Letting AI write code and run experiments is old news by now. But what happens when you hand it the whole research process, from picking a topic to writing the paper? Earlier "AI scientist" systems tended to stay stuck as glorified hyperparameter search engines, chasing short-term optimization with little room for genuine imagination. 🔬 A new paper from Sakana AI, "The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery," proposes an orchestration layer that unifies task setup, parallel investigation, retrospective evidence generation, and grounded paper writing. Multiple agents share a memory and run research cycles in parallel, splitting hypothesis proposal from implementation and verification. 🏆 A paper it actually wrote, on an extended error-diffusion learning rule, passed external peer review and was accepted as a talk at ALIFE 2026. Across a broader evaluation spanning 5 ML tasks, 4 coding harnesses, and 70 research campaigns, final experiment scores correlated with paper review scores at r=0.44. The system also discovered real algorithmic improvements on its own, like lifting retrieval accuracy from 51.7% to 98.0% by fixing LoRA routing. It's a concrete look at what research looks like once AI owns the process all the way through to the paper. URL: #AIScientist# #AutomatedDiscovery#