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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.19549 |
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| _version_ | 1866915520720142336 |
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| author | Xu, Derong Wen, Yi Jia, Pengyue Zhang, Yingyi zhang, wenlin Wang, Yichao Guo, Huifeng Tang, Ruiming Zhao, Xiangyu Chen, Enhong Xu, Tong |
| author_facet | Xu, Derong Wen, Yi Jia, Pengyue Zhang, Yingyi zhang, wenlin Wang, Yichao Guo, Huifeng Tang, Ruiming Zhao, Xiangyu Chen, Enhong Xu, Tong |
| contents | Large Language Models (LLMs) have recently been widely adopted in conversational agents. However, the increasingly long interactions between users and agents accumulate extensive dialogue records, making it difficult for LLMs with limited context windows to maintain a coherent long-term dialogue memory and deliver personalized responses. While retrieval-augmented memory systems have emerged to address this issue, existing methods often depend on single-granularity memory segmentation and retrieval. This approach falls short in capturing deep memory connections, leading to partial retrieval of useful information or substantial noise, resulting in suboptimal performance. To tackle these limits, we propose MemGAS, a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval. MemGAS is based on multi-granularity memory units and employs Gaussian Mixture Models to cluster and associate new memories with historical ones. An entropy-based router adaptively selects optimal granularity by evaluating query relevance distributions and balancing information completeness and noise. Retrieved memories are further refined via LLM-based filtering. Experiments on four long-term memory benchmarks demonstrate that MemGAS outperforms state-of-the-art methods on both question answer and retrieval tasks, achieving superior performance across different query types and top-K settings. \footnote{https://github.com/quqxui/MemGAS} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19549 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents Xu, Derong Wen, Yi Jia, Pengyue Zhang, Yingyi zhang, wenlin Wang, Yichao Guo, Huifeng Tang, Ruiming Zhao, Xiangyu Chen, Enhong Xu, Tong Computation and Language Large Language Models (LLMs) have recently been widely adopted in conversational agents. However, the increasingly long interactions between users and agents accumulate extensive dialogue records, making it difficult for LLMs with limited context windows to maintain a coherent long-term dialogue memory and deliver personalized responses. While retrieval-augmented memory systems have emerged to address this issue, existing methods often depend on single-granularity memory segmentation and retrieval. This approach falls short in capturing deep memory connections, leading to partial retrieval of useful information or substantial noise, resulting in suboptimal performance. To tackle these limits, we propose MemGAS, a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval. MemGAS is based on multi-granularity memory units and employs Gaussian Mixture Models to cluster and associate new memories with historical ones. An entropy-based router adaptively selects optimal granularity by evaluating query relevance distributions and balancing information completeness and noise. Retrieved memories are further refined via LLM-based filtering. Experiments on four long-term memory benchmarks demonstrate that MemGAS outperforms state-of-the-art methods on both question answer and retrieval tasks, achieving superior performance across different query types and top-K settings. \footnote{https://github.com/quqxui/MemGAS} |
| title | From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.19549 |