/**
 * Pool-axis VoI scorer — pure functions, no LLM, no I/O (IND-417).
 *
 * VoI = H_norm × coverage^1.5 × novelty
 *
 *   H_norm    score-weighted entropy over the axis sides, normalized by
 *             log2(k). Entropy is computed over candidate *score mass*
 *             (confidence weights), not head counts, so a balanced split of
 *             low-confidence junk-tail candidates doesn't inflate VoI.
 *   coverage  fraction of total pool score mass with a verified side
 *             assignment. The ^1.5 exponent is the anti-vague-axis knob:
 *             axes the LLM couldn't ground in evidence lose fast.
 *   novelty   1 − max cosine similarity between the axis embedding and
 *             reference embeddings (existing premises, intent sentences).
 *             An axis the user has already answered elsewhere scores ~0.
 */
import type { MinedDiscriminator, PoolCandidate, ScoredDiscriminator } from "./discriminator.types.js";
/** Cosine similarity of two equal-length vectors, in [-1, 1]. 0 for degenerate input. */
export declare function cosineSimilarity(a: number[], b: number[]): number;
/**
 * Novelty of an axis vs reference texts: 1 − max cosine similarity, clamped
 * to [0, 1]. No references → 1 (fully novel).
 */
export declare function computeNovelty(axisEmbedding: number[], referenceEmbeddings: number[][]): number;
/**
 * Score one mined axis against its pool.
 *
 * @param axis        Mined axis with verified assignments (one per candidate).
 * @param candidates  The pool (provides score mass per candidate id).
 * @param novelty     Precomputed novelty in [0,1] (see {@link computeNovelty}).
 */
export declare function scoreDiscriminator(axis: MinedDiscriminator, candidates: PoolCandidate[], novelty: number): ScoredDiscriminator;