✨The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality
📝 Summary:
The FACTS Leaderboard is a new comprehensive benchmark evaluating LLMs' factual accuracy. It uses four sub-leaderboards: image-based, closed-book, search-augmented, and document-grounded, to holistically assess factuality with automated judges.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10791
• PDF: https://arxiv.org/pdf/2512.10791
• Project Page: https://www.kaggle.com/benchmarks/google/facts
==================================
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📝 Summary:
The FACTS Leaderboard is a new comprehensive benchmark evaluating LLMs' factual accuracy. It uses four sub-leaderboards: image-based, closed-book, search-augmented, and document-grounded, to holistically assess factuality with automated judges.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10791
• PDF: https://arxiv.org/pdf/2512.10791
• Project Page: https://www.kaggle.com/benchmarks/google/facts
==================================
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✨Evaluating Gemini Robotics Policies in a Veo World Simulator
📝 Summary:
A generative evaluation system using a frontier video model (Veo) enables comprehensive policy evaluation in robotics, including nominal performance, out-of-distribution generalization, and safety che...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10675
• PDF: https://arxiv.org/pdf/2512.10675
==================================
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📝 Summary:
A generative evaluation system using a frontier video model (Veo) enables comprehensive policy evaluation in robotics, including nominal performance, out-of-distribution generalization, and safety che...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10675
• PDF: https://arxiv.org/pdf/2512.10675
==================================
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✨Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
📝 Summary:
Fed-SE, a Federated Self-Evolution framework, enhances LLM agents in privacy-constrained environments by local parameter-efficient fine-tuning and global aggregation in a low-rank subspace. AI-generat...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08870
• PDF: https://arxiv.org/pdf/2512.08870
==================================
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📝 Summary:
Fed-SE, a Federated Self-Evolution framework, enhances LLM agents in privacy-constrained environments by local parameter-efficient fine-tuning and global aggregation in a low-rank subspace. AI-generat...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08870
• PDF: https://arxiv.org/pdf/2512.08870
==================================
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✨Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation
📝 Summary:
This study systematically explores reinforcement learning for text-to-3D generation, addressing reward designs, RL algorithms, and introducing a new benchmark. It develops AR3D-R1, the first RL-enhanced text-to-3D model, demonstrating RLs effectiveness across 3D generation stages.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10949
• PDF: https://arxiv.org/pdf/2512.10949
• Github: https://github.com/Ivan-Tang-3D/3DGen-R1
==================================
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📝 Summary:
This study systematically explores reinforcement learning for text-to-3D generation, addressing reward designs, RL algorithms, and introducing a new benchmark. It develops AR3D-R1, the first RL-enhanced text-to-3D model, demonstrating RLs effectiveness across 3D generation stages.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10949
• PDF: https://arxiv.org/pdf/2512.10949
• Github: https://github.com/Ivan-Tang-3D/3DGen-R1
==================================
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✨OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification
📝 Summary:
The Outcome-based Process Verifier (OPV) improves the verification of complex reasoning chains in large language models by combining outcome-based and process-based verification with iterative active ...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10756
• PDF: https://arxiv.org/pdf/2512.10756
==================================
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📝 Summary:
The Outcome-based Process Verifier (OPV) improves the verification of complex reasoning chains in large language models by combining outcome-based and process-based verification with iterative active ...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10756
• PDF: https://arxiv.org/pdf/2512.10756
==================================
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✨MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos
📝 Summary:
MoCapAnything is a reference-guided framework that reconstructs rotation-based animations from monocular video for arbitrary rigged 3D assets, enabling cross-species retargeting and scalable 3D motion...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10881
• PDF: https://arxiv.org/pdf/2512.10881
==================================
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📝 Summary:
MoCapAnything is a reference-guided framework that reconstructs rotation-based animations from monocular video for arbitrary rigged 3D assets, enabling cross-species retargeting and scalable 3D motion...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10881
• PDF: https://arxiv.org/pdf/2512.10881
==================================
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✨Stronger Normalization-Free Transformers
📝 Summary:
Derf, a novel point-wise normalization function, outperforms existing alternatives across various domains, enhancing generalization without increased fitting capacity. AI-generated summary Although no...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10938
• PDF: https://arxiv.org/pdf/2512.10938
==================================
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📝 Summary:
Derf, a novel point-wise normalization function, outperforms existing alternatives across various domains, enhancing generalization without increased fitting capacity. AI-generated summary Although no...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10938
• PDF: https://arxiv.org/pdf/2512.10938
==================================
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✨Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving
📝 Summary:
OPV, an iterative active learning framework with Rejection Fine-Tuning, enhances verification of long reasoning chains in large language models, achieving state-of-the-art results and improving accura...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10739
• PDF: https://arxiv.org/pdf/2512.10739
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
OPV, an iterative active learning framework with Rejection Fine-Tuning, enhances verification of long reasoning chains in large language models, achieving state-of-the-art results and improving accura...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10739
• PDF: https://arxiv.org/pdf/2512.10739
==================================
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✨Confucius Code Agent: An Open-sourced AI Software Engineer at Industrial Scale
📝 Summary:
Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains ...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10398
• PDF: https://arxiv.org/pdf/2512.10398
• Github: https://github.com/facebook/confucius
==================================
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📝 Summary:
Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains ...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10398
• PDF: https://arxiv.org/pdf/2512.10398
• Github: https://github.com/facebook/confucius
==================================
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✨Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task
📝 Summary:
A spatiotemporal reasoning framework enhances multimodal large language models for video question answering by strategically scheduling tools to improve spatial and temporal understanding. AI-generate...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10359
• PDF: https://arxiv.org/pdf/2512.10359
• Github: https://github.com/fansunqi/VideoTool
==================================
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📝 Summary:
A spatiotemporal reasoning framework enhances multimodal large language models for video question answering by strategically scheduling tools to improve spatial and temporal understanding. AI-generate...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10359
• PDF: https://arxiv.org/pdf/2512.10359
• Github: https://github.com/fansunqi/VideoTool
==================================
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✨H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos
📝 Summary:
A video-to-video translation framework converts human-object interaction videos into realistic robot manipulation videos using unpaired training data and a generative model. AI-generated summary Robot...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09406
• PDF: https://arxiv.org/pdf/2512.09406
• Project Page: https://showlab.github.io/H2R-Grounder/
• Github: https://github.com/showlab/H2R-Grounder
==================================
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📝 Summary:
A video-to-video translation framework converts human-object interaction videos into realistic robot manipulation videos using unpaired training data and a generative model. AI-generated summary Robot...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09406
• PDF: https://arxiv.org/pdf/2512.09406
• Project Page: https://showlab.github.io/H2R-Grounder/
• Github: https://github.com/showlab/H2R-Grounder
==================================
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✨Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning
📝 Summary:
InternGeometry, an LLM agent, surpasses human performance on IMO geometry problems. It uses iterative proposition verification and a dynamic memory mechanism, combined with Complexity-Boosting Reinforcement Learning, to achieve this with very limited training data.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10534
• PDF: https://arxiv.org/pdf/2512.10534
==================================
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📝 Summary:
InternGeometry, an LLM agent, surpasses human performance on IMO geometry problems. It uses iterative proposition verification and a dynamic memory mechanism, combined with Complexity-Boosting Reinforcement Learning, to achieve this with very limited training data.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10534
• PDF: https://arxiv.org/pdf/2512.10534
==================================
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✨VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction
📝 Summary:
VQRAE, a Vector Quantization Representation AutoEncoder, unifies multimodal understanding, generation, and reconstruction using a unified tokenizer with continuous semantic features and discrete token...
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2511.23386
• PDF: https://arxiv.org/pdf/2511.23386
==================================
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📝 Summary:
VQRAE, a Vector Quantization Representation AutoEncoder, unifies multimodal understanding, generation, and reconstruction using a unified tokenizer with continuous semantic features and discrete token...
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2511.23386
• PDF: https://arxiv.org/pdf/2511.23386
==================================
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✨From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models
📝 Summary:
A benchmark framework evaluates Vision-Language Models in understanding microscopic spatial relationships, showing potential but highlighting the need for domain-specific knowledge integration. AI-gen...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10867
• PDF: https://arxiv.org/pdf/2512.10867
==================================
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📝 Summary:
A benchmark framework evaluates Vision-Language Models in understanding microscopic spatial relationships, showing potential but highlighting the need for domain-specific knowledge integration. AI-gen...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10867
• PDF: https://arxiv.org/pdf/2512.10867
==================================
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✨MoRel: Long-Range Flicker-Free 4D Motion Modeling via Anchor Relay-based Bidirectional Blending with Hierarchical Densification
📝 Summary:
MoRel is a 4D Gaussian Splatting framework for long-range dynamic videos. It uses Anchor Relay-based Bidirectional Blending and Hierarchical Densification to achieve temporally consistent, flicker-free reconstruction with efficient memory use.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09270
• PDF: https://arxiv.org/pdf/2512.09270
• Project Page: https://cmlab-korea.github.io/MoRel/
• Github: https://github.com/CMLab-Korea/MoRel-arXiv
==================================
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#GaussianSplatting #4DMotionModeling #ComputerVision #DeepLearning #NeuralRendering
📝 Summary:
MoRel is a 4D Gaussian Splatting framework for long-range dynamic videos. It uses Anchor Relay-based Bidirectional Blending and Hierarchical Densification to achieve temporally consistent, flicker-free reconstruction with efficient memory use.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09270
• PDF: https://arxiv.org/pdf/2512.09270
• Project Page: https://cmlab-korea.github.io/MoRel/
• Github: https://github.com/CMLab-Korea/MoRel-arXiv
==================================
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✨MOA: Multi-Objective Alignment for Role-Playing Agents
📝 Summary:
MOA is a reinforcement-learning framework for role-playing agents that uses multi-objective optimization and thought-augmented rollout. It simultaneously improves multiple skills like domain knowledge and linguistic style, addressing limitations of prior methods. MOA outperforms strong baselines,...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09756
• PDF: https://arxiv.org/pdf/2512.09756
==================================
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#AI #ReinforcementLearning #MultiObjectiveOptimization #RolePlayingAgents #MachineLearning
📝 Summary:
MOA is a reinforcement-learning framework for role-playing agents that uses multi-objective optimization and thought-augmented rollout. It simultaneously improves multiple skills like domain knowledge and linguistic style, addressing limitations of prior methods. MOA outperforms strong baselines,...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09756
• PDF: https://arxiv.org/pdf/2512.09756
==================================
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✨Thinking with Images via Self-Calling Agent
📝 Summary:
sCoT is a novel visual reasoning paradigm that reformulates interleaved multimodal CoT as a language-only CoT with self-calling subagents. It improves reasoning performance and efficiency by avoiding explicit multimodal interleaving and using group-relative policy optimization.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08511
• PDF: https://arxiv.org/pdf/2512.08511
• Github: https://github.com/YWenxi/think-with-images-through-self-calling
==================================
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#VisualReasoning #MultimodalAI #LLMs #AIagents #AIResearch
📝 Summary:
sCoT is a novel visual reasoning paradigm that reformulates interleaved multimodal CoT as a language-only CoT with self-calling subagents. It improves reasoning performance and efficiency by avoiding explicit multimodal interleaving and using group-relative policy optimization.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08511
• PDF: https://arxiv.org/pdf/2512.08511
• Github: https://github.com/YWenxi/think-with-images-through-self-calling
==================================
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✨T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground
📝 Summary:
T-pro 2.0 is an open-weight Russian LLM for hybrid reasoning and efficient inference. It uses a Cyrillic-dense tokenizer and EAGLE speculative decoding for low latency. The project releases model weights and benchmarks to foster reproducible research.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10430
• PDF: https://arxiv.org/pdf/2512.10430
==================================
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#LLM #AI #NaturalLanguageProcessing #HybridReasoning #EfficientInference
📝 Summary:
T-pro 2.0 is an open-weight Russian LLM for hybrid reasoning and efficient inference. It uses a Cyrillic-dense tokenizer and EAGLE speculative decoding for low latency. The project releases model weights and benchmarks to foster reproducible research.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10430
• PDF: https://arxiv.org/pdf/2512.10430
==================================
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✨ReViSE: Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning
📝 Summary:
The ReViSE framework enables reason-informed video editing by addressing the disconnect between models reasoning and editing capabilities. It uses a self-reflective learning mechanism with an internal VLM to provide intrinsic feedback. This significantly enhances editing accuracy and visual fidel...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09924
• PDF: https://arxiv.org/pdf/2512.09924
• Github: https://github.com/Liuxinyv/ReViSE
==================================
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#VideoEditing #AI #MachineLearning #VLM #SelfReflectiveLearning
📝 Summary:
The ReViSE framework enables reason-informed video editing by addressing the disconnect between models reasoning and editing capabilities. It uses a self-reflective learning mechanism with an internal VLM to provide intrinsic feedback. This significantly enhances editing accuracy and visual fidel...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09924
• PDF: https://arxiv.org/pdf/2512.09924
• Github: https://github.com/Liuxinyv/ReViSE
==================================
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#VideoEditing #AI #MachineLearning #VLM #SelfReflectiveLearning
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✨StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space
📝 Summary:
StereoSpace generates stereo images from monocular input using viewpoint-conditioned diffusion, avoiding explicit depth or warping. It leverages a canonical rectified space for sharp parallax and robust results on complex scenes. This establishes a scalable, depth-free stereo synthesis solution.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10959
• PDF: https://arxiv.org/pdf/2512.10959
• Project Page: https://huggingface.co/spaces/prs-eth/stereospace_web
• Github: https://github.com/prs-eth/stereospace
🔹 Models citing this paper:
• https://huggingface.co/prs-eth/stereospace-v1-0
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prs-eth/stereospace_web
==================================
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#StereoVision #DiffusionModels #ComputerVision #DeepLearning #ImageSynthesis
📝 Summary:
StereoSpace generates stereo images from monocular input using viewpoint-conditioned diffusion, avoiding explicit depth or warping. It leverages a canonical rectified space for sharp parallax and robust results on complex scenes. This establishes a scalable, depth-free stereo synthesis solution.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10959
• PDF: https://arxiv.org/pdf/2512.10959
• Project Page: https://huggingface.co/spaces/prs-eth/stereospace_web
• Github: https://github.com/prs-eth/stereospace
🔹 Models citing this paper:
• https://huggingface.co/prs-eth/stereospace-v1-0
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prs-eth/stereospace_web
==================================
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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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Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
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Master Python with step-by-step courses – from basics to advanced projects and practical applications.
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Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY
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Admin: @HusseinSheikho
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