@inproceedings{wang-etal-2026-memguard,
title = "{M}em{G}uard: Persisting Verifier Signals for {LLM}-Agent Memory Governance",
author = "Wang, Haoyu and
Dong, Guangyuan and
He, Liang and
Zhang, Zijing and
Luo, Jiachen and
Xue, Chao and
Liu, Chuang and
Tang, Hao",
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.423/",
pages = "9677--9708",
ISBN = "979-8-89176-459-0",
abstract = "LLM agents are moving from single-prompt use to long task streams in which reusable memory becomes a core capability for terminal, software-engineering, and web tasks. Such memory is useful only when stored experience remains reliable across hundreds of interactions, but two failure modes break that assumption in practice. The first is unreliable admission: failed trajectories, accidental successes, and misleading observations enter memory because they appear relevant, then mislead later decisions. The second is memory drift: long-running banks accumulate duplicate, stale, and conflicting records that retrieval alone cannot repair. MemGuard{'}s key distinction is to treat verifier output not as a one-shot filter, but as persistent lifecycle metadata. It converts multi-criteria score-token verification into reward, confidence, label, and uncertainty descriptors that are attached to every candidate before activation and reused during retrieval, conflict resolution, summarization, and archival. We evaluate MemGuard on Terminal-Bench 2.0, SWE-Bench Verified, WebArena, and Mind2Web across four backbones, comparing against four memory baselines plus a verifier-only control under matched runtime budgets. Averaged over five seeds, MemGuard achieves the best success metric and lowest average steps in all 16 backbone{--}benchmark settings, improving over ReasoningBank, the strongest prior baseline among the memory methods we evaluate, with a largest gain of 7.9 success-rate points on WebArena, 5.6 step-success-rate points on Mind2Web, and 2.4{--}3.5 points on terminal and software-engineering benchmarks. Code is available at [https://github.com/whyyyyy123/MemGuard](https://github.com/whyyyyy123/MemGuard)."
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<abstract>LLM agents are moving from single-prompt use to long task streams in which reusable memory becomes a core capability for terminal, software-engineering, and web tasks. Such memory is useful only when stored experience remains reliable across hundreds of interactions, but two failure modes break that assumption in practice. The first is unreliable admission: failed trajectories, accidental successes, and misleading observations enter memory because they appear relevant, then mislead later decisions. The second is memory drift: long-running banks accumulate duplicate, stale, and conflicting records that retrieval alone cannot repair. MemGuard’s key distinction is to treat verifier output not as a one-shot filter, but as persistent lifecycle metadata. It converts multi-criteria score-token verification into reward, confidence, label, and uncertainty descriptors that are attached to every candidate before activation and reused during retrieval, conflict resolution, summarization, and archival. We evaluate MemGuard on Terminal-Bench 2.0, SWE-Bench Verified, WebArena, and Mind2Web across four backbones, comparing against four memory baselines plus a verifier-only control under matched runtime budgets. Averaged over five seeds, MemGuard achieves the best success metric and lowest average steps in all 16 backbone–benchmark settings, improving over ReasoningBank, the strongest prior baseline among the memory methods we evaluate, with a largest gain of 7.9 success-rate points on WebArena, 5.6 step-success-rate points on Mind2Web, and 2.4–3.5 points on terminal and software-engineering benchmarks. Code is available at [https://github.com/whyyyyy123/MemGuard](https://github.com/whyyyyy123/MemGuard).</abstract>
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%0 Conference Proceedings
%T MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance
%A Wang, Haoyu
%A Dong, Guangyuan
%A He, Liang
%A Zhang, Zijing
%A Luo, Jiachen
%A Xue, Chao
%A Liu, Chuang
%A Tang, Hao
%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 wang-etal-2026-memguard
%X LLM agents are moving from single-prompt use to long task streams in which reusable memory becomes a core capability for terminal, software-engineering, and web tasks. Such memory is useful only when stored experience remains reliable across hundreds of interactions, but two failure modes break that assumption in practice. The first is unreliable admission: failed trajectories, accidental successes, and misleading observations enter memory because they appear relevant, then mislead later decisions. The second is memory drift: long-running banks accumulate duplicate, stale, and conflicting records that retrieval alone cannot repair. MemGuard’s key distinction is to treat verifier output not as a one-shot filter, but as persistent lifecycle metadata. It converts multi-criteria score-token verification into reward, confidence, label, and uncertainty descriptors that are attached to every candidate before activation and reused during retrieval, conflict resolution, summarization, and archival. We evaluate MemGuard on Terminal-Bench 2.0, SWE-Bench Verified, WebArena, and Mind2Web across four backbones, comparing against four memory baselines plus a verifier-only control under matched runtime budgets. Averaged over five seeds, MemGuard achieves the best success metric and lowest average steps in all 16 backbone–benchmark settings, improving over ReasoningBank, the strongest prior baseline among the memory methods we evaluate, with a largest gain of 7.9 success-rate points on WebArena, 5.6 step-success-rate points on Mind2Web, and 2.4–3.5 points on terminal and software-engineering benchmarks. Code is available at [https://github.com/whyyyyy123/MemGuard](https://github.com/whyyyyy123/MemGuard).
%U https://aclanthology.org/2026.emnlp-main.423/
%P 9677-9708
Markdown (Informal)
[MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance](https://aclanthology.org/2026.emnlp-main.423/) (Wang et al., EMNLP 2026)
ACL
- Haoyu Wang, Guangyuan Dong, Liang He, Zijing Zhang, Jiachen Luo, Chao Xue, Chuang Liu, and Hao Tang. 2026. MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9677–9708, Budapest, Hungary. Association for Computational Linguistics.