💡Our view: RSI is a systems engineering problem, not just a model problem. Progress depends on the environment a research agent works in: what resources it can call, what it can change, and how it runs experiments. That environment should reflect real production workflows and be easy for agents to use. At @Evolvent_AI, we built RSIGym around Everything as a Service. Agents can call established research services and iterate on data, training settings, and harness code. We also introduce RSI-Index to measure how well frontier agents jointly improve a target model’s weights and harness. Across 6 research agents and 5 benchmarks, Opus 5 leads at 0.4809. 🧵 We’re releasing the code, experiment configurations, research trajectories, and evaluation logs. Try your own improvement methods with RSIGym. 🌐 Website: 💻 GitHub: 📄 arXiv:

New RSIBench-Data ( result: Kimi K3 + Kimi Code achieves a 27.317% weighted score (aime,gpqa use 0.1, others 0.2) across six benchmarks, ranking #1# among the five model–harness combinations we tested. It scores 50% on SWE-bench Verified and 17% on SWE-bench Pro. A strong result for automated RSI research. Congratulate!