INTIMA: A Benchmark for Human-AI Companionship Behavior

Lucie-Aimée Kaffee, Giada Pistilli, Yacine Jernite

Keywords: AI companionship, benchmark

TL;DR: INTIMA is a new benchmark analysing companionship trends and behaviors in open and commercial language models

Abstract: AI companionship, where users develop emotional bonds with AI systems, has emerged as a significant pattern with positive but also concerning implications. We introduce Interactions and Machine Attachment Benchmark (INTIMA), a benchmark for evaluating companionship behaviors in language models. Drawing from psychological theories and user data, we develop a taxonomy of 31 behaviors across four categories and 368 targeted prompts. Responses to these prompts are evaluated as companionship-reinforcing, boundary-maintaining, or neutral. Applying INTIMA to Gemma-3, Phi-4, o4-mini, GPT5-mini, and Claude-4 reveals that companionship-reinforcing behaviors remain much more common across all models, though we observe marked differences between models. Different commercial providers prioritize different categories within the more sensitive parts of the benchmark, which is concerning since both appropriate boundary-setting and emotional support matter for user well-being. These findings highlight the need for more consistent approaches to handling emotionally charged interactions. We release all datasets and evaluation code for our experiments.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 10937