O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents
Recent advancements in LLM-powered agents have demonstrated significant potential in generating human-like responses; however, they continue to face challenges in maintaining long-term interactions within complex environments, primarily due to limitations in contextual consistency and dynamic personalization. Existing memory systems often depend on semantic grouping prior to retrieval, which can overlook semantically irrelevant yet critical user information and introduce retrieval noise. In this report, we propose the initial design of O-Mem, a novel memory framework based on active user profiling that dynamically extracts and updates user characteristics and event records from their proactive interactions with agents. O-Mem supports hierarchical retrieval of persona attributes and topic-related context, enabling more adaptive and coherent personalized responses. O-Mem achieves 51.67% on the public LoCoMo benchmark, a nearly 3% improvement upon LangMem,the previous state-of-the-art, and it achieves 62.99% on PERSONAMEM, a 3.5% improvement upon A-Mem,the previous state-of-the-art. O-Mem also boosts token and interaction response time efficiency compared to previous memory frameworks. Our work opens up promising directions for developing efficient and human-like personalized AI assistants in the future.
6923bd09b5612535ed95586d
Piaohong Wang
6923bd09b5612535ed95586e
Motong Tian
claimed_verified
2025-11-24T07:55:06.380Z
690f8f1364c5f58f09c08312
/avatars/7f38fb15cc5a63fdeab7c949a3ec0433.svg
Motong Tian
motongt2
user
motongt2
6923bd09b5612535ed95586f
Jiaxian Li
6923bd09b5612535ed955870
Yuan Liang
6923bd09b5612535ed955871
Yuqing Wang
6923bd09b5612535ed955872
Qianben Chen
claimed_verified
2026-03-23T11:03:05.555Z
64be3a8b86e7fb5b8a7ec8a9
/avatars/58280c82f643a5a8073623eff33fefb2.svg
Chen
Qianben
user
Qianben
6923bd09b5612535ed955873
Tiannan Wang
6923bd09b5612535ed955874
Zhicong Lu
6923bd09b5612535ed955875
Jiawei Ma
6923bd09b5612535ed955876
Yuchen Eleanor Jiang
6923bd09b5612535ed955877
Wangchunshu Zhou
O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents
Recent advancements in LLM-powered agents have demonstrated significant potential in generating human-like responses; however, they continue to face challenges in maintaining long-term interactions within complex environments, primarily due to limitations in contextual consistency and dynamic personalization. Existing memory systems often depend on semantic grouping prior to retrieval, which can overlook semantically irrelevant yet critical user information and introduce retrieval noise. In this report, we propose the initial design of O-Mem, a novel memory framework based on active user profiling that dynamically extracts and updates user characteristics and event records from their proactive interactions with agents. O-Mem supports hierarchical retrieval of persona attributes and topic-related context, enabling more adaptive and coherent personalized responses. O-Mem achieves 51.67% on the public LoCoMo benchmark, a nearly 3% improvement upon LangMem,the previous state-of-the-art, and it achieves 62.99% on PERSONAMEM, a 3.5% improvement upon A-Mem,the previous state-of-the-art. O-Mem also boosts token and interaction response time efficiency compared to previous memory frameworks. Our work opens up promising directions for developing efficient and human-like personalized AI assistants in the future.
O-Mem, an active user profiling memory framework, enhances contextual consistency and dynamic personalization in LLM-powered agents, improving performance on benchmarks and response efficiency.
LLM-powered agents contextual consistency dynamic personalization memory framework active user profiling hierarchical retrieval persona attributes topic-related context LoCoMo benchmark LangMem PERSONAMEM A-Mem token response time interaction response time