Jesse_Chen
75fc456d7e
Spoken collect no longer renders a second visual prompt; choice legends stay screen-reader only and live cards share the assistant inset. Exhaustion collect avoids colliding with the opening question id, and a successful billed turn no longer surfaces run_failed after the exit gate. Co-authored-by: Cursor <cursoragent@cursor.com>
668 lines
25 KiB
TypeScript
668 lines
25 KiB
TypeScript
import assert from "node:assert/strict";
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import { readFileSync } from "node:fs";
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import test from "node:test";
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import { candidateSetId } from "../src/lib/rectification-agentic/core/build-state.ts";
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import { asInferenceState } from "../src/lib/rectification-agentic/core/compose-receipt.ts";
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import {
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inspectDiscriminatorProbes,
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buildCandidateContrastPacket,
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} from "../src/lib/rectification-agentic/core/candidate-contrast-packet.ts";
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import {
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decideRectification,
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isNonConvergingRangeOffer,
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nonConvergingRangeNarration,
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REPRESENTATIVE_MINUTE_DISCLAIMER,
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} from "../src/lib/rectification-agentic/core/rectification-decision.ts";
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import { INFERENCE_ALGORITHM_VERSION } from "../src/lib/rectification-agentic/core/types.ts";
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import type { ConflictProbe, InferenceState } from "../src/lib/rectification-agentic/core/types.ts";
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import {
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decideAfterInferenceChange,
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decideFromDossier,
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type DecisionDossier,
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} from "../src/lib/rectification-agentic/v9/decision-from-dossier.ts";
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import {
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exhaustionSpokenCollectFollowup,
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projectRectificationChoiceCard,
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spokenFollowupForUser,
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} from "../src/lib/rectification-agentic/v9/method-followup.ts";
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import { persistNextInterviewIfIdle } from "../src/lib/rectification-agentic/v9/answer-choice.ts";
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import { informationGainAmongActive } from "../src/lib/rectification-agentic/v9/probe-question-contract.ts";
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import { projectCurrentQuestion } from "../src/lib/rectification-agentic/v9/turn-decision.ts";
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import { stableFollowupQuestionId } from "../src/lib/rectification-agentic/v9/server-focus.ts";
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import { RECTIFICATION_SKILL_VERSION } from "../src/lib/rectification-agentic/v9/case-status.ts";
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import { evidenceLedgerFingerprint } from "../src/lib/rectification-agentic/v9/tool-service.ts";
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import {
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OPEN_ENGINE_CAPABILITY_CEILING,
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CASE_ID,
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FOCUS_ID,
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TURN_ID,
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USER_ID,
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candidateSnapshotFixture,
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computeFixture,
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dossierFixture,
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fakeAccounting,
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receiptHandlers,
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} from "./rectification-v9-test-support.ts";
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const EXISTENCE_OPTIONS = [
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{ label: "明确发生且时间吻合", answer_class: "yes" as const },
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{ label: "发生过但程度较弱", answer_class: "weak_yes" as const },
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{ label: "明确没有发生", answer_class: "no" as const },
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{ label: "这段记不清楚", answer_class: "unsure" as const },
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];
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const TIMES = [
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"04:47", "04:51", "04:53", "04:59", "05:00", "05:07", "05:12", "05:14", "05:15",
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] as const;
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const ELIMINATED = new Set(["05:00", "05:07", "05:12", "05:14", "05:15"]);
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const ACTIVE = ["04:47", "04:51", "04:53", "04:59"] as const;
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const SCORES: Record<string, number> = {
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"04:47": 16,
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"04:51": 20,
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"04:53": 16,
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"04:59": 10,
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"05:00": 4,
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"05:07": 3,
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"05:12": 2,
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"05:14": 1,
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"05:15": 1,
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};
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const PROBABILITY: Record<string, number> = {
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"04:47": 0.25,
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"04:51": 0.4,
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"04:53": 0.25,
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"04:59": 0.1,
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};
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const EVIDENCE = [
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{
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id: "e-career-entry",
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status: "confirmed" as const,
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domain: "career",
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datePrecision: "month" as const,
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occurredFrom: "2020-04-01",
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occurredTo: null,
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eventKind: "career_entry",
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summary: "2020-04-01 career_entry",
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},
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{
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id: "e-career-exit",
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status: "confirmed" as const,
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domain: "career",
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datePrecision: "month" as const,
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occurredFrom: "2020-10-01",
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occurredTo: null,
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eventKind: "career_exit",
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summary: "2020-10-01 career_exit",
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},
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{
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id: "e-rel-start",
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status: "confirmed" as const,
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domain: "relationship",
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datePrecision: "month" as const,
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occurredFrom: "2024-05-01",
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occurredTo: null,
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eventKind: "relationship_start",
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summary: "2024-05-01 relationship_start",
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},
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{
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id: "e-rel-end",
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status: "confirmed" as const,
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domain: "relationship",
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datePrecision: "day" as const,
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occurredFrom: "2024-08-08",
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occurredTo: null,
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eventKind: "relationship_end",
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summary: "2024-08-08 relationship_end",
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},
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] as const;
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const DUAL_EXIT = "可以先按当前区间看盘，也可以再补一件记得时间的经历";
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function existenceProbe(input: {
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key: string;
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year: number;
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question: string;
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gain: number;
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source: string;
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yes: readonly string[];
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no: readonly string[];
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}): ConflictProbe {
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return {
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id: `probe:${input.key}`,
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semantic_key: input.key,
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candidate_split_hash: input.key,
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domain: "career",
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year: input.year,
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question: input.question,
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candidate_ids: [...new Set([...input.yes, ...input.no])],
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expected_outcomes: [
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{ answer_class: "yes", supports: input.yes, conflicts: input.no },
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{ answer_class: "weak_yes", supports: [], conflicts: [] },
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{ answer_class: "no", supports: input.no, conflicts: input.yes },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: input.gain,
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source: input.source,
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choice_kind: "existence",
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style_options: EXISTENCE_OPTIONS,
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};
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}
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const D9: ConflictProbe = {
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id: "contrast:varga.d9.巨蟹座/狮子座",
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semantic_key: "varga.d9.巨蟹座/狮子座",
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candidate_split_hash: "varga.d9.巨蟹座/狮子座",
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domain: "relationship",
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year: 0,
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question: "亲密关系里更接近下面哪一种相处方式？",
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candidate_ids: ["05:00", "05:07"],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:07"] },
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{ answer_class: "weak_yes", supports: ["05:07"], conflicts: ["05:00"] },
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{ answer_class: "no", supports: [], conflicts: [] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 1.1,
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source: "varga_contrast",
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choice_kind: "varga_style",
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};
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const D10: ConflictProbe = {
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id: "contrast:varga.d10.天秤座/天蝎座",
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semantic_key: "varga.d10.天秤座/天蝎座",
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candidate_split_hash: "varga.d10.天秤座/天蝎座",
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domain: "career",
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year: 0,
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question: "平时做事更接近下面哪一种职责风格？",
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candidate_ids: ["05:00", "05:07"],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:07"] },
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{ answer_class: "weak_yes", supports: ["05:07"], conflicts: ["05:00"] },
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{ answer_class: "no", supports: [], conflicts: [] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 1.05,
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source: "varga_contrast",
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choice_kind: "varga_style",
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};
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const CAREER_2023_05 = existenceProbe({
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key: "career.2023.05.dasha_boundary",
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year: 2023,
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question: "2023 年 5 月前后有没有入职或换工作？",
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gain: 0.9,
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source: "dasha_boundary",
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yes: ["05:00"],
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no: ["05:07"],
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});
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const RELOCATION_2015_05: ConflictProbe = {
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...existenceProbe({
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key: "relocation.2015.05.dasha_boundary",
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year: 2015,
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question: "2015 年 5 月前后有没有搬家或长期住到外地？",
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gain: 0.7,
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source: "dasha_boundary",
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yes: ["05:00"],
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no: ["05:07"],
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}),
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domain: "relocation",
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};
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/** Live remaining probe: splits after/before 05:00. Zero split on 04:47–04:59. */
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const CAREER_2024_04 = existenceProbe({
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key: "career.2024.04.dasha_boundary",
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year: 2024,
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question: "2024 年 4 月前后有没有入职或换工作？",
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gain: 1.1712,
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source: "dasha_boundary",
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yes: ["05:00", "05:07", "05:12", "05:14", "05:15"],
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no: ["04:47", "04:51", "04:53", "04:59"],
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});
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/** Live remaining probe: isolates 05:15. Zero split on 04:47–04:59. */
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const CAREER_2023_ACTIVATION = existenceProbe({
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key: "career.2023.dasha_activation",
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year: 2023,
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question: "2023 年前后大运有没有启动？",
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gain: 0.56,
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source: "dasha_activation",
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yes: ["05:15"],
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no: ["04:47", "04:51", "04:53", "04:59", "05:00", "05:07", "05:12", "05:14"],
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});
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/** P1 fixture: dated career probe that still splits the active four minutes. */
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const CAREER_ACTIVE_SPLIT = existenceProbe({
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key: "career.2022.dasha_boundary",
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year: 2022,
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question: "2022 年前后有没有入职或换工作？",
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gain: 1.4,
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source: "dasha_boundary",
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yes: ["04:47", "04:51"],
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no: ["04:53", "04:59"],
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});
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function uuidAt(index: number): string {
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return `88888888-8888-4888-8888-8888888888${(10 + index).toString(16).padStart(2, "0")}`;
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}
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function liveState(remaining: readonly ConflictProbe[]): InferenceState {
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const probes = [D9, D10, CAREER_2023_05, RELOCATION_2015_05, ...remaining];
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const rankedActive = [...ACTIVE].sort((left, right) => (
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(PROBABILITY[right] ?? 0) - (PROBABILITY[left] ?? 0)
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|| (SCORES[right] ?? 0) - (SCORES[left] ?? 0)
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|| left.localeCompare(right)
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));
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const candidates = TIMES.map((time, index) => {
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const eliminated = ELIMINATED.has(time);
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const activeRank = (rankedActive as readonly string[]).indexOf(time);
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return {
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id: time,
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time,
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cluster_range: [time, time] as const,
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prior_score: SCORES[time] ?? 0,
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posterior_score: SCORES[time] ?? 0,
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probability: eliminated ? 0 : (PROBABILITY[time] ?? 0),
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status: eliminated ? "eliminated" as const : "active" as const,
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rank: eliminated ? ACTIVE.length + index : activeRank + 1,
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strong_conflict_count: eliminated ? 3 : 0,
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};
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});
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const answered = [D9, D10, CAREER_2023_05, RELOCATION_2015_05].map((probe) => ({
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probe_id: probe.id,
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semantic_key: probe.semantic_key,
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candidate_split_hash: probe.candidate_split_hash,
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answer_class: "no" as const,
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classified_from: "choice" as const,
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}));
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const raw = {
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algorithm_version: INFERENCE_ALGORITHM_VERSION,
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candidate_set_id: candidateSetId("04:47", "05:15", TIMES),
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revision: 5,
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phase: "discrimination" as const,
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result_status: "discriminating" as const,
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range_start: "04:47",
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range_end: "05:15",
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candidates,
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events: [
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{ id: "e-career-entry", domain: "career", year: 2020, precision: "month" as const, usage: "training" as const },
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{ id: "e-career-exit", domain: "career", year: 2020, precision: "month" as const, usage: "training" as const },
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{ id: "e-rel-start", domain: "relationship", year: 2024, precision: "month" as const, usage: "training" as const },
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{ id: "e-rel-end", domain: "relationship", year: 2024, precision: "day" as const, usage: "training" as const },
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],
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probes,
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answered_probes: answered,
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rounds: [],
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last_inference_round: null,
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entropy: 1.2,
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representative_time: "04:51",
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credible_range: ["04:47", "04:53"] as const,
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holdout_passed: null,
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};
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const loaded = asInferenceState(raw);
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assert.ok(loaded, "live inference fixture must pass asInferenceState");
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return loaded;
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}
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function liveDossier(
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remaining: readonly ConflictProbe[],
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extra: {
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declinedTopics?: ReadonlyArray<Record<string, unknown>>;
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status?: string;
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eventProbes?: boolean;
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} = {},
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): DecisionDossier {
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const state = liveState(remaining);
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const fingerprint = evidenceLedgerFingerprint(EVIDENCE as never);
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return {
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evidence: EVIDENCE,
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conversationSummary: {
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activeFocus: null,
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declinedSkippedTopics: extra.declinedTopics ?? [{ target_domain: "family", status: "declined" }],
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},
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latestResult: {
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resultId: "55555555-5555-4555-8555-555555555555",
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selectionAllowed: true,
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confirmationAllowed: false,
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evidenceLedgerFingerprint: fingerprint,
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candidates: TIMES.map((time, index) => ({
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candidateId: uuidAt(index),
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time,
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rank: index + 1,
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relativeSupport: Math.round(SCORES[time] ?? 0),
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})),
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representativeTime: "04:51",
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decisionReceipt: {
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accept_allowed: true,
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acceptance_allowed: true,
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propose_allowed: true,
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selection_allowed: true,
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confirmation_allowed: false,
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inference_state: state,
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...(extra.eventProbes
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? {
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discriminating_event_probes: remaining.map((probe) => ({
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year: probe.year,
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year_label: `${probe.year} 年前后`,
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domain: probe.domain,
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event_family: "入职、换工作或职责加重",
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source: probe.source,
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tracks: ["vimshottari", "narayana"],
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tracks_agree: true,
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unique_minute_claim: false,
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user_meaning: probe.question,
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role: "distinguish",
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information_gain: probe.information_gain,
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semantic_key: probe.semantic_key,
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candidate_split_hash: probe.candidate_split_hash,
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candidate_ids: probe.candidate_ids,
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expected_outcomes: probe.expected_outcomes,
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choice_kind: probe.choice_kind,
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style_options: probe.style_options,
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})),
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}
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: {}),
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},
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},
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case: { acceptedTime: null, status: extra.status },
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};
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}
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function snapshotCandidates() {
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return TIMES.map((time, index) => ({
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candidate_id: uuidAt(index),
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rank: index + 1,
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time,
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relative_support: Math.round(SCORES[time] ?? 0),
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tied_minute_count: 1,
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}));
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}
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function rpcDossier(decision: DecisionDossier) {
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const evidence = EVIDENCE.map((item) => ({
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id: item.id,
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source_turn_id: TURN_ID,
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subject: "self",
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event_kind: item.eventKind,
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domain: item.domain,
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occurred_from: item.occurredFrom,
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occurred_to: item.occurredTo,
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date_precision: item.datePrecision,
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summary: `${item.occurredFrom} ${item.eventKind}`,
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status: item.status,
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supersedes_evidence_id: null,
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created_at: "2026-08-29T00:00:00.000Z",
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}));
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return dossierFixture({
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evidence,
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latestResult: candidateSnapshotFixture({
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selectionAllowed: true,
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confirmationAllowed: false,
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representativeTime: "04:51",
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evidenceLedgerFingerprint: evidenceLedgerFingerprint(EVIDENCE as never),
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candidates: snapshotCandidates(),
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decisionReceipt: {
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accept_allowed: true,
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acceptance_allowed: true,
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propose_allowed: true,
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selection_allowed: true,
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confirmation_allowed: false,
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...(decision.latestResult?.decisionReceipt ?? {}),
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},
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}),
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conversationSummary: {
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confirmed_evidence_summary: [],
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pending_revisions: [],
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active_focus: null,
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declined_skipped_topics: decision.conversationSummary.declinedSkippedTopics,
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candidate_divergence_summary: null,
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missing_evidence_categories: [],
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last_result_policy: null,
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summary_version: 1,
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updated_at: "2026-08-30T00:00:00.000Z",
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},
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});
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}
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test("skill version stays 10.0.14 for the range-offer dead-end fix", () => {
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assert.equal(RECTIFICATION_SKILL_VERSION, "10.0.14");
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});
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test("pre-fix dual-exit constant is gone; range narration carries numbers and the disclaimer", () => {
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const decision = "../src/lib/rectification-agentic/core/rectification-decision.ts";
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const answer = "../src/lib/rectification-agentic/v9/answer-choice.ts";
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const route = "../src/app/api/rectification/agent/route.ts";
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for (const relative of [decision, answer, route]) {
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const source = readFileSync(new URL(relative, import.meta.url), "utf8");
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assert.doesNotMatch(source, new RegExp(DUAL_EXIT));
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assert.doesNotMatch(source, /NON_CONVERGING_RANGE_NARRATION|DISCRIMINATOR_EXHAUSTED_NARRATION/);
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assert.match(source, /nonConvergingRangeNarration/);
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}
|
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const text = nonConvergingRangeNarration({
|
||
credibleRange: ["04:47", "04:53"],
|
||
representativeTime: "04:51",
|
||
});
|
||
assert.match(text, /04:47–04:53/);
|
||
assert.match(text, /04:51/);
|
||
assert.match(text, new RegExp(REPRESENTATIVE_MINUTE_DISCLAIMER));
|
||
assert.doesNotMatch(text, /可以先按当前区间看盘/);
|
||
});
|
||
|
||
test("zero-split among active is dropped with an explicit reason, not kept silent", () => {
|
||
const after = informationGainAmongActive(CAREER_2024_04.expected_outcomes, ACTIVE);
|
||
const activation = informationGainAmongActive(CAREER_2023_ACTIVATION.expected_outcomes, ACTIVE);
|
||
const split = informationGainAmongActive(CAREER_ACTIVE_SPLIT.expected_outcomes, ACTIVE);
|
||
assert.equal(after.splits, false);
|
||
assert.equal(activation.splits, false);
|
||
assert.equal(split.splits, true);
|
||
const packet = buildCandidateContrastPacket({
|
||
candidateSetVersion: candidateSetId("04:47", "05:15", TIMES),
|
||
engineProbes: [CAREER_2024_04, CAREER_2023_ACTIVATION].map((probe) => ({
|
||
semantic_key: probe.semantic_key,
|
||
candidate_split_hash: probe.candidate_split_hash,
|
||
domain: probe.domain,
|
||
year: probe.year,
|
||
user_meaning: probe.question,
|
||
information_gain: probe.information_gain,
|
||
expected_outcomes: probe.expected_outcomes,
|
||
choice_kind: probe.choice_kind,
|
||
style_options: probe.style_options,
|
||
})),
|
||
providedDomains: ["career", "relationship"],
|
||
candidateTimes: [...TIMES],
|
||
});
|
||
assert.equal(packet.probes.some((item) => item.semanticKey === CAREER_2024_04.semantic_key), true);
|
||
const inspected = inspectDiscriminatorProbes(packet, { topCandidateTimes: ACTIVE });
|
||
assert.equal(inspected.selected, null);
|
||
assert.equal(inspected.dropped.some((item) => (
|
||
item.semantic_key === CAREER_2024_04.semantic_key && item.reason === "no_split_among_active"
|
||
)), true);
|
||
assert.equal(inspected.dropped.some((item) => (
|
||
item.semantic_key === CAREER_2023_ACTIVATION.semantic_key && item.reason === "no_split_among_active"
|
||
)), true);
|
||
});
|
||
|
||
test("P1: dated career probes live only in inference_state still ask when they split active minutes", () => {
|
||
const dossier = liveDossier([CAREER_ACTIVE_SPLIT]);
|
||
const fromDossier = decideFromDossier(dossier, { birthDate: "1997-08-08" });
|
||
assert.equal(fromDossier.nextAction, "ask_candidate_discriminator");
|
||
assert.equal(fromDossier.probe?.semanticKey, CAREER_ACTIVE_SPLIT.semantic_key);
|
||
assert.equal(isNonConvergingRangeOffer(fromDossier), false);
|
||
const after = decideAfterInferenceChange({
|
||
dossier,
|
||
state: liveState([CAREER_ACTIVE_SPLIT]),
|
||
userStopped: false,
|
||
birthDate: "1997-08-08",
|
||
});
|
||
assert.equal(after.nextAction, "ask_candidate_discriminator");
|
||
assert.equal(after.probe?.semanticKey, CAREER_ACTIVE_SPLIT.semantic_key);
|
||
});
|
||
|
||
test("live remaining probes with zero active split enter dropped_probes and the range-offer branch", () => {
|
||
const remaining = [CAREER_2024_04, CAREER_2023_ACTIVATION];
|
||
const dossier = liveDossier(remaining);
|
||
const decision = decideFromDossier(dossier, { birthDate: "1997-08-08" });
|
||
assert.equal(decision.probe, null);
|
||
assert.equal(decision.nextAction, "offer_provisional_range");
|
||
assert.equal(isNonConvergingRangeOffer(decision), true);
|
||
assert.equal(decision.canAdopt, false);
|
||
assert.equal(decision.selectionAllowed, false);
|
||
assert.deepEqual(decision.credibleRange, ["04:47", "04:53"]);
|
||
assert.equal(decision.representativeTime, "04:51");
|
||
assert.equal(decision.droppedProbes.some((item) => (
|
||
item.semantic_key === CAREER_2024_04.semantic_key && item.reason === "no_split_among_active"
|
||
)), true);
|
||
assert.equal(decision.droppedProbes.some((item) => (
|
||
item.semantic_key === CAREER_2023_ACTIVATION.semantic_key && item.reason === "no_split_among_active"
|
||
)), true);
|
||
});
|
||
|
||
test("offerRangeWithoutAdopt persists a spoken collect and narrates the numeric range", async () => {
|
||
const dossier = liveDossier([CAREER_2024_04, CAREER_2023_ACTIVATION]);
|
||
const decision = decideFromDossier(dossier, { birthDate: "1997-08-08" });
|
||
assert.equal(isNonConvergingRangeOffer(decision), true);
|
||
const card = projectRectificationChoiceCard({
|
||
evidence: dossier.evidence,
|
||
declinedTopics: dossier.conversationSummary.declinedSkippedTopics,
|
||
sessionOutcome: decision.sessionOutcome,
|
||
selectionAllowed: decision.selectionAllowed,
|
||
});
|
||
assert.equal(card, null);
|
||
const collect = exhaustionSpokenCollectFollowup({
|
||
evidence: dossier.evidence,
|
||
declinedTopics: dossier.conversationSummary.declinedSkippedTopics,
|
||
});
|
||
assert.equal(collect?.domain, "education");
|
||
assert.equal(collect?.intent, "collect_method_evidence");
|
||
assert.equal(collect?.choice_frame, null);
|
||
const spoken = spokenFollowupForUser(collect);
|
||
assert.ok(spoken);
|
||
|
||
const accounting = fakeAccounting({
|
||
...receiptHandlers,
|
||
get_agentic_rectification_case_dossier: () => rpcDossier(dossier),
|
||
get_agentic_rectification_case_compute: () => computeFixture(),
|
||
set_agentic_rectification_conversation_focus: (_fn, args) => ({
|
||
focus: {
|
||
id: FOCUS_ID,
|
||
case_id: CASE_ID,
|
||
question_id: args.p_question_id,
|
||
intent: args.p_intent,
|
||
target_evidence_id: args.p_target_evidence_id,
|
||
target_domain: args.p_target_domain,
|
||
target_kind: args.p_target_kind,
|
||
expected_answer_schema: args.p_expected_answer_schema,
|
||
status: "active",
|
||
asked_at: "2026-08-30T00:00:00.000Z",
|
||
resolved_at: null,
|
||
},
|
||
idempotent: false,
|
||
}),
|
||
});
|
||
const idle = await persistNextInterviewIfIdle({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
});
|
||
assert.equal(idle.persisted, true);
|
||
assert.equal(idle.choiceReady, false);
|
||
assert.match(idle.hostNarration ?? "", /04:47–04:53/);
|
||
assert.match(idle.hostNarration ?? "", /04:51/);
|
||
assert.match(idle.hostNarration ?? "", new RegExp(REPRESENTATIVE_MINUTE_DISCLAIMER));
|
||
assert.doesNotMatch(idle.hostNarration ?? "", /升学|转学|考试/);
|
||
assert.doesNotMatch(idle.hostNarration ?? "", new RegExp(DUAL_EXIT));
|
||
const setFocus = accounting.calls.find((item) => item.fn === "set_agentic_rectification_conversation_focus");
|
||
assert.equal(setFocus?.args.p_intent, "collect_method_evidence");
|
||
assert.equal(setFocus?.args.p_target_domain, "education");
|
||
const schema = setFocus?.args.p_expected_answer_schema as Record<string, unknown> | undefined;
|
||
const question = projectCurrentQuestion({
|
||
id: FOCUS_ID,
|
||
questionId: String(setFocus?.args.p_question_id ?? ""),
|
||
intent: "collect_method_evidence",
|
||
targetDomain: "education",
|
||
expectedAnswerSchema: schema ?? null,
|
||
});
|
||
assert.equal(question?.kind, "collect_spoken");
|
||
assert.ok(question?.prompt);
|
||
assert.equal(Boolean(idle.hostNarration) && Boolean(question), true);
|
||
});
|
||
|
||
test("userStopped completes with a review-only provisional range", () => {
|
||
const stopped = decideRectification({
|
||
engineCeiling: OPEN_ENGINE_CAPABILITY_CEILING,
|
||
methodCoverageAll: false,
|
||
trainingGateOpen: true,
|
||
userStopped: true,
|
||
candidateScores: ACTIVE.map((time) => ({ time, score: SCORES[time] ?? 0 })),
|
||
});
|
||
assert.equal(stopped.sessionOutcome, "provisional_range_user_stopped");
|
||
// 原值是 true；用户停止只结束追问，不能绕过 coverage 和 holdout 门槛。
|
||
assert.equal(stopped.canAdopt, false);
|
||
assert.equal(stopped.canOfferRange, true);
|
||
assert.equal(isNonConvergingRangeOffer(stopped), false);
|
||
const dossier = liveDossier([CAREER_2024_04, CAREER_2023_ACTIVATION], { status: "paused" });
|
||
const fromDossier = decideFromDossier(dossier, { birthDate: "1997-08-08" });
|
||
assert.equal(fromDossier.sessionOutcome, "provisional_range_user_stopped");
|
||
// 原值是 true；该 dossier 只有两个同域日期事件，停止不等于证据达标。
|
||
assert.equal(fromDossier.canAdopt, false);
|
||
});
|
||
|
||
test("idle persist still decides from the dossier once and does not invent collect_evidence", () => {
|
||
const source = readFileSync(new URL("../src/lib/rectification-agentic/v9/answer-choice.ts", import.meta.url), "utf8");
|
||
const idle = source.slice(
|
||
source.indexOf("export async function persistNextInterviewIfIdle"),
|
||
source.indexOf("async function persistApplied"),
|
||
);
|
||
assert.equal(idle.split("decideFromDossier").length - 1, 1);
|
||
assert.match(idle, /sessionOutcome:\s*decision\.sessionOutcome/);
|
||
assert.doesNotMatch(idle, /sessionOutcome:\s*"collect_evidence"/);
|
||
assert.match(idle, /persistExhaustionCollect/);
|
||
});
|
||
|
||
function datedCollectEvidence(domain: string, year: string, extra: { eventKind?: string } = {}) {
|
||
return {
|
||
status: "confirmed" as const,
|
||
domain,
|
||
datePrecision: extra.eventKind ? "unknown" as const : "year" as const,
|
||
occurredFrom: extra.eventKind ? null : `${year}-01-01`,
|
||
occurredTo: null,
|
||
...(extra.eventKind ? { eventKind: extra.eventKind } : {}),
|
||
};
|
||
}
|
||
|
||
test("exhaustion after family declined and dated domains confirmed asks occupation", () => {
|
||
const next = exhaustionSpokenCollectFollowup({
|
||
evidence: [
|
||
datedCollectEvidence("education", "2016"),
|
||
datedCollectEvidence("career", "2020"),
|
||
datedCollectEvidence("relationship", "2018"),
|
||
datedCollectEvidence("finance", "2024"),
|
||
],
|
||
declinedTopics: [{ target_domain: "family", status: "declined" }],
|
||
});
|
||
assert.equal(next?.domain, "occupation");
|
||
assert.equal(next?.choice_frame, null);
|
||
assert.equal(spokenFollowupForUser(next), "你长期做什么工作？");
|
||
});
|
||
|
||
test("exhaustion generic fallback uses domain other, not unknown", () => {
|
||
const next = exhaustionSpokenCollectFollowup({
|
||
evidence: [
|
||
datedCollectEvidence("education", "2016"),
|
||
datedCollectEvidence("career", "2020"),
|
||
datedCollectEvidence("relationship", "2018"),
|
||
datedCollectEvidence("finance", "2024"),
|
||
datedCollectEvidence("relocation", "2022"),
|
||
datedCollectEvidence("health_pressure", "2021"),
|
||
datedCollectEvidence("occupation", "2020", { eventKind: "occupation_note" }),
|
||
],
|
||
declinedTopics: [{ target_domain: "family", status: "declined" }],
|
||
});
|
||
assert.equal(next?.domain, "other");
|
||
assert.equal(next?.intent, "collect_method_evidence");
|
||
assert.equal(stableFollowupQuestionId(next), "collect:other:collect_method_evidence");
|
||
assert.equal(spokenFollowupForUser(next), "可以再说一件记得大概时间的经历。");
|
||
});
|