Apr 2026· AGENT@ICSE· pp. 152-156· 0 citations· 20 references
Computer Science
TL;DR
Preliminary results indicate performance differences across LLMs, suggesting that model choice influences coverage, consistency, and hallucination rates.
Abstract
This paper presents MARARE, a real-time multi-agent system that transforms meeting dialogues into structured software requirements. One agent interacts with participants, while background agents extract and verify requirements collaboratively. Evaluation using the LLM-as-a-Judge method across five meetings (5–8 minutes each) shows a mean coverage of 80.0 ± 11.2 % (mean ± SD), semantic similarity of 0.86 ± 0.05, and hallucination rate of 14.3 ± 6.2 %. Preliminary results indicate performance differences across LLMs, suggesting that model choice influences coverage, consistency, and hallucination rates.
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Hao Li, Haoxiang Zhang, Jie M. Zhang et al.· Proceedings of the 32nd ACM...· 2 citations
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The AI-supported UX Strategy Evaluator, created by German UPA’s UX Strategy Working Group, addresses the gap by posing questions to UX strategists and UX managers, rather than attempting to provide answers or best practices.
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Autonomous artificial intelligence (AI) agents can now log into a learning management system, read course materials, and complete unproctored, asynchronous assessed work end-to-end with no student involvement. We document that capability and trace its consequences for assessment validity. Three demonstrations on a live...
Stavros P. Hadjisolomou, R. El-Haddad· Intersection: A Journal at t...· 0 citations
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