’ Project Page: https://groundingme.github.io/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MLLMs #VisualGrounding #AISafety #AIResearch #Benchmarking
π Summary:
GroundingME is a new benchmark revealing significant visual grounding gaps in MLLMs, which often hallucinate instead of rejecting ungroundable queries. State-of-the-art models only reach 45.1% accuracy, raising safety concerns. Data-mixture training shows promise in improving their ability to rec...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17495
β’ PDF: https://arxiv.org/pdf/2512.17495
β’ Project Page: https://groundingme.github.io/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MLLMs #VisualGrounding #AISafety #AIResearch #Benchmarking
β€1
β¨HERBench: A Benchmark for Multi-Evidence Integration in Video Question Answering
π Summary:
HERBench is a new VideoQA benchmark designed to test multi-evidence integration across time, revealing significant challenges for current Video-LLMs. It requires models to fuse at least three visual cues from distinct segments, with state-of-the-art models performing poorly due to retrieval and f...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14870
β’ PDF: https://arxiv.org/pdf/2512.14870
β’ Project Page: https://herbench.github.io/
β’ Github: https://github.com/DanBenAmi/HERBench
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/DanBenAmi/HERBench
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
HERBench is a new VideoQA benchmark designed to test multi-evidence integration across time, revealing significant challenges for current Video-LLMs. It requires models to fuse at least three visual cues from distinct segments, with state-of-the-art models performing poorly due to retrieval and f...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14870
β’ PDF: https://arxiv.org/pdf/2512.14870
β’ Project Page: https://herbench.github.io/
β’ Github: https://github.com/DanBenAmi/HERBench
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/DanBenAmi/HERBench
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€2
β¨An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges
π Summary:
This survey offers a structured guide to Vision-Language-Action VLA models in robotics. It breaks down five key challenges: representation, execution, generalization, safety, and datasets, serving as a roadmap for researchers.
πΉ Publication Date: Published on Dec 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.11362
β’ PDF: https://arxiv.org/pdf/2512.11362
β’ Project Page: https://suyuz1.github.io/Survery/
β’ Github: https://suyuz1.github.io/VLA-Survey-Anatomy/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VLAModels #Robotics #ArtificialIntelligence #VisionLanguage #AIResearch
π Summary:
This survey offers a structured guide to Vision-Language-Action VLA models in robotics. It breaks down five key challenges: representation, execution, generalization, safety, and datasets, serving as a roadmap for researchers.
πΉ Publication Date: Published on Dec 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.11362
β’ PDF: https://arxiv.org/pdf/2512.11362
β’ Project Page: https://suyuz1.github.io/Survery/
β’ Github: https://suyuz1.github.io/VLA-Survey-Anatomy/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VLAModels #Robotics #ArtificialIntelligence #VisionLanguage #AIResearch
β€1
β¨RadarGen: Automotive Radar Point Cloud Generation from Cameras
π Summary:
RadarGen synthesizes realistic automotive radar point clouds from camera images using diffusion models. It incorporates depth, semantic, and motion cues for physical plausibility, enabling scalable multimodal simulation and improving perception models.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17897
β’ PDF: https://arxiv.org/pdf/2512.17897
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AutomotiveRadar #PointClouds #DiffusionModels #ComputerVision #AutonomousDriving
π Summary:
RadarGen synthesizes realistic automotive radar point clouds from camera images using diffusion models. It incorporates depth, semantic, and motion cues for physical plausibility, enabling scalable multimodal simulation and improving perception models.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17897
β’ PDF: https://arxiv.org/pdf/2512.17897
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AutomotiveRadar #PointClouds #DiffusionModels #ComputerVision #AutonomousDriving
β€1
Media is too big
VIEW IN TELEGRAM
β¨3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework
π Summary:
3D-RE-GEN reconstructs single images into modifiable 3D textured mesh scenes with comprehensive backgrounds. It uses a compositional generative framework and novel optimization for artist-ready, physically realistic layouts, achieving state-of-the-art performance.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17459
β’ PDF: https://arxiv.org/pdf/2512.17459
β’ Project Page: https://3dregen.jdihlmann.com/
β’ Github: https://github.com/cgtuebingen/3D-RE-GEN
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#3DReconstruction #GenerativeAI #ComputerVision #DeepLearning #ComputerGraphics
π Summary:
3D-RE-GEN reconstructs single images into modifiable 3D textured mesh scenes with comprehensive backgrounds. It uses a compositional generative framework and novel optimization for artist-ready, physically realistic layouts, achieving state-of-the-art performance.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17459
β’ PDF: https://arxiv.org/pdf/2512.17459
β’ Project Page: https://3dregen.jdihlmann.com/
β’ Github: https://github.com/cgtuebingen/3D-RE-GEN
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#3DReconstruction #GenerativeAI #ComputerVision #DeepLearning #ComputerGraphics
β€1
This media is not supported in your browser
VIEW IN TELEGRAM
β¨Meta-RL Induces Exploration in Language Agents
π Summary:
LaMer, a Meta-RL framework, enhances LLM agents exploration and adaptation in RL tasks. It significantly improves their performance and generalization across diverse environments, proving Meta-RLs effectiveness for robust adaptation in language agents.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16848
β’ PDF: https://arxiv.org/pdf/2512.16848
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MetaRL #LLMAgents #ReinforcementLearning #NLP #AI
π Summary:
LaMer, a Meta-RL framework, enhances LLM agents exploration and adaptation in RL tasks. It significantly improves their performance and generalization across diverse environments, proving Meta-RLs effectiveness for robust adaptation in language agents.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16848
β’ PDF: https://arxiv.org/pdf/2512.16848
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MetaRL #LLMAgents #ReinforcementLearning #NLP #AI
β¨A Benchmark and Agentic Framework for Omni-Modal Reasoning and Tool Use in Long Videos
π Summary:
This paper introduces LongShOTBench, a diagnostic benchmark for long-form multimodal video understanding with open-ended questions and agentic tool use. It also presents LongShOTAgent, an agentic system for video analysis. Results show state-of-the-art models struggle significantly, highlighting ...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16978
β’ PDF: https://arxiv.org/pdf/2512.16978
β’ Project Page: https://mbzuai-oryx.github.io/LongShOT/
β’ Github: https://github.com/mbzuai-oryx/longshot
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/MBZUAI/longshot-bench
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VideoAI #MultimodalAI #AgenticAI #AIbenchmark #AIResearch
π Summary:
This paper introduces LongShOTBench, a diagnostic benchmark for long-form multimodal video understanding with open-ended questions and agentic tool use. It also presents LongShOTAgent, an agentic system for video analysis. Results show state-of-the-art models struggle significantly, highlighting ...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16978
β’ PDF: https://arxiv.org/pdf/2512.16978
β’ Project Page: https://mbzuai-oryx.github.io/LongShOT/
β’ Github: https://github.com/mbzuai-oryx/longshot
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/MBZUAI/longshot-bench
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VideoAI #MultimodalAI #AgenticAI #AIbenchmark #AIResearch