Learning User Preferences Through Interaction for Long-Term Collaboration
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, we conduct a human user study that demonstrates that memory helps improve user experience in real-world settings.
69615195138cc47cbd764e4e
Shuhaib Mehri
69615195138cc47cbd764e4f
Priyanka Kargupta
claimed_verified
2026-03-23T11:02:07.121Z
6476ae4083d4fdaedddf405f
https://cdn-avatars.huggingface.co/v1/production/uploads/6476ae4083d4fdaedddf405f/ViS5-L6bNTfP5Vg0ASbqM.jpeg
Priyanka Kargupta
pkargupta
user
pkargupta
69615195138cc47cbd764e50
Tal August
69615195138cc47cbd764e51
Dilek Hakkani-Tür
Learning User Preferences Through Interaction for Long-Term Collaboration
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, we conduct a human user study that demonstrates that memory helps improve user experience in real-world settings.
MultiSessionCollab benchmark evaluates agents' ability to learn and adapt to user preferences through persistent memory systems that enhance long-term collaboration quality.
multi-session collaboration user preference learning memory systems agent adaptation task success rate user experience