✨DreamOmni3: Scribble-based Editing and Generation
📝 Summary:
DreamOmni3 introduces scribble-based editing and generation for more flexible image creation beyond text prompts. It proposes new tasks, data synthesis, and a joint input scheme using colored scribbles on source images for precise localization and complex edits.
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22525
• PDF: https://arxiv.org/pdf/2512.22525
• Github: https://github.com/dvlab-research/DreamOmni3
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DreamOmni3 introduces scribble-based editing and generation for more flexible image creation beyond text prompts. It proposes new tasks, data synthesis, and a joint input scheme using colored scribbles on source images for precise localization and complex edits.
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22525
• PDF: https://arxiv.org/pdf/2512.22525
• Github: https://github.com/dvlab-research/DreamOmni3
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨End-to-End Test-Time Training for Long Context
📝 Summary:
This paper introduces End-to-End Test-Time Training TTT-E2E for long-context language models. It uses a standard Transformer that continually learns from context at test time, compressing information into its weights. TTT-E2E scales well with context length and offers constant inference latency, ...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23675
• PDF: https://arxiv.org/pdf/2512.23675
• Github: https://github.com/test-time-training/e2e
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper introduces End-to-End Test-Time Training TTT-E2E for long-context language models. It uses a standard Transformer that continually learns from context at test time, compressing information into its weights. TTT-E2E scales well with context length and offers constant inference latency, ...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23675
• PDF: https://arxiv.org/pdf/2512.23675
• Github: https://github.com/test-time-training/e2e
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨GraphLocator: Graph-guided Causal Reasoning for Issue Localization
📝 Summary:
The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in a...
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22469
• PDF: https://arxiv.org/pdf/2512.22469
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in a...
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22469
• PDF: https://arxiv.org/pdf/2512.22469
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Evaluating Parameter Efficient Methods for RLVR
📝 Summary:
This work evaluates 12 PEFT methods for RLVR in mathematical reasoning, challenging LoRAs default use. It finds that structural variants like DoRA outperform LoRA, while SVD-informed methods fail and extreme parameter reduction bottlenecks reasoning.
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23165
• PDF: https://arxiv.org/pdf/2512.23165
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#PEFT #RLVR #MathematicalReasoning #LoRA #DeepLearning
📝 Summary:
This work evaluates 12 PEFT methods for RLVR in mathematical reasoning, challenging LoRAs default use. It finds that structural variants like DoRA outperform LoRA, while SVD-informed methods fail and extreme parameter reduction bottlenecks reasoning.
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23165
• PDF: https://arxiv.org/pdf/2512.23165
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#PEFT #RLVR #MathematicalReasoning #LoRA #DeepLearning
✨UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
📝 Summary:
UltraShape 1.0 is a 3D diffusion framework that generates high-fidelity shapes using a two-stage process: coarse then refined geometry. It includes a novel data pipeline improving dataset quality, enabling strong geometric results on public data.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21185
• PDF: https://arxiv.org/pdf/2512.21185
• Project Page: https://pku-yuangroup.github.io/UltraShape-1.0/
• Github: https://pku-yuangroup.github.io/UltraShape-1.0/
🔹 Models citing this paper:
• https://huggingface.co/infinith/UltraShape
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#3DGeneration #DiffusionModels #GenerativeAI #ComputerGraphics #DeepLearning
📝 Summary:
UltraShape 1.0 is a 3D diffusion framework that generates high-fidelity shapes using a two-stage process: coarse then refined geometry. It includes a novel data pipeline improving dataset quality, enabling strong geometric results on public data.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21185
• PDF: https://arxiv.org/pdf/2512.21185
• Project Page: https://pku-yuangroup.github.io/UltraShape-1.0/
• Github: https://pku-yuangroup.github.io/UltraShape-1.0/
🔹 Models citing this paper:
• https://huggingface.co/infinith/UltraShape
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#3DGeneration #DiffusionModels #GenerativeAI #ComputerGraphics #DeepLearning
✨GateBreaker: Gate-Guided Attacks on Mixture-of-Expert LLMs
📝 Summary:
GateBreaker is the first framework to compromise MoE LLM safety by identifying and disabling ~3% of safety neurons in expert layers. This raises attack success rates from 7.4% to 64.9% across eight LLMs and generalizes to VLMs, showing concentrated and transferable safety vulnerabilities.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21008
• PDF: https://arxiv.org/pdf/2512.21008
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #AIsecurity #MoELLMs #AIvulnerability #GateBreaker
📝 Summary:
GateBreaker is the first framework to compromise MoE LLM safety by identifying and disabling ~3% of safety neurons in expert layers. This raises attack success rates from 7.4% to 64.9% across eight LLMs and generalizes to VLMs, showing concentrated and transferable safety vulnerabilities.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21008
• PDF: https://arxiv.org/pdf/2512.21008
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #AIsecurity #MoELLMs #AIvulnerability #GateBreaker
Forwarded from Machine Learning with Python
https://t.me/InsideAds_bot/open?startapp=r_148350890_utm_source-insideadsInternal-utm_medium-notification-utm_campaign-mailwithdrawnotify-variantA
I invite you to InsideAds — a platform to grow and monetize Telegram channels
monthly profits: +150$
I invite you to InsideAds — a platform to grow and monetize Telegram channels
monthly profits: +150$
Telegram
Inside Ads
Grow your channel through traffic exchange and buy real subscribers. Our AI will help monetize your audience by finding advertisers and creating ads.
Support: @InsideAds_Support_bot
Support: @InsideAds_Support_bot
❤2
🚀 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.
https://t.me/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.me/DataScience4
🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/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.me/datasets1
🧑🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/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.me/DataScienceN
📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53
⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/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.me/CodeProgrammer
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho
Please open Telegram to view this post
VIEW IN TELEGRAM
❤1
✨CosineGate: Semantic Dynamic Routing via Cosine Incompatibility in Residual Networks
📝 Summary:
CosineGate enables dynamic routing in residual networks using cosine incompatibility to skip redundant blocks. This reduces computation by up to 28.5 percent while matching or exceeding ResNet-20 accuracy, without auxiliary supervision.
🔹 Publication Date: Published on Dec 21, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22206
• PDF: https://arxiv.org/pdf/2512.22206
• Github: https://github.com/thotayogeswarreddy/CosineGate
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#DeepLearning #NeuralNetworks #DynamicRouting #ModelEfficiency #AIResearch
📝 Summary:
CosineGate enables dynamic routing in residual networks using cosine incompatibility to skip redundant blocks. This reduces computation by up to 28.5 percent while matching or exceeding ResNet-20 accuracy, without auxiliary supervision.
🔹 Publication Date: Published on Dec 21, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22206
• PDF: https://arxiv.org/pdf/2512.22206
• Github: https://github.com/thotayogeswarreddy/CosineGate
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#DeepLearning #NeuralNetworks #DynamicRouting #ModelEfficiency #AIResearch
👍1
✨Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models
📝 Summary:
Youtu-LLM is a lightweight 1.96B LLM, pre-trained from scratch with a compact architecture and a multi-stage curriculum focused on commonsense, STEM, and agentic tasks. It achieves state-of-the-art performance for sub-2B models, demonstrating strong intrinsic agentic capabilities.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24618
• PDF: https://arxiv.org/pdf/2512.24618
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #AI #AgenticAI #LightweightLLM #DeepLearning
📝 Summary:
Youtu-LLM is a lightweight 1.96B LLM, pre-trained from scratch with a compact architecture and a multi-stage curriculum focused on commonsense, STEM, and agentic tasks. It achieves state-of-the-art performance for sub-2B models, demonstrating strong intrinsic agentic capabilities.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24618
• PDF: https://arxiv.org/pdf/2512.24618
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #AI #AgenticAI #LightweightLLM #DeepLearning
Media is too big
VIEW IN TELEGRAM
✨GR-Dexter Technical Report
📝 Summary:
GR-Dexter introduces a hardware-model-data framework for bimanual dexterous-hand robot manipulation using VLA models. It combines a new 21-DoF hand, teleoperation for data, and diverse datasets. This framework achieves strong performance and robust generalization in real-world manipulation tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24210
• PDF: https://arxiv.org/pdf/2512.24210
• Project Page: https://byte-dexter.github.io/gr-dexter/
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#Robotics #DexterousManipulation #VLA #RobotHardware #MachineLearning
📝 Summary:
GR-Dexter introduces a hardware-model-data framework for bimanual dexterous-hand robot manipulation using VLA models. It combines a new 21-DoF hand, teleoperation for data, and diverse datasets. This framework achieves strong performance and robust generalization in real-world manipulation tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24210
• PDF: https://arxiv.org/pdf/2512.24210
• Project Page: https://byte-dexter.github.io/gr-dexter/
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#Robotics #DexterousManipulation #VLA #RobotHardware #MachineLearning
This media is not supported in your browser
VIEW IN TELEGRAM
✨SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time
📝 Summary:
SpaceTimePilot is a video diffusion model for dynamic scene rendering, offering independent control over spatial viewpoint and temporal motion. It achieves precise space-time disentanglement via a time-embedding, temporal-warping training, and a synthetic dataset.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25075
• PDF: https://arxiv.org/pdf/2512.25075
• Project Page: https://zheninghuang.github.io/Space-Time-Pilot/
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#VideoDiffusion #GenerativeAI #DynamicScenes #ComputerGraphics #DeepLearning
📝 Summary:
SpaceTimePilot is a video diffusion model for dynamic scene rendering, offering independent control over spatial viewpoint and temporal motion. It achieves precise space-time disentanglement via a time-embedding, temporal-warping training, and a synthetic dataset.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25075
• PDF: https://arxiv.org/pdf/2512.25075
• Project Page: https://zheninghuang.github.io/Space-Time-Pilot/
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#VideoDiffusion #GenerativeAI #DynamicScenes #ComputerGraphics #DeepLearning
✨PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation
📝 Summary:
Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly bas...
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24551
• PDF: https://arxiv.org/pdf/2512.24551
• Project Page: https://caiyuanhao1998.github.io/project/PhyGDPO/
• Github: https://github.com/caiyuanhao1998/Open-PhyGDPO
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly bas...
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24551
• PDF: https://arxiv.org/pdf/2512.24551
• Project Page: https://caiyuanhao1998.github.io/project/PhyGDPO/
• Github: https://github.com/caiyuanhao1998/Open-PhyGDPO
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Scaling Open-Ended Reasoning to Predict the Future
📝 Summary:
This work trains language models for open-ended future prediction using a new dataset synthesized from news. Their OpenForecaster 8B model matches larger proprietary models in accuracy, calibration, and consistency. All resources are open-sourced.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25070
• PDF: https://arxiv.org/pdf/2512.25070
• Project Page: https://www.openforecaster.github.io
• Github: https://github.com/OpenForecaster/scaling-forecasting-training
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLMs #FuturePrediction #AI #OpenSourceAI #MachineLearning
📝 Summary:
This work trains language models for open-ended future prediction using a new dataset synthesized from news. Their OpenForecaster 8B model matches larger proprietary models in accuracy, calibration, and consistency. All resources are open-sourced.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25070
• PDF: https://arxiv.org/pdf/2512.25070
• Project Page: https://www.openforecaster.github.io
• Github: https://github.com/OpenForecaster/scaling-forecasting-training
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLMs #FuturePrediction #AI #OpenSourceAI #MachineLearning
✨Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process
📝 Summary:
This paper introduces RISE, an unsupervised framework using sparse auto-encoders to discover and control LLM reasoning behaviors. It identifies interpretable reasoning vectors like reflection and backtracking, enabling targeted interventions and discovery of novel behaviors without retraining.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23988
• PDF: https://arxiv.org/pdf/2512.23988
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #AI #MachineLearning #AIReasoning #Interpretability
📝 Summary:
This paper introduces RISE, an unsupervised framework using sparse auto-encoders to discover and control LLM reasoning behaviors. It identifies interpretable reasoning vectors like reflection and backtracking, enabling targeted interventions and discovery of novel behaviors without retraining.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23988
• PDF: https://arxiv.org/pdf/2512.23988
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #AI #MachineLearning #AIReasoning #Interpretability
✨Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
📝 Summary:
The Agentic Learning Ecosystem ALE is a new infrastructure to streamline LLM agent development for real-world tasks. ALE comprises ROLL for optimization, ROCK for sandboxing, and iFlow CLI for context. Their agent ROME, built with ALE, shows strong benchmark performance.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24873
• PDF: https://arxiv.org/pdf/2512.24873
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AIAgents #LLMDevelopment #AgenticLearning #AIArchitecture #MachineLearning
📝 Summary:
The Agentic Learning Ecosystem ALE is a new infrastructure to streamline LLM agent development for real-world tasks. ALE comprises ROLL for optimization, ROCK for sandboxing, and iFlow CLI for context. Their agent ROME, built with ALE, shows strong benchmark performance.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24873
• PDF: https://arxiv.org/pdf/2512.24873
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AIAgents #LLMDevelopment #AgenticLearning #AIArchitecture #MachineLearning
✨Figure It Out: Improving the Frontier of Reasoning with Active Visual Thinking
📝 Summary:
Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performan...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24297
• PDF: https://arxiv.org/pdf/2512.24297
• Github: https://github.com/chenmeiqii/FIGR
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performan...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24297
• PDF: https://arxiv.org/pdf/2512.24297
• Github: https://github.com/chenmeiqii/FIGR
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Pretraining Frame Preservation in Autoregressive Video Memory Compression
📝 Summary:
We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temp...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23851
• PDF: https://arxiv.org/pdf/2512.23851
• Github: https://github.com/lllyasviel/PFP
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temp...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23851
• PDF: https://arxiv.org/pdf/2512.23851
• Github: https://github.com/lllyasviel/PFP
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Factorized Learning for Temporally Grounded Video-Language Models
📝 Summary:
Video-language models struggle with temporal grounding from coupled tasks. Our D^2VLM framework decouples grounding and textual response using evidence tokens. Factorized preference optimization explicitly optimizes temporal grounding for both tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24097
• PDF: https://arxiv.org/pdf/2512.24097
• Project Page: https://github.com/nusnlp/d2vlm
• Github: https://github.com/nusnlp/d2vlm
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Video-language models struggle with temporal grounding from coupled tasks. Our D^2VLM framework decouples grounding and textual response using evidence tokens. Factorized preference optimization explicitly optimizes temporal grounding for both tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24097
• PDF: https://arxiv.org/pdf/2512.24097
• Project Page: https://github.com/nusnlp/d2vlm
• Github: https://github.com/nusnlp/d2vlm
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
📝 Summary:
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for Joint Audio-Video (JAV) comprehension and generation. JavisGPT adopts a concise encoder-LLM-decoder architect...
🔹 Publication Date: Published on Dec 28, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.23377
• PDF: https://arxiv.org/pdf/2512.22905
• Project Page: https://javisverse.github.io/JavisGPT-page/
• Github: https://github.com/JavisVerse/JavisGPT
🔹 Models citing this paper:
• https://huggingface.co/JavisVerse/JavisGPT-v0.1-7B-Instruct
✨ Datasets citing this paper:
• https://huggingface.co/datasets/JavisVerse/MM-PreTrain
• https://huggingface.co/datasets/JavisVerse/JavisUnd-Eval
• https://huggingface.co/datasets/JavisVerse/AV-FineTune
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for Joint Audio-Video (JAV) comprehension and generation. JavisGPT adopts a concise encoder-LLM-decoder architect...
🔹 Publication Date: Published on Dec 28, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.23377
• PDF: https://arxiv.org/pdf/2512.22905
• Project Page: https://javisverse.github.io/JavisGPT-page/
• Github: https://github.com/JavisVerse/JavisGPT
🔹 Models citing this paper:
• https://huggingface.co/JavisVerse/JavisGPT-v0.1-7B-Instruct
✨ Datasets citing this paper:
• https://huggingface.co/datasets/JavisVerse/MM-PreTrain
• https://huggingface.co/datasets/JavisVerse/JavisUnd-Eval
• https://huggingface.co/datasets/JavisVerse/AV-FineTune
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
JavisDiT: Joint Audio-Video Diffusion Transformer with...
This paper introduces JavisDiT, a novel Joint Audio-Video Diffusion Transformer designed for synchronized audio-video generation (JAVG). Built upon the powerful Diffusion Transformer (DiT)...