Do Current Retrievers Cover All the Evidence? A Controlled Study of Conjunctive Cross-Page Retrieval

2026-07-27

AI Summary

This work addresses the challenge that existing retrieval systems struggle to ensure all conditions in multi-faceted queries are satisfied within a single long document, particularly when relevant evidence is scattered across different pages. To tackle this, the authors introduce the n-Clue benchmark and propose a “completeness-first” evaluation paradigm that requires retrieved documents to contain evidence for every query condition. Their approach integrates query decomposition, dense and sparse retrieval, vision–language joint modeling, and re-ranking strategies. Experimental results show that even the strongest hybrid system retrieves relevant documents for 81.1% of queries but achieves full condition coverage in only 35.8% of cases; purely vision-based systems perform substantially worse, with coverage below 5.3%, underscoring the difficulty of integrating cross-page, multi-condition evidence.

Abstract

Finding a long document relevant to a multi-part request is not the same as establishing that it contains every requested piece of evidence. We study this gap for conjunctive document retrieval, where two or three explicit conditions must be supported on different pages of one document. We use n-Clue as a controlled measurement instrument: 1{,}000 queries over 2{,}021 documents pair all-condition golds with naturally occurring documents that satisfy only a subset, and a complete-first success requires a top-10 gold to precede every released subset qrel. Across 70 configurations, condition-wise decomposition improves two dense backbones by 6.8--7.3 points and lexical--visual fusion adds 8.7, while four generic rerankers all reduce Gold-NDCG; these directions replicate on a four-source stress set. Scaling one dense family from 0.6B to 8B changes complete-first success by 0.0 points. The strongest displayed hybrid illustrates the resulting gap: it finds a gold for 81.1\% of queries but succeeds complete-first on only 35.8\%, and the gap persists across condition count, target length, candidate density, query rendering, and the four-source stress set. Finally, page-aware visual systems surface stored support for every condition on only 5.1--5.3\% of queries. These results identify condition coverage, rather than gold discovery alone, as the central bottleneck.

Problem

• conjunctive retrieval
• cross-page evidence
• condition coverage
• document retrieval
• multi-part query

Innovation

• conjunctive retrieval
• cross-page evidence
• condition coverage
• complete-first success
• n-Clue