✨RubiCap: Rubric-Guided Reinforcement Learning for Dense Image Captioning
📝 Summary:
RubiCap introduces a reinforcement learning framework for dense image captioning, using LLM-generated rubrics to provide fine-grained reward signals. This method overcomes limitations of supervised learning and prior RL, achieving superior performance on benchmarks and improving vision-language m...
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09160
• PDF: https://arxiv.org/pdf/2603.09160
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
RubiCap introduces a reinforcement learning framework for dense image captioning, using LLM-generated rubrics to provide fine-grained reward signals. This method overcomes limitations of supervised learning and prior RL, achieving superior performance on benchmarks and improving vision-language m...
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09160
• PDF: https://arxiv.org/pdf/2603.09160
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
📝 Summary:
Large pretrained models have a high density of task-specific experts around their weights. This enables a simple post-training method of random sampling and ensembling to be competitive with complex optimization techniques.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12228
• PDF: https://arxiv.org/pdf/2603.12228
• Project Page: https://thickets.mit.edu
• Github: https://github.com/sunrainyg/RandOpt
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Large pretrained models have a high density of task-specific experts around their weights. This enables a simple post-training method of random sampling and ensembling to be competitive with complex optimization techniques.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12228
• PDF: https://arxiv.org/pdf/2603.12228
• Project Page: https://thickets.mit.edu
• Github: https://github.com/sunrainyg/RandOpt
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨CREATE: Testing LLMs for Associative Creativity
📝 Summary:
CREATE is a new benchmark to evaluate LLMs associative creativity by generating diverse and specific concept paths. It scores models on path specificity, diversity, and quantity. Strong models perform well but saturation is hard to achieve, and thinking models dont always improve performance.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09970
• PDF: https://arxiv.org/pdf/2603.09970
• Project Page: https://manyawadhwa.github.io/projects/create/
• Github: https://github.com/ManyaWadhwa/CREATE
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
CREATE is a new benchmark to evaluate LLMs associative creativity by generating diverse and specific concept paths. It scores models on path specificity, diversity, and quantity. Strong models perform well but saturation is hard to achieve, and thinking models dont always improve performance.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09970
• PDF: https://arxiv.org/pdf/2603.09970
• Project Page: https://manyawadhwa.github.io/projects/create/
• Github: https://github.com/ManyaWadhwa/CREATE
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨WaDi: Weight Direction-aware Distillation for One-step Image Synthesis
📝 Summary:
Diffusion model inference is slow. WaDi focuses on weight direction changes during distillation to accelerate models into efficient one-step generators. This achieves state-of-the-art quality with significantly fewer parameters and broad versatility.
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08258
• PDF: https://arxiv.org/pdf/2603.08258
• Github: https://github.com/gudaochangsheng/WaDi
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#DiffusionModels #ImageSynthesis #ModelAcceleration #DeepLearning #AIResearch
📝 Summary:
Diffusion model inference is slow. WaDi focuses on weight direction changes during distillation to accelerate models into efficient one-step generators. This achieves state-of-the-art quality with significantly fewer parameters and broad versatility.
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08258
• PDF: https://arxiv.org/pdf/2603.08258
• Github: https://github.com/gudaochangsheng/WaDi
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#DiffusionModels #ImageSynthesis #ModelAcceleration #DeepLearning #AIResearch
✨AutoFigure-Edit: Generating Editable Scientific Illustration
📝 Summary:
AutoFigure-Edit generates editable scientific illustrations from text and reference images. It improves editability, style control, and efficiency by combining long-context understanding and native SVG editing for high-quality, flexible refinement.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.06674
• PDF: https://arxiv.org/pdf/2603.06674
• Project Page: https://deepscientist.cc/
• Github: https://github.com/ResearAI/AutoFigure-Edit
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #ScientificIllustration #ImageGeneration #SVG #DeepLearning
📝 Summary:
AutoFigure-Edit generates editable scientific illustrations from text and reference images. It improves editability, style control, and efficiency by combining long-context understanding and native SVG editing for high-quality, flexible refinement.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.06674
• PDF: https://arxiv.org/pdf/2603.06674
• Project Page: https://deepscientist.cc/
• Github: https://github.com/ResearAI/AutoFigure-Edit
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #ScientificIllustration #ImageGeneration #SVG #DeepLearning
✨Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
📝 Summary:
Nemotron 3 Nano is an efficient Mixture-of-Experts hybrid Mamba-Transformer model. It achieves better accuracy and up to 3.3x higher inference throughput than similar models, while using fewer active parameters and supporting 1M token contexts for enhanced agentic reasoning.
🔹 Publication Date: Published on Dec 23, 2025
🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/nemotron-3-nano-open-efficient-mixture-of-experts-hybrid-mamba-transformer-model-for-agentic-reasoning-1072-37bf9190
• PDF: https://arxiv.org/pdf/2512.20848
• Github: https://github.com/NVIDIA-NeMo/Nemotron
🔹 Models citing this paper:
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
✨ Spaces citing this paper:
• https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard
• https://huggingface.co/spaces/hadadxyz/ai
• https://huggingface.co/spaces/hadadxyz/blog
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#Nemotron3Nano #MixtureOfExperts #MambaTransformer #AgenticAI #LLM
📝 Summary:
Nemotron 3 Nano is an efficient Mixture-of-Experts hybrid Mamba-Transformer model. It achieves better accuracy and up to 3.3x higher inference throughput than similar models, while using fewer active parameters and supporting 1M token contexts for enhanced agentic reasoning.
🔹 Publication Date: Published on Dec 23, 2025
🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/nemotron-3-nano-open-efficient-mixture-of-experts-hybrid-mamba-transformer-model-for-agentic-reasoning-1072-37bf9190
• PDF: https://arxiv.org/pdf/2512.20848
• Github: https://github.com/NVIDIA-NeMo/Nemotron
🔹 Models citing this paper:
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
✨ Spaces citing this paper:
• https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard
• https://huggingface.co/spaces/hadadxyz/ai
• https://huggingface.co/spaces/hadadxyz/blog
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#Nemotron3Nano #MixtureOfExperts #MambaTransformer #AgenticAI #LLM
Arxivlens
Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning - AI Research Paper Analysis…
AI-powered analysis of 'Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning'. We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained…
❤2👍1
🚀 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.iss.one/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.
https://t.iss.one/DataScienceM
🧠 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.
https://t.iss.one/DataScience4
🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ
💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1
🧑🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC
😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT
💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab
🖊 Data Science Jupyter Notebooks
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.
https://t.iss.one/DataScienceN
📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.iss.one/DataScienceV
📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX
🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.iss.one/Python53
⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.iss.one/DataScienceY
━━━━━━━━━━━━━━━━━━
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 – 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 – 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 – 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 Notebooks—insights, 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 – uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.iss.one/Python53
Professional Academic Writing & Simulation Services
https://t.iss.one/DataScienceY
━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho
Please open Telegram to view this post
VIEW IN TELEGRAM
✨Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
📝 Summary:
Vision-R1 is a reasoning MLLM enhancing multimodal reasoning via Reinforcement Learning. It leverages a large, AI-generated multimodal CoT dataset and new training strategies to refine reasoning. This achieves high accuracy on multimodal math benchmarks.
🔹 Publication Date: Published on Mar 9, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.06749
• PDF: https://arxiv.org/pdf/2503.06749
• Github: https://github.com/Osilly/Vision-R1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Yuting6/ttrl
• https://huggingface.co/datasets/LoadingBFX/GeoQA-PLUS-aug-train-Vision-R1-cot-rewrite
• https://huggingface.co/datasets/LoadingBFX/GeoQA-train-Vision-R1-cot-rewrite
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MLLM #ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence
📝 Summary:
Vision-R1 is a reasoning MLLM enhancing multimodal reasoning via Reinforcement Learning. It leverages a large, AI-generated multimodal CoT dataset and new training strategies to refine reasoning. This achieves high accuracy on multimodal math benchmarks.
🔹 Publication Date: Published on Mar 9, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.06749
• PDF: https://arxiv.org/pdf/2503.06749
• Github: https://github.com/Osilly/Vision-R1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Yuting6/ttrl
• https://huggingface.co/datasets/LoadingBFX/GeoQA-PLUS-aug-train-Vision-R1-cot-rewrite
• https://huggingface.co/datasets/LoadingBFX/GeoQA-train-Vision-R1-cot-rewrite
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MLLM #ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence
❤1
✨LMEB: Long-horizon Memory Embedding Benchmark
📝 Summary:
LMEB is a new benchmark for evaluating embedding models' long-horizon memory retrieval abilities, a gap in traditional benchmarks. It assesses complex memory types and reveals that performance in standard passage retrieval does not generalize to these challenging scenarios.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12572
• PDF: https://arxiv.org/pdf/2603.12572
✨ Datasets citing this paper:
• https://huggingface.co/datasets/KaLM-Embedding/LMEB
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#EmbeddingModels #MemoryRetrieval #Benchmarks #MachineLearning #AIResearch
📝 Summary:
LMEB is a new benchmark for evaluating embedding models' long-horizon memory retrieval abilities, a gap in traditional benchmarks. It assesses complex memory types and reveals that performance in standard passage retrieval does not generalize to these challenging scenarios.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12572
• PDF: https://arxiv.org/pdf/2603.12572
✨ Datasets citing this paper:
• https://huggingface.co/datasets/KaLM-Embedding/LMEB
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#EmbeddingModels #MemoryRetrieval #Benchmarks #MachineLearning #AIResearch
✨VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
📝 Summary:
VQQA is a multi-agent framework that uses VLM critiques as semantic gradients for efficient, black-box video generation optimization via natural language. It resolves visual artifacts, significantly improving video quality for text-to-video and image-to-video tasks.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12310
• PDF: https://arxiv.org/pdf/2603.12310
• Project Page: https://yiwen-song.github.io/vqqa/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#VideoGeneration #AIAgents #VisionLanguageModels #GenerativeAI #MachineLearning
📝 Summary:
VQQA is a multi-agent framework that uses VLM critiques as semantic gradients for efficient, black-box video generation optimization via natural language. It resolves visual artifacts, significantly improving video quality for text-to-video and image-to-video tasks.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12310
• PDF: https://arxiv.org/pdf/2603.12310
• Project Page: https://yiwen-song.github.io/vqqa/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#VideoGeneration #AIAgents #VisionLanguageModels #GenerativeAI #MachineLearning
✨daVinci-Env: Open SWE Environment Synthesis at Scale
📝 Summary:
OpenSWE is the largest open framework for training software engineering agents, featuring 45,320 executable Python environments. It achieves state-of-the-art performance on SWE-bench Verified and shows substantial out-of-domain reasoning improvements.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13023
• PDF: https://arxiv.org/pdf/2603.13023
• Github: https://github.com/GAIR-NLP/OpenSWE
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#SoftwareEngineering #AIagents #MachineLearning #OpenSWE #DeepLearning
📝 Summary:
OpenSWE is the largest open framework for training software engineering agents, featuring 45,320 executable Python environments. It achieves state-of-the-art performance on SWE-bench Verified and shows substantial out-of-domain reasoning improvements.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13023
• PDF: https://arxiv.org/pdf/2603.13023
• Github: https://github.com/GAIR-NLP/OpenSWE
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#SoftwareEngineering #AIagents #MachineLearning #OpenSWE #DeepLearning
✨From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space
📝 Summary:
Multi-View GRPO enhances text-to-image flow model alignment by expanding condition space for richer reward mapping and improved sample relationship exploration. AI-generated summary Group Relative Pol...
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12648
• PDF: https://arxiv.org/pdf/2603.12648
• Project Page: https://bujiazi.github.io/mvgrpo.github.io/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Multi-View GRPO enhances text-to-image flow model alignment by expanding condition space for richer reward mapping and improved sample relationship exploration. AI-generated summary Group Relative Pol...
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12648
• PDF: https://arxiv.org/pdf/2603.12648
• Project Page: https://bujiazi.github.io/mvgrpo.github.io/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents
📝 Summary:
The Budget-Aware Value Tree BAVT optimizes LLM agent reasoning by dynamically balancing exploration and exploitation based on remaining compute. It uses a budget-conditioned node selection and residual value predictor for efficient search, outperforming brute-force methods with 4x less resources.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12634
• PDF: https://arxiv.org/pdf/2603.12634
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMAgents #AIResearch #Optimization #EfficientAI #ValueTreeSearch
📝 Summary:
The Budget-Aware Value Tree BAVT optimizes LLM agent reasoning by dynamically balancing exploration and exploitation based on remaining compute. It uses a budget-conditioned node selection and residual value predictor for efficient search, outperforming brute-force methods with 4x less resources.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12634
• PDF: https://arxiv.org/pdf/2603.12634
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMAgents #AIResearch #Optimization #EfficientAI #ValueTreeSearch
❤2
✨Visual-ERM: Reward Modeling for Visual Equivalence
📝 Summary:
Visual-ERM is a multimodal generative reward model providing fine-grained visual feedback for vision-to-code tasks. It significantly improves reinforcement learning performance for chart, table, and SVG parsing, demonstrating that fine-grained visual supervision is essential.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13224
• PDF: https://arxiv.org/pdf/2603.13224
• Github: https://github.com/InternLM/Visual-ERM
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#ReinforcementLearning #ComputerVision #GenerativeAI #AI #DataScience
📝 Summary:
Visual-ERM is a multimodal generative reward model providing fine-grained visual feedback for vision-to-code tasks. It significantly improves reinforcement learning performance for chart, table, and SVG parsing, demonstrating that fine-grained visual supervision is essential.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13224
• PDF: https://arxiv.org/pdf/2603.13224
• Github: https://github.com/InternLM/Visual-ERM
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#ReinforcementLearning #ComputerVision #GenerativeAI #AI #DataScience
✨SimRecon: SimReady Compositional Scene Reconstruction from Real Videos
📝 Summary:
SimRecon reconstructs cluttered scenes from real videos using a Perception-Generation-Simulation pipeline. It employs Active Viewpoint Optimization for visual fidelity and a Scene Graph Synthesizer for physical plausibility. This enables superior compositional scene representations for simulation...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02133
• PDF: https://arxiv.org/pdf/2603.02133
• Project Page: https://xiac20.github.io/SimRecon/
• Github: https://github.com/xiac20/SimRecon
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#SceneReconstruction #ComputerVision #AI #Simulation #3DReconstruction
📝 Summary:
SimRecon reconstructs cluttered scenes from real videos using a Perception-Generation-Simulation pipeline. It employs Active Viewpoint Optimization for visual fidelity and a Scene Graph Synthesizer for physical plausibility. This enables superior compositional scene representations for simulation...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02133
• PDF: https://arxiv.org/pdf/2603.02133
• Project Page: https://xiac20.github.io/SimRecon/
• Github: https://github.com/xiac20/SimRecon
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#SceneReconstruction #ComputerVision #AI #Simulation #3DReconstruction
✨LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation
📝 Summary:
LookaheadKV enhances KV cache eviction in LLMs by accurately predicting future importance scores. It uses parameter-efficient modules, avoiding costly draft generation while maintaining high accuracy. This lightweight method significantly reduces eviction overhead and speeds up inference.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10899
• PDF: https://arxiv.org/pdf/2603.10899
• Github: https://github.com/SamsungLabs/LookaheadKV
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #KVCache #ModelOptimization #DeepLearning #AI
📝 Summary:
LookaheadKV enhances KV cache eviction in LLMs by accurately predicting future importance scores. It uses parameter-efficient modules, avoiding costly draft generation while maintaining high accuracy. This lightweight method significantly reduces eviction overhead and speeds up inference.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10899
• PDF: https://arxiv.org/pdf/2603.10899
• Github: https://github.com/SamsungLabs/LookaheadKV
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #KVCache #ModelOptimization #DeepLearning #AI
✨Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation
📝 Summary:
Cheers is a unified multimodal model that decouples visual details from semantic representations for efficient joint optimization of understanding and generation. It employs a vision tokenizer, LLM-based Transformer, and cascaded flow matching. Cheers achieves state-of-the-art performance with 4x...
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12793
• PDF: https://arxiv.org/pdf/2603.12793
• Project Page: https://huggingface.co/ai9stars/Cheers
• Github: https://github.com/AI9Stars/Cheers
🔹 Models citing this paper:
• https://huggingface.co/ai9stars/Cheers
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MultimodalAI #LLM #ComputerVision #GenerativeAI #AIResearch
📝 Summary:
Cheers is a unified multimodal model that decouples visual details from semantic representations for efficient joint optimization of understanding and generation. It employs a vision tokenizer, LLM-based Transformer, and cascaded flow matching. Cheers achieves state-of-the-art performance with 4x...
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12793
• PDF: https://arxiv.org/pdf/2603.12793
• Project Page: https://huggingface.co/ai9stars/Cheers
• Github: https://github.com/AI9Stars/Cheers
🔹 Models citing this paper:
• https://huggingface.co/ai9stars/Cheers
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MultimodalAI #LLM #ComputerVision #GenerativeAI #AIResearch
✨OmniForcing: Unleashing Real-time Joint Audio-Visual Generation
📝 Summary:
OmniForcing transforms slow bidirectional audio-visual diffusion models into fast, real-time streaming generators. It tackles training instability and synchronization by using asymmetric alignment, a global prefix, and an audio sink token. This enables high-fidelity, synchronized generation at 25...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11647
• PDF: https://arxiv.org/pdf/2603.11647
• Project Page: https://omniforcing.com/
• Github: https://github.com/OmniForcing/OmniForcing
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#GenerativeAI #AudioVisual #RealtimeAI #DiffusionModels #DeepLearning
📝 Summary:
OmniForcing transforms slow bidirectional audio-visual diffusion models into fast, real-time streaming generators. It tackles training instability and synchronization by using asymmetric alignment, a global prefix, and an audio sink token. This enables high-fidelity, synchronized generation at 25...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11647
• PDF: https://arxiv.org/pdf/2603.11647
• Project Page: https://omniforcing.com/
• Github: https://github.com/OmniForcing/OmniForcing
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#GenerativeAI #AudioVisual #RealtimeAI #DiffusionModels #DeepLearning
✨HybridStitch: Pixel and Timestep Level Model Stitching for Diffusion Acceleration
📝 Summary:
HybridStitch accelerates text-to-image generation by intelligently combining large and small diffusion models. It uses the large model for complex image regions and the smaller model for simpler parts, even within a single denoising step. This approach speeds up generation by 1.83x on Stable Diff...
🔹 Publication Date: Published on Mar 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07815
• PDF: https://arxiv.org/pdf/2603.07815
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
HybridStitch accelerates text-to-image generation by intelligently combining large and small diffusion models. It uses the large model for complex image regions and the smaller model for simpler parts, even within a single denoising step. This approach speeds up generation by 1.83x on Stable Diff...
🔹 Publication Date: Published on Mar 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07815
• PDF: https://arxiv.org/pdf/2603.07815
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning
📝 Summary:
MM-CondChain benchmark evaluates multimodal large language models on deep compositional visual reasoning through multi-layer conditional workflows with mechanically verifiable conditions. AI-generated...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12266
• PDF: https://arxiv.org/pdf/2603.12266
• Project Page: https://accio-lab.github.io/MM-CondChain
• Github: https://accio-lab.github.io/MM-CondChain
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MM-CondChain benchmark evaluates multimodal large language models on deep compositional visual reasoning through multi-layer conditional workflows with mechanically verifiable conditions. AI-generated...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12266
• PDF: https://arxiv.org/pdf/2603.12266
• Project Page: https://accio-lab.github.io/MM-CondChain
• Github: https://accio-lab.github.io/MM-CondChain
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research