🚀Sage-A1.5 by Shanghai AI Lab is here — pushing 35B agentic intelligence to the next level 🔥 🤖 35B MoE (3B active) 🧩 Agentic Orchestration & Verification — spawns parallel subagents & self-corrects on the fly 🔄 RSI Data Flywheel — evolved via real scientific discovery workflows & tens of thousands of synthesized environments 🚀 Beats frontier giants on FS-Olympiad, FS-Research, CMT & PinchBench, and hits 61.2% on SWE-Bench Pro 💻 Runs locally via vLLM（@vllm_project）, SGLang（@sgl_project）, llama.cpp & LM Studio (Q4_K_M ~20GB) Scale the Horizon, Compose the Capabilities 👇 🔗 Model: @huggingface @ModelScope2022 🕹️ Try it free:

🚀 Introducing Intern-S2-397B, our new multimodal foundation model designed for long-horizon scientific research and powerful scientific agents. 1⃣Strong general capabilities across knowledge, coding, and agents, ranking among the top open-source models. 2⃣Outstanding performance on scientific tasks including Biology-Instructions and Mol-Instructions, with strong capabilities in scientific understanding, generation, and design. 3⃣Achieves leading results among open-source models on IMO-Proof and AdvancedMathBench, reaching the level of top closed-source models such as Gemini 3.1 Pro. 4⃣Explores a new architecture for continuously expanding specialized scientific knowledge, building a stronger foundation for long-cycle scientific research. 👏Now supported by vLLM (@vllm_project ) and SGLang ( @lmsysorg ) — with more ecosystem integrations on the way. 🤗 Model: @huggingface @ModelScope2022 🤗 Try it now:

🥳Introducing Intern-S2-Preview, an efficient 35B scientific multimodal foundation model. 1⃣Delivers performance comparable to the trillion-scale Intern-S1-Pro on core scientific tasks. 2⃣The first open-source model with material crystal structure generation capabilities and strong general capabilities. 3⃣Significantly stronger scientific agent capabilities on multiple benchmarks. 4⃣Improves MTP acceptance rate and token generation speed via shared-weight MTP + KL loss. 5⃣CoT compression shortens responses while preserving strong reasoning , improving both performance and efficiency. 🥰Now supported by vLLM (@vllm_project) and SGLang ( @lmsysorg ) — with more ecosystem integrations on the way. 🤗Model： @huggingface @ModelScope2022 🤗GitHub: 🤗Try it now at: