Yongbin Li
Head of the Qoder & Tongyi Lingma Models; Algorithm Scientist at Tongyi Lab
Li Yongbin graduated from Tsinghua University and serves as Head of the Qoder and Tongyi Lingma models, as well as an Algorithm Scientist at Tongyi Lab. His research focuses on large model technologies, including post-training, code models, character models, and AI agents. In the AI coding domain, he led the development of the industry-leading Qwen-Coder-Qoder large model and helped build products such as Qoder and Tongyi Lingma. He is also responsible for large model development across multiple application areas, including AI social companionship (Qwen-Character, Tongyi Stardust), AI customer service (Intelligent Customer Service, Tongyi Xiaomi), and AI-powered data analysis. In recent years, he has published over 100 papers at top international conferences, including ICLR, NeurIPS, ICML, ACL, EMNLP, and CVPR. His work has received more than 6,500 citations on Google Scholar, with an H-index of 37. He has also served as an Area Chair for leading international conferences such as ACL, NAACL, and WSDM.
Topic
From Assisted Coding to Autonomous Intelligence: The Agentic Evolution and Practice of Qoder in Complex Software Engineering
As AI Coding enters the era of autonomous agents, Qoder is building an Agentic Coding Platform designed for real-world, complex software systems. This session will unpack the core challenges of AI-native programming when dealing with large-scale codebases, and reveal how Qoder constructs efficient agentic workflows. It will also introduce Qwen-Coder-Qoder, a cutting-edge model deeply optimized for software engineering scenarios. Finally, drawing on real-world practices, the talk will offer forward-looking insights into the evolution of AI Coding—from code assistants to autonomous software engineers. Outline: Paradigm Shift: The Agentic Era of AI Coding Into the Deep End: Core Challenges in Complex Software Engineering Inside Qoder’s Architecture: Building Self-Evolving Agentic Workflows Vertical Breakthroughs: Technical Insights into Qwen-Coder-Qoder From Practice to Future: The Next Evolution of AI Coding
