✨Step-level Optimization for Efficient Computer-use Agents
📝 Summary:
Computer-use agents are inefficient when using large models for every step. This paper proposes an event-driven cascade that uses small policies by default, escalating to stronger models only when lightweight monitors detect high risk like stalls or semantic drift, thereby optimizing compute.
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27151
• PDF: https://arxiv.org/pdf/2604.27151
• Github: https://github.com/yale-nlp/StepWise
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#AI #AgentSystems #ResourceOptimization #EfficientAI #AdaptiveSystems
📝 Summary:
Computer-use agents are inefficient when using large models for every step. This paper proposes an event-driven cascade that uses small policies by default, escalating to stronger models only when lightweight monitors detect high risk like stalls or semantic drift, thereby optimizing compute.
🔹 Publication Date: Published on Apr 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27151
• PDF: https://arxiv.org/pdf/2604.27151
• Github: https://github.com/yale-nlp/StepWise
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#AI #AgentSystems #ResourceOptimization #EfficientAI #AdaptiveSystems
✨SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
📝 Summary:
SignRoundV2 is a post-training quantization method for LLMs. It achieves competitive, near full-precision accuracy even at extremely low-bits like 2-bits. This is done via layer-wise bit allocation and pre-tuning scale search.
🔹 Publication Date: Published on Dec 4, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04746
• PDF: https://arxiv.org/pdf/2512.04746
• Project Page: https://github.com/intel/auto-round
• Github: https://github.com/intel/auto-round
🔹 Models citing this paper:
• https://huggingface.co/Intel/MiroThinker-v1.5-30B-gguf-q2ks-mixed-AutoRound
• https://huggingface.co/Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLMs #Quantization #DeepLearning #AI #MachineLearning
📝 Summary:
SignRoundV2 is a post-training quantization method for LLMs. It achieves competitive, near full-precision accuracy even at extremely low-bits like 2-bits. This is done via layer-wise bit allocation and pre-tuning scale search.
🔹 Publication Date: Published on Dec 4, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04746
• PDF: https://arxiv.org/pdf/2512.04746
• Project Page: https://github.com/intel/auto-round
• Github: https://github.com/intel/auto-round
🔹 Models citing this paper:
• https://huggingface.co/Intel/MiroThinker-v1.5-30B-gguf-q2ks-mixed-AutoRound
• https://huggingface.co/Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLMs #Quantization #DeepLearning #AI #MachineLearning
✨Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
📝 Summary:
Nemotron 3 Nano Omni is a new efficient, open multimodal AI model. It natively supports audio, text, images, and video inputs, improving accuracy and efficiency over previous versions. It excels in document understanding and long audio-video comprehension.
🔹 Publication Date: Published on Apr 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24954
• PDF: https://arxiv.org/pdf/2604.24954
🔹 Models citing this paper:
• https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
• https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4
• https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8
✨ Spaces citing this paper:
• https://huggingface.co/spaces/akhaliq/Nemotron-3-Nano-Omni
• https://huggingface.co/spaces/developerjeremylive/Nemotron-3-Nano-Omni-etheroi
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#AI #MultimodalAI #DeepLearning #OpenSourceAI #AIResearch
📝 Summary:
Nemotron 3 Nano Omni is a new efficient, open multimodal AI model. It natively supports audio, text, images, and video inputs, improving accuracy and efficiency over previous versions. It excels in document understanding and long audio-video comprehension.
🔹 Publication Date: Published on Apr 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24954
• PDF: https://arxiv.org/pdf/2604.24954
🔹 Models citing this paper:
• https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
• https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4
• https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8
✨ Spaces citing this paper:
• https://huggingface.co/spaces/akhaliq/Nemotron-3-Nano-Omni
• https://huggingface.co/spaces/developerjeremylive/Nemotron-3-Nano-Omni-etheroi
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#AI #MultimodalAI #DeepLearning #OpenSourceAI #AIResearch
arXiv.org
Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 Nano Omni...
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✨DeepSeek-OCR: Contexts Optical Compression
📝 Summary:
DeepSeek-OCR compresses long contexts via optical 2D mapping to achieve high OCR precision with significantly reduced vision tokens. It shows 97% accuracy at 10x compression, outperforming other OCR models efficiently. This innovation holds practical value for document processing and LLM training...
🔹 Publication Date: Published on Oct 21, 2025
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/deepseek-ocr-contexts-optical-compression
• PDF: https://arxiv.org/pdf/2510.18234
• Github: https://github.com/deepseek-ai/DeepSeek-OCR
🔹 Models citing this paper:
• https://huggingface.co/deepseek-ai/DeepSeek-OCR
• https://huggingface.co/deepseek-ai/DeepSeek-OCR-2
• https://huggingface.co/unsloth/DeepSeek-OCR
✨ Spaces citing this paper:
• https://huggingface.co/spaces/merterbak/DeepSeek-OCR-Demo
• https://huggingface.co/spaces/davidpcm/openclaw-stock-analyst
• https://huggingface.co/spaces/khang119966/DeepSeek-OCR-DEMO
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#OCR #AI #DeepLearning #ContextCompression #LLM
📝 Summary:
DeepSeek-OCR compresses long contexts via optical 2D mapping to achieve high OCR precision with significantly reduced vision tokens. It shows 97% accuracy at 10x compression, outperforming other OCR models efficiently. This innovation holds practical value for document processing and LLM training...
🔹 Publication Date: Published on Oct 21, 2025
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/deepseek-ocr-contexts-optical-compression
• PDF: https://arxiv.org/pdf/2510.18234
• Github: https://github.com/deepseek-ai/DeepSeek-OCR
🔹 Models citing this paper:
• https://huggingface.co/deepseek-ai/DeepSeek-OCR
• https://huggingface.co/deepseek-ai/DeepSeek-OCR-2
• https://huggingface.co/unsloth/DeepSeek-OCR
✨ Spaces citing this paper:
• https://huggingface.co/spaces/merterbak/DeepSeek-OCR-Demo
• https://huggingface.co/spaces/davidpcm/openclaw-stock-analyst
• https://huggingface.co/spaces/khang119966/DeepSeek-OCR-DEMO
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#OCR #AI #DeepLearning #ContextCompression #LLM
Arxivexplained
DeepSeek-OCR: Contexts Optical Compression - Explained Simply
By Haoran Wei, Yaofeng Sun, Yukun Li. # DeepSeek-OCR: A Game-Changer for Processing Text-Heavy Documents
**The Problem:** Current AI syst...
**The Problem:** Current AI syst...
❤4👍1
Forwarded from Machine Learning with Python
Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
GitHub
GitHub - dair-ai/Mathematics-for-ML: 🧮 A collection of resources to learn mathematics for machine learning
🧮 A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
❤3
Forwarded from Machine Learning
🔖 A huge open-source course on AI Engineering from scratch
In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀
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#AI #MachineLearning #Python #Rust #OpenSource #Tech
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In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀
⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
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Forwarded from Data Analytics
📰 Anthropic is rolling out Claude Opus 4.8 🚀
The model has become significantly more honest in evaluating its own work and notices problems in its own code four times more often. 🔍✨
Plus, dynamic workflows have appeared — hundreds of AI subagents can work on large projects and migrations in parallel. 🤖⚡
⛓️ More details here
https://www.anthropic.com/news/claude-opus-4-8
#Anthropic #ClaudeOpus48 #AI #ArtificialIntelligence #TechNews #Innovation
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
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The model has become significantly more honest in evaluating its own work and notices problems in its own code four times more often. 🔍✨
Plus, dynamic workflows have appeared — hundreds of AI subagents can work on large projects and migrations in parallel. 🤖⚡
⛓️ More details here
https://www.anthropic.com/news/claude-opus-4-8
#Anthropic #ClaudeOpus48 #AI #ArtificialIntelligence #TechNews #Innovation
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AI & ML Papers
Photo
🔥 COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation
📅 Published on May 29
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.31264
• PDF: https://arxiv.org/pdf/2605.31264
━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus
#ArtificialIntelligence #ExpertKnowledgeDistillation #AI_skill_generation #HumanCenteredAI #KnowledgeGraphEmbedding
💡 The paper introduces COLLEAGUE SKILL, a system for automatically generating person-grounded AI skills from heterogeneous traces of expert knowledge. The problem addressed is that current methods for creating AI agents that mimic human expertise and judgment are limited, as they rely on fragmented evidence and lack a comprehensive workflow for distilling this knowledge into usable skills.
The method presented involves an automated trace-to-skill distillation process that takes materials from a target person or role and produces a versioned skill package with two tracks: a capability track for practices, mental models, and decision heuristics, and a bounded behavior track for communication style, interaction rules, and correction history. This package can be inspected, updated through natural-language feedback, and deployed across agent hosts.
The results of the system are significant, with the open-source repository having approximately 18.5k GitHub stars, 215 skills from 165 contributors, and over 100k cumulative stars across listed skill cards. The system demonstrates how person-grounded skills can be represented as portable, correctable packages, rather than opaque prompts or hidden memories, and provides a comprehensive workflow for generating and deploying these skills. The paper presents the artifact contract, generation workflow, correction lifecycle, deployment surface, and domain presets implemented in the system, showcasing its potential for creating more human-like AI agents.
📅 Published on May 29
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.31264
• PDF: https://arxiv.org/pdf/2605.31264
━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus
#ArtificialIntelligence #ExpertKnowledgeDistillation #AI_skill_generation #HumanCenteredAI #KnowledgeGraphEmbedding
GitHub
Hugging Face
The AI community building the future. Hugging Face has 443 repositories available. Follow their code on GitHub.
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Forwarded from Machine Learning with Python
🎁 SPOTO Mid-Year Sale – Grab Your IT Certification Success Kit!
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