/**
 * Graph metrics for the cortex memory graph.
 *
 * Fiedler value: algebraic connectivity of the Laplacian.
 *   - High → well-integrated knowledge graph
 *   - Low  → disconnected clusters (compartmentalised knowledge)
 *
 * PE saturation: trend analysis over prediction-error values for recent
 * identity-related observations. Used to decide whether adversarial
 * rumination should be triggered.
 */
import type { CortexStore } from '../core/store.js';
/**
 * Compute the Fiedler value (algebraic connectivity) of the memory graph.
 *
 * The Fiedler value is the second-smallest eigenvalue of the graph Laplacian.
 * It is zero for disconnected graphs and increases with connectivity.
 *
 * Algorithm (two-pass power iteration, O(n·iter)):
 *   Pass 1 — power-iterate L to find lambda_max (largest eigenvalue), keeping
 *             v orthogonal to the constant eigenvector (eigenvalue 0).
 *   Pass 2 — power-iterate the shifted matrix (lambda_max·I − L) with the same
 *             deflation. The dominant eigenvalue mu_max of the shifted matrix
 *             satisfies: Fiedler = lambda_max − mu_max.
 *
 * This avoids matrix inversion (no shifted-inverse iteration needed) and is
 * exact enough for memory graphs up to ~5 000 nodes.
 */
export declare function computeFiedlerValue(store: CortexStore): Promise<number>;
export interface PESaturationResult {
    /** Mean prediction error over the most recent 14-day window. */
    mean_pe: number;
    /** Trend relative to the prior 14-day window. */
    trend: 'rising' | 'stable' | 'declining';
    /**
     * True when the recent mean PE has fallen below 0.10 — signal that
     * adversarial rumination should be triggered to prevent stagnation.
     */
    saturated: boolean;
    recommendation: string;
}
/**
 * Detect PE saturation in identity-related observations.
 *
 * Queries the 'observations' collection for entries in the last 28 days
 * that have a non-null prediction_error. Computes the mean PE for the most
 * recent 14-day window vs the prior 14-day window and classifies the trend.
 *
 * A "saturated" state (mean_pe < 0.10) means the system is no longer
 * surprised by identity-type inputs — a sign that adversarial or
 * counterfactual prompting should be introduced to break out of the plateau.
 */
export declare function detectPESaturation(store: CortexStore): Promise<PESaturationResult>;
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