RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations

Arman Behnam, Sunglyoung Kim, Jiayi Yu, Eric Huang, Liangwei Yang

An AI companion that talks with someone for months should come to understand them. It should know who they are, remember what they said, and recognize when something from the past matters now. Testing this needs real conversations, but real conversations are private, so existing benchmarks use invented people and invented questions. We release RealCompanion, ten real relationships between people and an AI companion, with 27,218 messages over up to 120 days. For each person, we release the full conversation, a profile, a persona, chat test items, and question test items. Every label points to the messages that support it, and every chat label comes with the reasoning that produced it. The real data shows three things. First, people rarely refer back. Only 3.4% of their messages depend on something said earlier, and when one does, the earlier message is usually far away (a median of 2,157 messages back). Averages hide this. Looking at the most recent messages finds the needed one 95.9% of the time overall, but only 2.2% of the time when it is far back. Second, AI systems cannot tell when the past matters. The detectors we tested barely beat chance on real messages, and when the…