@inproceedings{meng-etal-2026-beliefmem,
title = "{BELIEFMEM}: Belief-Revising Memory for Conversational {LLM} Agents",
author = "Meng, Felix and
Wu, Junda and
McAuley, Julian",
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.576/",
pages = "12152--12166",
ISBN = "979-8-89176-462-0",
abstract = {Conversational LLM agents accumulate persistent state across sessions, but existing memory systems treat updates as append-or-replace heuristics, leaving contradictory beliefs to coexist in the store and forcing the answer model to reconcile them at query time. We argue this is the wrong abstraction: conversation is a stream of \textit{belief revisions}, and the right formal lens is the Alchourr{\'o}n{--}G{\"a}rdenfors{--}Makinson (AGM) framework, adapted to Hansson belief bases. We present , which operationalizes the AGM operator set as the write semantics of a history-preserving \textit{fact} belief base. A uniform LLM-driven resolver classifies every extracted claim into one of four actions (\textsc{exp}, \textsc{rev}, \textsc{dup}, \textsc{con}) and runs at two timings: per round against the recent session, and across sessions over a similarity graph. The store never deletes; superseded beliefs retain predecessor pointers, and a context-selection step decides per query whether to surface the head, the full chain, or neither. On LoCoMo (1{,}540 questions) reaches $\mathbf{85.5\%}$ over Mem0-graph) and $\mathbf{92.3\%}$ with a stronger backbone. On BEAM (400 questions) it reaches $\mathbf{54.5\%}$ ($\mathbf{+21.8}$ over the LIGHT mean).}
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<abstract>Conversational LLM agents accumulate persistent state across sessions, but existing memory systems treat updates as append-or-replace heuristics, leaving contradictory beliefs to coexist in the store and forcing the answer model to reconcile them at query time. We argue this is the wrong abstraction: conversation is a stream of belief revisions, and the right formal lens is the Alchourrón–Gärdenfors–Makinson (AGM) framework, adapted to Hansson belief bases. We present , which operationalizes the AGM operator set as the write semantics of a history-preserving fact belief base. A uniform LLM-driven resolver classifies every extracted claim into one of four actions (exp, rev, dup, con) and runs at two timings: per round against the recent session, and across sessions over a similarity graph. The store never deletes; superseded beliefs retain predecessor pointers, and a context-selection step decides per query whether to surface the head, the full chain, or neither. On LoCoMo (1,540 questions) reaches \mathbf85.5% over Mem0-graph) and \mathbf92.3% with a stronger backbone. On BEAM (400 questions) it reaches \mathbf54.5% (\mathbf+21.8 over the LIGHT mean).</abstract>
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%0 Conference Proceedings
%T BELIEFMEM: Belief-Revising Memory for Conversational LLM Agents
%A Meng, Felix
%A Wu, Junda
%A McAuley, Julian
%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 meng-etal-2026-beliefmem
%X Conversational LLM agents accumulate persistent state across sessions, but existing memory systems treat updates as append-or-replace heuristics, leaving contradictory beliefs to coexist in the store and forcing the answer model to reconcile them at query time. We argue this is the wrong abstraction: conversation is a stream of belief revisions, and the right formal lens is the Alchourrón–Gärdenfors–Makinson (AGM) framework, adapted to Hansson belief bases. We present , which operationalizes the AGM operator set as the write semantics of a history-preserving fact belief base. A uniform LLM-driven resolver classifies every extracted claim into one of four actions (exp, rev, dup, con) and runs at two timings: per round against the recent session, and across sessions over a similarity graph. The store never deletes; superseded beliefs retain predecessor pointers, and a context-selection step decides per query whether to surface the head, the full chain, or neither. On LoCoMo (1,540 questions) reaches \mathbf85.5% over Mem0-graph) and \mathbf92.3% with a stronger backbone. On BEAM (400 questions) it reaches \mathbf54.5% (\mathbf+21.8 over the LIGHT mean).
%U https://aclanthology.org/2026.findings-emnlp.576/
%P 12152-12166
Markdown (Informal)
[BELIEFMEM: Belief-Revising Memory for Conversational LLM Agents](https://aclanthology.org/2026.findings-emnlp.576/) (Meng et al., Findings 2026)
ACL