Puzzles to Paradigms: A Survey of Human--Agent Collaboration in Scientific Innovation

arXiv:2611.09917v1 Announce Type: cross Abstract: Agentic AI systems are increasingly embedded in scientific workflows, including literature synthesis, hypothesis generation, tool use, writing, and peer review. However, existing surveys and evaluations often emphasize task performance while underexamining how humans and agents coordinate under different epistemic conditions of scientific work. This survey introduces CSCW$\times$Kuhn, a coordination-aware framework that connects computer-supported cooperative work dimensions with distinct scientific contexts. Using this framework, we synthesize recent human--agent systems and benchmarks to examine how coordination demands and failure risks may vary across contexts. Our synthesis identifies five recurring failure modes: procedural drift, grounding substitution, evaluative flattening, proxy optimization, and epistemic black-boxing, and argues for evaluations that move beyond task accuracy and toward grounding, provenance, disagreement preservation, and long-horizon scientific reliability. Our corpus and coding artifact are available at https://github.com/jenlia024/CSCWxKuhn-artifact.