/**
 * BENCHMARK — the falsifiable proof that the brain is worth it: on a hidden response surface, does the
 * closed loop reach the optimum in FEWER experiments than random search and a systematic grid? Fewer
 * experiments is the entire value — each real experiment costs reagents, robot-time, money. Deterministic
 * + reproducible; the numbers are measured, not claimed.
 */
import { type Space, type Experiment } from "./space.js";
/** A multimodal surface with a global peak (≈1 at (7.2,3.4)) and a decoy local peak that traps naive search. */
export declare function multimodal(e: Experiment): number;
export declare const benchSpace: Space;
export interface BenchResult {
    brain: number | null;
    random: number;
    grid: number | null;
    target: number;
    budget: number;
}
/** Compare experiments-to-target for brain vs random (averaged over seeds) vs grid. Lower = better. */
export declare function benchmark(opts?: {
    budget?: number;
    target?: number;
    seeds?: number;
}): Promise<BenchResult>;
/** A rugged Rastrigin-style surface (many local optima; global max 0 at (6.3,3.7)) — where single
 * greedy optimisers get trapped and the portfolio's diversity pays off. */
export declare function rugged(e: Experiment): number;
export interface RobustnessRow {
    landscape: string;
    portfolio: number;
    kernelUcb: number;
    cmaes: number;
    resonance: number;
    random: number;
    portfolioIsBest: boolean;
    portfolioIsWorst: boolean;
}
/** Measure the portfolio vs each single arm across diverse landscapes (mean best over `seeds`). The
 * production thesis, falsifiable: the portfolio is never the worst and tracks the best arm per landscape —
 * and on the rugged surface the ensemble beats every single algorithm. */
export declare function robustnessBench(seeds?: number): Promise<RobustnessRow[]>;
export declare function benchGauntlet(): Promise<{
    score: 0 | 100;
    checks: Array<{
        name: string;
        pass: boolean;
        detail: string;
    }>;
}>;
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