@inproceedings{zhou-etal-2026-latticemind,
title = "{L}attice{M}ind: A Conflict-Aware Memory Primitive for Multi-Agent Systems",
author = "Zhou, Heng and
Zhang, Lian and
Fan, Yutao and
He, Tiancheng and
Chen, Siki and
Geng, Hejia and
Torr, Philip and
Yin, Zhenfei",
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.1564/",
pages = "35653--35672",
ISBN = "979-8-89176-459-0",
abstract = "Multi-agent LLM systems often fail not for lack of candidate answers, but because they have no persistent mechanism for deciding which incompatible claim should currently be trusted. Majority vote, debate, and judge-based selection choose an output without recording which claim wins, which is contested, or why a later update supersedes it. We present {LatticeMind}, a conflict-aware structured memory that handles contradiction at write time. It maintains explicit item status, applies cheap symbolic conflict checks, and routes the flagged cases to an LLM reconciler. On a label-blind ConflictBank evaluation that removes source-name hints, over a uniform draw of 200 examples from the full benchmark pool, LatticeMind reaches 0.965 accuracy versus 0.525 for the strongest baseline, with every gap significant at $p<10^{-24}$ by paired McNemar test. Ablations show that removing the checker or the reconciler costs 12 to 14 points. Our claims are scoped to credibility conflicts, where one claim should defeat another: the coordination branch of the design is a safety override that we do not validate empirically, and all prompt-based comparisons are conditioned on a single backbone. On three secondary planning benchmarks and one synthetic scheduling diagnostic the picture is mixed: LatticeMind beats naive merge on three of four, but does not replace deliberation methods on tasks rewarding iterative search."
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<abstract>Multi-agent LLM systems often fail not for lack of candidate answers, but because they have no persistent mechanism for deciding which incompatible claim should currently be trusted. Majority vote, debate, and judge-based selection choose an output without recording which claim wins, which is contested, or why a later update supersedes it. We present LatticeMind, a conflict-aware structured memory that handles contradiction at write time. It maintains explicit item status, applies cheap symbolic conflict checks, and routes the flagged cases to an LLM reconciler. On a label-blind ConflictBank evaluation that removes source-name hints, over a uniform draw of 200 examples from the full benchmark pool, LatticeMind reaches 0.965 accuracy versus 0.525 for the strongest baseline, with every gap significant at p<10⁻24 by paired McNemar test. Ablations show that removing the checker or the reconciler costs 12 to 14 points. Our claims are scoped to credibility conflicts, where one claim should defeat another: the coordination branch of the design is a safety override that we do not validate empirically, and all prompt-based comparisons are conditioned on a single backbone. On three secondary planning benchmarks and one synthetic scheduling diagnostic the picture is mixed: LatticeMind beats naive merge on three of four, but does not replace deliberation methods on tasks rewarding iterative search.</abstract>
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%0 Conference Proceedings
%T LatticeMind: A Conflict-Aware Memory Primitive for Multi-Agent Systems
%A Zhou, Heng
%A Zhang, Lian
%A Fan, Yutao
%A He, Tiancheng
%A Chen, Siki
%A Geng, Hejia
%A Torr, Philip
%A Yin, Zhenfei
%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 zhou-etal-2026-latticemind
%X Multi-agent LLM systems often fail not for lack of candidate answers, but because they have no persistent mechanism for deciding which incompatible claim should currently be trusted. Majority vote, debate, and judge-based selection choose an output without recording which claim wins, which is contested, or why a later update supersedes it. We present LatticeMind, a conflict-aware structured memory that handles contradiction at write time. It maintains explicit item status, applies cheap symbolic conflict checks, and routes the flagged cases to an LLM reconciler. On a label-blind ConflictBank evaluation that removes source-name hints, over a uniform draw of 200 examples from the full benchmark pool, LatticeMind reaches 0.965 accuracy versus 0.525 for the strongest baseline, with every gap significant at p<10⁻24 by paired McNemar test. Ablations show that removing the checker or the reconciler costs 12 to 14 points. Our claims are scoped to credibility conflicts, where one claim should defeat another: the coordination branch of the design is a safety override that we do not validate empirically, and all prompt-based comparisons are conditioned on a single backbone. On three secondary planning benchmarks and one synthetic scheduling diagnostic the picture is mixed: LatticeMind beats naive merge on three of four, but does not replace deliberation methods on tasks rewarding iterative search.
%U https://aclanthology.org/2026.emnlp-main.1564/
%P 35653-35672
Markdown (Informal)
[LatticeMind: A Conflict-Aware Memory Primitive for Multi-Agent Systems](https://aclanthology.org/2026.emnlp-main.1564/) (Zhou et al., EMNLP 2026)
ACL
- Heng Zhou, Lian Zhang, Yutao Fan, Tiancheng He, Siki Chen, Hejia Geng, Philip Torr, and Zhenfei Yin. 2026. LatticeMind: A Conflict-Aware Memory Primitive for Multi-Agent Systems. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 35653–35672, Budapest, Hungary. Association for Computational Linguistics.