🚀 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…

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…

🚀 We built Visual Aesthetic Benchmark (VAB). Arena is alive here: vab.bakelab.ai/arena Aesthetic judgment is one of the hardest ceilings for AI to crack right now. Not generating images, but truly understanding what “looks good.” We hand-curated 400 sets of artist works (fine art, photography, and illustration), featuring 2000+ hours of brand-new commissioned data created specifically for this benchmark — all grounded in 13K+ domain expert judgments across 7 core aesthetic dimensions (composition, lighting, technique, expression…) to ensure rigorous evaluation in highly subjective domains. We asked 20+ frontier AI models to judge visual aesthetics (fine art, photography, illustration) against domain experts. Frontier models are really not good at it yet. Best model, Claude Sonnet 4.6 hit 26.5%. Human experts: 68.9%. > Blog: vab.bakelab.ai/blog > Leaderboard: vab.bakelab.ai/#leaderboard

“Research on AI, by AI, for AI and all, shall not perish from the scientific community.” Thrilled to share our new work BadScientist—honored with the Best Paper Award at Agents4Science 🏆.

🧩 VLMs can finally solve logic puzzles! We created VisualSphinx - a 660K synthetic vision logic dataset that boosts vision logical reasoning by 15-35% with RL. Full pipeline from 4K seed problems → 660K multimodal puzzles, all open-sourced! ✨ Website: visualsphinx.github.io