ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents

Excerpt

arXiv:2609.37311v2 Announce Type: replace Abstract: Recent Recommendation Agents (RecAgents) offer a promising alternative by shifting recommendation to an active, user-side paradigm, where generative agents autonomously perceive external platforms, reason over user preferences, and execute decisions. However, existing RecAgents still suffer from two critical limitations: brittle item perception based on noisy and heterogeneous item pages, and inefficient long-context reasoning over extended use