π Master Data Science & Programming!
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π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
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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.me/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.me/DataScience4
π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
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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.me/DataScience9
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The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/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.
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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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π§ Learn Python Hub
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.me/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.me/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.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
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Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/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.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
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https://t.me/DataScienceY
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β¨Flowception: Temporally Expansive Flow Matching for Video Generation
π Summary:
Flowception, a non-autoregressive video generation framework, interleaves discrete frame insertions with continuous denoising, improving efficiency and performance over existing methods. AI-generated ...
πΉ Publication Date: Published on Dec 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.11438
β’ PDF: https://arxiv.org/pdf/2512.11438
β’ Project Page: https://flowception-meta.github.io/
==================================
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β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Flowception, a non-autoregressive video generation framework, interleaves discrete frame insertions with continuous denoising, improving efficiency and performance over existing methods. AI-generated ...
πΉ Publication Date: Published on Dec 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.11438
β’ PDF: https://arxiv.org/pdf/2512.11438
β’ Project Page: https://flowception-meta.github.io/
==================================
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β https://t.me/DataScienceT
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β¨Rethinking Expert Trajectory Utilization in LLM Post-training
π Summary:
The Sequential SFT-then-RL pipeline is identified as optimal for integrating expert trajectories, with guidelines for scaling and trajectory selection based on performance metrics. AI-generated summar...
πΉ Publication Date: Published on Dec 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.11470
β’ PDF: https://arxiv.org/pdf/2512.11470
β’ Github: https://github.com/LINs-lab/RETU
==================================
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β https://t.me/DataScienceT
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π Summary:
The Sequential SFT-then-RL pipeline is identified as optimal for integrating expert trajectories, with guidelines for scaling and trajectory selection based on performance metrics. AI-generated summar...
πΉ Publication Date: Published on Dec 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.11470
β’ PDF: https://arxiv.org/pdf/2512.11470
β’ Github: https://github.com/LINs-lab/RETU
==================================
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β¨FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos
π Summary:
FoundationMotion is an automated pipeline for creating large-scale motion datasets using object detection, trajectory extraction, and LLM-generated captions, improving motion understanding in models. ...
πΉ Publication Date: Published on Dec 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.10927
β’ PDF: https://arxiv.org/pdf/2512.10927
β’ Project Page: https://yulugan.com/projects/FoundationMotion.html
β’ Github: https://github.com/Wolfv0/FoundationMotion/tree/main
==================================
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β https://t.me/DataScienceT
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π Summary:
FoundationMotion is an automated pipeline for creating large-scale motion datasets using object detection, trajectory extraction, and LLM-generated captions, improving motion understanding in models. ...
πΉ Publication Date: Published on Dec 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.10927
β’ PDF: https://arxiv.org/pdf/2512.10927
β’ Project Page: https://yulugan.com/projects/FoundationMotion.html
β’ Github: https://github.com/Wolfv0/FoundationMotion/tree/main
==================================
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β https://t.me/DataScienceT
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β¨KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
π Summary:
A novel knowledge distillation framework compresses a high-performance ConvNeXtV2-Large model into a lightweight EfficientNet-B2 for efficient AMD and CNV classification in real-time clinical settings...
πΉ Publication Date: Published on Dec 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.09069
β’ PDF: https://arxiv.org/pdf/2512.09069
β’ Github: https://github.com/erfan-nourbakhsh/KD-OCT
==================================
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β https://t.me/DataScienceT
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π Summary:
A novel knowledge distillation framework compresses a high-performance ConvNeXtV2-Large model into a lightweight EfficientNet-B2 for efficient AMD and CNV classification in real-time clinical settings...
πΉ Publication Date: Published on Dec 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.09069
β’ PDF: https://arxiv.org/pdf/2512.09069
β’ Github: https://github.com/erfan-nourbakhsh/KD-OCT
==================================
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β https://t.me/DataScienceT
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β¨CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence
π Summary:
CoRe3D is a 3D reasoning framework for understanding and generation. It aligns high-level language intent with low-level 3D content using spatially grounded reasoning. This ensures consistent and accurate 3D outputs faithful to linguistic descriptions.
πΉ Publication Date: Published on Dec 14
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12768
β’ PDF: https://arxiv.org/pdf/2512.12768
==================================
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β https://t.me/DataScienceT
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π Summary:
CoRe3D is a 3D reasoning framework for understanding and generation. It aligns high-level language intent with low-level 3D content using spatially grounded reasoning. This ensures consistent and accurate 3D outputs faithful to linguistic descriptions.
πΉ Publication Date: Published on Dec 14
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12768
β’ PDF: https://arxiv.org/pdf/2512.12768
==================================
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β https://t.me/DataScienceT
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β¨AutoMV: An Automatic Multi-Agent System for Music Video Generation
π Summary:
AutoMV, a multi-agent system, generates coherent full-length music videos directly from songs. It processes music attributes for agents to script and generate scenes, ensuring consistency. AutoMV outperforms existing methods, nearing professional MV quality.
πΉ Publication Date: Published on Dec 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12196v1
β’ PDF: https://arxiv.org/pdf/2512.12196
β’ Project Page: https://github.com/multimodal-art-projection/AutoMV
β’ Github: https://github.com/multimodal-art-projection/AutoMV
==================================
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β https://t.me/DataScienceT
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π Summary:
AutoMV, a multi-agent system, generates coherent full-length music videos directly from songs. It processes music attributes for agents to script and generate scenes, ensuring consistency. AutoMV outperforms existing methods, nearing professional MV quality.
πΉ Publication Date: Published on Dec 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12196v1
β’ PDF: https://arxiv.org/pdf/2512.12196
β’ Project Page: https://github.com/multimodal-art-projection/AutoMV
β’ Github: https://github.com/multimodal-art-projection/AutoMV
==================================
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β https://t.me/DataScienceT
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β¨I-Scene: 3D Instance Models are Implicit Generalizable Spatial Learners
π Summary:
A pre-trained 3D instance generator is reprogrammed to learn spatial understanding directly from geometric cues. This enables generalization to new layouts, showing it is an implicit spatial learner, pointing to foundation models for 3D scene generation.
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13683
β’ PDF: https://arxiv.org/pdf/2512.13683
==================================
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β https://t.me/DataScienceT
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π Summary:
A pre-trained 3D instance generator is reprogrammed to learn spatial understanding directly from geometric cues. This enables generalization to new layouts, showing it is an implicit spatial learner, pointing to foundation models for 3D scene generation.
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13683
β’ PDF: https://arxiv.org/pdf/2512.13683
==================================
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β https://t.me/DataScienceT
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β¨VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse
π Summary:
The rapid scaling of Large Language Models (LLMs) has achieved remarkable performance, but it also leads to prohibitive memory costs. Existing parameter-efficient approaches such as pruning and quanti...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14531
β’ PDF: https://arxiv.org/pdf/2512.14531
β’ Github: https://github.com/huawei-noah/noah-research/tree/master/VersatileFFN
==================================
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β https://t.me/DataScienceT
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π Summary:
The rapid scaling of Large Language Models (LLMs) has achieved remarkable performance, but it also leads to prohibitive memory costs. Existing parameter-efficient approaches such as pruning and quanti...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14531
β’ PDF: https://arxiv.org/pdf/2512.14531
β’ Github: https://github.com/huawei-noah/noah-research/tree/master/VersatileFFN
==================================
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β https://t.me/DataScienceT
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β¨Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling
π Summary:
Scone integrates composition and distinction in image generation by using a two-stage training scheme with semantic alignment and attention-based masking, outperforming existing models on benchmarks. ...
πΉ Publication Date: Published on Dec 14
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12675
β’ PDF: https://arxiv.org/pdf/2512.12675
β’ Github: https://github.com/Ryann-Ran/Scone
πΉ Models citing this paper:
β’ https://huggingface.co/Ryann829/Scone
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Ryann829/Scone-S2I-57K
β’ https://huggingface.co/datasets/Ryann829/SconeEval
==================================
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π Summary:
Scone integrates composition and distinction in image generation by using a two-stage training scheme with semantic alignment and attention-based masking, outperforming existing models on benchmarks. ...
πΉ Publication Date: Published on Dec 14
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12675
β’ PDF: https://arxiv.org/pdf/2512.12675
β’ Github: https://github.com/Ryann-Ran/Scone
πΉ Models citing this paper:
β’ https://huggingface.co/Ryann829/Scone
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Ryann829/Scone-S2I-57K
β’ https://huggingface.co/datasets/Ryann829/SconeEval
==================================
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β¨WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling
π Summary:
WorldPlay is a streaming video diffusion model that achieves real-time, interactive world modeling with long-term geometric consistency by using a Dual Action Representation, Reconstituted Context Mem...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14614
β’ PDF: https://arxiv.org/pdf/2512.14614
β’ Project Page: https://3d-models.hunyuan.tencent.com/world/
==================================
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β https://t.me/DataScienceT
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π Summary:
WorldPlay is a streaming video diffusion model that achieves real-time, interactive world modeling with long-term geometric consistency by using a Dual Action Representation, Reconstituted Context Mem...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14614
β’ PDF: https://arxiv.org/pdf/2512.14614
β’ Project Page: https://3d-models.hunyuan.tencent.com/world/
==================================
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β https://t.me/DataScienceT
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β¨Olmo 3
π Summary:
Olmo 3, a family of state-of-the-art fully-open language models at 7B and 32B parameter scales, excels in long-context reasoning, function calling, coding, instruction following, general chat, and kno...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13961
β’ PDF: https://arxiv.org/pdf/2512.13961
β’ Project Page: https://playground.allenai.org/
==================================
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β https://t.me/DataScienceT
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π Summary:
Olmo 3, a family of state-of-the-art fully-open language models at 7B and 32B parameter scales, excels in long-context reasoning, function calling, coding, instruction following, general chat, and kno...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13961
β’ PDF: https://arxiv.org/pdf/2512.13961
β’ Project Page: https://playground.allenai.org/
==================================
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β¨Feedforward 3D Editing via Text-Steerable Image-to-3D
π Summary:
Steer3D enables text-based editing of AI-generated 3D assets by adapting ControlNet for image-to-3D generation with flow-matching training and Direct Preference Optimization. AI-generated summary Rece...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13678
β’ PDF: https://arxiv.org/pdf/2512.13678
β’ Project Page: https://glab-caltech.github.io/steer3d/
β’ Github: https://glab-caltech.github.io/steer3d/#demo
πΉ Models citing this paper:
β’ https://huggingface.co/ziqima/Steer3D
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/ziqima/Steer3D-Data
β’ https://huggingface.co/datasets/ziqima/Edit3D-Bench
==================================
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β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Steer3D enables text-based editing of AI-generated 3D assets by adapting ControlNet for image-to-3D generation with flow-matching training and Direct Preference Optimization. AI-generated summary Rece...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13678
β’ PDF: https://arxiv.org/pdf/2512.13678
β’ Project Page: https://glab-caltech.github.io/steer3d/
β’ Github: https://glab-caltech.github.io/steer3d/#demo
πΉ Models citing this paper:
β’ https://huggingface.co/ziqima/Steer3D
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/ziqima/Steer3D-Data
β’ https://huggingface.co/datasets/ziqima/Edit3D-Bench
==================================
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β https://t.me/DataScienceT
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β¨MMGR: Multi-Modal Generative Reasoning
π Summary:
MMGR is a new benchmark assessing video and image model reasoning across physical, logical, and spatial domains. It uncovers major performance gaps, showing models struggle with abstract reasoning and planning, often prioritizing visual plausibility over true causal correctness.
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14691
β’ PDF: https://arxiv.org/pdf/2512.14691
==================================
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β https://t.me/DataScienceT
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π Summary:
MMGR is a new benchmark assessing video and image model reasoning across physical, logical, and spatial domains. It uncovers major performance gaps, showing models struggle with abstract reasoning and planning, often prioritizing visual plausibility over true causal correctness.
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14691
β’ PDF: https://arxiv.org/pdf/2512.14691
==================================
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β https://t.me/DataScienceT
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β¨RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics
π Summary:
RoboTracer, a 3D-aware visual language model, enhances spatial tracing by combining supervised and reinforcement fine-tuning with a universal spatial encoder and regression-supervised decoder, achievi...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13660
β’ PDF: https://arxiv.org/pdf/2512.13660
β’ Project Page: https://zhoues.github.io/RoboTracer/
β’ Github: https://zhoues.github.io/RoboTracer/
==================================
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β https://t.me/DataScienceT
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π Summary:
RoboTracer, a 3D-aware visual language model, enhances spatial tracing by combining supervised and reinforcement fine-tuning with a universal spatial encoder and regression-supervised decoder, achievi...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13660
β’ PDF: https://arxiv.org/pdf/2512.13660
β’ Project Page: https://zhoues.github.io/RoboTracer/
β’ Github: https://zhoues.github.io/RoboTracer/
==================================
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β https://t.me/DataScienceT
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β¨RecGPT-V2 Technical Report
π Summary:
RecGPT-V2 enhances recommender systems by integrating a Hierarchical Multi-Agent System, Hybrid Representation Inference, Meta-Prompting, constrained reinforcement learning, and an Agent-as-a-Judge fr...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14503
β’ PDF: https://arxiv.org/pdf/2512.14503
==================================
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β https://t.me/DataScienceT
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π Summary:
RecGPT-V2 enhances recommender systems by integrating a Hierarchical Multi-Agent System, Hybrid Representation Inference, Meta-Prompting, constrained reinforcement learning, and an Agent-as-a-Judge fr...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14503
β’ PDF: https://arxiv.org/pdf/2512.14503
==================================
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β¨MemFlow: Flowing Adaptive Memory for Consistent and Efficient Long Video Narratives
π Summary:
MemFlow dynamically updates a memory bank by retrieving relevant historical frames for each video chunk, ensuring narrative coherence and generation efficiency with minimal computational overhead. AI-...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14699
β’ PDF: https://arxiv.org/pdf/2512.14699
==================================
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β https://t.me/DataScienceT
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π Summary:
MemFlow dynamically updates a memory bank by retrieving relevant historical frames for each video chunk, ensuring narrative coherence and generation efficiency with minimal computational overhead. AI-...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14699
β’ PDF: https://arxiv.org/pdf/2512.14699
==================================
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β https://t.me/DataScienceT
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β¨A4-Agent: An Agentic Framework for Zero-Shot Affordance Reasoning
π Summary:
A4-Agent, a training-free framework, decouples affordance prediction into three stages using specialized pre-trained models to enhance generalization and performance in real-world settings. AI-generat...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14442
β’ PDF: https://arxiv.org/pdf/2512.14442
β’ Project Page: https://zixinzhang02.github.io/A4-Agent-page/
β’ Github: https://zixinzhang02.github.io/A4-Agent-page/
==================================
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β https://t.me/DataScienceT
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π Summary:
A4-Agent, a training-free framework, decouples affordance prediction into three stages using specialized pre-trained models to enhance generalization and performance in real-world settings. AI-generat...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14442
β’ PDF: https://arxiv.org/pdf/2512.14442
β’ Project Page: https://zixinzhang02.github.io/A4-Agent-page/
β’ Github: https://zixinzhang02.github.io/A4-Agent-page/
==================================
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β https://t.me/DataScienceT
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β¨Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed
π Summary:
AR-to-dLM conversion enhances diffusion language models' efficiency and speed while maintaining task accuracy through refined attention patterns and token masking strategies. AI-generated summary Diff...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14067
β’ PDF: https://arxiv.org/pdf/2512.14067
==================================
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β https://t.me/DataScienceT
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π Summary:
AR-to-dLM conversion enhances diffusion language models' efficiency and speed while maintaining task accuracy through refined attention patterns and token masking strategies. AI-generated summary Diff...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14067
β’ PDF: https://arxiv.org/pdf/2512.14067
==================================
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β¨Comparative Analysis of LLM Abliteration Methods: A Cross-Architecture Evaluation
π Summary:
Four abliteration tools are evaluated for their effectiveness in removing refusal representations from large language models, with findings showing variability in capability preservation and distribut...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13655
β’ PDF: https://arxiv.org/pdf/2512.13655
==================================
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β https://t.me/DataScienceT
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π Summary:
Four abliteration tools are evaluated for their effectiveness in removing refusal representations from large language models, with findings showing variability in capability preservation and distribut...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13655
β’ PDF: https://arxiv.org/pdf/2512.13655
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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β¨WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling
π Summary:
WorldPlay is a streaming video diffusion model that achieves real-time, interactive world modeling with long-term geometric consistency by using a Dual Action Representation, Reconstituted Context Mem...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14614
β’ PDF: https://arxiv.org/pdf/2512.14614
β’ Project Page: https://3d-models.hunyuan.tencent.com/world/
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
WorldPlay is a streaming video diffusion model that achieves real-time, interactive world modeling with long-term geometric consistency by using a Dual Action Representation, Reconstituted Context Mem...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14614
β’ PDF: https://arxiv.org/pdf/2512.14614
β’ Project Page: https://3d-models.hunyuan.tencent.com/world/
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research