arXiv (All)
open access
A Domain-Aware Pipeline for Structured Extraction and Retrieval-Augmented Question Answering over arXiv Superconductivity Literature
Scientific literature in materials physics is growing faster than researchers can manually inspect it, yet most retrieval tools rely on keyword matching or generic question answering that ignores domain-specific structure in PDF files. This paper presents an end-to-end pipeline that transforms arXiv
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
- Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models#1123666
- Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models#919441
- Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use#798912
- Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use#797498
- RDL-Bench: A Benchmark for Task-Aware Question Answering over Relational Databases#1356718
Record · ID 1457545
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