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home / papers / 2026 / MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search

MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search

Paper · 2026 · Sheng Zhang, Junyi Li, Yingyi Zhang et al.

Summary

Agentic (deep) search lets an LLM alternate between reasoning and retrieval. Each round adds thinking traces and retrieved passages, so the context grows and key evidence gets buried. The authors call this memory dilution. Existing fixes that summarize, filter or prune memory often miss the shifting intent of follow-up queries and drop useful connections. MemSearch-o1 instead builds memory bottom-up from the query. Each new search query is split with spaCy into seed groups: subjects, actions, degree modifiers and temporal markers. Guided by these seeds, the LLM extracts short fragment sentences from retrieved documents and uses them in the next reasoning round. After the loop, a contribution function scores each fragment by relevance to the original question and by its bridge potential to other fragments. Greedy search then chains the selected fragments into a coherent memory path for the final answer. On eight QA benchmarks with several LLMs, the method reportedly outperforms RAG and agentic baselines that manage memory. This suggests structured, token-level memory helps large models use their reasoning ability.

Key points

  • Memory seeds are formed with the spaCy toolkit using parts of speech. Nouns and pronouns become subjects or temporal markers, verbs become actions, and adjectives and adverbs become degree modifiers.
  • The retracing score has two terms: relevance of a fragment to the original query, plus its bridge potential toward other fragments. A greedy search then builds a semantically smooth path.
  • The evaluation covers eight QA benchmarks and multiple LLM backbones. Baselines include RAG methods and agentic search with memory management, such as Search-o1.
  • In UMAP case studies with Qwen2.5-72B-Instruct, MemSearch-o1 explored a wider memory region than Search-o1 (Refined), even when both retrieved the same region. Its retrieval also landed closer to ground-truth answers.
  • Gains are limited for small models under 3B parameters, which struggle with extraction and with following search instructions. Code is available at github.com/Applied-Machine-Learning-Lab/ACL2026_MemSearch-o1.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢Huawei TechnologiesShenzhen · China75 works · 12,975 citations🎓City University of Hong KongHong Kong · China45 works · 3,014 citations🎓Dalian University of TechnologyDalian · China12 works · 46 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Sheng Zhang, Junyi Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Xiaowei Qian, Wenlin Zhang, Maolin Wang, Yong Liu and Xiangyu Zhao. 2026. MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.41

@inproceedings{zhang2026memsearch,
  title = {{MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search}},
  author = {Sheng Zhang, Junyi Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Xiaowei Qian, Wenlin Zhang, Maolin Wang, Yong Liu and Xiangyu Zhao},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.41},
  url = {https://doi.org/10.18653/v1/2026.acl-long.41}
}

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