Radical AI's self-driving lab demonstrates that automating the physical experimentation loop — not just the modeling — can achieve a throughput in materials discovery that prior state-of-the-art programs could not match.
Teams building multi-agent LLM pipelines can use behavioral economics game benchmarks as a cheap pre-screening tool to identify which open-weight models will cooperate effectively before investing in full-scale deployments.