SOURCE-LINKED INTELLIGENCE
ThinkFlow: Self-Evolving Probabilistic Latent Memory for Lifelong Conversational Agents
Lifelong conversational agents rely on memory systems to maintain deep, context-aware interactions with users. However, existing explicit textual memory pipelines suffer from a severe information bottleneck, often losing subtle behavioral patterns and emotional shifts. Furthermore, being typically static post-deployment, they cannot autonomously adapt to personal habits and preferences without manual feedback. Cognitive science, however, suggests that humans maintain mental models purely in a latent space and continuously refine them through predictive coding. Inspired by this, we propose \tex
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T11:21:39.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.