arXiv (All)
open access
AFCS: Atomic-Fact-Conditioned Synthesis for Factual Knowledge Pretraining
Synthetic data is increasingly used in language-model pretraining. Existing knowledge-corpus synthesis pipelines, including WRAP and Nemotron-CC, usually take a source passage or document segment as input and generate rewritten or QA-style samples from that text. This can improve surface form and pr
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
- W-RAG: Source-Aware Retrieval for Enterprise Document Generation from Heterogeneous Knowledge Bases#482217
- Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning#1037721
- From Chunks to Functional Evidence: Function-Aware Retrieval for EDA Documentation QA#1233400
- RCER: Reconstructing Cross-Evidence Relations for Iterative Retrieval-Augmented Generation#1345221
- Robust Hierarchical Structures for Agentic Document Analysis#1108870
Record · ID 1355022
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