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Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory

Paper · 2026 · Derong Xu, Shuochen Liu, Pengfei Luo et al.

Summary

LLM agents that personalize conversations over many sessions need a long-term user memory, because the context window cannot hold the whole history. Current memory systems either use fixed, hand-designed update rules or train update policies with RL on sparse final-answer rewards, which makes long-horizon training unstable. The authors propose MemCoE, a framework based on memory schema theory: prefrontal regions set a schema, and the hippocampus fills in episodic details.

MemCoE works in two stages:

  • Memory Guideline Induction (MGI) treats the memory-update prompt as a learnable text parameter. It refines this prompt using contrastive feedback on memory-augmented trajectories, treated as textual gradients and aggregated over batches.
  • Guideline-Aligned Memory Policy Optimization (GMPO) turns the resulting guideline into process-level rewards. It then trains the memory-evolution policy with multi-turn RL.

Splitting the problem into how memory is organized and what gets stored narrows the policy's action space. On PersonaMem, PrefEval and PersonaBench, MemCoE beats retrieval-based and RL-based memory baselines and stays robust, efficient and transferable.

Key points

  • Evaluation covers three personalization benchmarks (PersonaMem, PrefEval, PersonaBench), spanning explicit and implicit preferences, different history sizes and noisy evidence.
  • On PersonaMem, the MGI-induced guideline reaches 53.28 (32K) and 43.76 (128K). This is about +10.4% and +11.3% relative to a manually written prompt and beats an LLM-rewritten prompt, with results stable across three seeds.
  • The per-round token budget shows a trade-off. Very small budgets (1K–2K) force many update rounds, causing compounding errors and forgetting. Very large budgets (8K–32K) make each update step harder, so a moderate budget works best.
  • The induced guideline transfers across different LLMs and generalizes when query types shift.
  • The main limitation is that GMPO depends on an LLM-based scorer for its rewards. Code is released at github.com/Applied-Machine-Learning-Lab/ACL2026_MemCoE.

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

🎓University of Science and Technology of ChinaHefei · China145 works · 114,409 citations🎓Xi'an Jiaotong UniversityXi'an · China42 works · 36,431 citations🎓City University of Hong KongHong Kong · China59 works · 3,480 citations🎓Dalian University of TechnologyDalian · China20 works · 46 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksNeuroscience · 186 worksCognitive Neuroscience · 185 works

Cite

Cite as Derong Xu, Shuochen Liu, Pengfei Luo, Pengyue Jia, Yingyi Zhang, Yi Wen, Yimin Deng, Wenlin Zhang, Enhong Chen, Xiangyu Zhao and Tong Xu. 2026. Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory. 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.2084

@inproceedings{xu2026learning,
  title = {{Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory}},
  author = {Derong Xu, Shuochen Liu, Pengfei Luo, Pengyue Jia, Yingyi Zhang, Yi Wen, Yimin Deng, Wenlin Zhang, Enhong Chen, Xiangyu Zhao and Tong Xu},
  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.2084},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2084}
}

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