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A Dense Subset Index for Collective Query Coverage

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
collective retrievalsubset searchsubmodular functions
Abstract:

In traditional information retrieval, corpus items compete with each other to occupy top ranks in response to a query. In contrast, in many recent retrieval scenarios associated with complex, multi-hop question answering or text-to-SQL, items are not self-complete: they must instead collaborate, i.e., information from multiple items must be combined to respond to the query. In the context of modern dense retrieval, this need translates into finding a small collection of corpus items whose contextual word vectors collectively cover the contextual word vectors of the query. The central challenge is to retrieve a near-optimal collection of covering items in time that is sublinear in corpus size. By establishing coverage as a submodular objective, we enable successive dense index probes to quickly assemble an item collection that achieves near-optimal coverage. Successive query vectors are iteratively `edited', and the dense index is built using random projections of a novel, lifted dense vector space. Beyond rigorous theoretical guarantees, we report on a scalable implementation of this new form of vector database. Extensive experiments establish the empirical success of DISCo, in terms of the best coverage vs. query latency tradeoffs.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces a coverage-based retrieval framework using submodular facility location objectives to assemble collections of corpus items that collectively cover multi-hop queries. It resides in the Submodular Coverage Optimization leaf under Coverage-Optimized Retrieval Architectures, where it is currently the only paper. This isolation suggests the specific combination of submodular theory with dense retrieval for collective coverage represents a relatively unexplored niche within the broader taxonomy of twenty papers across multiple coverage-related directions.

The taxonomy reveals neighboring research in Multi-Aspect Iterative Retrieval and Collective Scoring for Structured Data, which address coverage through iterative expansion or set-based scoring but without formal submodular guarantees. Document Expansion techniques like topic-controlled query generation enhance coverage through augmentation rather than principled subset selection. The paper's lifted vector space and random projection approach distinguishes it from these alternatives by providing theoretical approximation guarantees for marginal gain computation, positioning it at the intersection of coverage optimization theory and practical dense retrieval infrastructure.

Among fourteen candidates examined across three contributions, none were identified as clearly refuting the work. The coverage-based objective examined three candidates with zero refutations, the lifted vector representation examined one candidate with zero refutations, and the DISCO system examined ten candidates with zero refutations. This limited search scope—focused on top-K semantic matches and citation expansion—suggests that within the examined literature, no substantial prior work directly anticipates the combination of submodular coverage objectives with dense retrieval infrastructure, though the analysis does not claim exhaustive coverage of all related optimization or retrieval literature.

Based on the examined candidates and taxonomy structure, the work appears to occupy a distinct position combining formal optimization theory with dense retrieval practice. The absence of sibling papers in its taxonomy leaf and the lack of refuting candidates among fourteen examined works suggest novelty within the surveyed scope, though the limited search scale means potentially relevant work in broader optimization or information retrieval venues may exist outside this analysis.

Taxonomy

Core-task Taxonomy Papers
20
3
Claimed Contributions
14
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Collective query coverage in dense retrieval focuses on ensuring that a retrieval system can effectively handle diverse query sets by selecting or organizing documents to maximize coverage across multiple information needs. The field structure suggested by the taxonomy reveals several complementary research directions. Coverage-Optimized Retrieval Architectures explore methods that explicitly optimize for broad query coverage, often using submodular or set-based objectives to select representative document subsets. Document Expansion and Augmentation Techniques enhance retrieval by enriching documents with synthetic queries or contextual information, as seen in works like Doc2Query Plus[11]. Multi-Retriever and Hybrid Retrieval Systems combine multiple retrieval strategies to improve robustness, while Retrieval for Generative and Question Answering Tasks addresses the integration of retrieval with downstream generation models such as Passage Retrieval Generative[2]. Domain-Specific Dense Retrieval Applications tailor methods to specialized contexts like legal or e-commerce search, and Retrieval Quality Prediction and Evaluation develops metrics to assess coverage and relevance. Zero-Shot and Generalization Challenges tackle the difficulty of maintaining performance across unseen queries and domains. Particularly active lines of work include coverage-aware indexing and evaluation frameworks that measure how well retrieval systems serve diverse query distributions. Dense Subset Index[0] sits within the Coverage-Optimized Retrieval Architectures branch, specifically addressing submodular coverage optimization to build compact yet comprehensive indexes. This approach contrasts with augmentation-focused methods like LADER[10] or query expansion techniques, instead emphasizing principled subset selection to maximize collective coverage. Nearby works such as Coherence Query Measures[1] and Concept Coverage Queries[8] explore related themes of measuring and optimizing coverage, though they differ in whether they focus on evaluation metrics or query-level representations. The central trade-off across these branches involves balancing computational efficiency, coverage breadth, and retrieval precision, with Dense Subset Index[0] contributing a formal optimization perspective to this landscape.

Claimed Contributions

Coverage-based collective retrieval objective using submodular facility location

The authors formulate collective retrieval as maximizing a facility-location style coverage objective F(S,Q) that measures how well a subset of corpus items collectively covers query atom embeddings. This objective is proven to be monotone and submodular, enabling greedy approximation algorithms with theoretical guarantees.

3 retrieved papers
Lifted vector representation and random projection method for marginal gain approximation

The authors introduce augmented vector representations that encode coverage state into query vectors while keeping corpus vectors static, followed by a random projection technique that approximates the marginal gain as dot products. This transformation makes the coverage objective compatible with approximate nearest neighbor indexing.

1 retrieved paper
DISCO: scalable multi-vector dense retrieval system with theoretical guarantees

The authors develop DISCO, a complete implementation of their coverage-based retrieval framework featuring specialized indexing structures, multi-stage pruning strategies, and query processing algorithms. The system achieves provable approximation guarantees while delivering practical efficiency improvements of over 100× speedup compared to greedy baselines.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Coverage-based collective retrieval objective using submodular facility location

The authors formulate collective retrieval as maximizing a facility-location style coverage objective F(S,Q) that measures how well a subset of corpus items collectively covers query atom embeddings. This objective is proven to be monotone and submodular, enabling greedy approximation algorithms with theoretical guarantees.

Contribution

Lifted vector representation and random projection method for marginal gain approximation

The authors introduce augmented vector representations that encode coverage state into query vectors while keeping corpus vectors static, followed by a random projection technique that approximates the marginal gain as dot products. This transformation makes the coverage objective compatible with approximate nearest neighbor indexing.

Contribution

DISCO: scalable multi-vector dense retrieval system with theoretical guarantees

The authors develop DISCO, a complete implementation of their coverage-based retrieval framework featuring specialized indexing structures, multi-stage pruning strategies, and query processing algorithms. The system achieves provable approximation guarantees while delivering practical efficiency improvements of over 100× speedup compared to greedy baselines.

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