arXiv (All)
open access
Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion
Graph-based retrieval-augmented generation (RAG) is widely used for multimodal, cross-document question answering. However, building corpus-level graphs is expensive, slow to query, and difficult to maintain. We present TrioRAG, a graph-free multimodal framework that integrates evidence from three c
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
- Controlled Evaluation of Graph and Multimodal Augmentation in RAG for Document Question Answering#800105
- CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation#972579
- Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment#985564
- Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement#625200
- Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement#622927
Record · ID 1014011
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