@inproceedings{xu-etal-2026-loopguard,
title = "{L}oop{G}uard: Breaking Self-Reinforcing Attention Loops via Dynamic {KV} Cache Intervention",
author = "Xu, Dongjie and
Wu, Hao and
Shi, Weijie and
Cui, Yue and
Liu, Yuanjun and
Li, Jiawei and
MA, Haolun and
Liu, An and
Zhu, Jia and
Xu, Jiajie",
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.1223/",
pages = "25883--25898",
ISBN = "979-8-89176-462-0",
abstract = "Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition loops. We find that this degeneration is driven by collapsed attention patterns, where a subset of heads locks onto a narrow suffix of the history, and is further stabilized by inference-time KV cache reuse. Crucially, since many existing KV cache policies rely on attention-based importance, this collapse can produce spuriously high scores for repetitive tokens, causing cache management to inadvertently amplify repetition. To study this phenomenon in a controlled and reproducible manner, we introduce LoopBench, a benchmark with explicit loop-inducing conditions and loop-oriented metrics that quantify repetition severity and generation instability beyond downstream task scores. Building on these insights, we propose LoopGuard, a lightweight, plug-in KV cache guard that detects loop onset online and disrupts the feedback cycle by pruning repetitive tail spans under a fixed cache budget. Experiments on LoopBench show that LoopGuard reduces loop incidence by over 90 percentage points, while restoring output diversity and reducing token waste."
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<abstract>Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition loops. We find that this degeneration is driven by collapsed attention patterns, where a subset of heads locks onto a narrow suffix of the history, and is further stabilized by inference-time KV cache reuse. Crucially, since many existing KV cache policies rely on attention-based importance, this collapse can produce spuriously high scores for repetitive tokens, causing cache management to inadvertently amplify repetition. To study this phenomenon in a controlled and reproducible manner, we introduce LoopBench, a benchmark with explicit loop-inducing conditions and loop-oriented metrics that quantify repetition severity and generation instability beyond downstream task scores. Building on these insights, we propose LoopGuard, a lightweight, plug-in KV cache guard that detects loop onset online and disrupts the feedback cycle by pruning repetitive tail spans under a fixed cache budget. Experiments on LoopBench show that LoopGuard reduces loop incidence by over 90 percentage points, while restoring output diversity and reducing token waste.</abstract>
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%0 Conference Proceedings
%T LoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention
%A Xu, Dongjie
%A Wu, Hao
%A Shi, Weijie
%A Cui, Yue
%A Liu, Yuanjun
%A Li, Jiawei
%A MA, Haolun
%A Liu, An
%A Zhu, Jia
%A Xu, Jiajie
%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 xu-etal-2026-loopguard
%X Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition loops. We find that this degeneration is driven by collapsed attention patterns, where a subset of heads locks onto a narrow suffix of the history, and is further stabilized by inference-time KV cache reuse. Crucially, since many existing KV cache policies rely on attention-based importance, this collapse can produce spuriously high scores for repetitive tokens, causing cache management to inadvertently amplify repetition. To study this phenomenon in a controlled and reproducible manner, we introduce LoopBench, a benchmark with explicit loop-inducing conditions and loop-oriented metrics that quantify repetition severity and generation instability beyond downstream task scores. Building on these insights, we propose LoopGuard, a lightweight, plug-in KV cache guard that detects loop onset online and disrupts the feedback cycle by pruning repetitive tail spans under a fixed cache budget. Experiments on LoopBench show that LoopGuard reduces loop incidence by over 90 percentage points, while restoring output diversity and reducing token waste.
%U https://aclanthology.org/2026.findings-emnlp.1223/
%P 25883-25898
Markdown (Informal)
[LoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention](https://aclanthology.org/2026.findings-emnlp.1223/) (Xu et al., Findings 2026)
ACL
- Dongjie Xu, Hao Wu, Weijie Shi, Yue Cui, Yuanjun Liu, Jiawei Li, Haolun MA, An Liu, Jia Zhu, and Jiajie Xu. 2026. LoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 25883–25898, Budapest, Hungary. Association for Computational Linguistics.