SOURCE-LINKED INTELLIGENCE
Efficiently Linking Unstructured Data for Multi-step Reasoning
Modern LLMs and AI agents increasingly support data engineering workflows that integrate evidence from unstructured sources. Such pipelines typically do data retrieval, integration, and ranking before proceeding to more complex agentic reasoning or actions, e.g., for scientific discovery. The core retrieval problem in these workflows jointly executes multi-attribute filtering, multi-vector search, exact relational joins, and thresholded embedding-similarity joins. Given a planned query and monotone scoring function, our DASE query engine constructs and ranks candidate evidence tuples. It compr
Read original source ↗ Open in workspace
- recordType
- paper
- region
- Global
Evidence & attribution
- arXiv · Artificial Intelligence · 2026-09-16T23:16:45.000Z
- arXiv · AI, language, vision and robotics · 2026-09-16T23:16:45.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.