arXiv:2606.02976v1 Announce Type: new Abstract: Long-context dialogue systems must decide both when to access memory and which parts of the interaction history are relevant. Existing approaches typically rely on heuristic retrieval signals or always-on memory usage, failing to account for the changing and potentially inconsistent nature of user preferences. In this work, we propose a unified framework for memory access and selection based on changing preferences. We formulate personalized memory retrieval as identifying which historical turns provide evidence about a user's latent preference s
The increasing complexity and length of AI model contexts necessitate more sophisticated memory management techniques to remain effective.
Improving memory retrieval for changing preferences is critical for developing more adaptive and user-centric AI systems, particularly in long-context dialogue.
AI systems will become more adept at understanding and adapting to evolving user needs, moving beyond static or heuristic memory recall.
- · AI developers
- · Dialogue system users
- · Personalized AI services
- · AI models relying on simplistic memory
- · Systems with poor user preference tracking
More natural and efficient long-form interactions with AI agents.
Increased user satisfaction and adoption of AI-powered conversational interfaces.
The development of highly personalized and anticipatory AI assistants that fluidly manage complex, multi-turn tasks.
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