/**
 * The promotion gate: does the learned embedding model actually recover the
 * relationships the hand-written synonym graph encodes — and does it do so for
 * the Korean / long-tail terms, which is exactly where pure-TS SGNS is weakest
 * and where the decision to (eventually) add a real multilingual encoder will be
 * made? Reported separately so a flip-to-default is made on a number, not a hope.
 */
import type { EmbeddingModel } from "./embedding-model.js";
export type RecallReport = {
    k: number;
    /** Overall recall@k across all in-vocab synonym pairs. */
    recall: number;
    pairsEvaluated: number;
    /** Korean-term subset (a group member containing Hangul). */
    koRecall: number;
    koPairsEvaluated: number;
    /** English/other subset. */
    enRecall: number;
    enPairsEvaluated: number;
    /** Fraction of hand-graph terms that exist in the learned vocab at all. */
    vocabCoverage: number;
    termsTotal: number;
    termsInVocab: number;
};
/**
 * For every hand-authored synonym group, treat each in-vocab member as a query
 * and check how many of its in-vocab group-mates land in the model's top-k
 * neighbours. Recall = hits / possible. A term-mate that is out-of-vocab cannot
 * be recalled and is excluded from the denominator (coverage is reported
 * separately so the two effects are not conflated).
 */
export declare function evalSynonymRecall(model: EmbeddingModel, groups: string[][], k?: number): RecallReport;