China AI Native Industry Insights – 20260608 – Alibaba | 阶跃星辰 | 腾讯混元 | more
Explore TongyiLab’s AgentScope 2.0, ResNet’s 2026 prize win, Tencent’s Stem algorithm. Discover more in Today’s China AI Native Industry Insights.
1. Alibaba TongyiLab releases AgentScope 2.0 multi-agent framework with system-level transparency features
Alibaba’s TongyiLab announced AgentScope 2.0, a production-ready agent framework focused on system-level transparency for multi-agent applications. The new version includes built-in retry and fallback mechanisms, an observable event system, a smarter permission system, and decoupled workspace with unified agent service. The framework is designed to work with increasingly capable LLMs while maintaining complete visibility into agent operations and decision-making processes.
Read more: https://github.com/agentscope-ai/agentscope
Video Credit: @Ali_TongyiLab on X
2. ResNet paper co-authored by StepFun Chief Scientist Zhang Xiangyu receives CVPR 2026 Longuet-Higgins Prize
CVPR 2026 announced that the 2015 paper Deep Residual Learning for Image Recognition (ResNet), co-authored by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, has received the Longuet-Higgins Prize. The prize recognizes research that has demonstrated long-term impact on both academic research and industrial development in computer vision. ResNet introduced residual learning to solve deep neural network training challenges and has become a foundational architecture in modern deep learning, with over 320,000 citations making it the most cited paper of the 21st century. The residual connection concept has expanded beyond computer vision to natural language processing, speech, multimodal systems, and other AI domains.
Read more: https://mp.weixin.qq.com/s/ZVgqdH_fE42jO4kcI-lF3g
Video Credit: Hyperframes
3. Tencent Hunyuan proposes Stem sparse attention algorithm reducing time-to-first-token by 3.6x, paper accepted to ICML 2026
Tencent Hunyuan announced the Stem sparse attention algorithm, which has been accepted to the machine learning conference ICML 2026. The algorithm introduces Token Position Decay (TPD) and Output-Aware Metric (OAM) innovations to achieve near-dense attention accuracy using only 25% of computational resources. When integrated into the Hunyuan Hy3 preview model with optimized HPC operators, Stem reduces time-to-first-token by 3.6x for 128K context lengths. The algorithm addresses bottlenecks in long-context inference by reallocating computational budget based on causal information flow and evaluating token importance through both attention scores and value vector magnitudes. Both the Stem algorithm and HPC operator implementations have been open-sourced on GitHub.
Read more: https://mp.weixin.qq.com/s/XneOSvjt-7A-DU546cGoZA
Video Credit: Hyperframes
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