most citedRank1: Test-Time Compute for Reranking in Information Retrieval

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cs.IR2026

ColBERTSaR: Sparsified ColBERT Index via Product Quantization

Eugene Yang, Andrew Yates, Dawn Lawrie +3

While ColBERT is an effective neural retrieval architecture, it requires a heavy index structure to support candidate set retrieval based on approximated token embeddings, gatherin…

cs.IR2026

Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability

Jia-Huei Ju, Eugene Yang, Trevor Adriaanse +2

Long-form Retrieval-Augmented Generation (RAG) brings the challenge of coverage-based ranking, because ranking methods must ensure the inclusion of comprehensive relevant nuggets (…

cs.IR2026

CoverageBench: Evaluating Information Coverage across Tasks and Domains

Saron Samuel, Andrew Yates, Dawn Lawrie +4

We wish to measure the information coverage of an ad hoc retrieval algorithm, that is, how much of the range of available relevant information is covered by the search results. Inf…

cs.IR2026

Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage

Saron Samuel, Alexander Martin, Eugene Yang +5

Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation. While the relat…

cs.IR2026

NeuCLIRTech: Chinese Monolingual and Cross-Language Information Retrieval Evaluation in a Challenging Domain

Dawn Lawrie, James Mayfield, Eugene Yang +6

Measuring advances in retrieval requires test collections with relevance judgments that can faithfully distinguish systems. This paper presents NeuCLIRTech, an evaluation collectio…

cs.IR2026

Overview of the TREC 2025 RAGTIME Track

Dawn Lawrie, Sean MacAvaney, James Mayfield +3

The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multilingual source documents. The track has cr…