@inproceedings{ma-etal-2026-failuremem,
title = "{F}ailure{M}em: A Failure-Aware Multimodal Framework for Autonomous Software Repair",
author = "Ma, Ruize and
Jiang, Yilei and
Zhang, Shilin and
Ma, Zheng and
Feng, Yi and
Wang, Zhi and
Yue, Xiangyu and
Li, Chuanyi and
Ng, Vincent and
Lu, Lewei",
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.2305/",
pages = "52387--52402",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal Automated Program Repair (MAPR) extends traditional program repair by requiring models to jointly reason over source code, textual issue descriptions, and visual artifacts such as GUI screenshots. While recent LLM-based repair systems have shown promising results, existing approaches face several limitations: rigid workflow pipelines restrict exploration during debugging, visual reasoning is often performed over full-page screenshots without localized grounding, and failed repair attempts are rarely transformed into reusable knowledge. To address these challenges, we propose FailureMem, a multimodal repair framework that integrates three key mechanisms: a hybrid workflow{--}agent architecture that balances structured localization with flexible reasoning, active perception tools that enable region-level visual grounding, and a Failure Memory Bank that converts past repair attempts into reusable guidance. Experiments on SWE-bench Multimodal demonstrate FailureMem improves the resolved rate over GUIRepair by 3.7{\%}."
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<abstract>Multimodal Automated Program Repair (MAPR) extends traditional program repair by requiring models to jointly reason over source code, textual issue descriptions, and visual artifacts such as GUI screenshots. While recent LLM-based repair systems have shown promising results, existing approaches face several limitations: rigid workflow pipelines restrict exploration during debugging, visual reasoning is often performed over full-page screenshots without localized grounding, and failed repair attempts are rarely transformed into reusable knowledge. To address these challenges, we propose FailureMem, a multimodal repair framework that integrates three key mechanisms: a hybrid workflow–agent architecture that balances structured localization with flexible reasoning, active perception tools that enable region-level visual grounding, and a Failure Memory Bank that converts past repair attempts into reusable guidance. Experiments on SWE-bench Multimodal demonstrate FailureMem improves the resolved rate over GUIRepair by 3.7%.</abstract>
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%0 Conference Proceedings
%T FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair
%A Ma, Ruize
%A Jiang, Yilei
%A Zhang, Shilin
%A Ma, Zheng
%A Feng, Yi
%A Wang, Zhi
%A Yue, Xiangyu
%A Li, Chuanyi
%A Ng, Vincent
%A Lu, Lewei
%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 ma-etal-2026-failuremem
%X Multimodal Automated Program Repair (MAPR) extends traditional program repair by requiring models to jointly reason over source code, textual issue descriptions, and visual artifacts such as GUI screenshots. While recent LLM-based repair systems have shown promising results, existing approaches face several limitations: rigid workflow pipelines restrict exploration during debugging, visual reasoning is often performed over full-page screenshots without localized grounding, and failed repair attempts are rarely transformed into reusable knowledge. To address these challenges, we propose FailureMem, a multimodal repair framework that integrates three key mechanisms: a hybrid workflow–agent architecture that balances structured localization with flexible reasoning, active perception tools that enable region-level visual grounding, and a Failure Memory Bank that converts past repair attempts into reusable guidance. Experiments on SWE-bench Multimodal demonstrate FailureMem improves the resolved rate over GUIRepair by 3.7%.
%U https://aclanthology.org/2026.emnlp-main.2305/
%P 52387-52402
Markdown (Informal)
[FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair](https://aclanthology.org/2026.emnlp-main.2305/) (Ma et al., EMNLP 2026)
ACL
- Ruize Ma, Yilei Jiang, Shilin Zhang, Zheng Ma, Yi Feng, Zhi Wang, Xiangyu Yue, Chuanyi Li, Vincent Ng, and Lewei Lu. 2026. FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 52387–52402, Budapest, Hungary. Association for Computational Linguistics.