arXiv:2606.04660v1 Announce Type: new Abstract: Lifelong digital companions must integrate cross-session cues, continually update their understanding of users, and adapt to shifting privacy boundaries. Existing evaluations fail to capture this, testing memory recall and short-term empathy in isolation. To bridge this gap, we introduce \benchmark, a benchmark centered on multi-session \textit{Memory-Emotion-Environment} loops. By modeling users as persistent worlds with layered profiles and event trajectories, \benchmark uses multi-agent simulation to project environmental dynamics into dialogu
The rapid advancement in AI capabilities and increasing focus on autonomous agents necessitate more robust and comprehensive methods for evaluating their long-term performance and user integration.
This benchmark provides critical infrastructure for developing agents that can operate effectively as lifelong companions, impacting user trust, adoption, and the foundational design of human-AI interaction.
The focus shifts from isolated, short-term evaluations to integrated, multi-session assessments of AI agents, emphasizing memory, emotion, and environmental adaptation.
- · AI agent developers
- · Personalized AI service providers
- · Human-computer interaction researchers
- · AI evaluation methodologies focused solely on short-term tasks
- · AI models lacking strong persistent memory and adaptive capabilities
Improved, more reliable, and context-aware AI digital companions emerge.
Increased user reliance on AI agents for complex, ongoing tasks and personal support.
Ethical and societal frameworks for lifelong AI companionship become a central policy concern.
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Read at arXiv cs.CL