Compare long-context LLMs with the dual-layer GraphRAG pipeline

Determine whether a modern long-context large language model given the full corpus achieves performance comparable to the vector-RAG and Lexical_ReAct systems on small- to moderate-sized CMC document collections.

Background

The paper proposes a dual-layer GraphRAG architecture for CMC process-development documents and argues that its principal advantages emerge for multi-hundred-document programs, where flat retrieval faces cost, scale, structural-reasoning, and update challenges.

For the 52,000-word testbed of 38 documents, the authors suggest that providing the entire corpus to a modern long-context LLM could produce results comparable to those of the vector-RAG baseline or Lexical_ReAct. However, no such comparison is conducted, so the relative value of GraphRAG in small- to moderate-sized corpora remains unresolved.

References

Indeed, for a testbed of 52{,}000 (38 documents), a long-context LLM given the full corpus might score comparably to either the vector-RAG or Lexical_ReAct. While the comparison is not tested here, we argue that the graph's motivating regime is found in multi-hundred-document programs mentioned in \autoref{sec:corpus}.

— From Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Development  (2609.11493 - Amirmoshiri et al., 10 Sep 2026) in Section 4, Summary, Limitations and Outlook, subsection “Large Context LLM vs GraphRAG”