✨TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
📝 Summary:
TESO is a new metaheuristic framework for simulation optimization that tackles noisy, complex problems. It integrates Tabu List and Elite Memory strategies to dynamically balance exploration and exploitation, demonstrating improved performance.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24007
• PDF: https://arxiv.org/pdf/2512.24007
• Github: https://github.com/bulentsoykan/TESO
==================================
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#SimulationOptimization #Metaheuristics #Optimization #BlackBoxOptimization #TabuSearch
📝 Summary:
TESO is a new metaheuristic framework for simulation optimization that tackles noisy, complex problems. It integrates Tabu List and Elite Memory strategies to dynamically balance exploration and exploitation, demonstrating improved performance.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24007
• PDF: https://arxiv.org/pdf/2512.24007
• Github: https://github.com/bulentsoykan/TESO
==================================
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#SimulationOptimization #Metaheuristics #Optimization #BlackBoxOptimization #TabuSearch
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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🚀 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!
🔰 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.me/CodeProgrammer
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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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The largest Arabic-speaking group for Python developers to share knowledge and help.
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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.
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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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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
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The largest Arabic-speaking group for Python developers to share knowledge and help.
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✨Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
📝 Summary:
Dolphin is a novel multimodal model for document image parsing. It uses an analyze-then-parse approach with heterogeneous anchor prompting, achieving state-of-the-art performance and superior efficiency.
🔹 Publication Date: Published on May 20, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.14059
• PDF: https://arxiv.org/pdf/2505.14059
• Github: https://github.com/bytedance/dolphin
==================================
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#DocumentParsing #MultimodalAI #DeepLearning #ComputerVision #AI
📝 Summary:
Dolphin is a novel multimodal model for document image parsing. It uses an analyze-then-parse approach with heterogeneous anchor prompting, achieving state-of-the-art performance and superior efficiency.
🔹 Publication Date: Published on May 20, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.14059
• PDF: https://arxiv.org/pdf/2505.14059
• Github: https://github.com/bytedance/dolphin
==================================
For more data science resources:
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#DocumentParsing #MultimodalAI #DeepLearning #ComputerVision #AI
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🚀 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!
🔰 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.me/CodeProgrammer
🔖 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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🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
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Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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💾 Kaggle Data Hub
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The largest Arabic-speaking group for Python developers to share knowledge and help.
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🖊 Data Science Jupyter Notebooks
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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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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
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An active community group for discussing data challenges and networking with peers.
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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 Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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✨Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization
📝 Summary:
Youtu-Agent scales LLM agent productivity, automating generation and enabling continuous evolution. Its hybrid optimization, using in-context learning and scalable reinforcement learning, yields top performance and boosted capabilities.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24615
• PDF: https://arxiv.org/pdf/2512.24615
• Project Page: https://tencentcloudadp.github.io/youtu-agent/
• Github: https://github.com/TencentCloudADP/youtu-tip
==================================
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#LLM #AIAgents #ReinforcementLearning #MachineLearning #AI
📝 Summary:
Youtu-Agent scales LLM agent productivity, automating generation and enabling continuous evolution. Its hybrid optimization, using in-context learning and scalable reinforcement learning, yields top performance and boosted capabilities.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24615
• PDF: https://arxiv.org/pdf/2512.24615
• Project Page: https://tencentcloudadp.github.io/youtu-agent/
• Github: https://github.com/TencentCloudADP/youtu-tip
==================================
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#LLM #AIAgents #ReinforcementLearning #MachineLearning #AI
✨SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning
📝 Summary:
SenseNova-MARS empowers Vision-Language Models with interleaved visual reasoning and dynamic tool use like search and cropping via reinforcement learning. It achieves state-of-the-art performance on complex visual tasks, outperforming proprietary models on new and existing benchmarks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24330
• PDF: https://arxiv.org/pdf/2512.24330
• Github: https://github.com/OpenSenseNova/SenseNova-MARS
✨ Datasets citing this paper:
• https://huggingface.co/datasets/sensenova/SenseNova-MARS-Data
• https://huggingface.co/datasets/sensenova/HR-MMSearch
==================================
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✓ https://t.me/DataScienceT
#MultimodalAI #ReinforcementLearning #VisionLanguageModels #AgenticAI #ComputerVision
📝 Summary:
SenseNova-MARS empowers Vision-Language Models with interleaved visual reasoning and dynamic tool use like search and cropping via reinforcement learning. It achieves state-of-the-art performance on complex visual tasks, outperforming proprietary models on new and existing benchmarks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24330
• PDF: https://arxiv.org/pdf/2512.24330
• Github: https://github.com/OpenSenseNova/SenseNova-MARS
✨ Datasets citing this paper:
• https://huggingface.co/datasets/sensenova/SenseNova-MARS-Data
• https://huggingface.co/datasets/sensenova/HR-MMSearch
==================================
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#MultimodalAI #ReinforcementLearning #VisionLanguageModels #AgenticAI #ComputerVision
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✨Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
📝 Summary:
Avatar Forcing creates real-time interactive talking head avatars. It uses diffusion forcing for low-latency reactions to user input and a label-free preference optimization for expressive, preferred motion, achieving 6.8x speedup.
🔹 Publication Date: Published on Jan 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00664
• PDF: https://arxiv.org/pdf/2601.00664
• Project Page: https://taekyungki.github.io/AvatarForcing/
• Github: https://github.com/TaekyungKi/AvatarForcing
==================================
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#AvatarGeneration #RealTimeAI #GenerativeAI #ComputerVision #AIResearch
📝 Summary:
Avatar Forcing creates real-time interactive talking head avatars. It uses diffusion forcing for low-latency reactions to user input and a label-free preference optimization for expressive, preferred motion, achieving 6.8x speedup.
🔹 Publication Date: Published on Jan 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00664
• PDF: https://arxiv.org/pdf/2601.00664
• Project Page: https://taekyungki.github.io/AvatarForcing/
• Github: https://github.com/TaekyungKi/AvatarForcing
==================================
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#AvatarGeneration #RealTimeAI #GenerativeAI #ComputerVision #AIResearch
✨Deep Delta Learning
📝 Summary:
The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly...
🔹 Publication Date: Published on Jan 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00417
• PDF: https://arxiv.org/pdf/2601.00417
• Github: https://github.com/yifanzhang-pro/deep-delta-learning
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly...
🔹 Publication Date: Published on Jan 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00417
• PDF: https://arxiv.org/pdf/2601.00417
• Github: https://github.com/yifanzhang-pro/deep-delta-learning
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Fast-weight Product Key Memory
📝 Summary:
FwPKM introduces a dynamic, fast-weight episodic memory mechanism for sequence modeling that balances storage capacity and efficiency, achieving strong performance on long-context tasks like Needle in...
🔹 Publication Date: Published on Jan 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00671
• PDF: https://arxiv.org/pdf/2601.00671
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
FwPKM introduces a dynamic, fast-weight episodic memory mechanism for sequence modeling that balances storage capacity and efficiency, achieving strong performance on long-context tasks like Needle in...
🔹 Publication Date: Published on Jan 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00671
• PDF: https://arxiv.org/pdf/2601.00671
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation
📝 Summary:
MLLMs struggle with hallucinations on counterfactual videos. DualityForge synthesizes counterfactual video data and QA pairs through diffusion-based editing to address this. This method significantly reduces model hallucinations and improves general performance.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24271
• PDF: https://arxiv.org/pdf/2512.24271
• Project Page: https://amap-ml.github.io/Taming-Hallucinations/
• Github: https://github.com/AMAP-ML/Taming-Hallucinations
==================================
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#MLLMs #VideoUnderstanding #AIHallucinations #GenerativeAI #MachineLearning
📝 Summary:
MLLMs struggle with hallucinations on counterfactual videos. DualityForge synthesizes counterfactual video data and QA pairs through diffusion-based editing to address this. This method significantly reduces model hallucinations and improves general performance.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24271
• PDF: https://arxiv.org/pdf/2512.24271
• Project Page: https://amap-ml.github.io/Taming-Hallucinations/
• Github: https://github.com/AMAP-ML/Taming-Hallucinations
==================================
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#MLLMs #VideoUnderstanding #AIHallucinations #GenerativeAI #MachineLearning
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✨NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos
📝 Summary:
NeoVerse is a 4D world model for reconstruction and video generation. It scales to in-the-wild monocular videos using pose-free feed-forward reconstruction and online degradation simulation, achieving state-of-the-art performance.
🔹 Publication Date: Published on Jan 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00393
• PDF: https://arxiv.org/pdf/2601.00393
• Project Page: https://neoverse-4d.github.io/
• Github: https://neoverse-4d.github.io
==================================
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✓ https://t.me/DataScienceT
#4DWorldModel #VideoGeneration #ComputerVision #DeepLearning #AI
📝 Summary:
NeoVerse is a 4D world model for reconstruction and video generation. It scales to in-the-wild monocular videos using pose-free feed-forward reconstruction and online degradation simulation, achieving state-of-the-art performance.
🔹 Publication Date: Published on Jan 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00393
• PDF: https://arxiv.org/pdf/2601.00393
• Project Page: https://neoverse-4d.github.io/
• Github: https://neoverse-4d.github.io
==================================
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#4DWorldModel #VideoGeneration #ComputerVision #DeepLearning #AI
✨MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing
📝 Summary:
MorphAny3D offers a training-free framework for high-quality 3D morphing, even across categories. It leverages Structured Latent representations with novel attention mechanisms MCA, TFSA for structural coherence and temporal consistency. This achieves state-of-the-art results and supports advance...
🔹 Publication Date: Published on Jan 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00204
• PDF: https://arxiv.org/pdf/2601.00204
• Project Page: https://xiaokunsun.github.io/MorphAny3D.github.io
• Github: https://github.com/XiaokunSun/MorphAny3D
==================================
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#3DMorphing #ComputerGraphics #DeepLearning #StructuredLatent #AIResearch
📝 Summary:
MorphAny3D offers a training-free framework for high-quality 3D morphing, even across categories. It leverages Structured Latent representations with novel attention mechanisms MCA, TFSA for structural coherence and temporal consistency. This achieves state-of-the-art results and supports advance...
🔹 Publication Date: Published on Jan 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00204
• PDF: https://arxiv.org/pdf/2601.00204
• Project Page: https://xiaokunsun.github.io/MorphAny3D.github.io
• Github: https://github.com/XiaokunSun/MorphAny3D
==================================
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#3DMorphing #ComputerGraphics #DeepLearning #StructuredLatent #AIResearch
✨Nested Learning: The Illusion of Deep Learning Architectures
📝 Summary:
Nested Learning NL models ML as nested optimization problems. It enables expressive algorithms for higher-order learning and continual adaptation, introducing optimizers, self-modifying models, and continuum memory systems.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24695
• PDF: https://arxiv.org/pdf/2512.24695
==================================
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#NestedLearning #MachineLearning #DeepLearning #Optimization #AI
📝 Summary:
Nested Learning NL models ML as nested optimization problems. It enables expressive algorithms for higher-order learning and continual adaptation, introducing optimizers, self-modifying models, and continuum memory systems.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24695
• PDF: https://arxiv.org/pdf/2512.24695
==================================
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#NestedLearning #MachineLearning #DeepLearning #Optimization #AI
nature papers: 1400$
Q1 and Q2 papers 900$
Q3 and Q4 papers 500$
Doctoral thesis (complete) 700$
M.S thesis 300$
paper simulation 200$
Contact me
https://t.me/m/-nTmpj5vYzNk
Q1 and Q2 papers 900$
Q3 and Q4 papers 500$
Doctoral thesis (complete) 700$
M.S thesis 300$
paper simulation 200$
Contact me
https://t.me/m/-nTmpj5vYzNk
ML Research Hub pinned «nature papers: 1400$ Q1 and Q2 papers 900$ Q3 and Q4 papers 500$ Doctoral thesis (complete) 700$ M.S thesis 300$ paper simulation 200$ Contact me https://t.me/m/-nTmpj5vYzNk»
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✨AdaGaR: Adaptive Gabor Representation for Dynamic Scene Reconstruction
📝 Summary:
AdaGaR reconstructs dynamic 3D scenes from monocular video. It introduces an Adaptive Gabor Representation for detail and stability, and Cubic Hermite Splines for temporal continuity. This method achieves state-of-the-art performance.
🔹 Publication Date: Published on Jan 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00796
• PDF: https://arxiv.org/pdf/2601.00796
• Project Page: https://jiewenchan.github.io/AdaGaR/
==================================
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✓ https://t.me/DataScienceT
#3DReconstruction #ComputerVision #DynamicScenes #MonocularVideo #GaborRepresentation
📝 Summary:
AdaGaR reconstructs dynamic 3D scenes from monocular video. It introduces an Adaptive Gabor Representation for detail and stability, and Cubic Hermite Splines for temporal continuity. This method achieves state-of-the-art performance.
🔹 Publication Date: Published on Jan 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00796
• PDF: https://arxiv.org/pdf/2601.00796
• Project Page: https://jiewenchan.github.io/AdaGaR/
==================================
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#3DReconstruction #ComputerVision #DynamicScenes #MonocularVideo #GaborRepresentation
❤1
✨InfoSynth: Information-Guided Benchmark Synthesis for LLMs
📝 Summary:
InfoSynth automatically generates novel and diverse coding benchmarks for LLMs. It uses information-theoretic metrics and genetic algorithms to create scalable self-verifying problems, overcoming manual effort and training data contamination.
🔹 Publication Date: Published on Jan 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00575
• PDF: https://arxiv.org/pdf/2601.00575
• Project Page: https://ishirgarg.github.io/infosynth_web/
• Github: https://github.com/ishirgarg/infosynth
==================================
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#LLM #AI #Benchmarking #GenerativeAI #DeepLearning
📝 Summary:
InfoSynth automatically generates novel and diverse coding benchmarks for LLMs. It uses information-theoretic metrics and genetic algorithms to create scalable self-verifying problems, overcoming manual effort and training data contamination.
🔹 Publication Date: Published on Jan 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00575
• PDF: https://arxiv.org/pdf/2601.00575
• Project Page: https://ishirgarg.github.io/infosynth_web/
• Github: https://github.com/ishirgarg/infosynth
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#LLM #AI #Benchmarking #GenerativeAI #DeepLearning
✨Diversity or Precision? A Deep Dive into Next Token Prediction
📝 Summary:
This paper proposes a pre-training objective that reshapes the token-output distribution for better RL exploration. It uses reward-shaping to balance diversity and precision in next-token prediction. Contrary to intuition, a precision-oriented prior surprisingly yields a superior exploration spac...
🔹 Publication Date: Published on Dec 28, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22955
• PDF: https://arxiv.org/pdf/2512.22955
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#NextTokenPrediction #ReinforcementLearning #LLM #NLP #AIResearch
📝 Summary:
This paper proposes a pre-training objective that reshapes the token-output distribution for better RL exploration. It uses reward-shaping to balance diversity and precision in next-token prediction. Contrary to intuition, a precision-oriented prior surprisingly yields a superior exploration spac...
🔹 Publication Date: Published on Dec 28, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22955
• PDF: https://arxiv.org/pdf/2512.22955
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#NextTokenPrediction #ReinforcementLearning #LLM #NLP #AIResearch
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✨OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
📝 Summary:
OmniVCus introduces a system for feedforward multi-subject video customization with multimodal controls. It proposes a data pipeline, VideoCus-Factory, and a diffusion Transformer framework with novel embedding mechanisms. This enables more subjects and precise editing, significantly outperformin...
🔹 Publication Date: Published on Jun 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.23361
• PDF: https://arxiv.org/pdf/2506.23361
• Project Page: https://caiyuanhao1998.github.io/project/OmniVCus/
• Github: https://github.com/caiyuanhao1998/Open-OmniVCus
🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/OmniVCus
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Test
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train
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#VideoGeneration #DiffusionModels #MultimodalAI #DeepLearning #ComputerVision
📝 Summary:
OmniVCus introduces a system for feedforward multi-subject video customization with multimodal controls. It proposes a data pipeline, VideoCus-Factory, and a diffusion Transformer framework with novel embedding mechanisms. This enables more subjects and precise editing, significantly outperformin...
🔹 Publication Date: Published on Jun 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.23361
• PDF: https://arxiv.org/pdf/2506.23361
• Project Page: https://caiyuanhao1998.github.io/project/OmniVCus/
• Github: https://github.com/caiyuanhao1998/Open-OmniVCus
🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/OmniVCus
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Test
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train
==================================
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#VideoGeneration #DiffusionModels #MultimodalAI #DeepLearning #ComputerVision
arXiv.org
OmniVCus: Feedforward Subject-driven Video Customization with...
Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Another challenging...
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