Open challenges for agentic recommender systems: autonomy–control balance, external knowledge integration, and dynamic multimodal evaluation

Develop principled methods to (i) balance autonomy with controllability in agentic recommender systems, (ii) effectively incorporate external knowledge into recommendation pipelines, and (iii) design evaluation protocols for dynamic multimodal settings.

Background

The paper situates MemRerank within the emerging landscape of agentic recommender systems, where LLMs coordinate tools and memory. In summarizing broader perspectives, it highlights foundational challenges that remain unresolved.

Specifically, achieving the right autonomy–control trade-off, integrating external knowledge sources reliably, and devising robust evaluations for dynamic multimodal interactions are identified as open issues that the community has yet to solve.