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arXiv · 2606.10677

Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory

Suozhao Ji·Baodong Wu·Zehao Wang·Lei Xia·Qingping Li·Ruisong Wang·Wenbo Ding·Zhenhua Zhu·Boxun Li·Guohao Dai·Yu Wang
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Abstract

Long-term LLM agents need persistent memory that can track changing facts and provide relevant evidence across sessions. Existing memory systems often store observations as isolated records, summaries, or indexed fragments, which makes evidence aggregation, fact revision, and memory maintenance difficult. We propose Infini Memory, a maintainable text-based persistent memory architecture that treats agent memory as topic-structured documents. Each topic document serves as a semantic unit for collecting related evidence, preserving metadata, and revising facts over time. New observations are first staged in a buffer and periodically consolidated into coherent textual contexts. At inference time, an agentic retrieval procedure lets the LLM read memory through iterative tool calls rather than a single retrieval step. On MemoryAgentBench, Infini Memory achieves 64.7% overall score. Ablations show that topic-structured maintenance and iterative evidence inspection improve complementary aspects of long-term memory use.

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Suozhao Ji, Baodong Wu, Zehao Wang, Lei Xia, Qingping Li, Ruisong Wang, Wenbo Ding, Zhenhua Zhu, Boxun Li, Guohao Dai, Yu Wang. 2026-06-09. Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory. https://arxiv.org/abs/2606.10677

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