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REMem: Reasoning with Episodic Memory in Language Agent
arXiv – CS AI|Yiheng Shu, Saisri Padmaja Jonnalagedda, Xiang Gao, Bernal Jim\'enez Guti\'errez, Weijian Qi, Kamalika Das, Huan Sun, Yu Su||5 views
🤖AI Summary
Researchers have developed REMem, a new framework that enables AI language agents to form and reason with episodic memory similar to humans. The system uses a two-phase approach with offline memory graph indexing and online agentic retrieval, showing significant improvements over existing memory systems like Mem0 and HippoRAG 2.
Key Takeaways
- →REMem introduces episodic memory capabilities to language agents through a hybrid memory graph that links time-aware experiences and facts.
- →The framework operates in two phases: offline indexing of experiences and online inference with iterative retrieval tools.
- →REMem achieved 3.4% improvement on episodic recollection tasks and 13.4% improvement on reasoning tasks compared to state-of-the-art systems.
- →Current language agents primarily rely on semantic memory and struggle with recollecting and reasoning over interaction histories.
- →The system demonstrates better refusal behavior for unanswerable questions, improving reliability and accuracy.
#episodic-memory#language-agents#ai-reasoning#memory-systems#retrieval-augmented-generation#arxiv#machine-learning#artificial-intelligence
Read Original →via arXiv – CS AI
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