@inproceedings{xia-etal-2026-unimem,
title = "{U}ni{M}em: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams",
author = "Xia, Siyu and
Zhang, Chenheng and
Wu, Yanting and
Li, Haoxuan and
Chai, Jiajun and
Wang, Xiaohan and
Yin, Guojun and
Lin, Wei and
Lin, Zhouchen and
Zhang, Haifeng and
Wang, Jun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1490/",
pages = "34066--34085",
ISBN = "979-8-89176-459-0",
abstract = "Memory is essential for LLM agents to accumulate task experience and reuse task-specific execution strategies. However, real-world deployment over boundary-agnostic and evolving task streams exposes a fundamental stability-plasticity dilemma. External retrieval-based memory can rapidly absorb new evidence, but it often fails to internalize recurring execution patterns and incurs inference-time retrieval overhead. Parametric memory enables stable and efficient execution once learned, but typically relies on explicit task boundaries and fixed parameter budgets. Inspired by the human brain, which balances plasticity and stability through complementary episodic storage and gradual consolidation, we propose UniMem, a self-routing framework for autonomous memory management. UniMem uses learnable routing tokens as memory controllers, enabling adaptive coordination between complementary memory pathways: novel or sparse tasks are retained in an episodic buffer for retrieval-augmented execution, while recurring and reliable patterns are consolidated into expandable parametric memory. By decoupling task identification from task execution with routing tokens and parametric memory blocks, UniMem expands memory on demand without task labels during deployment or uncontrolled parameter growth. Experiments on long-horizon streaming task sequences show that UniMem typically achieves strong aggregate results while maintaining routing and execution stability, with an average gain of 4.0 EM over Replay LoRA."
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<abstract>Memory is essential for LLM agents to accumulate task experience and reuse task-specific execution strategies. However, real-world deployment over boundary-agnostic and evolving task streams exposes a fundamental stability-plasticity dilemma. External retrieval-based memory can rapidly absorb new evidence, but it often fails to internalize recurring execution patterns and incurs inference-time retrieval overhead. Parametric memory enables stable and efficient execution once learned, but typically relies on explicit task boundaries and fixed parameter budgets. Inspired by the human brain, which balances plasticity and stability through complementary episodic storage and gradual consolidation, we propose UniMem, a self-routing framework for autonomous memory management. UniMem uses learnable routing tokens as memory controllers, enabling adaptive coordination between complementary memory pathways: novel or sparse tasks are retained in an episodic buffer for retrieval-augmented execution, while recurring and reliable patterns are consolidated into expandable parametric memory. By decoupling task identification from task execution with routing tokens and parametric memory blocks, UniMem expands memory on demand without task labels during deployment or uncontrolled parameter growth. Experiments on long-horizon streaming task sequences show that UniMem typically achieves strong aggregate results while maintaining routing and execution stability, with an average gain of 4.0 EM over Replay LoRA.</abstract>
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%0 Conference Proceedings
%T UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams
%A Xia, Siyu
%A Zhang, Chenheng
%A Wu, Yanting
%A Li, Haoxuan
%A Chai, Jiajun
%A Wang, Xiaohan
%A Yin, Guojun
%A Lin, Wei
%A Lin, Zhouchen
%A Zhang, Haifeng
%A Wang, Jun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xia-etal-2026-unimem
%X Memory is essential for LLM agents to accumulate task experience and reuse task-specific execution strategies. However, real-world deployment over boundary-agnostic and evolving task streams exposes a fundamental stability-plasticity dilemma. External retrieval-based memory can rapidly absorb new evidence, but it often fails to internalize recurring execution patterns and incurs inference-time retrieval overhead. Parametric memory enables stable and efficient execution once learned, but typically relies on explicit task boundaries and fixed parameter budgets. Inspired by the human brain, which balances plasticity and stability through complementary episodic storage and gradual consolidation, we propose UniMem, a self-routing framework for autonomous memory management. UniMem uses learnable routing tokens as memory controllers, enabling adaptive coordination between complementary memory pathways: novel or sparse tasks are retained in an episodic buffer for retrieval-augmented execution, while recurring and reliable patterns are consolidated into expandable parametric memory. By decoupling task identification from task execution with routing tokens and parametric memory blocks, UniMem expands memory on demand without task labels during deployment or uncontrolled parameter growth. Experiments on long-horizon streaming task sequences show that UniMem typically achieves strong aggregate results while maintaining routing and execution stability, with an average gain of 4.0 EM over Replay LoRA.
%U https://aclanthology.org/2026.emnlp-main.1490/
%P 34066-34085
Markdown (Informal)
[UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams](https://aclanthology.org/2026.emnlp-main.1490/) (Xia et al., EMNLP 2026)
ACL
- Siyu Xia, Chenheng Zhang, Yanting Wu, Haoxuan Li, Jiajun Chai, Xiaohan Wang, Guojun Yin, Wei Lin, Zhouchen Lin, Haifeng Zhang, and Jun Wang. 2026. UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 34066–34085, Budapest, Hungary. Association for Computational Linguistics.