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[2602.13530] REMem: Reasoning with Episodic Memory in Language Agent
Summary
The paper presents REMem, a novel framework for enhancing language agents' episodic memory, enabling better recollection and reasoning over past interactions, outperforming existing systems.
Why It Matters
As AI continues to evolve, enhancing language agents' capabilities with episodic memory is crucial for developing more human-like interactions. REMem addresses significant gaps in current memory systems, paving the way for more effective AI applications in various fields.
Key Takeaways
- REMem introduces a two-phase framework for episodic memory in language agents.
- The framework includes offline indexing and online inference for improved memory handling.
- REMem outperforms existing memory systems like Mem0 and HippoRAG 2 in key benchmarks.
Computer Science > Artificial Intelligence arXiv:2602.13530 (cs) [Submitted on 13 Feb 2026] Title:REMem: Reasoning with Episodic Memory in Language Agent Authors:Yiheng Shu, Saisri Padmaja Jonnalagedda, Xiang Gao, Bernal Jiménez Gutiérrez, Weijian Qi, Kamalika Das, Huan Sun, Yu Su View a PDF of the paper titled REMem: Reasoning with Episodic Memory in Language Agent, by Yiheng Shu and 7 other authors View PDF HTML (experimental) Abstract:Humans excel at remembering concrete experiences along spatiotemporal contexts and performing reasoning across those events, i.e., the capacity for episodic memory. In contrast, memory in language agents remains mainly semantic, and current agents are not yet capable of effectively recollecting and reasoning over interaction histories. We identify and formalize the core challenges of episodic recollection and reasoning from this gap, and observe that existing work often overlooks episodicity, lacks explicit event modeling, or overemphasizes simple retrieval rather than complex reasoning. We present REMem, a two-phase framework for constructing and reasoning with episodic memory: 1) Offline indexing, where REMem converts experiences into a hybrid memory graph that flexibly links time-aware gists and facts. 2) Online inference, where REMem employs an agentic retriever with carefully curated tools for iterative retrieval over the memory graph. Comprehensive evaluation across four episodic memory benchmarks shows that REMem substantially outpe...
