arXiv (All)
open access
Retrieval from Within: An Intrinsic Capability of Attention-Based Models
Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems. We ask whether an attention-based encoder-decoder can instead retrieve directly from its own internal representations. We introduce INTRA (INTrinsic Retrieval via Attention), a framework where decoder
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
- UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities#821696
- Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation#1104536
- Amplify What You Gaze At: Target Saliency Boosting in Text-to-Image Generation#1105292
- EnComp: Lightweight Encoder-Only Context Compression for Retrieval-Augmented Question Answering#1063857
- To Copy or Not to Copy: Copying Is Easier to Induce Than Recall#799941
Record · ID 1155187
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