@inproceedings{liu-etal-2026-repair,
title = "{R}e{P}air: Turning Retrieval Failures into Counterfactual Hard Pairs",
author = "Liu, Siyi and
Zhu, Xiaorong and
Du, Enjun and
Zuo, Xinyu and
Duan, Lisheng and
Liang, Haijin and
Ma, Jin and
Pu, Junfu and
Zhang, Yongqi",
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.1922/",
pages = "43774--43800",
ISBN = "979-8-89176-459-0",
abstract = "Vision-language retrieval with CLIP-style dual encoders achieves strong cross-modal performance, yet practical accuracy often hinges on localized semantic distinctions where top-ranked near misses differ from the true match by a single critical detail. Hard-sample mining can select confusable candidates but cannot construct corrected counterparts; synthetic augmentation can generate novel samples but, without conditioning on actual model failures, targets irrelevant dimensions of hardness. We observe that a top-ranked false positive is a counterfactual scaffold{---}sharing most of the query{'}s semantics while differing in a localized failure-causing residual. Minimally correcting this residual yields a hard positive of the ground truth in the same modality; the corrected and unedited versions form a hard negative pair that straddles the decision boundary, producing complementary pull{--}push supervision. We introduce RePair, guided by three principles{---}Validity, Minimality, and Locality{---}which mines false positives bidirectionally, applies LLM-guided counterfactual editing, and trains with a local hard-pair contrastive objective. On Flickr30K and COCO30K, RePair outperforms controlled augmentation baselines with only 107K synthetic samples{---}26{\%}{--}75{\%} fewer than comparable methods{---}confirming failure-conditioned repair is more data-efficient than error-agnostic augmentation."
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<abstract>Vision-language retrieval with CLIP-style dual encoders achieves strong cross-modal performance, yet practical accuracy often hinges on localized semantic distinctions where top-ranked near misses differ from the true match by a single critical detail. Hard-sample mining can select confusable candidates but cannot construct corrected counterparts; synthetic augmentation can generate novel samples but, without conditioning on actual model failures, targets irrelevant dimensions of hardness. We observe that a top-ranked false positive is a counterfactual scaffold—sharing most of the query’s semantics while differing in a localized failure-causing residual. Minimally correcting this residual yields a hard positive of the ground truth in the same modality; the corrected and unedited versions form a hard negative pair that straddles the decision boundary, producing complementary pull–push supervision. We introduce RePair, guided by three principles—Validity, Minimality, and Locality—which mines false positives bidirectionally, applies LLM-guided counterfactual editing, and trains with a local hard-pair contrastive objective. On Flickr30K and COCO30K, RePair outperforms controlled augmentation baselines with only 107K synthetic samples—26%–75% fewer than comparable methods—confirming failure-conditioned repair is more data-efficient than error-agnostic augmentation.</abstract>
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%0 Conference Proceedings
%T RePair: Turning Retrieval Failures into Counterfactual Hard Pairs
%A Liu, Siyi
%A Zhu, Xiaorong
%A Du, Enjun
%A Zuo, Xinyu
%A Duan, Lisheng
%A Liang, Haijin
%A Ma, Jin
%A Pu, Junfu
%A Zhang, Yongqi
%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 liu-etal-2026-repair
%X Vision-language retrieval with CLIP-style dual encoders achieves strong cross-modal performance, yet practical accuracy often hinges on localized semantic distinctions where top-ranked near misses differ from the true match by a single critical detail. Hard-sample mining can select confusable candidates but cannot construct corrected counterparts; synthetic augmentation can generate novel samples but, without conditioning on actual model failures, targets irrelevant dimensions of hardness. We observe that a top-ranked false positive is a counterfactual scaffold—sharing most of the query’s semantics while differing in a localized failure-causing residual. Minimally correcting this residual yields a hard positive of the ground truth in the same modality; the corrected and unedited versions form a hard negative pair that straddles the decision boundary, producing complementary pull–push supervision. We introduce RePair, guided by three principles—Validity, Minimality, and Locality—which mines false positives bidirectionally, applies LLM-guided counterfactual editing, and trains with a local hard-pair contrastive objective. On Flickr30K and COCO30K, RePair outperforms controlled augmentation baselines with only 107K synthetic samples—26%–75% fewer than comparable methods—confirming failure-conditioned repair is more data-efficient than error-agnostic augmentation.
%U https://aclanthology.org/2026.emnlp-main.1922/
%P 43774-43800
Markdown (Informal)
[RePair: Turning Retrieval Failures into Counterfactual Hard Pairs](https://aclanthology.org/2026.emnlp-main.1922/) (Liu et al., EMNLP 2026)
ACL
- Siyi Liu, Xiaorong Zhu, Enjun Du, Xinyu Zuo, Lisheng Duan, Haijin Liang, Jin Ma, Junfu Pu, and Yongqi Zhang. 2026. RePair: Turning Retrieval Failures into Counterfactual Hard Pairs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 43774–43800, Budapest, Hungary. Association for Computational Linguistics.