@inproceedings{wang-etal-2026-comma,
title = "{C}o{MMA}: Conflict-Aware Read-Time Control for Conversational Memory {QA}",
author = "Wang, Zixiao and
Du, Xingbo and
Zhang, Duzhen and
Song, Le",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.88/",
pages = "1925--1944",
ISBN = "979-8-89176-439-2",
abstract = "Memory-augmented assistants must answer questions from long-horizon conversations in which facts are not only missing, but also revised, corrected, or mutually inconsistent. Existing methods largely optimize retrieval, storage, or updating, and many controllers still treat failure primarily as insufficient evidence. We argue that conversational memory QA also requires an explicit model of inconsistency: when remembered claims disagree, a system should not simply retrieve more or guess, but may need to resolve versions, return a qualified answer, ask for clarification, or abstain. We introduce CoMMA, a transparent read-time controller with explicit conflict state for conversational memory QA. CoMMA extracts provenance-aware claims from retrieved memories, groups potentially incompatible claims into conflict clusters, infers lightweight question intent, and chooses among direct answer, qualified answer, clarification, and abstention. In an evaluation centered on the full LongMemEval-Oracle benchmark, a LongMemEval conflict-focused diagnostic slice, and FactConsolidation-20, CoMMA performs best on the main LongMemEval answer-quality comparison, is the only method with non-zero conflict F1 on the conflict-focused diagnostic slice, and ties the strongest write-time overwrite baseline on FactConsolidation-20. These results suggest that making conflict explicit at read time improves both decision quality and the auditability of memory-aware behavior."
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%0 Conference Proceedings
%T CoMMA: Conflict-Aware Read-Time Control for Conversational Memory QA
%A Wang, Zixiao
%A Du, Xingbo
%A Zhang, Duzhen
%A Song, Le
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F wang-etal-2026-comma
%X Memory-augmented assistants must answer questions from long-horizon conversations in which facts are not only missing, but also revised, corrected, or mutually inconsistent. Existing methods largely optimize retrieval, storage, or updating, and many controllers still treat failure primarily as insufficient evidence. We argue that conversational memory QA also requires an explicit model of inconsistency: when remembered claims disagree, a system should not simply retrieve more or guess, but may need to resolve versions, return a qualified answer, ask for clarification, or abstain. We introduce CoMMA, a transparent read-time controller with explicit conflict state for conversational memory QA. CoMMA extracts provenance-aware claims from retrieved memories, groups potentially incompatible claims into conflict clusters, infers lightweight question intent, and chooses among direct answer, qualified answer, clarification, and abstention. In an evaluation centered on the full LongMemEval-Oracle benchmark, a LongMemEval conflict-focused diagnostic slice, and FactConsolidation-20, CoMMA performs best on the main LongMemEval answer-quality comparison, is the only method with non-zero conflict F1 on the conflict-focused diagnostic slice, and ties the strongest write-time overwrite baseline on FactConsolidation-20. These results suggest that making conflict explicit at read time improves both decision quality and the auditability of memory-aware behavior.
%U https://aclanthology.org/2026.aacl-long.88/
%P 1925-1944
Markdown (Informal)
[CoMMA: Conflict-Aware Read-Time Control for Conversational Memory QA](https://aclanthology.org/2026.aacl-long.88/) (Wang et al., AACL-IJCNLP 2026)
ACL
- Zixiao Wang, Xingbo Du, Duzhen Zhang, and Le Song. 2026. CoMMA: Conflict-Aware Read-Time Control for Conversational Memory QA. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1925–1944, Hengqin, China. Association for Computational Linguistics.