🚀 A milestone for OpenRSI: 3 OpenRSI agent-generated optimizations have been merged upstream into SGLang-Omni! All three PRs underwent rigorous maintainer code review and verification by the SGLang-Omni team. 😉 Real code. Proven improvements. Lasting impact for the open-source community. More to come! 👏 Huge thanks to @zhuofengli96475 and @GenAI_is_real for leading this effort, and to the SGLang team @sgl_project for their support and collaboration! Try OpenRSI harness now: github.com/OpenRSI-Foundatio…

To take ASI seriously is to accept a weak-to-strong premise: human intelligence can build a process that eventually produces intelligence beyond itself. RSI is the last piece of that puzzle of ASI. It serves as the key to scaling on insights: human insights are limited by bandwidth, while agents could scale the generation of ideas and sweep a far larger region of method-space. As the early project initiator, we are fully aware that today, everyone is excited about RSI, yet the barriers to participation also keep everyone outside the door. We believe that a model's RSI capabilities intrinsically come from researchers, independent labs, and domain teams alike, and we think the benefits should ultimately return to everyone who builds models. This is why we take our name as OpenRSI-Index. We aim to keep RSI open through shared platforms and tools, so more people can participate and benefit. We could shape RSI together, set RSI’s standards, and challenge frontier models with our own related real research work. For this goal, we build RSI-Anything, a Human-AI collaboration pipeline. Through about one hour of conversation, the pipeline could help turn a real research question into a runnable autoresearch environment. We want researchers and agents to solve these problems together, sharing new insights, methods, and results with the community. Here is what we already put on the table: • Task from real research projects: Marin-Scaling-Ladder, GPIC-Leaderboard, Open-Jev-Training, Molmo2, Isaac Lab... • Ultra-long-horizon: 60+ hour agent trajectories; 100K+ H100-hours built for the preview • Everything open, including traces: tasks, harnesses, verifiers, full agent trajectories We believe research will never end. A game turns zero-sum only when the pie is too small to share. However, research is definitely the field with the highest ceiling there is. What shifts is the mindset: it frees researchers to find and formulate the crazier, more valuable, more exciting problems in the world.

Let RSI benefit everyone and let everyone shape RSI. Excited to be one of the project initiators helping the design of the signiture task and organization. I also think most of the research can be formulated as 1) hill climbing a current task, and 2) formalize a new task. We would like to share the process with everyone, rather than locking them only in some places.

As AI systems enter a recursive self-improvement loop, the central question is whether it can systematically move beyond human-designed methods to genuinely extend the scientific and intelligence frontier. What’s missing is a neutral, open standard for evaluating these capabilities in real, production-scale intelligence development. Today, we’re releasing OpenRSI-Index v0.1: evaluating whether AI can recursively improve itself and push the boundaries of intelligence and science, on production-scale clusters with 1k GPUs. We turn fully open-source projects into autoresearch environments, with agent trajectories lasting 60+ hours. Building v0.1 took 100K+ H100-hours. We’re building an ecosystem with and for the research community: let RSI benefit everyone, and let everyone shape RSI together. We invite task contributors and compute partners to build this open benchmark with us - all contributors will be included as paper authors. Shape RSI with us: 🌐 Website: index.openrsi.foundation/ind… 🛠️ GitHub: github.com/OpenRSI-Foundatio… 🤝 Contribute: github.com/OpenRSI-Foundatio…