Qingsong Li
Author directory2026
FlexSciEval: Structure-Grounded Multi-agent Evaluation for Flexible Electronics Research
Ke Li | Xinyi Yang | Runzhe Zhan | Qianyong Hu | Derek F. Wong | Qingsong Li | Zhiyuan Liu
Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
Ke Li | Xinyi Yang | Runzhe Zhan | Qianyong Hu | Derek F. Wong | Qingsong Li | Zhiyuan Liu
Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
Evaluating research in flexible electronics requires weighing evidence across multiple technical and manufacturing dimensions. However, current LLM-based approaches largely rely on unstructured text and generic evaluation criteria, limiting their ability to assess scientific work at the device level. We present FlexSciEval, a multi-agent framework that first turns each paper or research plan into an auditable structured record, then uses dimension-specific agents to score academic impact, fabrication realizability, and novelty. We also construct a 60-paper expert-annotated benchmark for validation and scale the framework to 4,158 papers. FlexSciEval reduces overall mean absolute error by 10.4% compared with direct LLM evaluation on the benchmark annotated by experts, although gains vary by model and dimension. These results indicate that grounding LLM evaluation in structured and dimension-specific evidence improves the reliability of automated scientific assessment in the field of flexible electronics, providing a practical basis for more reliable agent-based evaluation in experimental device research in the future.