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.

翻译：长期大语言模型（LLM）智能体需要具备持久记忆能力，以追踪动态变化的事实并在跨会话中提供相关证据。现有记忆系统通常将观测结果存储为孤立记录、摘要或索引片段，这导致证据聚合、事实修正及记忆维护变得困难。我们提出Infini Memory——一种可维护的基于文本的持久记忆架构，它将智能体记忆视为主题结构化文档。每个主题文档作为语义单元，用于收集相关证据、保留元数据并随时间修正事实。新观测结果首先在缓冲中暂存，然后定期整合为连贯的文本上下文。在推理阶段，智能体化检索流程允许LLM通过迭代工具调用（而非单步检索）读取记忆。在MemoryAgentBench上，Infini Memory取得了64.7%的总分。消融实验表明，主题结构化维护与迭代式证据检查能够互补提升长期记忆使用的不同方面。