arXiv (All)
open access
BoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrieval
Open-ended queries in modern Retrieval-Augmented Generation (RAG) are increasingly "diffuse," requiring a large set of documents to be assembled into a finite LLM context window. To ensure retrieval quality, systems use fast dual-encoders and more expensive cross-encoders (CEs) to score candidates.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Page-Aware Retrieval-Augmented Generation for EvalLLM 2026: A Five-Variant Study on French PDFs#1129397
- ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering#926251
- SCoNE: Selective Context-aware Neuron Editing for Robust Retrieval-Augmented Generation#820708
- RandSlot: Learning Compact Visual Document Representations with Random Soft Tokens#1117030
- Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings#926301
Record · ID 1063424
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive