Recent advances in large language models (LLMs) have scaled the potential for reasoning and agentic search, wherein models autonomously plan, retrieve, and reason over external knowledge to answer complex queries. However, the iterative think-search loop accumulates long system memories, leading to memory dilution problem. In addition, existing memory management methods struggle to capture fine-grained semantic relations between queries and documents and often lose substantial information. Therefore, we propose MemSearch-o1, an agentic search framework built on reasoning-aligned memory growth and retracing. MemSearch-o1 dynamically grows fine-grained memory fragments from memory seed tokens from the queries, then retraces and deeply refines the memory via a contribution function, and finally reorganizes a globally connected memory path. This shifts memory management from stream-like concatenation to structured, token-level growth with path-based reasoning. Experiments on eight benchmark datasets show that MemSearch-o1 substantially mitigates memory dilution, and more effectively activates the reasoning potential of diverse LLMs, establishing a solid foundation for memory-aware agentic intelligence.

翻译：近年来，大语言模型（LLM）的进展显著提升了推理与智能搜索的潜力——模型能够自主规划、检索并推理外部知识以回答复杂查询。然而，迭代式“思考-搜索”循环会累积长程系统记忆，导致记忆稀释问题。此外，现有记忆管理方法难以捕捉查询与文档间的细粒度语义关联，且常丢失大量信息。为此，我们提出MemSearch-o1，一种基于推理对齐式记忆增长与回溯的智能搜索框架。MemSearch-o1从查询中的记忆种子令牌动态生成细粒度记忆片段，通过贡献函数进行回溯与深度精炼，最终重组为全局关联的记忆路径。这使记忆管理从流式拼接转变为基于令牌的结构化增长与路径推理。在八个基准数据集上的实验表明，MemSearch-o1显著缓解了记忆稀释问题，并能更有效地激活不同LLM的推理潜力，为具备记忆感知能力的智能体研究奠定了坚实基础。