Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation

arXiv:2606.18781v1 Announce Type: new Abstract: Dense retrieval ranks one query vector against one document vector. On long documents, this interface can fail when a short but decisive span is weakened during document encoding before ranking. We study this failure mode as document-side early compression and introduce the Evidence Dilution Index (EDI) to measure how far a document-level representation falls below the strongest chunk-level evidence within the same gold document. Guided by this view, we propose DICE (Document Inference via Chunk Evidence), a training-free document-side strategy t

Source: arXiv cs.CL — read the full report at the original publisher.

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