Year: 2026
Type: conference-paper
Abstract: Dense retrievers excel at first-stage candidate generation but lack effective reranking in zeroresource 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 pseudopositive 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 crossquery 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.
Authors: Shiyan Liu, Yichen Li
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166814974