arXiv:2510.16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states. Existing approaches, including retrieval-augmented generation and explicit memory systems, primarily operate at the fact level, making it difficult to distill stable preferences and deep user traits from evolving and potentially conflicting dialogues.To address this challenge, we propose RGMem, a self-evolving memory framework inspired by
The increasing sophistication of LLMs and the demand for more personalized, continuous interactions are driving innovation in memory architectures.
This development addresses a fundamental limitation in current LLM-based agents, enabling deeper and more stable understanding of user states across sessions.
LLM agents can move beyond episodic interactions to long-term, evolving relationships, leading to more human-like and effective autonomous systems.
- · AI developers
- · Conversational AI platforms
- · SaaS providers
- · End-users of AI agents
- · Legacy chatbot systems
- · Basic retrieval-augmented generation
LLM agents will exhibit improved long-term memory and personalization capabilities.
This will accelerate the deployment of autonomous AI agents across various sectors, performing complex, multi-session tasks.
The enhanced agency of AI systems could lead to new forms of digital labor and potentially reshape white-collar workflows more profoundly.
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Read at arXiv cs.AI