1.7 KiB
1.7 KiB
Paper: Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory
type: paper title: "Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory" authors: Suozhao Ji, Baodong Wu, Zehao Wang, Lei Xia, Qingping Li, Ruisong Wang, Wenbo Ding, Zhenhua Zhu, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.10677 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-09 updated_at: 2026-06-09 status: skimmed relevance: high topics:
- agent-evaluation
- memory
- rag
- tool-use methods:
- topic-documents
- buffered-consolidation
- agentic-retrieval benchmarks:
- MemoryAgentBench
- LongMemEval models:
datasets:
- cs.AI
- cs.CL related_concepts:
- semantic-memory
- selective-forgetting related_jobs:
related_experiments:
- KC-001-agent-memory-pilot related_projects:
- learning/agent-memory collection_score: 18 collection_queries: agent-memory
One-line Takeaway
长期事实应被维护为带证据和时间的主题文档,并允许 Agent 多步检查,而不是只保存孤立记录和一次检索结果。
Pilot Skim
- problem: 孤立记录难以聚合证据、修订事实和维护跨 session 一致性。
- method: observation 先进入 buffer,再巩固到 topic documents;读取时迭代 search、grep 和 read_lines。
- evidence: MemoryAgentBench overall 64.7;LongMemEval 上 summary-only 41.7、summary+BM25 76.0、agentic 79.3。
- boundary: multi-hop selective forgetting 仍困难;多步 retrieval 和周期维护增加计算成本。