@inproceedings{wang-etal-2026-jfta,
title = "{JFTA}-Bench: Evaluate {LLM}{'}s Ability of Tracking and Analyzing Malfunctions Using Fault Trees",
author = "Wang, Yuhui and
Yang, Zhixiong and
Zhang, Ming and
Dou, Shihan and
Xi, Zhiheng and
Liang, Di and
Zhou, Enyu and
Jin, Senjie and
Shen, Yujiong and
Zhu, Dingwei and
Dong, Yi and
Chen, Yujia and
Shi, Haibo and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
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.1261/",
pages = "26653--26678",
ISBN = "979-8-89176-462-0",
abstract = "In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by large language models, which can assist in tracking and analyzing malfunctions, we propose a novel textual representation of fault trees. Building on it, we construct a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments, evaluating a model{'}s ability to assist in malfunction localization, which contains 3130 entries and 40.75 turns per entry on average. We train an end-to-end model to generate vague information to reflect user behavior and introduce long-range rollback and recovery procedures to simulate user error scenarios, enabling assessment of a model{'}s integrated capabilities in task tracking and error recovery, and Gemini 2.5 pro achieves the best performance."
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<abstract>In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by large language models, which can assist in tracking and analyzing malfunctions, we propose a novel textual representation of fault trees. Building on it, we construct a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments, evaluating a model’s ability to assist in malfunction localization, which contains 3130 entries and 40.75 turns per entry on average. We train an end-to-end model to generate vague information to reflect user behavior and introduce long-range rollback and recovery procedures to simulate user error scenarios, enabling assessment of a model’s integrated capabilities in task tracking and error recovery, and Gemini 2.5 pro achieves the best performance.</abstract>
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%0 Conference Proceedings
%T JFTA-Bench: Evaluate LLM’s Ability of Tracking and Analyzing Malfunctions Using Fault Trees
%A Wang, Yuhui
%A Yang, Zhixiong
%A Zhang, Ming
%A Dou, Shihan
%A Xi, Zhiheng
%A Liang, Di
%A Zhou, Enyu
%A Jin, Senjie
%A Shen, Yujiong
%A Zhu, Dingwei
%A Dong, Yi
%A Chen, Yujia
%A Shi, Haibo
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%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 wang-etal-2026-jfta
%X In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by large language models, which can assist in tracking and analyzing malfunctions, we propose a novel textual representation of fault trees. Building on it, we construct a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments, evaluating a model’s ability to assist in malfunction localization, which contains 3130 entries and 40.75 turns per entry on average. We train an end-to-end model to generate vague information to reflect user behavior and introduce long-range rollback and recovery procedures to simulate user error scenarios, enabling assessment of a model’s integrated capabilities in task tracking and error recovery, and Gemini 2.5 pro achieves the best performance.
%U https://aclanthology.org/2026.findings-emnlp.1261/
%P 26653-26678
Markdown (Informal)
[JFTA-Bench: Evaluate LLM’s Ability of Tracking and Analyzing Malfunctions Using Fault Trees](https://aclanthology.org/2026.findings-emnlp.1261/) (Wang et al., Findings 2026)
ACL
- Yuhui Wang, Zhixiong Yang, Ming Zhang, Shihan Dou, Zhiheng Xi, Di Liang, Enyu Zhou, Senjie Jin, Yujiong Shen, Dingwei Zhu, Yi Dong, Yujia Chen, Haibo Shi, Tao Gui, Qi Zhang, and Xuanjing Huang. 2026. JFTA-Bench: Evaluate LLM’s Ability of Tracking and Analyzing Malfunctions Using Fault Trees. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 26653–26678, Budapest, Hungary. Association for Computational Linguistics.