/**
 * evermind/eval.ts — the pre/post-merge regression check.
 *
 * Each merge produces a NEW version from the PREVIOUS one. To tell whether it helped
 * or quietly regressed (catastrophic forgetting), both models are scored on a small
 * held-out set of previously-learned examples and their mean next-token loss compared.
 *
 * Forward-only: the loss is the same mean cross-entropy the trainer reports, computed
 * from the logits alone, so scoring never pays for a backward pass or touches grads.
 */
import type { LogitsModel } from "../bench/types.js";
/** One held-out example: the learned text, optionally with the task prompt it answered. */
export interface EvalExample {
    prompt?: string;
    text: string;
}
/** Max tokens scored per example — bounds the forward cost of eval. */
export declare const EVAL_MAX_TOKENS = 128;
/** The tokenizer surface eval needs (the engine's `BPETokenizer` satisfies it). */
export interface EvalTokenizer {
    encode(text: string): number[];
}
/** Mean next-token cross-entropy over one token sequence, or null when it has no target. */
export declare function sequenceLoss(model: LogitsModel, ids: number[]): number | null;
/**
 * Mean loss of `model` across `examples` (unscorable ones skipped), each example
 * weighted equally. Null when nothing was scorable, so a caller can decline to
 * record a meaningless eval point.
 */
export declare function meanEvalLoss(model: LogitsModel, tokenizer: EvalTokenizer, examples: readonly EvalExample[], maxTokens?: number): number | null;
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