Agent4Spec is presented, an autonomous multi-agent framework featuring two distinct pipelines: a validation pipeline that detects and self-corrects OpenAPI syntax errors, and a market analysis pipeline that conducts competitive research, identifies functional gaps against industry leaders, and automatically generates m...
Yong-Jian Tang, C. Moraru, Doruk Tuncel et al.· 0 citations
Existing LLM-based approaches for software Requirements Engineering (RE) typically rely on basic prompting strategies or rudimentary agent collaboration, under-utilizing the full potential of multi-agent systems. Meanwhile, available datasets focus on isolated subtasks, such as requirements extraction, classification,...
Yong-Jian Tang, Lin-Han Li, Thomas A. Runkler· Proceedings of the 1st Inter...· 1 citation
Large language models (LLMs) increasingly underpin scientific AI applications that reason over structured knowledge, from biomedical question answering to materials informatics. However, their logical reasoning often falls short, producing factual inaccuracies unacceptable in these settings. Reliable evaluation remains...
Nishtha N. Vaidya, S. Grimm, T. Hubauer et al.· 0 citations
This study systematically evaluates self-evaluation-based uncertainty quantification across different language models of varying sizes on question-answering tasks spanning general to specialized knowledge domains. Using various self-evaluation methods where models judge their own predictions, we examine how model scale...
Idil Kapikiran, Thomas Decker, Thomas A. Runkler· 0 citations
Overall, AutoCoC contributes a reproducible and scalable alternative to expert-authored concept hierarchies by automatically deriving domain-specific prompt structures from documentation, making concept-based prompting more practical for structured knowledge generation.
Hurile Borjigin, Nishtha N. Vaidya, Thomas A. Runkler et al.· SKILLED-LLMs@FLoC· 0 citations
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