🔥 Announcing our new paper: "SR-Scientist: Scientific Equation Discovery With Agentic AI" Most current work using LLMs for scientific discovery, like AlphaEvolve, follows a rigid "generate → evaluate → refine" loop. We challenge this paradigm for equation discovery. Our work, SR-Scientist, empowers an LLM to act as an autonomous agent, discovering scientific equations through long-horizon, tool-driven data analysis and equation evaluation—much like a human scientist. We further enhance its capabilities with multi-turn RL. 📈 Key Results: 1️⃣Consistently outperforms SOTA methods by a 6% to 35% absolute margin. 2️⃣Achieves significant performance gains after RL training. 3️⃣Demonstrates robustness to noise and generalization to out-of-domain data. 💡 Key Insights: 1️⃣ Long-horizon exploration is vital for performance. 2️⃣ Enabling agents to conduct their own data analysis is crucial. 3️⃣ An experience buffer is key for continuous optimization. 📄 Paper: arxiv.org/abs/2510.11661 💻 Code: github.com/GAIR-NLP/SR-Scien…