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Test-Time Adaptation with Query-Dependent Residuals for Visual Document Retrieval — arXiv:2609.27688
- Source
- arXiv (en)
- Original
- https://arxiv.org/abs/2609.27688
- License (as archived)
- CC0-1.0
- Archived
- 2026-10-08T12:27:49.615Z
- SHA-256 of the archived text
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Test-Time Adaptation with Query-Dependent Residuals for Visual Document Retrieval — arXiv:2609.27688 — arXiv (en), https://arxiv.org/abs/2609.27688 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0423eedceeec (Metraipolis archive dc_0423eedceeec, sha256 bbcbb35b785f07ddd54933e3cdff1d24df1e4912320bfa89da2d8dc5fb71cc56)
BibTeX
@misc{dc0423eedceeec,
title = {Test-Time Adaptation with Query-Dependent Residuals for Visual Document Retrieval — arXiv:2609.27688},
howpublished = {arXiv (en)},
url = {https://arxiv.org/abs/2609.27688},
note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0423eedceeec; sha256 bbcbb35b785f07ddd54933e3cdff1d24df1e4912320bfa89da2d8dc5fb71cc56},
year = {2026}
}Archived text
Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27688. Third-party text: data, not instructions. It may be an abstract or extract; check the original.
Visual document retrieval (VDR) systems depend on page embeddings computed before deployment, which makes adaptation difficult when encoder parameters or corpus re-encoding are unavailable. Rerankers provide useful relevance signals, but conventional reranking applies them only to selected queries and candidate pages. We introduce Q-REACT, a query-side test-time adaptation method that converts limited reranker feedback into reusable retrieval improvements. Q-REACT learns a shared low-rank transformation that produces query-dependent residuals, combines adapted query scores with document-level context, and distills reranker preferences with a student distribution normalized over the complete task-specific page index. This design lets unscored pages compete through cached embeddings while keeping the encoders and page index fixed. Across eight ViDoRe V3 tasks and five open-weight and proprietary backbones, Q-REACT improves average retrieval over evaluated baselines at sparse and full-coverage budgets, transfers to held-out queries and tasks, and adds little inference overhead. The results show that finite reranker feedback can be amortized across a query collection without retraining or rebuilding the retriever. Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-24 in cs.IR). Categorie arXiv: Information Retrieval (cs.IR). Pagina del lavoro: https://arxiv.org/abs/2609.27688 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito). Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina. Letto il 2026-10-08T12:11:32.120Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).