AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents
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2026
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| author | Zhu, Wenhui Chen, Xiwen Wang, Zhipeng Wang, Jingjing Dong, Xuanzhao Huang, Minzhou Cai, Rui Sang, Hejian Wang, Hao Qiu, Peijie Deng, Yueyue Tiwari, Prayag Rappazzo, Brendan Hogan Wang, Yalin |
| author_facet | Zhu, Wenhui Chen, Xiwen Wang, Zhipeng Wang, Jingjing Dong, Xuanzhao Huang, Minzhou Cai, Rui Sang, Hejian Wang, Hao Qiu, Peijie Deng, Yueyue Tiwari, Prayag Rappazzo, Brendan Hogan Wang, Yalin |
| contents | Long-horizon LLM agents require memory systems that remain accurate under fixed context budgets. However, existing systems struggle with two persistent challenges in long-term dialogue: (i) \textbf{disconnected evidence}, where multi-hop answers require linking facts distributed across time, and (ii) \textbf{state updates}, where evolving information (e.g., schedule changes) creates conflicts with older static logs. We propose AriadneMem, a structured memory system that addresses these failure modes via a decoupled two-phase pipeline. In the \textbf{offline construction phase}, AriadneMem employs \emph{entropy-aware gating} to filter noise and low-information message before LLM extraction and applies \emph{conflict-aware coarsening} to merge static duplicates while preserving state transitions as temporal edges. In the \textbf{online reasoning phase}, rather than relying on expensive iterative planning, AriadneMem executes \emph{algorithmic bridge discovery} to reconstruct missing logical paths between retrieved facts, followed by \emph{single-call topology-aware synthesis}. On LoCoMo experiments with GPT-4o, AriadneMem improves \textbf{Multi-Hop F1 by 15.2\%} and \textbf{Average F1 by 9.0\%} over strong baselines. Crucially, by offloading reasoning to the graph layer, AriadneMem reduces \textbf{total runtime by 77.8\%} using only \textbf{497} context tokens. The code is available at https://github.com/LLM-VLM-GSL/AriadneMem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03290 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents Zhu, Wenhui Chen, Xiwen Wang, Zhipeng Wang, Jingjing Dong, Xuanzhao Huang, Minzhou Cai, Rui Sang, Hejian Wang, Hao Qiu, Peijie Deng, Yueyue Tiwari, Prayag Rappazzo, Brendan Hogan Wang, Yalin Computation and Language Artificial Intelligence Information Retrieval Machine Learning Long-horizon LLM agents require memory systems that remain accurate under fixed context budgets. However, existing systems struggle with two persistent challenges in long-term dialogue: (i) \textbf{disconnected evidence}, where multi-hop answers require linking facts distributed across time, and (ii) \textbf{state updates}, where evolving information (e.g., schedule changes) creates conflicts with older static logs. We propose AriadneMem, a structured memory system that addresses these failure modes via a decoupled two-phase pipeline. In the \textbf{offline construction phase}, AriadneMem employs \emph{entropy-aware gating} to filter noise and low-information message before LLM extraction and applies \emph{conflict-aware coarsening} to merge static duplicates while preserving state transitions as temporal edges. In the \textbf{online reasoning phase}, rather than relying on expensive iterative planning, AriadneMem executes \emph{algorithmic bridge discovery} to reconstruct missing logical paths between retrieved facts, followed by \emph{single-call topology-aware synthesis}. On LoCoMo experiments with GPT-4o, AriadneMem improves \textbf{Multi-Hop F1 by 15.2\%} and \textbf{Average F1 by 9.0\%} over strong baselines. Crucially, by offloading reasoning to the graph layer, AriadneMem reduces \textbf{total runtime by 77.8\%} using only \textbf{497} context tokens. The code is available at https://github.com/LLM-VLM-GSL/AriadneMem. |
| title | AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents |
| topic | Computation and Language Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2603.03290 |