@inproceedings{liu-li-2026-test,
title = "Test-Time Training for Zero-Resource Dense Retrieval Reranking",
author = "Liu, Shiyan and
Li, Yichen",
editor = "Chen, Canyu and
Zhang, Yuji and
Li, Zoey Sha and
Wang, Zihan and
Wang, Qineng and
Su, Jinyan and
Kargupta, Priyanka and
Marjanovi{\'c}, Sara Vera and
Pan, Jeff Z. and
Bansal, Mohit and
Augenstein, Isabelle and
Han, Jiawei and
Ji, Heng and
Li, Manling",
booktitle = "Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models ({K}now{FM} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.knowfm-1.8/",
pages = "105--114",
ISBN = "979-8-89176-403-3",
abstract = "Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings. Existing approaches face a fundamental dilemma: cross-encoders deliver strong reranking quality but require costly supervised training and incur high latency, while unsupervised BM25 reranking consistently degrades dense retrieval performance on most of BEIR benchmarks. We propose DART (Dense Adaptive Reranking at Test-time), which resolves this dilemma by adapting the scoring function at inference time. For each query, the top-ranked documents serve as pseudo-positive examples and the bottom-ranked as pseudo-negative examples, providing noisy but readily available supervision to adapt a bilinear scoring matrix $W$ via a small number of gradient updates. We further introduce a confidence-weighted margin loss and a cross-query momentum buffer that warm-starts adaptation across queries. On six BEIR benchmarks, DART achieves a mean per-dataset relative NDCG@10 gain of +2.1{\%} over the dense retrieval baseline with under 10ms additional latency per query, demonstrating a powerful capability for zero-shot performance enhancement and cross-domain generalization."
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<abstract>Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings. Existing approaches face a fundamental dilemma: cross-encoders deliver strong reranking quality but require costly supervised training and incur high latency, while unsupervised BM25 reranking consistently degrades dense retrieval performance on most of BEIR benchmarks. We propose DART (Dense Adaptive Reranking at Test-time), which resolves this dilemma by adapting the scoring function at inference time. For each query, the top-ranked documents serve as pseudo-positive examples and the bottom-ranked as pseudo-negative examples, providing noisy but readily available supervision to adapt a bilinear scoring matrix W via a small number of gradient updates. We further introduce a confidence-weighted margin loss and a cross-query momentum buffer that warm-starts adaptation across queries. On six BEIR benchmarks, DART achieves a mean per-dataset relative NDCG@10 gain of +2.1% over the dense retrieval baseline with under 10ms additional latency per query, demonstrating a powerful capability for zero-shot performance enhancement and cross-domain generalization.</abstract>
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%0 Conference Proceedings
%T Test-Time Training for Zero-Resource Dense Retrieval Reranking
%A Liu, Shiyan
%A Li, Yichen
%Y Chen, Canyu
%Y Zhang, Yuji
%Y Li, Zoey Sha
%Y Wang, Zihan
%Y Wang, Qineng
%Y Su, Jinyan
%Y Kargupta, Priyanka
%Y Marjanović, Sara Vera
%Y Pan, Jeff Z.
%Y Bansal, Mohit
%Y Augenstein, Isabelle
%Y Han, Jiawei
%Y Ji, Heng
%Y Li, Manling
%S Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-403-3
%F liu-li-2026-test
%X Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings. Existing approaches face a fundamental dilemma: cross-encoders deliver strong reranking quality but require costly supervised training and incur high latency, while unsupervised BM25 reranking consistently degrades dense retrieval performance on most of BEIR benchmarks. We propose DART (Dense Adaptive Reranking at Test-time), which resolves this dilemma by adapting the scoring function at inference time. For each query, the top-ranked documents serve as pseudo-positive examples and the bottom-ranked as pseudo-negative examples, providing noisy but readily available supervision to adapt a bilinear scoring matrix W via a small number of gradient updates. We further introduce a confidence-weighted margin loss and a cross-query momentum buffer that warm-starts adaptation across queries. On six BEIR benchmarks, DART achieves a mean per-dataset relative NDCG@10 gain of +2.1% over the dense retrieval baseline with under 10ms additional latency per query, demonstrating a powerful capability for zero-shot performance enhancement and cross-domain generalization.
%U https://aclanthology.org/2026.knowfm-1.8/
%P 105-114
Markdown (Informal)
[Test-Time Training for Zero-Resource Dense Retrieval Reranking](https://aclanthology.org/2026.knowfm-1.8/) (Liu & Li, KnowFM 2026)
ACL