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The article examines how the peer review system is struggling to cope with the exponential growth of research publications and AI-assisted papers, leaving volunteer reviewers overwhelmed and leading to errors and delays, prompting calls for reform.
This paper presents an agentic AI scientific community of virtual labs that autonomously discover neural operator architectures for PDE problems. Using LLM planners, numerical workers, and reviewers under a citation-based economy, the system produces high-accuracy hybrid architectures, with results suggesting no universal winner among operator families.