arXiv cs.CL· Xiaobin Chen, Dian Jin, Lin Shu, Defeng Sun, Xiaoming Tao·· 12 小时前AI 评分38
SLICE-Mem：面向高效 LLM 智能体记忆的显著性门控轻量信息整理框架
SLICE-Mem: Salience-Gated Lightweight Information Curation for Efficient LLM Agent Memory
AI 导读
SLICE-Mem 是一个轻量 LLM 智能体记忆框架，用紧凑分类器筛选值得保留的用户轮次，再以语义依存抽取器转为简洁记忆条目，在线流程无需生成式 LLM 调用。
正文
Abstract:Long-term memory enables large language model (LLM) agents to maintain personalized and temporally coherent interactions, but repeated LLM inference for memory admission, extraction, and organization introduces substantial cost and latency. We present SLICE-Mem, a lightweight memory framework that combines salience-guided admission and predicate-centered information extraction. A compact classifier identifies user turns worth retaining, and a semantic-dependency-based extractor converts admitted turns into concise memory entries. This online pipeline requires no generative LLM calls, while cross-entry reconciliation is deferred to an offline consolidation stage. We evaluate SLICE-Mem on the weekly subset of Memora and LongMemEval-S using 8B and 30B Qwen backbones for memory construction and consolidation on both benchmarks. Compared with the generative-memory baseline MemOS under the Qwen3-30B configuration, SLICE-Mem's online pipeline achieves 46.60 versus 46.68 FAMA (Forgetting-Aware Memory Accuracy) on Memora Weekly and 56.8% versus 59.3% accuracy on LongMemEval-S, while reducing total LLM token consumption by 92.4% and 99.5%, respectively. These results demonstrate that SLICE-Mem can preserve useful evidence for long-term memory with substantially less reliance on LLM-based memory construction.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33015 [cs.CL] |
| (or arXiv:2610.33015v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33015 arXiv-issued DOI via DataCite |
Submission history
From: Xiaobin Chen [view email]
[v1]
Thu, 8 Oct 2026 05:17:23 UTC (410 KB)
来源：arXiv cs.CL · arxiv.org