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Published equation contexts

cquery(Q)=CindexQ+cmarginalc_{\text{query}}(Q) = \frac{C_{\text{index}}}{Q} + c_{\text{marginal}}

Why this formula appears here

This is the one family among the five whose defining engineering trade-off is temporal rather than architectural: cost is shifted from query time to index time. Writing CindexC_{\text{index}} for the one-time cost of building the graph and its community summaries, and cmarginalc_{\text{marginal}} for the cost of answering a single query against the finished structure, the amortised cost of a query workload of size Q against one graph is cquery(Q)=CindexQ+cmarginalc_{\text{query}}(Q) = \frac{C_{\text{index}}}{Q} + c_{\text{marginal}}. When Q is small — a corpus queried rarely, or one still being explored — CindexC_{\text{index}} dominates and the naive or hybrid architectures are cheaper by a wide margin, because they pay nothing until a query arrives. When Q is large against a stable…

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cqueryc_{\text{query}}

Symbol c_query

cqc_query is part of the quantity the equation computes from the expression on the right.

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cmarginalc_{\text{marginal}}

Symbol c_marginal

cmc_marginal is one of the signed contributions combined to compute the quantity on the left.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.

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Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

cquery(Q)=CindexQ+cmarginal.c_{\text{query}}(Q) = \frac{C_{\text{index}}}{Q} + c_{\text{marginal}}.

Equation 18 · AI Agents & Systems

Naive, Graph, and Agentic: A Systems Comparison of RAG Architectures

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

This is the one family among the five whose defining engineering trade-off is temporal rather than architectural: cost is shifted from query time to index time. Writing CindexC_{\text{index}} for the one-time cost of building the graph and its community summaries, and cmarginalc_{\text{marginal}} for the cost of answering a single query against the finished structure, the amortised cost of a query workload of size Q against one graph is cquery(Q)=CindexQ+cmarginalc_{\text{query}}(Q) = \frac{C_{\text{index}}}{Q} + c_{\text{marginal}}. When Q is small — a corpus queried rarely, or one still being explored — CindexC_{\text{index}} dominates and the naive or hybrid architectures are cheaper by a wide margin, because they pay nothing until a query arrives. When Q is large against a stable…

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