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AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents
arXiv – CS AI|Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Jingjing Wang, Xuanzhao Dong, Minzhou Huang, Rui Cai, Hejian Sang, Hao Wang, Peijie Qiu, Yueyue Deng, Prayag Tiwari, Brendan Hogan Rappazzo, Yalin Wang|
🤖AI Summary
Researchers have developed AriadneMem, a new memory system for long-horizon LLM agents that addresses challenges in maintaining accurate memory under fixed context budgets. The system uses a two-phase pipeline with entropy-aware gating and conflict-aware coarsening to improve multi-hop reasoning while reducing runtime by 77.8% and using only 497 context tokens.
Key Takeaways
- →AriadneMem improves Multi-Hop F1 by 15.2% and Average F1 by 9.0% over existing baselines in LLM agent memory systems.
- →The system addresses two key challenges: disconnected evidence requiring multi-hop reasoning and state updates that conflict with older information.
- →AriadneMem reduces total runtime by 77.8% while using only 497 context tokens through efficient graph-based reasoning.
- →The two-phase pipeline includes offline construction with noise filtering and online reasoning with algorithmic bridge discovery.
- →The research demonstrates significant efficiency gains in long-term dialogue systems for LLM agents.
Mentioned in AI
Models
GPT-4OpenAI
#llm-agents#memory-systems#ai-research#natural-language-processing#arxiv#machine-learning#dialogue-systems#context-optimization
Read Original →via arXiv – CS AI
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