Evolving Scientific Intelligence: From Fragmented Evidence to Distributed Experimental Loops
Yuheng Ding
Abstract
Scientific discovery advances when evidence from different investigations is brought into contact. Yet paper-centric communication leaves experimental results fragmented, delayed, or unreported, creating a "scientific memory wall" between growing computational capabilities and the evidence accessible to AI systems. We envision a different dynamic: in a network of intelligent laboratories, a failed or overlooked experiment in one domain could trigger a hypothesis in another, whose testing generates evidence that stimulates further investigations. Such chains of information collision and empirical validation could transform isolated discovery loops into a cumulative scientific process. We propose Evolving Scientific Intelligence (ESI), an architectural blueprint for enabling these evidence-driven chain reactions. ESI preserves experimental records in shared, computable scientific memory, where evidence from different domains can be recombined into testable hypotheses. It jointly selects candidates and evaluation methods according to scientific value, reliability, and cost, then coordinates physical tests across distributed experimental nodes. Their feedback revises hypotheses and evaluators, while slower meta-control reassesses research objectives and decision criteria to guard against self-reinforcing errors and misguided optimization. We call for an Information Manhattan Project to build the infrastructure for scientific intelligence that evolves through experimentally grounded chains of discovery.