MetaSyn benchmark shows LLM pipelines recover at most 52.7% of ground-truth included studies due to screening failures on PI/ECO eligibility, despite 90.9% retrieval recall at K=200.
Marshall and Byron C
2 Pith papers cite this work. Polarity classification is still indexing.
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Agentic AI in engineering and manufacturing is currently most useful for structured repetitive work and data tasks, but adoption is limited by fragmented data, regulatory constraints, legacy systems, and needs for verification and human oversight.
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Benchmarking LLM Agents on Meta-Analysis Articles from Nature Portfolio
MetaSyn benchmark shows LLM pipelines recover at most 52.7% of ground-truth included studies due to screening failures on PI/ECO eligibility, despite 90.9% retrieval recall at K=200.
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Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities
Agentic AI in engineering and manufacturing is currently most useful for structured repetitive work and data tasks, but adoption is limited by fragmented data, regulatory constraints, legacy systems, and needs for verification and human oversight.