/**
 * GAIA Hardness Predictor — Training Data Loader (ADR-136 Track Q)
 *
 * Loads labelled training examples from prior bench-run result JSONs
 * (iter-15, iter-23, iter-28 outputs) and converts them into the
 * `LabeledExample[]` format consumed by `HardnessPredictor.train()`.
 *
 * Expected result JSON schema (matches gaia-bench --output json):
 * {
 *   level: number,
 *   model: string,
 *   summary: { total, passed, passRate, estCostUsd, meanTurns, meanWallMs },
 *   results: [
 *     {
 *       task_id: string, question: string, model: string, correct: boolean,
 *       answer: string | null, expected_output: string, error?: string,
 *       turns?: number, wallMs?: number, inputTokens?: number, outputTokens?: number
 *     }
 *   ]
 * }
 *
 * The file may contain either:
 *   (a) a single JSON object (one model run), or
 *   (b) a JSON array of objects (multi-model run from --models a,b,c), or
 *   (c) a text preamble followed by JSON (raw output from gaia-bench text mode
 *       — we scan for the first '[' or '{' and parse from there).
 *
 * Missing files are silently skipped (returns empty array).
 * Malformed files emit a warning to stderr and are skipped.
 *
 * Default search paths (tried in order, first found wins per iter):
 *   /tmp/gaia-l1-full.json
 *   /tmp/gaia-l1-haiku.json
 *   /tmp/gaia-all-p1b.json
 *   /tmp/gaia-all-p2.json
 *   <custom paths passed by caller>
 *
 * Refs: ADR-136, #2156
 */
import type { LabeledExample } from './predictor.js';
/** Default candidate paths for historical bench-run result JSONs. */
export declare const DEFAULT_RESULT_PATHS: readonly string[];
/**
 * Load labelled training examples from historical bench-run result JSONs.
 *
 * @param additionalPaths - Extra file paths to scan beyond the defaults.
 * @param verbose - If true, log loaded example counts to stderr.
 * @returns Deduplicated array of LabeledExample (dedup by task_id, last write wins).
 */
export declare function loadTrainingData(additionalPaths?: string[], verbose?: boolean): LabeledExample[];
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