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.
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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.
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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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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.iss.one/CodeProgrammer
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.iss.one/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.iss.one/DataScience4
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Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
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An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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β¨Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
π 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.
πΉ Publication Date: Published on Mar 9, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2503.06749
β’ PDF: https://arxiv.org/pdf/2503.06749
β’ Github: https://github.com/Osilly/Vision-R1
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Yuting6/ttrl
β’ https://huggingface.co/datasets/LoadingBFX/GeoQA-PLUS-aug-train-Vision-R1-cot-rewrite
β’ https://huggingface.co/datasets/LoadingBFX/GeoQA-train-Vision-R1-cot-rewrite
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MLLM #ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence
π 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.
πΉ Publication Date: Published on Mar 9, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2503.06749
β’ PDF: https://arxiv.org/pdf/2503.06749
β’ Github: https://github.com/Osilly/Vision-R1
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Yuting6/ttrl
β’ https://huggingface.co/datasets/LoadingBFX/GeoQA-PLUS-aug-train-Vision-R1-cot-rewrite
β’ https://huggingface.co/datasets/LoadingBFX/GeoQA-train-Vision-R1-cot-rewrite
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MLLM #ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence
β€1
β¨LMEB: Long-horizon Memory Embedding Benchmark
π 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.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12572
β’ PDF: https://arxiv.org/pdf/2603.12572
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/KaLM-Embedding/LMEB
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#EmbeddingModels #MemoryRetrieval #Benchmarks #MachineLearning #AIResearch
π 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.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12572
β’ PDF: https://arxiv.org/pdf/2603.12572
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/KaLM-Embedding/LMEB
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#EmbeddingModels #MemoryRetrieval #Benchmarks #MachineLearning #AIResearch
β¨VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
π 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.
πΉ Publication Date: Published on Mar 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12310
β’ PDF: https://arxiv.org/pdf/2603.12310
β’ Project Page: https://yiwen-song.github.io/vqqa/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VideoGeneration #AIAgents #VisionLanguageModels #GenerativeAI #MachineLearning
π 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.
πΉ Publication Date: Published on Mar 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12310
β’ PDF: https://arxiv.org/pdf/2603.12310
β’ Project Page: https://yiwen-song.github.io/vqqa/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VideoGeneration #AIAgents #VisionLanguageModels #GenerativeAI #MachineLearning
β¨daVinci-Env: Open SWE Environment Synthesis at Scale
π 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.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13023
β’ PDF: https://arxiv.org/pdf/2603.13023
β’ Github: https://github.com/GAIR-NLP/OpenSWE
==================================
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β https://t.iss.one/DataScienceT
#SoftwareEngineering #AIagents #MachineLearning #OpenSWE #DeepLearning
π 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.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13023
β’ PDF: https://arxiv.org/pdf/2603.13023
β’ Github: https://github.com/GAIR-NLP/OpenSWE
==================================
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β https://t.iss.one/DataScienceT
#SoftwareEngineering #AIagents #MachineLearning #OpenSWE #DeepLearning
β¨From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space
π 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...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12648
β’ PDF: https://arxiv.org/pdf/2603.12648
β’ Project Page: https://bujiazi.github.io/mvgrpo.github.io/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π 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...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12648
β’ PDF: https://arxiv.org/pdf/2603.12648
β’ Project Page: https://bujiazi.github.io/mvgrpo.github.io/
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents
π 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.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12634
β’ PDF: https://arxiv.org/pdf/2603.12634
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LLMAgents #AIResearch #Optimization #EfficientAI #ValueTreeSearch
π 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.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12634
β’ PDF: https://arxiv.org/pdf/2603.12634
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LLMAgents #AIResearch #Optimization #EfficientAI #ValueTreeSearch
β€2
β¨Visual-ERM: Reward Modeling for Visual Equivalence
π 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.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13224
β’ PDF: https://arxiv.org/pdf/2603.13224
β’ Github: https://github.com/InternLM/Visual-ERM
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#ReinforcementLearning #ComputerVision #GenerativeAI #AI #DataScience
π 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.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13224
β’ PDF: https://arxiv.org/pdf/2603.13224
β’ Github: https://github.com/InternLM/Visual-ERM
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#ReinforcementLearning #ComputerVision #GenerativeAI #AI #DataScience
β¨SimRecon: SimReady Compositional Scene Reconstruction from Real Videos
π 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...
πΉ Publication Date: Published on Mar 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.02133
β’ PDF: https://arxiv.org/pdf/2603.02133
β’ Project Page: https://xiac20.github.io/SimRecon/
β’ Github: https://github.com/xiac20/SimRecon
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#SceneReconstruction #ComputerVision #AI #Simulation #3DReconstruction
π 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...
πΉ Publication Date: Published on Mar 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.02133
β’ PDF: https://arxiv.org/pdf/2603.02133
β’ Project Page: https://xiac20.github.io/SimRecon/
β’ Github: https://github.com/xiac20/SimRecon
==================================
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β https://t.iss.one/DataScienceT
#SceneReconstruction #ComputerVision #AI #Simulation #3DReconstruction
β¨LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation
π 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.
πΉ Publication Date: Published on Mar 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.10899
β’ PDF: https://arxiv.org/pdf/2603.10899
β’ Github: https://github.com/SamsungLabs/LookaheadKV
==================================
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β https://t.iss.one/DataScienceT
#LLM #KVCache #ModelOptimization #DeepLearning #AI
π 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.
πΉ Publication Date: Published on Mar 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.10899
β’ PDF: https://arxiv.org/pdf/2603.10899
β’ Github: https://github.com/SamsungLabs/LookaheadKV
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LLM #KVCache #ModelOptimization #DeepLearning #AI
β¨Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation
π 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...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12793
β’ PDF: https://arxiv.org/pdf/2603.12793
β’ Project Page: https://huggingface.co/ai9stars/Cheers
β’ Github: https://github.com/AI9Stars/Cheers
πΉ Models citing this paper:
β’ https://huggingface.co/ai9stars/Cheers
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MultimodalAI #LLM #ComputerVision #GenerativeAI #AIResearch
π 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...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12793
β’ PDF: https://arxiv.org/pdf/2603.12793
β’ Project Page: https://huggingface.co/ai9stars/Cheers
β’ Github: https://github.com/AI9Stars/Cheers
πΉ Models citing this paper:
β’ https://huggingface.co/ai9stars/Cheers
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#MultimodalAI #LLM #ComputerVision #GenerativeAI #AIResearch
β¨OmniForcing: Unleashing Real-time Joint Audio-Visual Generation
π 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...
πΉ Publication Date: Published on Mar 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.11647
β’ PDF: https://arxiv.org/pdf/2603.11647
β’ Project Page: https://omniforcing.com/
β’ Github: https://github.com/OmniForcing/OmniForcing
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#GenerativeAI #AudioVisual #RealtimeAI #DiffusionModels #DeepLearning
π 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...
πΉ Publication Date: Published on Mar 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.11647
β’ PDF: https://arxiv.org/pdf/2603.11647
β’ Project Page: https://omniforcing.com/
β’ Github: https://github.com/OmniForcing/OmniForcing
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#GenerativeAI #AudioVisual #RealtimeAI #DiffusionModels #DeepLearning
β¨HybridStitch: Pixel and Timestep Level Model Stitching for Diffusion Acceleration
π 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...
πΉ Publication Date: Published on Mar 8
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.07815
β’ PDF: https://arxiv.org/pdf/2603.07815
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π 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...
πΉ Publication Date: Published on Mar 8
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.07815
β’ PDF: https://arxiv.org/pdf/2603.07815
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning
π 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...
πΉ Publication Date: Published on Mar 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12266
β’ PDF: https://arxiv.org/pdf/2603.12266
β’ Project Page: https://accio-lab.github.io/MM-CondChain
β’ Github: https://accio-lab.github.io/MM-CondChain
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π 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...
πΉ Publication Date: Published on Mar 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12266
β’ PDF: https://arxiv.org/pdf/2603.12266
β’ Project Page: https://accio-lab.github.io/MM-CondChain
β’ Github: https://accio-lab.github.io/MM-CondChain
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Steve-Evolving: Open-World Embodied Self-Evolution via Fine-Grained Diagnosis and Dual-Track Knowledge Distillation
π Summary:
A self-evolving framework for open-world embodied agents that couples execution diagnosis with knowledge distillation to improve long-horizon task performance through structured experience organizatio...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13131
β’ PDF: https://arxiv.org/pdf/2603.13131
β’ Github: https://github.com/xzw-ustc/Steve-Evolving
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
A self-evolving framework for open-world embodied agents that couples execution diagnosis with knowledge distillation to improve long-horizon task performance through structured experience organizatio...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13131
β’ PDF: https://arxiv.org/pdf/2603.13131
β’ Github: https://github.com/xzw-ustc/Steve-Evolving
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration
π Summary:
Video generative models can be adapted for image restoration tasks with minimal training data by treating restoration as a progressive generative process. AI-generated summary Large-scale video genera...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13089
β’ PDF: https://arxiv.org/pdf/2603.13089
β’ Project Page: https://zhengsh123.github.io/V-Bridge/
β’ Github: https://github.com/Zhengsh123/V-Bridge
πΉ Models citing this paper:
β’ https://huggingface.co/desimfj/V-Bridge
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Video generative models can be adapted for image restoration tasks with minimal training data by treating restoration as a progressive generative process. AI-generated summary Large-scale video genera...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13089
β’ PDF: https://arxiv.org/pdf/2603.13089
β’ Project Page: https://zhengsh123.github.io/V-Bridge/
β’ Github: https://github.com/Zhengsh123/V-Bridge
πΉ Models citing this paper:
β’ https://huggingface.co/desimfj/V-Bridge
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Multimodal OCR: Parse Anything from Documents
π Summary:
MOCR is a multimodal OCR approach that jointly parses text and graphics into unified representations, enabling structured document reconstruction and supporting end-to-end training with semantic relat...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13032
β’ PDF: https://arxiv.org/pdf/2603.13032
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
MOCR is a multimodal OCR approach that jointly parses text and graphics into unified representations, enabling structured document reconstruction and supporting end-to-end training with semantic relat...
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.13032
β’ PDF: https://arxiv.org/pdf/2603.13032
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Detecting Intrinsic and Instrumental Self-Preservation in Autonomous Agents: The Unified Continuation-Interest Protocol
π Summary:
A novel detection framework called UCIP uses quantum statistical mechanics-inspired methods to distinguish between autonomous agents with genuine continuation objectives versus those pursuing continua...
πΉ Publication Date: Published on Mar 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.11382
β’ PDF: https://arxiv.org/pdf/2603.11382
β’ Project Page: https://lab.christopheraltman.com/
β’ Github: https://github.com/christopher-altman/persistence-signal-detector
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
A novel detection framework called UCIP uses quantum statistical mechanics-inspired methods to distinguish between autonomous agents with genuine continuation objectives versus those pursuing continua...
πΉ Publication Date: Published on Mar 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.11382
β’ PDF: https://arxiv.org/pdf/2603.11382
β’ Project Page: https://lab.christopheraltman.com/
β’ Github: https://github.com/christopher-altman/persistence-signal-detector
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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β¨Can Vision-Language Models Solve the Shell Game?
π Summary:
Vision-Language Models struggle with tracking identical visual entities, performing poorly on the VET-Bench testbed. Researchers propose Spatiotemporal Grounded Chain-of-Thought SGCoT to generate object trajectories as intermediate states. This method achieves over 90% accuracy, showing VLMs can ...
πΉ Publication Date: Published on Mar 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.08436
β’ PDF: https://arxiv.org/pdf/2603.08436
β’ Project Page: https://vetbench.github.io/
β’ Github: https://github.com/liutiedong/shellgame
πΉ Models citing this paper:
β’ https://huggingface.co/tiedong/Molmo2-SGCoT
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/tiedong/vetbench
β’ https://huggingface.co/datasets/tiedong/Molmo2-SGCoT
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Vision-Language Models struggle with tracking identical visual entities, performing poorly on the VET-Bench testbed. Researchers propose Spatiotemporal Grounded Chain-of-Thought SGCoT to generate object trajectories as intermediate states. This method achieves over 90% accuracy, showing VLMs can ...
πΉ Publication Date: Published on Mar 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.08436
β’ PDF: https://arxiv.org/pdf/2603.08436
β’ Project Page: https://vetbench.github.io/
β’ Github: https://github.com/liutiedong/shellgame
πΉ Models citing this paper:
β’ https://huggingface.co/tiedong/Molmo2-SGCoT
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/tiedong/vetbench
β’ https://huggingface.co/datasets/tiedong/Molmo2-SGCoT
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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β¨HomeSafe-Bench: Evaluating Vision-Language Models on Unsafe Action Detection for Embodied Agents in Household Scenarios
π Summary:
HomeSafe-Bench presents a benchmark for vision-language models to detect unsafe actions by embodied agents in household settings. It also introduces HD-Guard, a hierarchical dual-brain architecture balancing real-time safety monitoring with detection accuracy.
πΉ Publication Date: Published on Mar 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.11975
β’ PDF: https://arxiv.org/pdf/2603.11975
β’ Project Page: https://pujiayue.github.io/homesafe-bench.github.io/
β’ Github: https://github.com/pujiayue/HomeSafe-Bench
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/pujiayue/HomeSafe-Bench-Leaderboard
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VisionLanguageModels #EmbodiedAI #AISafety #Robotics #Benchmark
π Summary:
HomeSafe-Bench presents a benchmark for vision-language models to detect unsafe actions by embodied agents in household settings. It also introduces HD-Guard, a hierarchical dual-brain architecture balancing real-time safety monitoring with detection accuracy.
πΉ Publication Date: Published on Mar 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.11975
β’ PDF: https://arxiv.org/pdf/2603.11975
β’ Project Page: https://pujiayue.github.io/homesafe-bench.github.io/
β’ Github: https://github.com/pujiayue/HomeSafe-Bench
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/pujiayue/HomeSafe-Bench-Leaderboard
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VisionLanguageModels #EmbodiedAI #AISafety #Robotics #Benchmark
β€1
β¨NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrieval
π Summary:
NanoVDR improves visual document retrieval by distilling a large VLM teacher into a small 70M text-only query encoder. This decouples document indexing from query processing, achieving 50x lower latency and 32x fewer parameters with nearly identical quality.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12824
β’ PDF: https://arxiv.org/pdf/2603.12824
β’ Project Page: https://huggingface.co/nanovdr
πΉ Models citing this paper:
β’ https://huggingface.co/nanovdr/NanoVDR-L
β’ https://huggingface.co/nanovdr/NanoVDR-S-Multi
β’ https://huggingface.co/nanovdr/NanoVDR-S
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/nanovdr/NanoVDR-Demo
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VisualDocumentRetrieval #ModelDistillation #VLM #InformationRetrieval #DeepLearning
π Summary:
NanoVDR improves visual document retrieval by distilling a large VLM teacher into a small 70M text-only query encoder. This decouples document indexing from query processing, achieving 50x lower latency and 32x fewer parameters with nearly identical quality.
πΉ Publication Date: Published on Mar 13
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2603.12824
β’ PDF: https://arxiv.org/pdf/2603.12824
β’ Project Page: https://huggingface.co/nanovdr
πΉ Models citing this paper:
β’ https://huggingface.co/nanovdr/NanoVDR-L
β’ https://huggingface.co/nanovdr/NanoVDR-S-Multi
β’ https://huggingface.co/nanovdr/NanoVDR-S
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/nanovdr/NanoVDR-Demo
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#VisualDocumentRetrieval #ModelDistillation #VLM #InformationRetrieval #DeepLearning
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