VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions
Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on understanding the user beyond what is explicitly stated, as user intent is often reflected in fragmented daily interactions and requires both personalized modeling and proactive interaction. However, existing agent benchmarks primarily evaluate reasoning and tool use, largely overlooking the challenges of inferring and leveraging user preferences in realistic scenarios. To address this gap, we introduce VitaBench 2.0, a benchmark for evaluating personalized and proactive agent behavior in long-term user interact...
| Authors | Yuxin Chen, Yi Zhang, Zhengzhou Cai et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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