arXiv:2606.04120v1 Announce Type: cross Abstract: Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context windows with raw retrieval degrades reasoning quality, while training memory agents via standard reinforcement learning creates a severe credit assignment bottleneck in a multi-stage pipeline. To solve this, we introduce SALIMORY, a framework that trains a single language model to manage a cognitively-structured memory-spanning user facts, preferences, and working memory. By introducing a hierarchica
The development of more sophisticated conversational AI agents necessitates better memory management solutions to move beyond limited context windows and achieve true lifelong companionship.
This research addresses a fundamental bottleneck in AI agent development, promising more coherent, personalized, and persistent interactions that are crucial for advanced applications.
Current limitations of conversational memory, such as degrading reasoning with large context windows or credit assignment problems in training, are potentially overcome by integrating a cognitively-structured memory system.
- · AI agent developers
- · Conversational AI platforms
- · Personalized AI assistants
- · Users of AI companions
- · AI systems relying solely on context window expansion
- · Traditional reinforcement learning approaches for memory management
SaliMory enables conversational agents to maintain more accurate and persistent user knowledge over extended interactions.
Improved memory leads to more effective and trustworthy AI agents that can handle complex and long-term user relationships.
The ability of AI agents to act as truly 'lifelong companions' could profoundly change human-computer interaction and daily life.
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Read at arXiv cs.AI