Mustafa Omer Gul, Philippe Laban, Tanya Goyal, Jennifer Neville · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31512 · open-access PDF
사용자는 점점 더 확장된 다중 회전 작업을 위해 AI 시스템에 의존하지만, 증거는 LLM 기반 시스템 성능이 장기간의 상호 작용보다 악화된다는 것을 나타냅니다. 그러한 설정에서 AI 보조자를 평가하고 개선하려면 복잡한 사용자 의도를 충실하게 따르는 사용자 시뮬레이터가 필요하며 현실적인 대화 행동을 보여줍니다.
Machine translation (Meta M2M100); the English original follows.
Users increasingly rely on AI systems for extended, multi-turn tasks, yet evidence indicates that LLM-based system performance degrades over prolonged interaction. To evaluate and improve AI assistants in such settings, we need user simulators that faithfully follow complex user intents while exhibiting realistic conversational behavior. Existing approaches manifest a trade-off: prompted assistants can follow complex user intents but lack behavioral fidelity, while specialized user simulators better approximate conversational behavior but struggle to remain faithful to complex intents. In this
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