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    <abstract>Emotional Support Conversation (ESC) systems help seekers understand and cope with distress through multi-turn interaction. Existing ESC systems typically improve responses by adding auxiliary support knowledge or prompting LLMs to reason over dialogue context. However, they usually pass inferred seeker information to the generator as a single state, so factual circumstances and mental state inferences are stored, selected, and updated in the same way. This blurs the difference between persistent facts and transient personal states, making it difficult to retain stable grounding while adapting to psychological changes across turns. Therefore, in this paper, we propose MIA, a Personal-Factual Adaptive Memory framework for multi-turn emotional support conversation. MIA first infers Emotional Theory-of-Mind factors and separates them into factual and personal states. It then uses a Personal-Factual Discriminator to decide which state should guide the next response, and an Obsolescence Removal Mechanism to remove outdated memory before generation. This design enables the generator to preserve factual grounding, focus on the turn-relevant support orientation, and avoid stale psychological assumptions. Experiments on ESConv and CPsyCounD show that MIA better understands the seeker’s situation, provides more constructive guidance, and maintains more coherent support across turns. Our codes are available at: https://github.com/ Wh2018/MIA.</abstract>
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%0 Conference Proceedings
%T Remembering Facts, Updating Minds: Personal-Factual Adaptive Memory for Multi-Turn Emotional Support
%A Wang, Hao
%A Zhang, Qiuyun
%A LI, Peilin
%A Ma, Ke
%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 wang-etal-2026-remembering
%X Emotional Support Conversation (ESC) systems help seekers understand and cope with distress through multi-turn interaction. Existing ESC systems typically improve responses by adding auxiliary support knowledge or prompting LLMs to reason over dialogue context. However, they usually pass inferred seeker information to the generator as a single state, so factual circumstances and mental state inferences are stored, selected, and updated in the same way. This blurs the difference between persistent facts and transient personal states, making it difficult to retain stable grounding while adapting to psychological changes across turns. Therefore, in this paper, we propose MIA, a Personal-Factual Adaptive Memory framework for multi-turn emotional support conversation. MIA first infers Emotional Theory-of-Mind factors and separates them into factual and personal states. It then uses a Personal-Factual Discriminator to decide which state should guide the next response, and an Obsolescence Removal Mechanism to remove outdated memory before generation. This design enables the generator to preserve factual grounding, focus on the turn-relevant support orientation, and avoid stale psychological assumptions. Experiments on ESConv and CPsyCounD show that MIA better understands the seeker’s situation, provides more constructive guidance, and maintains more coherent support across turns. Our codes are available at: https://github.com/ Wh2018/MIA.
%U https://aclanthology.org/2026.findings-emnlp.1142/
%P 24183-24205

Markdown (Informal)

[Remembering Facts, Updating Minds: Personal-Factual Adaptive Memory for Multi-Turn Emotional Support](https://aclanthology.org/2026.findings-emnlp.1142/) (Wang et al., Findings 2026)

• Remembering Facts, Updating Minds: Personal-Factual Adaptive Memory for Multi-Turn Emotional Support (Wang et al., Findings 2026)

ACL

• Hao Wang, Qiuyun Zhang, Peilin LI, and Ke Ma. 2026. Remembering Facts, Updating Minds: Personal-Factual Adaptive Memory for Multi-Turn Emotional Support. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 24183–24205, Budapest, Hungary. Association for Computational Linguistics.