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
The increasing prevalence of large language models handling long documents for diverse applications necessitates improved retrieval methods to maintain accuracy and prevent information loss.
Improving long-document retrieval directly enhances the effectiveness and reliability of AI systems, impacting fields from professional research to real-time information processing.
This research offers a method to significantly improve the ability of AI models to accurately process and retrieve information from lengthy texts, reducing the current limitation of 'early compression'.
- · AI developers
- · Information retrieval companies
- · Enterprises using LLMs for RAG
- · Academics and researchers
- · Inefficient search algorithms
- · Systems relying on inadequate document embeddings
More accurate and reliable AI responses when dealing with extensive textual data.
Accelerated development and adoption of AI systems in fields requiring deep understanding of long-form content, such as legal or medical research.
Enhanced AI capabilities could lead to new forms of automated analysis and synthesis previously limited by retrieval shortcomings.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL