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QQ

This entry links every published equation using this exact notation. Its meaning may change between equations.

Meanings in context

Used in 42 equations

Clifetime=Creuse⏟compress  +  Ccurate+Ctrain⏟train small  +  Csearch⏟NAS  +  Q⋅cˉtok,withMresident≤BmemC_{\text{lifetime}} = \underbrace{C_{\text{reuse}}}_{\text{compress}} \;+\; \underbrace{C_{\text{curate}} + C_{\text{train}}}_{\text{train small}} \;+\; \underbrace{C_{\text{search}}}_{\text{NAS}} \;+\; Q \cdot \bar c_{\text{tok}}, \qquad \text{with} \quad M_{\text{resident}} \le B_{\text{mem}}

Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared · Equation 33

This equation states a bound: one expression must stay on the indicated side of the other under the article’s assumptions.

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Q(st,at)←Q(st,at)+α[rt+1+γmax⁡aQ(st+1,a)−Q(st,at)].Q(s_t,a_t) \leftarrow Q(s_t,a_t) + \alpha\Big[r_{t+1} + \gamma \max_{a} Q(s_{t+1},a) - Q(s_t,a_t)\Big].

From Scripted Bots to Autonomous Agents: A History of AI Agent Architecture · Equation 9

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.

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score(D,Q)=∑i=1nIDF(qi)⋅f(qi,D) (k1+1)f(qi,D)+k1(1−b+b ∣D∣avgdl)\mathrm{score}(D,Q) = \sum_{i=1}^{n} \mathrm{IDF}(q_i) \cdot \frac{f(q_i, D)\,(k_1+1)}{f(q_i, D) + k_1\left(1 - b + b\,\dfrac{|D|}{\mathrm{avgdl}}\right)}

What Actually Happens Between a Query and an Answer in RAG · Equation 1

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

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score(D,Q)=∑qi∈QIDF(qi)⋅f(qi,D)⋅(k1+1)f(qi,D)+k1⋅(1−b+b⋅∣D∣avgdl),\mathrm{score}(D, Q) = \sum_{q_i \in Q} \mathrm{IDF}(q_i) \cdot \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \dfrac{|D|}{\mathrm{avgdl}}\right)},

From BM25 to Agentic Retrieval: A History of Retrieval-Augmented Generation · Equation 8

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

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