✨MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier
📝 Summary:
MOOSE-Star enables tractable training for generative scientific reasoning by tackling its intractable combinatorial complexity. It uses decomposed subtasks, hierarchical search to reduce complexity to logarithmic, and bounded composition, allowing scalable training and inference.
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03756
• PDF: https://arxiv.org/pdf/2603.03756
• Github: https://github.com/ZonglinY/MOOSE-Star
🔹 Models citing this paper:
• https://huggingface.co/ZonglinY/MOOSE-Star-HC-R1D-7B
• https://huggingface.co/ZonglinY/MOOSE-Star-IR-R1D-7B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/ZonglinY/TOMATO-Star
• https://huggingface.co/datasets/ZonglinY/TOMATO-Star-SFT-Data-R1D-32B
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MOOSE-Star enables tractable training for generative scientific reasoning by tackling its intractable combinatorial complexity. It uses decomposed subtasks, hierarchical search to reduce complexity to logarithmic, and bounded composition, allowing scalable training and inference.
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03756
• PDF: https://arxiv.org/pdf/2603.03756
• Github: https://github.com/ZonglinY/MOOSE-Star
🔹 Models citing this paper:
• https://huggingface.co/ZonglinY/MOOSE-Star-HC-R1D-7B
• https://huggingface.co/ZonglinY/MOOSE-Star-IR-R1D-7B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/ZonglinY/TOMATO-Star
• https://huggingface.co/datasets/ZonglinY/TOMATO-Star-SFT-Data-R1D-32B
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
MOOSE-Star: Unlocking Tractable Training for Scientific Discovery...
While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the direct modeling of the generative reasoning...
✨HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images
📝 Summary:
HiFi-Inpaint generates high-fidelity human-product images using shared enhancement attention and detail-aware loss with a new 40K-image dataset. AI-generated summary Human-product images , which showc...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02210
• PDF: https://arxiv.org/pdf/2603.02210
• Project Page: https://correr-zhou.github.io/HiFi-Inpaint/
• Github: https://github.com/Correr-Zhou/HiFi-Inpaint
✨ Datasets citing this paper:
• https://huggingface.co/datasets/donghao-zhou/HP-Image-40K
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
HiFi-Inpaint generates high-fidelity human-product images using shared enhancement attention and detail-aware loss with a new 40K-image dataset. AI-generated summary Human-product images , which showc...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02210
• PDF: https://arxiv.org/pdf/2603.02210
• Project Page: https://correr-zhou.github.io/HiFi-Inpaint/
• Github: https://github.com/Correr-Zhou/HiFi-Inpaint
✨ Datasets citing this paper:
• https://huggingface.co/datasets/donghao-zhou/HP-Image-40K
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval
📝 Summary:
DARE is a retrieval model that improves R package retrieval by embedding data distribution information into function representations. It significantly outperforms existing models, enabling more reliable R code generation and statistical analysis.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04743
• PDF: https://arxiv.org/pdf/2603.04743
• Project Page: https://ama-cmfai.github.io/DARE_webpage/
• Github: https://ama-cmfai.github.io/DARE_webpage/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DARE is a retrieval model that improves R package retrieval by embedding data distribution information into function representations. It significantly outperforms existing models, enabling more reliable R code generation and statistical analysis.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04743
• PDF: https://arxiv.org/pdf/2603.04743
• Project Page: https://ama-cmfai.github.io/DARE_webpage/
• Github: https://ama-cmfai.github.io/DARE_webpage/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Locality-Attending Vision Transformer
📝 Summary:
Vision transformers are enhanced for segmentation tasks through a Gaussian kernel modulation that improves local attention while maintaining classification performance. AI-generated summary Vision tra...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04892
• PDF: https://arxiv.org/pdf/2603.04892
• Github: https://github.com/sinahmr/LocAtViT
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Vision transformers are enhanced for segmentation tasks through a Gaussian kernel modulation that improves local attention while maintaining classification performance. AI-generated summary Vision tra...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04892
• PDF: https://arxiv.org/pdf/2603.04892
• Github: https://github.com/sinahmr/LocAtViT
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
This media is not supported in your browser
VIEW IN TELEGRAM
✨RealWonder: Real-Time Physical Action-Conditioned Video Generation
📝 Summary:
RealWonder enables real-time action-conditioned video generation by integrating 3D reconstruction, physics simulation, and a distilled video generator to simulate physical consequences of 3D actions. ...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05449
• PDF: https://arxiv.org/pdf/2603.05449
• Project Page: https://liuwei283.github.io/RealWonder/
• Github: https://github.com/liuwei283/RealWonder
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
RealWonder enables real-time action-conditioned video generation by integrating 3D reconstruction, physics simulation, and a distilled video generator to simulate physical consequences of 3D actions. ...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05449
• PDF: https://arxiv.org/pdf/2603.05449
• Project Page: https://liuwei283.github.io/RealWonder/
• Github: https://github.com/liuwei283/RealWonder
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨KARL: Knowledge Agents via Reinforcement Learning
📝 Summary:
A reinforcement learning system for enterprise search agents achieves superior performance through diverse training data generation and multi-task learning approaches. AI-generated summary We present ...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05218
• PDF: https://arxiv.org/pdf/2603.05218
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A reinforcement learning system for enterprise search agents achieves superior performance through diverse training data generation and multi-task learning approaches. AI-generated summary We present ...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05218
• PDF: https://arxiv.org/pdf/2603.05218
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
📝 Summary:
Timer-S1 is a scalable Mixture-of-Experts time series model with 8.3B parameters that uses serial scaling and novel TimeMoE blocks to improve long-term forecasting accuracy. AI-generated summary We in...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04791
• PDF: https://arxiv.org/pdf/2603.04791
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Timer-S1 is a scalable Mixture-of-Experts time series model with 8.3B parameters that uses serial scaling and novel TimeMoE blocks to improve long-term forecasting accuracy. AI-generated summary We in...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04791
• PDF: https://arxiv.org/pdf/2603.04791
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨DreamWorld: Unified World Modeling in Video Generation
📝 Summary:
DreamWorld introduces a unified framework for video generation that integrates multiple types of world knowledge through joint modeling of temporal dynamics, spatial geometry, and semantic consistency...
🔹 Publication Date: Published on Feb 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00466
• PDF: https://arxiv.org/pdf/2603.00466
• Github: https://github.com/ABU121111/DreamWorld
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DreamWorld introduces a unified framework for video generation that integrates multiple types of world knowledge through joint modeling of temporal dynamics, spatial geometry, and semantic consistency...
🔹 Publication Date: Published on Feb 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00466
• PDF: https://arxiv.org/pdf/2603.00466
• Github: https://github.com/ABU121111/DreamWorld
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline
📝 Summary:
MM-Lifelong dataset captures natural video sequences across multiple temporal scales to evaluate multimodal lifelong understanding, revealing limitations in current approaches and introducing a recurs...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05484
• PDF: https://arxiv.org/pdf/2603.05484
• Project Page: https://huggingface.co/datasets/CG-Bench/MM-Lifelong
• Github: https://github.com/cg1177/Recursive-Multimodal-Agent
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MM-Lifelong dataset captures natural video sequences across multiple temporal scales to evaluate multimodal lifelong understanding, revealing limitations in current approaches and introducing a recurs...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05484
• PDF: https://arxiv.org/pdf/2603.05484
• Project Page: https://huggingface.co/datasets/CG-Bench/MM-Lifelong
• Github: https://github.com/cg1177/Recursive-Multimodal-Agent
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨On-Policy Self-Distillation for Reasoning Compression
📝 Summary:
OPSDC enables efficient reasoning model compression by having models distill concise behavior from their own outputs, achieving significant token reduction while maintaining accuracy. AI-generated sum...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05433
• PDF: https://arxiv.org/pdf/2603.05433
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
OPSDC enables efficient reasoning model compression by having models distill concise behavior from their own outputs, achieving significant token reduction while maintaining accuracy. AI-generated sum...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05433
• PDF: https://arxiv.org/pdf/2603.05433
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data
📝 Summary:
A bimanual robotic grasping framework is presented that generates diverse grasp data through optimization and planning, enabling effective zero-shot sim-to-real transfer with high success rates on nov...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05312
• PDF: https://arxiv.org/pdf/2603.05312
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A bimanual robotic grasping framework is presented that generates diverse grasp data through optimization and planning, enabling effective zero-shot sim-to-real transfer with high success rates on nov...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05312
• PDF: https://arxiv.org/pdf/2603.05312
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Distribution-Conditioned Transport
📝 Summary:
Distribution-conditioned transport framework enables generalization to unseen distribution pairs and supports semi-supervised learning for scientific applications. AI-generated summary Learning a tran...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04736
• PDF: https://arxiv.org/pdf/2603.04736
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Distribution-conditioned transport framework enables generalization to unseen distribution pairs and supports semi-supervised learning for scientific applications. AI-generated summary Learning a tran...
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04736
• PDF: https://arxiv.org/pdf/2603.04736
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨SageBwd: A Trainable Low-bit Attention
📝 Summary:
Research investigates why low-bit attention methods like SageBwd exhibit performance gaps during pre-training and identifies key factors for stable training with reduced precision. AI-generated summar...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02170
• PDF: https://arxiv.org/pdf/2603.02170
• Project Page: https://github.com/thu-ml/SageAttention
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Research investigates why low-bit attention methods like SageBwd exhibit performance gaps during pre-training and identifies key factors for stable training with reduced precision. AI-generated summar...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02170
• PDF: https://arxiv.org/pdf/2603.02170
• Project Page: https://github.com/thu-ml/SageAttention
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios
📝 Summary:
AgentVista presents a comprehensive benchmark for multimodal agents requiring long-horizon tool interactions across multiple modalities and complex real-world scenarios. AI-generated summary Real-worl...
🔹 Publication Date: Published on Feb 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23166
• PDF: https://arxiv.org/pdf/2602.23166
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
AgentVista presents a comprehensive benchmark for multimodal agents requiring long-horizon tool interactions across multiple modalities and complex real-world scenarios. AI-generated summary Real-worl...
🔹 Publication Date: Published on Feb 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23166
• PDF: https://arxiv.org/pdf/2602.23166
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨RoboPocket: Improve Robot Policies Instantly with Your Phone
📝 Summary:
RoboPocket enables efficient, robot-free policy iteration via smartphones. It uses augmented reality to visualize policy weaknesses, guiding data collection, and asynchronous online finetuning to update policies quickly. This doubles data and sample efficiency.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05504
• PDF: https://arxiv.org/pdf/2603.05504
• Project Page: https://robo-pocket.github.io
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
RoboPocket enables efficient, robot-free policy iteration via smartphones. It uses augmented reality to visualize policy weaknesses, guiding data collection, and asynchronous online finetuning to update policies quickly. This doubles data and sample efficiency.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05504
• PDF: https://arxiv.org/pdf/2603.05504
• Project Page: https://robo-pocket.github.io
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Large Multimodal Models as General In-Context Classifiers
📝 Summary:
Large Multimodal Models demonstrate superior performance in closed-world classification with in-context learning and excel in open-world scenarios when equipped with the proposed CIRCLE method for pse...
🔹 Publication Date: Published on Feb 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23229
• PDF: https://arxiv.org/pdf/2602.23229
• Project Page: https://circle-lmm.github.io/
• Github: https://github.com/marco-garosi/CIRCLE
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Large Multimodal Models demonstrate superior performance in closed-world classification with in-context learning and excel in open-world scenarios when equipped with the proposed CIRCLE method for pse...
🔹 Publication Date: Published on Feb 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23229
• PDF: https://arxiv.org/pdf/2602.23229
• Project Page: https://circle-lmm.github.io/
• Github: https://github.com/marco-garosi/CIRCLE
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
📝 Summary:
GLiNER2 is a unified transformer-based framework that supports multiple NLP tasks with improved efficiency and accessibility compared to large language models. AI-generated summary Information extract...
🔹 Publication Date: Published on Jul 24, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.18546
• PDF: https://arxiv.org/pdf/2507.18546
• Github: https://github.com/fastino-ai/GLiNER2
🔹 Models citing this paper:
• https://huggingface.co/fastino/gliner2-base-v1
• https://huggingface.co/fastino/gliner2-large-v1
• https://huggingface.co/fastino/gliner2-multi-v1
✨ Spaces citing this paper:
• https://huggingface.co/spaces/sitammeur/GLiNER2-Suite
• https://huggingface.co/spaces/fastino/gliner2-official-demo
• https://huggingface.co/spaces/sohom004/testdup
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
GLiNER2 is a unified transformer-based framework that supports multiple NLP tasks with improved efficiency and accessibility compared to large language models. AI-generated summary Information extract...
🔹 Publication Date: Published on Jul 24, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.18546
• PDF: https://arxiv.org/pdf/2507.18546
• Github: https://github.com/fastino-ai/GLiNER2
🔹 Models citing this paper:
• https://huggingface.co/fastino/gliner2-base-v1
• https://huggingface.co/fastino/gliner2-large-v1
• https://huggingface.co/fastino/gliner2-multi-v1
✨ Spaces citing this paper:
• https://huggingface.co/spaces/sitammeur/GLiNER2-Suite
• https://huggingface.co/spaces/fastino/gliner2-official-demo
• https://huggingface.co/spaces/sohom004/testdup
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
GLiNER2: An Efficient Multi-Task Information Extraction System...
Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large...
✨Mozi: Governed Autonomy for Drug Discovery LLM Agents
📝 Summary:
Mozi is a dual-layer framework for reliable drug discovery LLM agents, solving issues of tool-use governance and long-horizon reliability. It uses a control plane for isolated tool-use and replanning, plus a workflow plane for structured stages with human oversight, ensuring robust, auditable res...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03655
• PDF: https://arxiv.org/pdf/2603.03655
• Project Page: https://ai4s.idea.edu.cn/ai4s/mozi
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMAgents #DrugDiscovery #AI #MachineLearning #AutonomousAgents
📝 Summary:
Mozi is a dual-layer framework for reliable drug discovery LLM agents, solving issues of tool-use governance and long-horizon reliability. It uses a control plane for isolated tool-use and replanning, plus a workflow plane for structured stages with human oversight, ensuring robust, auditable res...
🔹 Publication Date: Published on Mar 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03655
• PDF: https://arxiv.org/pdf/2603.03655
• Project Page: https://ai4s.idea.edu.cn/ai4s/mozi
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMAgents #DrugDiscovery #AI #MachineLearning #AutonomousAgents
✨SkillNet: Create, Evaluate, and Connect AI Skills
📝 Summary:
SkillNet is an open infrastructure that systematically creates, evaluates, and organizes AI skills using a unified ontology. This overcomes the lack of skill accumulation in current agents, significantly boosting performance by 40 percent in rewards and reducing execution steps by 30 percent.
🔹 Publication Date: Published on Feb 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04448
• PDF: https://arxiv.org/pdf/2603.04448
• Project Page: https://skillnet.openkg.cn/
• Github: https://github.com/zjunlp/SkillNet
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #AISkills #AIAgents #Ontology #MachineLearning
📝 Summary:
SkillNet is an open infrastructure that systematically creates, evaluates, and organizes AI skills using a unified ontology. This overcomes the lack of skill accumulation in current agents, significantly boosting performance by 40 percent in rewards and reducing execution steps by 30 percent.
🔹 Publication Date: Published on Feb 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04448
• PDF: https://arxiv.org/pdf/2603.04448
• Project Page: https://skillnet.openkg.cn/
• Github: https://github.com/zjunlp/SkillNet
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #AISkills #AIAgents #Ontology #MachineLearning
✨Interactive Benchmarks
📝 Summary:
Interactive Benchmarks propose a new framework to assess AI intelligence by evaluating active information acquisition and reasoning under constraints. This approach offers a robust assessment, revealing significant room for model improvement in interactive scenarios.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04737
• PDF: https://arxiv.org/pdf/2603.04737
• Project Page: https://github.com/interactivebench/interactivebench
• Github: https://github.com/interactivebench/interactivebench
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #AIBenchmarks #InteractiveAI #AIIntelligence #MachineLearning
📝 Summary:
Interactive Benchmarks propose a new framework to assess AI intelligence by evaluating active information acquisition and reasoning under constraints. This approach offers a robust assessment, revealing significant room for model improvement in interactive scenarios.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04737
• PDF: https://arxiv.org/pdf/2603.04737
• Project Page: https://github.com/interactivebench/interactivebench
• Github: https://github.com/interactivebench/interactivebench
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #AIBenchmarks #InteractiveAI #AIIntelligence #MachineLearning