Interactive Memory Learning for Long-Term Conversations
Recent advancements in large language models have significantly enhanced the capabilities of agents in modeling long-term conversations. Despite these successes, existing approaches typically adopt a static heuristic paradigm, where information is passively archived without adaptive memory valuation. Consequently, these methods fail to self-evolve or align their memory management with evolving user needs. To address this, we propose ICML (InteraCtive Memory Learning), a multi-agent framework that transforms the memory mechanism from a passive archive into a learnable, interactive memory policy. Specifically, we first employ a session synthesis pipeline to generate expert data, facilitatin...
| Authors | Cai Ke, Jiangyue Yan, Han Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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