arXiv:2607.00017v1 Announce Type: cross Abstract: Long-term conversational agents are expected to remember past interactions, but memory is useful only when the right evidence is recalled for the right user. Existing memory-augmented LLM agents have made progress in building compact memory banks, yet retrieval is still often driven by query-centered similarity or fixed ranking rules, leaving user-conditioned relevance underexplored.To address this gap, we propose Profile-guided Personalized Retrieval Optimization (PPRO), a retrieval-centric framework that makes memory retrieval both user-aware
The proliferation of long-term conversational AI agents necessitates more sophisticated memory management and personalized recall to maintain user relevance and utility.
This development enhances the practical effectiveness and user experience of AI agents, making them more adaptable and valuable across various applications and collapsing more complex workflows.
AI agents are moving beyond query-centric retrieval to user-aware, personalized memory recall, significantly improving their ability to understand and respond to individual user needs over time.
- · AI agent developers
- · Conversational AI platforms
- · Enterprise software users
- · Personalized assistant services
- · Generic AI retrieval methods
- · Developers relying solely on brute-force memory
- · AI solutions lacking personalization features
AI agents become significantly more effective and personalized in long-term interactions, reducing user friction.
Increased adoption of AI agents across various industries as their utility and trustworthiness grow due to enhanced personalization.
The integration of deeply personalized AI agents could redefine human-computer interaction, making AI feel more like a tailored extension of the user rather than a generic tool.
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Read at arXiv cs.CL