[2601.02702] MultiSessionCollab: Learning User Preferences with Memory to Improve Long-Term Collaboration

Computer Science > Artificial Intelligence arXiv:2601.02702 (cs) [Submitted on 6 Jan 2026 (v1), last revised 3 Mar 2026 (this version, v2)] Title:MultiSessionCollab: Learning User Preferences with Memory to Improve Long-Term Collaboration Authors:Shuhaib Mehri, Priyanka Kargupta, Tal August, Dilek Hakkani-Tür View a PDF of the paper titled MultiSessionCollab: Learning User Preferences with Memory to Improve Long-Term Collaboration, by Shuhaib Mehri and 3 other authors View PDF HTML (experimental) Abstract:As conversational agents accumulate experience collaborating with users, adapting to user preferences is essential for fostering long-term relationships and improving collaboration quality over time. We introduce MultiSessionCollab, a benchmark that evaluates how well agents can learn user preferences and leverage them to improve collaboration quality throughout multiple sessions. To develop agents that succeed in this setting, we present long-term collaborative agents equipped with a memory that persists and refines user preference as interaction experience accumulates. Moreover, we demonstrate that learning signals can be derived from user simulator behavior in MultiSessionCollab to train agents to generate more comprehensive reflections and update their memory more effectively. Extensive experiments show that equipping agents with memory improves long-term collaboration, yielding higher task success rates, more efficient interactions, and reduced user effort. Finally, ...