import type { Embedder } from '../influence-core/index.js';
import type { ContextUnit } from './missingContext.js';
import type { AblationRunStats, OutcomeComparator } from './types.js';
/**
 * Re-run the agent with `units` ADDED BACK into the context (`[]` = baseline,
 * restore nothing). Returns the run's output. Mirror of `AblationRunner`.
 */
export type RestorationRunner = (units: readonly ContextUnit[], run: {
    readonly seed: number;
}) => Promise<string>;
/** The rerun configuration that upgrades the dropped list to the causal tier. */
export interface RestorationRerun {
    readonly runner: RestorationRunner;
    /** The original (buggy) output the reruns are compared against. */
    readonly originalOutput: string;
    /** Seeded reruns per probe. Default 3. Never below 2 (no single-run verdicts). */
    readonly samples?: number;
    /** Outcome-flip comparator. Default: similarity < `flipThreshold`. */
    readonly outcomeChanged?: OutcomeComparator;
    /** Similarity floor for the DEFAULT comparator. Default 0.8. */
    readonly flipThreshold?: number;
    /**
     * Restore only the first K dropped candidates. Default 5. COST: confirmation
     * calls your model `samples × (K + 1)` times (the +1 is the baseline) — real,
     * seeded re-runs. Keep `maxCandidates`/`samples` low, or pre-rank candidates,
     * to bound spend. Candidates beyond K are listed without a verdict.
     */
    readonly maxCandidates?: number;
}
export interface RestorationProbeConfig {
    readonly rerun: RestorationRerun;
    readonly embedder: Embedder;
}
/**
 * Run ONE restoration probe: call the consumer's runner with `units` restored
 * once per seed, measure each output's similarity to the original, count flips.
 * `[]` units = the un-restored baseline. Mirror of `runAblationProbe`.
 */
export declare function runRestorationProbe(config: RestorationProbeConfig, units: readonly ContextUnit[]): Promise<AblationRunStats>;