Google’s Coscientist finds what took Researchers a Decade
The article at https://www.techspot.com/news/106874-ai-accelerates-superbug-solution-completing-two-days-what.html highlights a Google AI CoScientist project featuring a multi-agent system that generates original hypotheses without any gradient-based training. It runs on base LLMs, Gemini 2.0, which engage in back-and-forth arguments. This shows how “test-time compute scaling” without RL can create genuinely creative ideas. System overview The system starts with base LLMs that are not trained through gradient descent. Instead, multiple agents collaborate, challenge, and refine each other’s ideas. The process hinges on hypothesis creation, critical feedback, and iterative refinement. Hypothesis Production and Feedback An agent first proposes a set of hypotheses. Another agent then critiques...
