@inproceedings{ke-etal-2026-interactive,
title = "Interactive Memory Learning for Long-Term Conversations",
author = "Ke, Cai and
Yan, Jiangyue and
Zhang, Han and
Liu, Xin and
Yuan, Zike and
Yu, Yue and
Wang, Hui and
Xu, Ruifeng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.683/",
pages = "14336--14358",
ISBN = "979-8-89176-462-0",
abstract = "Recent advancements in large language models have significantly enhanced the capabilities of agents in modeling long-term conversations. Despite these successes, existing approaches typically adopt a static heuristic paradigm, where information is passively archived without adaptive memory valuation. Consequently, these methods fail to self-evolve or align their memory management with evolving user needs. To address this, we propose **ICML** (**I**ntera**C**tive **M**emory **L**earning), a multi-agent framework that transforms the memory mechanism from a passive archive into a learnable, interactive memory policy. Specifically, we first employ a session synthesis pipeline to generate expert data, facilitating rapid test-time adaptation in unseen scenarios. Building on this, **ICML** utilizes an online reinforcement learning mechanism where a Planner agent selectively encodes high-value information and a Trigger agent dynamically retrieves it to optimize response quality, whereby the two agents co-evolve through continuous interaction feedback. Crucially, both agents are synchronized through a delayed reward mechanism that propagates future feedback back to earlier storage decisions, ensuring memory policies are precisely aligned with user expectations. Experimental results demonstrate that **ICML** significantly outperforms strong baselines, exhibiting the unique capability to continuously improve response quality as interactions accumulate."
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<abstract>Recent advancements in large language models have significantly enhanced the capabilities of agents in modeling long-term conversations. Despite these successes, existing approaches typically adopt a static heuristic paradigm, where information is passively archived without adaptive memory valuation. Consequently, these methods fail to self-evolve or align their memory management with evolving user needs. To address this, we propose **ICML** (**I**ntera**C**tive **M**emory **L**earning), a multi-agent framework that transforms the memory mechanism from a passive archive into a learnable, interactive memory policy. Specifically, we first employ a session synthesis pipeline to generate expert data, facilitating rapid test-time adaptation in unseen scenarios. Building on this, **ICML** utilizes an online reinforcement learning mechanism where a Planner agent selectively encodes high-value information and a Trigger agent dynamically retrieves it to optimize response quality, whereby the two agents co-evolve through continuous interaction feedback. Crucially, both agents are synchronized through a delayed reward mechanism that propagates future feedback back to earlier storage decisions, ensuring memory policies are precisely aligned with user expectations. Experimental results demonstrate that **ICML** significantly outperforms strong baselines, exhibiting the unique capability to continuously improve response quality as interactions accumulate.</abstract>
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%0 Conference Proceedings
%T Interactive Memory Learning for Long-Term Conversations
%A Ke, Cai
%A Yan, Jiangyue
%A Zhang, Han
%A Liu, Xin
%A Yuan, Zike
%A Yu, Yue
%A Wang, Hui
%A Xu, Ruifeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ke-etal-2026-interactive
%X Recent advancements in large language models have significantly enhanced the capabilities of agents in modeling long-term conversations. Despite these successes, existing approaches typically adopt a static heuristic paradigm, where information is passively archived without adaptive memory valuation. Consequently, these methods fail to self-evolve or align their memory management with evolving user needs. To address this, we propose **ICML** (**I**ntera**C**tive **M**emory **L**earning), a multi-agent framework that transforms the memory mechanism from a passive archive into a learnable, interactive memory policy. Specifically, we first employ a session synthesis pipeline to generate expert data, facilitating rapid test-time adaptation in unseen scenarios. Building on this, **ICML** utilizes an online reinforcement learning mechanism where a Planner agent selectively encodes high-value information and a Trigger agent dynamically retrieves it to optimize response quality, whereby the two agents co-evolve through continuous interaction feedback. Crucially, both agents are synchronized through a delayed reward mechanism that propagates future feedback back to earlier storage decisions, ensuring memory policies are precisely aligned with user expectations. Experimental results demonstrate that **ICML** significantly outperforms strong baselines, exhibiting the unique capability to continuously improve response quality as interactions accumulate.
%U https://aclanthology.org/2026.findings-emnlp.683/
%P 14336-14358
Markdown (Informal)
[Interactive Memory Learning for Long-Term Conversations](https://aclanthology.org/2026.findings-emnlp.683/) (Ke et al., Findings 2026)
ACL
- Cai Ke, Jiangyue Yan, Han Zhang, Xin Liu, Zike Yuan, Yue Yu, Hui Wang, and Ruifeng Xu. 2026. Interactive Memory Learning for Long-Term Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 14336–14358, Budapest, Hungary. Association for Computational Linguistics.