Scientific agents stop working alone: a shared research state turns failed experiments into the next agent's starting point
Self-improvement in scientific agent swarms no longer rests on one agent's repeated trial and error. Previously, a failed experiment could not show whether the hypothesis or its implementation was at fault, and a program without overall gains could still hide a component another agent needed. SwarmRSI places hypotheses, programs and experimental evidence into a shared graph as inheritable research states, uses other agents' feedback to decide whether to revise a hypothesis or repair a program, retests retained hypotheses on other agents' programs, and combines complementary components. It reaches an average of 76.2 on OpenProblems and matches published SOTA on 44.4% of NatureBench tasks, the highest rate among compared methods, with medals on 81.8% of MLE-Bench Lite tasks.