Ant Open Source

🚀 Ring-2.6-1T is now open source. A trillion-scale flagship thinking model built for real-world complex tasks: Agent workflows, coding & engineering, long-horizon tasks, complex reasoning, research, and enterprise automation. It is designed to move beyond “answering” toward execution: understanding context, planning steps, calling tools, and staying stable across long task chains. Highlights： - Advanced agentic workflow support. - Reasoning effort levels: high for agentic tasks, xhigh for complex reasoning. - Scalable asynchronous RL via the IcePop algorithm, enabling stable, trillion-scale training for long-horizon agentic RL.

Ling-2.6-flash is now officially open-sourced! A fast, token-efficient Instruct model built for real-world agent workflows. 104B total parameters · 7.4B active parameters Available in BF16, FP8, and INT4 variants for different deployment needs. Key strengths: - Fast generation: 215 tokens/s on Artificial Analysis Output Speed - High token efficiency: only 15M tokens on the full AA Intelligence Index evaluation - Real task execution: strong performance across coding, document processing, and lightweight agent workflows - Improved experience: better Chinese-English switching and smoother compatibility with mainstream coding frameworks

🎉 After one year of teamwork, we are excited to release our 3D foundation model — LingBot-Map! Unlike DA3/VGGT, LingBot-Map is a purely autoregressive model for streaming 3D reconstruction ⚡ It achieves ~20 FPS on 518×378 resolution over sequences exceeding 10,000 frames — and beyond 🚀 Two key insights behind LingBot-Map: 🔑 Keep SLAM's structural wisdom: build Geometric Context Attention with long-context modeling while maintaining a compact streaming state 🔑 Make everything end-to-end learnable — no optimization, no post-processing Let's check out our demos 👇

🚀 Exciting news for the spatial perception community! 📷 For too long, the lack of large-scale, real-world depth datasets has been a major bottleneck. Today, we are open-sourcing the RGB-D dataset built for training our spatial perception model LingBot-Depth — and it's massive. 👇