AI-Powered Operator Network Operations Automation
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Developed AI-based automation systems for operator network operations, improving efficiency and reducing manual intervention in network management.
Posts by Collection
portfolio
AI-Based Base Station Error Handling and Diagnosis
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Developed intelligent systems for automated base station error detection, diagnosis, and resolution, reducing downtime and maintenance costs.
Road Detection and Autonomous Driving
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Research and development on road detection and autonomous driving systems, focusing on computer vision and perception for autonomous vehicles.
Robot Reinforcement Learning
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Research on reinforcement learning for robotic control and manipulation, developing intelligent agents for robotic tasks.
Text-to-Scene Generation
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Generating 3D scenes from natural language descriptions, bridging the gap between language understanding and 3D scene representation.
MLLM-MCTS-CoT: Monte Carlo Tree Search for Multimodal Reasoning
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Combining Monte Carlo Tree Search (MCTS) with Chain-of-Thought reasoning for enhanced reasoning in multimodal large language models.
Multimodal Large Language Model Chain-of-Thought
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Research on Chain-of-Thought (CoT) reasoning for multimodal large language models, improving reasoning capabilities in vision-language tasks.
Automated Evaluation Framework Improvements (lmms-eval)
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Improving automated evaluation frameworks based on lmms-eval for comprehensive assessment of multimodal and language models.
openRLHF and lightRLHF Framework Improvements
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Algorithm improvements and enhancements for the openRLHF reinforcement learning framework, including lightRLHF - a lightweight version based on openRLHF improvements.
veRL Framework Improvements
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Framework integration and improvements for veRL (Volcano Engine Reinforcement Learning), a flexible, efficient and production-ready RL training library for large language models.
Research MCP Servers
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A collection of Model Context Protocol (MCP) servers for research workflows, including arXiv integration and other research tools.
Multi-Agent Research Assistant
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An intelligent multi-agent system for research assistance, enabling collaborative problem-solving among multiple AI agents.
Multi-round Reinforcement Learning and Self-Evolving Agents
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Exploring multi-round reinforcement learning and self-evolving agent architectures for developing adaptive and continuously improving AI systems.
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
Download Paper | Download Slides
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Paper Title Number 5, with math \(E=mc^2\)
Published in GitHub Journal of Bugs, 2024
This paper is about a famous math equation, \(E=mc^2\)
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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SafeWork-R1: Coevolving Safety and Intelligence under the AI-45° Law
Published in arXiv, 2025
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framework, which incorporates large-scale, progressive, safety-oriented reinforcement learning post-training, supported by a suite of multi-principled verifiers.
Recommended citation: Shanghai AI Lab et al. (2025). "SafeWork-R1: Coevolving Safety and Intelligence under the AI-45° Law." arXiv preprint arXiv:2507.18576.
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Large Language Model Reinforcement Learning Algorithm Optimization
Published in Under Review, 2025
A novel approach for optimizing reinforcement learning algorithms in large language model training, focusing on improving training efficiency and stability.
Recommended citation:
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talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.