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 or reviews these hypotheses. The interplay between proposal and critique drives the early phase of exploration and ensures each idea receives scrutiny before moving forward.
Agent Tournaments To filter and refine the pool of ideas, the system conducts tournaments where two hypotheses go head-to-head, and the stronger one prevails. The selection is informed by the critiques and debates previously attached to each hypothesis.
Evolution and Refinement A specialized evolution agent then takes the best hypothesis from a tournament and refines it using the critiques. This updated hypothesis is submitted once more to additional tournaments. The repeated loop of proposing, debating, selecting, and refining systematically sharpens each idea’s quality.
Meta-Review A meta-review agent oversees all outputs, reviews, hypotheses, and debates. It draws on insights from each round of feedback and suggests broader or deeper improvements to guide the next generation of hypotheses.
Future Role of RL Though gradient-based training is absent in the current setup, the authors note that reinforcement learning might be integrated down the line to enhance the system’s capabilities. For now, the focus remains on agents’ ability to critique and refine one another’s ideas during inference.
Power of LLM Judgment A standout aspect of the project is how effectively the language models serve as judges. Their capacity to generate creative theories appears to scale alongside their aptitude for evaluating and critiquing them. This result signals the value of “judgment-based” processes in pushing AI toward more powerful, reliable, and novel outputs.
Conclusion Through discussion, self-reflection, and iterative testing, Google AI CoScientist leverages multi-agent debates to produce innovative hypotheses—without further gradient-based training or RL. It underscores the potential of “test-time compute scaling” to cultivate not only effective but truly novel solutions, especially when LLMs play the role of critics and referees.
[link] [comments]