Jyotisha/frontend/tests/rectification-eight-method.test.ts

import assert from "node:assert/strict";
import { readFileSync } from "node:fs";
import test from "node:test";

import { buildMethodFollowupPlan, buildNextUserAction, conversationalSessionOutcome, isOfferBlockingFollowup, spokenFollowupForUser, blockingMethodsCovered } from "../src/lib/rectification-agentic/v9/method-followup.ts";
import { trainingScoreableGate } from "../src/lib/rectification-agentic/v9/evidence-model.ts";
import {
  mentionedVargaKeysFromLedgerEvidence,
  buildCandidateContrastPacket,
} from "../src/lib/rectification-agentic/core/candidate-contrast-packet.ts";
import {
  internalObservationsFromWindowScan,
  parseWindowScan,
} from "../src/lib/rectification-agentic/v9/varga-observations.ts";
import { WINDOW_SCAN_DISPLAY_LAYER_ORDER } from "../src/lib/rectification-agentic/v9/refinement-packet.ts";
import { RECTIFICATION_SKILL_VERSION } from "../src/lib/rectification-agentic/v9/case-status.ts";
import { buildInferenceState } from "../src/lib/rectification-agentic/core/build-state.ts";
import { decideRectification } from "../src/lib/rectification-agentic/core/rectification-decision.ts";
import { readVedastroMinuteSensitiveStatus } from "../src/lib/rectification-agentic/v9/confirmation-gate.ts";
import { authoritativeCandidateProjection } from "../src/lib/rectification-agentic/v9/inference-adapter.ts";
import { createRectificationV9Tools, latestResultToolProjection } from "../src/mastra/rectification-v9-tools.ts";
import { PUBLIC_RECTIFICATION_TOOLS } from "../src/lib/rectification-agentic/v9/public-receipt.ts";
import {
  candidateRangeFingerprint,
  evidenceLedgerFingerprint,
  parseV9CaseDossier,
  RectificationToolServiceError,
} from "../src/lib/rectification-agentic/v9/tool-service.ts";
import {
  resolveExactSkillPackage,
} from "../src/lib/skill-package-registry.ts";
import {
  OPEN_ENGINE_CAPABILITY_CEILING,
  CASE_ID,
  CANDIDATE_ID,
  FOCUS_ID,
  RESULT_ID,
  SECOND_CANDIDATE_ID,
  TURN_ID,
  USER_ID,
  candidateSnapshotFixture,
  computeFixture,
  conversationSummaryFixture,
  dossierFixture,
  activeFocusFixture,
  fakeAccounting,
  receiptHandlers,
} from "./rectification-v9-test-support.ts";

const THIRD_CANDIDATE_ID = "88888888-8888-4888-8888-888888888883";
const EDUCATION_ID = "44444444-4444-4444-8444-444444444441";
const RELATIONSHIP_ID = "44444444-4444-4444-8444-444444444442";
const FAMILY_ID = "44444444-4444-4444-8444-444444444443";
const CAREER_ID = "44444444-4444-4444-8444-444444444444";
const UNIQUE_MINUTE_COPY = /±5 分钟确定性/;

// 本单回退 3847e9c9 把训练门未开的下一问改成家人的断言。见各断言旁三栏。

function collectDecision(candidates: readonly { time: string; relativeSupport?: number }[]) {
  return decideRectification({
    engineCeiling: OPEN_ENGINE_CAPABILITY_CEILING,
    methodCoverageAll: false,
    trainingGateOpen: false,
    candidateScores: candidates.map((item) => ({ time: item.time, score: item.relativeSupport ?? 0 })),
  });
}

function producedInferenceState(
  candidates: readonly Readonly<{ id: string; time: string; relative_support: number }>[],
) {
  const times = candidates.map((item) => item.time).sort();
  return buildInferenceState({
    range_start: times[0]!,
    range_end: times[times.length - 1]!,
    candidates: candidates.map((item) => ({
      id: item.id,
      time: item.time,
      relative_support: item.relative_support,
    })),
    events: [],
    probes: [],
  });
}

const DYNAMIC_STYLE_OPTIONS = [
  { label: "明确发生且时间吻合", answer_class: "yes" as const },
  { label: "发生过但程度较弱", answer_class: "weak_yes" as const },
  { label: "明确没有发生", answer_class: "no" as const },
  { label: "不记得这段经历", answer_class: "unsure" as const },
];

const CLASSIC_COVERAGE = [
  { status: "confirmed", domain: "education", datePrecision: "year" as const, occurredFrom: "2016-01-01", occurredTo: null },
  { status: "confirmed", domain: "relationship", datePrecision: "year" as const, occurredFrom: "2018-01-01", occurredTo: null },
  { status: "confirmed", domain: "career", datePrecision: "year" as const, occurredFrom: "2019-01-01", occurredTo: null },
  { status: "confirmed", domain: "family", datePrecision: "year" as const, occurredFrom: "2020-01-01", occurredTo: null },
  { status: "confirmed", domain: "appearance", datePrecision: "unknown" as const, occurredFrom: null, occurredTo: null },
  { status: "confirmed", domain: "marks", datePrecision: "unknown" as const, occurredFrom: null, occurredTo: null },
  { status: "confirmed", domain: "occupation", datePrecision: "unknown" as const, occurredFrom: null, occurredTo: null },
  { status: "confirmed", domain: "horary", datePrecision: "day" as const, occurredFrom: "2024-01-01", occurredTo: null },
];

function datedEvidence(
  domain: string,
  year: string,
  extra: {
    eventKind?: string | null;
    summary?: string | null;
    datePrecision?: "year" | "month" | "day";
  } = {},
) {
  return {
    status: "confirmed" as const,
    domain,
    datePrecision: extra.datePrecision ?? ("year" as const),
    occurredFrom: `${year}-01-01`,
    occurredTo: null,
    ...(extra.eventKind !== undefined ? { eventKind: extra.eventKind } : {}),
    ...(extra.summary !== undefined ? { summary: extra.summary } : {}),
  };
}

const CAREER_CONFLICT_PROBE = {
  year: 2018,
  year_label: "2018 年前后",
  domain: "career" as const,
  event_family: "入职、升职或职责明显加重",
  source: "dasha_activation" as const,
  tracks: ["vimshottari", "narayana"] as const,
  tracks_agree: true,
  unique_minute_claim: false as const,
  user_meaning: "年份锁定 2018 年前后。请写成一句自然语言，问是否入职或职责加重。",
  role: "distinguish" as const,
  phase: "candidate_discriminator" as const,
  information_gain: 0.21,
  semantic_key: "career.2018.dasha_activation",
  candidate_set_version: "set-test",
  candidate_split_hash: "set-test:career:2018",
  candidate_ids: ["05:00", "05:20"],
  expected_outcomes: [
    { answer_class: "yes", supports: ["05:00"], conflicts: ["05:20"] },
    { answer_class: "no", supports: ["05:20"], conflicts: ["05:00"] },
  ],
  style_options: DYNAMIC_STYLE_OPTIONS,
};

const EDUCATION_QUALITY_PROBE = {
  year: 2016,
  year_label: "2016 年前后",
  domain: "education" as const,
  event_family: "高考或重要考试发挥明显失常、压力很大",
  source: "known_event_quality" as const,
  tracks: ["vimshottari", "narayana"] as const,
  tracks_agree: true,
  unique_minute_claim: false as const,
  user_meaning: "年份锁定 2016 年前后。已有高考或考试经历。请写成一句自然语言，问那次是否发挥失常或压力特别大。",
  role: "clarify" as const,
  phase: "event_clarification" as const,
  choice_kind: "event_quality" as const,
  information_gain: 0,
  semantic_key: "education.2016",
  style_options: DYNAMIC_STYLE_OPTIONS,
};

const CAREER_QUALITY_PROBE = {
  year: 2020,
  year_label: "2020 年前后",
  domain: "career" as const,
  event_family: "入职、升职或职责明显加重",
  source: "known_event_quality" as const,
  tracks: ["vimshottari", "narayana"] as const,
  tracks_agree: true,
  unique_minute_claim: false as const,
  user_meaning: "年份锁定 2020 年前后。已有相关经历。请写成一句自然语言，问入职、升职或职责明显加重有没有发生过。不得改年份。",
  role: "clarify" as const,
  phase: "event_clarification" as const,
  choice_kind: "event_quality" as const,
  information_gain: 0,
  semantic_key: "career.2020",
  style_options: DYNAMIC_STYLE_OPTIONS,
};

const ENGINE_SCORE = {
  success: true,
  endpoint: "rectification_v5_score",
  result_id: "e4fbf2e0-85dc-5b42-a5a3-34e5dd4b7e62",
  algorithm_version: "rectification-event-contract-v2",
  event_contract_version: "rectification-event-contract-v2",
  decision_policy_version: "rectification-candidate-policy-v2",
  execution_ledger_version: "rectification-execution-ledger-v2",
  candidate_decisions: [
    { candidate_id: CANDIDATE_ID, time: "04:50", rank: 1, relative_support: 57, tied_minute_count: 1 },
    { candidate_id: SECOND_CANDIDATE_ID, time: "04:51", rank: 2, relative_support: 25, tied_minute_count: 2 },
    { candidate_id: THIRD_CANDIDATE_ID, time: "04:52", rank: 3, relative_support: 18, tied_minute_count: 2 },
  ],
  decision_receipt: {
    receipt_version: "candidate-decision-receipt-v2",
    contract_version: "v2",
    event_contract_version: "rectification-event-contract-v2",
    policy_version: "rectification-candidate-policy-v2",
    decision_policy_version: "rectification-candidate-policy-v2",
    display_allowed: true,
    selection_allowed: true,
    acceptance_allowed: true,
    propose_allowed: true,
    confirmation_allowed: false,
    accept_allowed: true,
    confirm_allowed: false,
    representative_candidate_id: CANDIDATE_ID,
    representative_time: "04:50",
    overall_confidence: "high",
    margin_percent: 42.5,
  },
  execution_ledger: [
    { ledger_version: "rectification-execution-ledger-v2", stage: "technique_layer", method: "d1-rashi", status: "executed", source: "python-engine" },
    { ledger_version: "rectification-execution-ledger-v2", stage: "technique_layer", method: "d9-navamsa", status: "executed", source: "python-engine" },
  ],
  diagnostics: {
    window_scan: {
      scanned: true,
      confirmation_allowed: false,
      unique_minute_claim: false,
      d9_lagna_count: 2,
      d10_lagna_count: 1,
      d9_candidates_differ: true,
      d10_candidates_differ: false,
      d9_sign_names: ["白羊座", "天蝎"],
    },
  },
};

const educationEvidence = {
  id: EDUCATION_ID,
  source_turn_id: TURN_ID,
  subject: "self",
  event_kind: "education_milestone",
  domain: "education",
  occurred_from: "2016-06-01",
  occurred_to: null,
  date_precision: "month",
  summary: "2016年6月一次学业节点",
  status: "confirmed",
  supersedes_evidence_id: null,
  created_at: "2026-08-12T10:00:06.000Z",
};

const relationshipEvidence = {
  id: RELATIONSHIP_ID,
  source_turn_id: TURN_ID,
  subject: "self",
  event_kind: "relationship_start",
  domain: "relationship",
  occurred_from: "2018-01-01",
  occurred_to: null,
  date_precision: "year",
  summary: "2018年一段感情开始",
  status: "confirmed",
  supersedes_evidence_id: null,
  created_at: "2026-08-12T10:00:07.000Z",
};

const familyEvidence = {
  id: FAMILY_ID,
  source_turn_id: TURN_ID,
  subject: "family",
  event_kind: "family_event",
  domain: "family",
  occurred_from: "2020-01-01",
  occurred_to: null,
  date_precision: "year",
  summary: "2020年家人相关变化",
  status: "confirmed",
  supersedes_evidence_id: null,
  created_at: "2026-08-12T10:00:08.000Z",
};

const careerEvidence = {
  id: CAREER_ID,
  source_turn_id: TURN_ID,
  subject: "self",
  event_kind: "career_entry",
  domain: "career",
  occurred_from: "2019-07-01",
  occurred_to: null,
  date_precision: "year",
  summary: "2019年开始工作",
  status: "confirmed",
  supersedes_evidence_id: null,
  created_at: "2026-08-12T10:00:09.000Z",
};

const methodCoverageTieEvidence = [
  { ...educationEvidence, domain: "education" },
  {
    id: "44444444-4444-4444-8444-444444444442",
    source_turn_id: TURN_ID,
    subject: "self",
    event_kind: "relationship_start",
    domain: "relationship",
    occurred_from: "2018-01-01",
    occurred_to: null,
    date_precision: "year",
    summary: "感情变化",
    status: "confirmed",
    supersedes_evidence_id: null,
    created_at: "2026-08-12T10:00:07.000Z",
  },
  {
    id: "44444444-4444-4444-8444-444444444443",
    source_turn_id: TURN_ID,
    subject: "self",
    event_kind: "career_entry",
    domain: "career",
    occurred_from: "2019-01-01",
    occurred_to: null,
    date_precision: "year",
    summary: "工作变化",
    status: "confirmed",
    supersedes_evidence_id: null,
    created_at: "2026-08-12T10:00:08.000Z",
  },
  {
    id: "44444444-4444-4444-8444-444444444446",
    source_turn_id: TURN_ID,
    subject: "self",
    event_kind: "family_event",
    domain: "family",
    occurred_from: "2020-01-01",
    occurred_to: null,
    date_precision: "year",
    summary: "家人变化",
    status: "confirmed",
    supersedes_evidence_id: null,
    created_at: "2026-08-12T10:00:09.000Z",
  },
  {
    id: "44444444-4444-4444-8444-444444444445",
    source_turn_id: TURN_ID,
    subject: "self",
    event_kind: "occupation_note",
    domain: "occupation",
    occurred_from: null,
    occurred_to: null,
    date_precision: "unknown",
    summary: "长期一直是程序员",
    status: "confirmed",
    supersedes_evidence_id: null,
    created_at: "2026-08-12T10:00:10.000Z",
  },
];

function scoreableFingerprintForRawEvidence(evidence: unknown[]): string {
  const parsed = parseV9CaseDossier(dossierFixture({ evidence }));
  assert.ok(parsed);
  return evidenceLedgerFingerprint(parsed.evidence);
}

function stubEngine(response: unknown) {
  const previous = globalThis.fetch;
  globalThis.fetch = (async () => ({
    ok: true,
    status: 200,
    json: async () => response,
  })) as unknown as typeof fetch;
  return () => {
    globalThis.fetch = previous;
  };
}

test("eight-method routing asks relationship after dated education, not relocation", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      datePrecision: "month",
      occurredFrom: "2016-06-01",
      occurredTo: null,
    }],
  });
  // 原值: relatives / family
  // 新值: d9_relationship / relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(plan.next_followup?.domain, "relationship");
  assert.equal(plan.stop_domain_rotation, true);
  assert.deepEqual([...plan.not_in_rotation], ["relocation"]);
  assert.equal(plan.methods.find((item) => item.method_id === "appearance")?.status, "skipped_by_policy");
});

test("dasha conflict probe does not jump ahead of method rotation before acceptance event quality", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [datedEvidence("education", "2016")],
    eventProbes: [CAREER_CONFLICT_PROBE],
  });
  // 原值: method_coverage / relatives
  // 新值: method_coverage / d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.notEqual(plan.next_followup?.source, "event_probe");
});

test("known exam quality does not create a scoring card after one recorded event", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [datedEvidence("education", "2016")],
    eventProbes: [EDUCATION_QUALITY_PROBE, CAREER_CONFLICT_PROBE],
  });
  // 原值: method_coverage / relatives
  // 新值: method_coverage / d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.notEqual(plan.next_followup?.choice_kind, "event_quality");
});

test("career known-event quality does not jump the adoption gate", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      datedEvidence("career", "2020", { eventKind: "career_entry", datePrecision: "month" }),
      datedEvidence("career", "2020", { eventKind: "career_exit", datePrecision: "month" }),
    ],
    eventProbes: [CAREER_QUALITY_PROBE, CAREER_CONFLICT_PROBE],
  });
  // 原值: method_coverage / relatives
  // 新值: method_coverage / d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.notEqual(plan.next_followup?.source, "event_probe");
  assert.notEqual(plan.next_followup?.choice_kind, "event_quality");
});

test("encoded exam quality does not stamp another card and keeps method rotation", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [datedEvidence("education", "2016", { summary: "2016年高考发挥异常" })],
    eventProbes: [EDUCATION_QUALITY_PROBE, CAREER_CONFLICT_PROBE],
  });
  // 原值: method_coverage / relatives
  // 新值: method_coverage / d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(plan.next_followup?.choice_frame, null);
});

test("dasha conflict probe jumps after four scoreable events leave three training domains", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      datedEvidence("education", "2016"),
      datedEvidence("education", "2020"),
      datedEvidence("relationship", "2018"),
      datedEvidence("family", "2023"),
    ],
    eventProbes: [CAREER_CONFLICT_PROBE],
  });
  assert.equal(plan.next_followup?.source, "event_probe");
  assert.equal(plan.next_followup?.intent, "distinguish_candidates");
  assert.equal(plan.next_followup?.domain, "career");
  assert.equal(plan.next_followup?.choice_frame?.scoring, true);
  assert.equal(plan.next_followup?.choice_frame?.period, "2018 年前后");
  assert.deepEqual(plan.next_followup?.candidate_ids, ["05:00", "05:20"]);
  assert.ok((plan.next_followup?.information_gain ?? 0) > 0);
  assert.equal(isOfferBlockingFollowup(plan.next_followup, plan.methods), true);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
  }), "discriminate_candidates");
});

test("dasha conflict probe keeps the engine month on the choice card", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      datedEvidence("education", "2016"),
      datedEvidence("education", "2020"),
      datedEvidence("relationship", "2018"),
      datedEvidence("family", "2023"),
    ],
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      month: 3,
      year_label: "2018 年 3 月前后",
      source: "dasha_boundary",
      user_meaning: "时间范围锁定 2018 年 3 月前后；领域锁定 career。",
      semantic_key: "career.2018.03.dasha_boundary",
    }],
  });
  assert.equal(plan.next_followup?.source, "event_probe");
  assert.equal(plan.next_followup?.choice_frame?.period, "2018 年 3 月前后");
  assert.equal(plan.next_followup?.year_label, "2018 年 3 月前后");
  assert.equal(plan.next_followup?.probe_month, 3);
  assert.match(plan.next_followup?.user_prompt_hint ?? "", /2018 年 3 月前后/);
});

test("three scoreable events in one domain still rotate methods instead of reverse-inferring", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      datedEvidence("education", "2012"),
      datedEvidence("education", "2016"),
      datedEvidence("education", "2020"),
    ],
    eventProbes: [CAREER_CONFLICT_PROBE],
  });
  // 原值: method_coverage / relatives
  // 新值: method_coverage / d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(plan.next_followup?.choice_frame, null);
});

test("occupation_note does not count toward reverse-inference event quality", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      datedEvidence("education", "2016"),
      datedEvidence("education", "2020"),
      {
        status: "confirmed",
        domain: "occupation",
        datePrecision: "unknown",
        occurredFrom: null,
        occurredTo: null,
        eventKind: "occupation_note",
      },
    ],
    eventProbes: [CAREER_CONFLICT_PROBE],
  });
  // 原值: method_coverage / relatives
  // 新值: method_coverage / d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(plan.next_followup?.choice_frame, null);
});

test("age-band probe does not jump ahead of uncovered relationship", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      datePrecision: "year",
      occurredFrom: "2016-01-01",
      occurredTo: null,
    }],
    eventProbes: [{
      year: 2018,
      year_label: "2018 年前后",
      domain: "relocation",
      event_family: "搬家、离乡或长期异地",
      source: "age_band",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: false,
      unique_minute_claim: false,
      user_meaning: "年份锁定 2018 年前后。",
      role: "reverse_verify",
    }],
  });
  // 原值: relatives
  // 新值: d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(plan.next_followup?.choice_frame, null);
});

test("user-stop action records stated events when the ledger is empty", () => {
  const action = buildNextUserAction({
    scorableCount: 0,
    evidenceCount: 0,
    hasLatestResult: false,
    selectionAllowed: false,
    sessionOutcome: "collect_evidence",
    nextFollowup: null,
    workingTime: "12:00",
  });
  assert.equal(action.id, "record_stated_events");
  assert.equal(action.on_user_stop.id, "record_stated_events");
  assert.match(action.on_user_stop.user_meaning, /batch/);
});

test("user-stop action offers a range when candidates already exist", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      datePrecision: "month",
      occurredFrom: "2016-06-01",
      occurredTo: null,
    }],
  });
  const withCandidates = buildNextUserAction({
    scorableCount: 1,
    evidenceCount: 1,
    hasLatestResult: true,
    selectionAllowed: false,
    sessionOutcome: "collect_evidence",
    nextFollowup: plan.next_followup,
    workingTime: "12:00",
  });
  assert.equal(withCandidates.id, "ask_method_followup");
  assert.equal(withCandidates.on_user_stop.id, "offer_provisional_range");
  const withoutCandidates = buildNextUserAction({
    scorableCount: 0,
    evidenceCount: 1,
    hasLatestResult: false,
    selectionAllowed: false,
    sessionOutcome: "collect_evidence",
    nextFollowup: plan.next_followup,
    workingTime: "12:00",
  });
  assert.equal(withoutCandidates.id, "ask_method_followup");
  assert.equal(withoutCandidates.on_user_stop.id, "explain_current_window");
  assert.match(withoutCandidates.on_user_stop.user_meaning, /12:00/);
  assert.match(withoutCandidates.on_user_stop.user_meaning, /不要只说会话会保留/);
  assert.equal(plan.methods.find((item) => item.method_id === "marks")?.status, "skipped_by_policy");
  assert.equal(plan.methods.find((item) => item.method_id === "horary")?.status, "uncovered");
  assert.equal(plan.methods.find((item) => item.method_id === "occupation")?.status, "uncovered");
  // 原值: relatives
  // 新值: d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.match(plan.next_followup?.user_prompt_hint ?? "", /自然语言/);
  assert.doesNotMatch(plan.next_followup?.user_prompt_hint ?? "", /A\/B\/C\/D/);
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.doesNotMatch(JSON.stringify(plan), UNIQUE_MINUTE_COPY);
});

test("selectionAllowed with remaining method follow-up keeps collecting and offers a range on stop", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      datePrecision: "month",
      occurredFrom: "2016-06-01",
      occurredTo: null,
    }],
  });
  const action = buildNextUserAction({
    scorableCount: 3,
    evidenceCount: 3,
    hasLatestResult: true,
    selectionAllowed: true,
    sessionOutcome: "collect_evidence",
    nextFollowup: plan.next_followup,
    workingTime: "05:07",
  });
  // 原值: relatives
  // 新值: d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.method_id, "d9_relationship");
  assert.equal(action.id, "ask_method_followup");
  assert.equal(action.on_user_stop.id, "offer_provisional_range");
});

test("adopt_representative defers method follow-up instead of asking this turn", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      datePrecision: "month",
      occurredFrom: "2016-06-01",
      occurredTo: null,
    }],
    sessionOutcome: "adopt_representative",
  });
  assert.equal(plan.next_followup, null);
  // 原值: relatives
  // 新值: d9_relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.deferred_followup?.method_id, "d9_relationship");
  assert.equal(plan.session_outcome, "adopt_representative");
});

test("adopt_representative ignores leftover distinguish focus", () => {
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
    sessionOutcome: "adopt_representative",
    activeFocus: {
      intent: "distinguish_candidates",
      targetDomain: "relocation",
      targetKind: "home_change",
    },
  });
  assert.equal(plan.next_followup, null);
  assert.equal(plan.session_outcome, "adopt_representative");
});

test("declined relationship skips to career and leaves horary uncovered", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      datePrecision: "year",
      occurredFrom: "2016-01-01",
      occurredTo: null,
    }],
    declinedTopics: [{ target_domain: "relationship", status: "declined" }],
  });
  // 原值: relatives / family
  // 新值: d10_career / career
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.method_id, "d10_career");
  assert.equal(plan.next_followup?.domain, "career");
  assert.deepEqual([...plan.do_not_poll], []);
  assert.equal(plan.methods.find((item) => item.method_id === "horary")?.status, "uncovered");
});

test("D9 differ keeps sign names for the type-table report and still forbids unique-minute claims", () => {
  const scan = parseWindowScan({
    scanned: true,
    confirmation_allowed: true,
    unique_minute_claim: true,
    d9_lagna_count: 2,
    d10_lagna_count: 1,
    d9_candidates_differ: true,
    d10_candidates_differ: false,
    d9_sign_names: ["白羊座", "天蝎"],
    type_table: "热情冲动",
    transitions: [{
      layer: "d9",
      at: "05:14",
      user_meaning: "白羊座在 05:14 换成天蝎",
    }],
  });
  assert.ok(scan);
  assert.equal(scan.confirmation_allowed, false);
  assert.equal(scan.unique_minute_claim, false);
  assert.equal(scan.d9_candidates_differ, true);
  assert.deepEqual(scan.d9_sign_names, ["白羊座", "天蝎"]);
  assert.deepEqual(scan.transitions, [{
    layer: "d9",
    at: "05:14",
    user_meaning: "D9 在 05:14 发生变化",
  }]);
  const observations = internalObservationsFromWindowScan(scan);
  assert.deepEqual(observations, [
    { layer: "d9", candidates_differ: true, ask_theme: "relationship_style" },
    { layer: "d10", candidates_differ: false, ask_theme: null },
    { layer: "d4", candidates_differ: false, ask_theme: null },
    { layer: "d5", candidates_differ: false, ask_theme: null },
    { layer: "d7", candidates_differ: false, ask_theme: null },
    { layer: "d12", candidates_differ: false, ask_theme: null },
    { layer: "d11", candidates_differ: false, ask_theme: null },
    { layer: "d30", candidates_differ: false, ask_theme: null },
  ]);
  assert.doesNotMatch(JSON.stringify({ scan, observations }), UNIQUE_MINUTE_COPY);
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE,
    observations,
  });
  assert.equal(plan.next_followup, null);
  assert.doesNotMatch(JSON.stringify(plan), UNIQUE_MINUTE_COPY);
});

test("D24-only window change folds into education follow-up without a second layer", () => {
  const scan = parseWindowScan({
    scanned: true,
    d9_lagna_count: 1,
    d10_lagna_count: 1,
    d5_lagna_count: 1,
    d24_lagna_count: 2,
    d5_candidates_differ: false,
    d24_candidates_differ: true,
    transitions: [
      { layer: "d24", at: "05:14" },
      { layer: "pada", at: "05:14" },
    ],
  });
  assert.ok(scan);
  assert.equal(scan.d24_candidates_differ, true);
  assert.equal(scan.d5_candidates_differ, false);
  assert.deepEqual(scan.transitions, [
    { layer: "d24", at: "05:14", user_meaning: "D24 在 05:14 发生变化" },
    { layer: "pada", at: "05:14", user_meaning: "Nakshatra pada 在 05:14 发生变化" },
  ]);
  const observations = internalObservationsFromWindowScan(scan);
  assert.equal(observations.find((item) => item.layer === "d5")?.candidates_differ, true);
  assert.equal(observations.find((item) => item.layer === "d5")?.ask_theme, "education_style");
  assert.deepEqual(
    observations.map((item) => item.layer),
    ["d9", "d10", "d4", "d5", "d7", "d12", "d11", "d30"],
  );
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE,
    observations,
  });
  assert.equal(plan.next_followup?.method_id, "d5_education");
  assert.equal(plan.next_followup?.ask_theme, "education_style");
});

test("D11-only window change folds into finance follow-up without delaying adopt", () => {
  const scan = parseWindowScan({
    scanned: true,
    d9_lagna_count: 1,
    d10_lagna_count: 1,
    d2_lagna_count: 1,
    d11_lagna_count: 2,
    d30_lagna_count: 2,
    d2_candidates_differ: false,
    d11_candidates_differ: true,
    d30_candidates_differ: true,
    transitions: [
      { layer: "d11", at: "05:14" },
      { layer: "d30", at: "05:14" },
      { layer: "bhava", at: "05:14" },
      { layer: "pranapada", at: "05:14" },
    ],
  });
  assert.ok(scan);
  assert.equal(scan.d11_candidates_differ, true);
  assert.equal(scan.d2_candidates_differ, false);
  assert.deepEqual(scan.transitions, [
    { layer: "d11", at: "05:14", user_meaning: "D11 在 05:14 发生变化" },
    { layer: "d30", at: "05:14", user_meaning: "D30 在 05:14 发生变化" },
    { layer: "bhava", at: "05:14", user_meaning: "Bhava Lagna 在 05:14 发生变化" },
    { layer: "pranapada", at: "05:14", user_meaning: "Pranapada Lagna 在 05:14 发生变化" },
  ]);
  const observations = internalObservationsFromWindowScan(scan);
  assert.equal(observations.find((item) => item.layer === "d11")?.candidates_differ, true);
  assert.equal(observations.find((item) => item.layer === "d11")?.ask_theme, "finance_change");
  assert.equal(observations.find((item) => item.layer === "d30")?.ask_theme, "health_pressure");
  assert.deepEqual(
    observations.map((item) => item.layer),
    ["d9", "d10", "d4", "d5", "d7", "d12", "d11", "d30"],
  );
  const plan = buildMethodFollowupPlan({
    evidence: [
      ...CLASSIC_COVERAGE,
      { status: "confirmed", domain: "finance", datePrecision: "year", occurredFrom: "2021-01-01", occurredTo: null },
    ],
    observations,
  });
  assert.equal(plan.next_followup?.method_id, "d2_finance");
  assert.equal(plan.next_followup?.source, "varga_observation");
});

test("window scan displays KP sub-lord changes without opening confirmation", () => {
  assert.deepEqual(
    ["kp1", "kp4", "kp7", "kp10"].every((layer) => WINDOW_SCAN_DISPLAY_LAYER_ORDER.includes(layer as typeof WINDOW_SCAN_DISPLAY_LAYER_ORDER[number])),
    true,
  );
  const scan = parseWindowScan({
    scanned: true,
    confirmation_allowed: true,
    unique_minute_claim: true,
    d9_lagna_count: 1,
    d10_lagna_count: 1,
    transitions: [
      { layer: "kp1", at: "05:14" },
      { layer: "kp10", at: "05:15" },
    ],
  });
  assert.ok(scan);
  assert.equal(scan.confirmation_allowed, false);
  assert.equal(scan.unique_minute_claim, false);
  assert.deepEqual(scan.transitions, [
    { layer: "kp1", at: "05:14", user_meaning: "KP 1宫子主 在 05:14 发生变化" },
    { layer: "kp10", at: "05:15", user_meaning: "KP 10宫子主 在 05:15 发生变化" },
  ]);
});

test("read-case follows method plan and keeps D9/D10 type tables when SQL missing categories rotate", async () => {
  const accounting = fakeAccounting({
    ...receiptHandlers,
    get_agentic_rectification_case_dossier: () => dossierFixture({
      evidence: [educationEvidence, familyEvidence, careerEvidence],
      conversationSummary: {
        confirmed_evidence_summary: [],
        pending_revisions: [],
        active_focus: null,
        declined_skipped_topics: [],
        candidate_divergence_summary: null,
        missing_evidence_categories: ["relocation", "health", "finance"],
        last_result_policy: null,
        summary_version: 1,
        updated_at: "2026-08-12T10:00:06.000Z",
      },
      latestResult: {
        ...candidateSnapshotFixture({
          confirmationAllowed: true,
          representativeTime: "04:45",
          candidates: [
            { candidate_id: CANDIDATE_ID, rank: 1, time: "04:45", relative_support: 40, tied_minute_count: 25 },
            { candidate_id: SECOND_CANDIDATE_ID, rank: 2, time: "04:46", relative_support: 35, tied_minute_count: 25 },
            { candidate_id: THIRD_CANDIDATE_ID, rank: 3, time: "04:47", relative_support: 25, tied_minute_count: 25 },
          ],
        }),
        selection_allowed: true,
        confirmation_allowed: true,
        decision_receipt: {
          receipt_version: "candidate-decision-receipt-v2",
          policy_version: "rectification-candidate-policy-v2",
          selection_allowed: true,
          acceptance_allowed: true,
          propose_allowed: true,
          confirmation_allowed: false,
          representative_candidate_id: CANDIDATE_ID,
          overall_confidence: "medium",
          window_scan: {
            scanned: true,
            confirmation_allowed: false,
            unique_minute_claim: false,
            d9_lagna_count: 2,
            d10_lagna_count: 1,
            d9_candidates_differ: true,
            d10_candidates_differ: false,
            d9_sign_names: ["白羊", "天蝎"],
          },
        },
      },
    }),
    get_agentic_rectification_case_compute: () => computeFixture(),
  });
  const tools = createRectificationV9Tools({
    userId: USER_ID,
    caseId: CASE_ID,
    turnId: TURN_ID,
    accounting: accounting.client as never,
  });
  const projection = await (tools["rectification-read-case"] as unknown as {
    execute(input: unknown): Promise<{
      conversation_summary: { missing_evidence_categories: string[] };
      method_followup_plan: {
        next_followup: { method_id: string; domain: string | null } | null;
        deferred_followup: { method_id: string; domain: string | null } | null;
        session_outcome: string;
      };
      internal_observations: Array<{ layer: string; ask_theme: string | null }>;
      latest_result: {
        confirmation_allowed: boolean;
        indistinguishable_width_minutes: number;
        window_scan: { d9_candidates_differ: boolean } | null;
        session_outcome: string;
      };
    }>;
  }).execute({ caseId: CASE_ID, projection: "full_diagnostics" });
  assert.deepEqual(projection.conversation_summary.missing_evidence_categories, ["relocation", "health", "finance"]);
  // 原值: d2_finance / finance
  // 新值: d9_relationship / relationship
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(projection.method_followup_plan.next_followup?.method_id, "d9_relationship");
  assert.equal(projection.method_followup_plan.next_followup?.domain, "relationship");
  assert.equal(projection.method_followup_plan.deferred_followup, null);
  assert.equal(projection.method_followup_plan.session_outcome, "collect_evidence");
  assert.equal(
    (projection as { next_user_action?: { id?: string; on_user_stop?: { id?: string } } }).next_user_action?.id,
    "ask_method_followup",
  );
  assert.equal(
    (projection as { next_user_action?: { on_user_stop?: { id?: string } } }).next_user_action?.on_user_stop?.id,
    "offer_provisional_range",
  );
  assert.equal(projection.latest_result.session_outcome, "collect_evidence");
  assert.equal(projection.internal_observations.find((item) => item.layer === "d9")?.ask_theme, "relationship_style");
  assert.equal(projection.latest_result.confirmation_allowed, false);
  assert.ok(projection.latest_result.indistinguishable_width_minutes >= 25);
  assert.equal(projection.latest_result.window_scan?.d9_candidates_differ, true);
  assert.doesNotMatch(JSON.stringify(projection), UNIQUE_MINUTE_COPY);
  assert.doesNotMatch(JSON.stringify(projection), /A\/B\/C\/D/);
  assert.match(JSON.stringify(projection.method_followup_plan.next_followup), /自然语言/);
});

test("accepted batch evidence resolves a spoken collect focus before the next question", async () => {
  const restore = stubEngine(ENGINE_SCORE);
  let collectFocusResolved = false;
  try {
    const accounting = fakeAccounting({
      ...receiptHandlers,
      get_agentic_rectification_case_dossier: () => dossierFixture({
        evidence: [educationEvidence],
        latestResult: null,
        conversationSummary: conversationSummaryFixture({
          activeFocus: collectFocusResolved
            ? null
            : activeFocusFixture({
              intent: "collect_method_evidence",
              targetDomain: "education",
              expectedAnswerSchema: { collect: true, prompt: "有没有记得住时间的升学经历？" },
            }),
        }),
      }),
      get_agentic_rectification_case_compute: () => computeFixture(),
      record_agentic_rectification_evidence_batch: () => ({
        items: [{
          index: 0,
          outcome: "accepted",
          evidence_id: EDUCATION_ID,
          status: "confirmed",
          idempotent: false,
          clarification_fields: [],
          error_code: null,
        }],
        accepted_count: 1,
        needs_clarification_count: 0,
        rejected_count: 0,
        focus_id: null,
      }),
      resolve_agentic_rectification_conversation_focus: (_fn, args) => {
        collectFocusResolved = true;
        return {
          focus_id: args.p_focus_id,
          status: args.p_status,
          evidence_id: args.p_evidence_id,
          idempotent: false,
        };
      },
      persist_agentic_rectification_candidate_v2: () => ({
        ...candidateSnapshotFixture(),
        cached: false,
      }),
    });
    const tools = createRectificationV9Tools({
      userId: USER_ID,
      caseId: CASE_ID,
      turnId: TURN_ID,
      userMessage: "2016年6月高考结束",
      accounting: accounting.client as never,
    });
    const result = await (tools["rectification-record-evidence-batch"] as unknown as {
      execute(input: unknown): Promise<{
        accepted_count: number;
        rescore: { status: string; executed_methods: string[]; error_code: string | null };
        open_question: { prompt?: string } | null;
      }>;
    }).execute({
      caseId: CASE_ID,
      items: [{
        quote: "2016年6月高考结束",
        proposedKind: "education_milestone",
        subject: "self",
        domain: "education",
        datePrecision: "month",
        occurredFrom: "2016-06",
        summary: "2016年6月一次学业节点",
      }],
    });
    assert.equal(result.accepted_count, 1);
    assert.equal(result.rescore.status, "completed");
    assert.ok(result.rescore.executed_methods.includes("d1-rashi"));
    assert.equal(accounting.calls[0]?.fn, "get_agentic_rectification_case_dossier");
    const resolvedFocus = accounting.calls.find((call) => call.fn === "resolve_agentic_rectification_conversation_focus");
    assert.equal(resolvedFocus?.args.p_focus_id, FOCUS_ID);
    assert.equal(resolvedFocus?.args.p_status, "resolved");
    assert.equal(resolvedFocus?.args.p_evidence_id, EDUCATION_ID);
    const recordIndex = accounting.calls.findIndex((call) => call.fn === "record_agentic_rectification_evidence_batch");
    const resolveIndex = accounting.calls.findIndex((call) => call.fn === "resolve_agentic_rectification_conversation_focus");
    assert.ok(recordIndex >= 0 && resolveIndex > recordIndex);
    assert.doesNotMatch(result.open_question?.prompt ?? "", /升学经历/);
    const persistCall = accounting.calls.find((call) => call.fn === "persist_agentic_rectification_candidate_v2");
    assert.ok(persistCall);
    const receipt = persistCall.args.p_decision_receipt as { window_scan?: { d9_candidates_differ?: boolean; d9_sign_names?: unknown } };
    assert.equal(receipt.window_scan?.d9_candidates_differ, true);
    assert.deepEqual((receipt.window_scan as { d9_sign_names?: string[] } | undefined)?.d9_sign_names, ["白羊座", "天蝎"]);
    const completedReceipt = accounting.calls.find((call) =>
      call.fn === "insert_agentic_rectification_tool_receipt"
      && call.args.p_tool_name === "rectification-record-evidence-batch"
      && call.args.p_status === "completed"
    );
    assert.ok(completedReceipt);
    assert.ok((completedReceipt.args.p_executed_methods as string[]).includes("d1-rashi"));
    assert.equal(
      accounting.calls.some((call) =>
        call.fn === "transition_agentic_rectification_case_status"
        && call.args.p_to_status === "candidate_ready"
      ),
      false,
    );
    assert.doesNotMatch(JSON.stringify(result), UNIQUE_MINUTE_COPY);
  } finally {
    restore();
  }
});

test("cached candidates retry a failed VedAstro validation without recomputing ranking", async () => {
  const validationResponse = {
    status: "passed",
    can_confirm_exact_minute: true,
    event_validation: {
      search_events_primary_supports_local_winner: true,
    },
    minute_sensitive_validation: {
      status: "passed",
    },
  };
  const restore = stubEngine(validationResponse);
  try {
    const compute = computeFixture();
    const evidence = [educationEvidence];
    const evidenceFingerprint = evidenceLedgerFingerprint([{
      id: educationEvidence.id,
      sourceTurnId: educationEvidence.source_turn_id,
      subject: educationEvidence.subject,
      eventKind: educationEvidence.event_kind,
      domain: educationEvidence.domain,
      occurredFrom: educationEvidence.occurred_from,
      occurredTo: educationEvidence.occurred_to,
      datePrecision: educationEvidence.date_precision,
      summary: educationEvidence.summary,
      status: educationEvidence.status,
      supersedesEvidenceId: educationEvidence.supersedes_evidence_id,
      createdAt: educationEvidence.created_at,
    }]);
    const rangeFingerprint = candidateRangeFingerprint(
      compute.candidate_range,
      compute.baseline_profile_fingerprint,
    );
    const cached = {
      ...candidateSnapshotFixture({
        representativeTime: "05:02",
        evidenceLedgerFingerprint: evidenceFingerprint,
        decisionReceipt: {
          inference_state: producedInferenceState([
            { id: CANDIDATE_ID, time: "05:02", relative_support: 58 },
            { id: SECOND_CANDIDATE_ID, time: "04:55", relative_support: 42 },
          ]),
          gates: {
            exact_confirmation: {
              external_validation_status: "failed",
              vedastro_event_validation: {
                status: "failed",
                search_events_primary_supports_local_winner: false,
                can_confirm_exact_minute: false,
                failure: { code: "timeout" },
              },
            },
          },
        },
      }),
      candidate_range_fingerprint: rangeFingerprint,
    };
    const refreshedReceipt = {
      ...cached.decision_receipt,
      gates: {
        exact_confirmation: {
          external_validation_status: "passed",
          vedastro_event_validation: {
            status: "passed",
            search_events_primary_supports_local_winner: true,
            can_confirm_exact_minute: true,
            failure: null,
          },
        },
      },
    };
    const parsedCached = parseV9CaseDossier(dossierFixture({ evidence, latestResult: cached }));
    assert.equal(parsedCached?.latestResult?.selectionAllowed, true);
    assert.equal(parsedCached?.latestResult?.evidenceLedgerFingerprint, evidenceFingerprint);
    assert.equal(parsedCached?.latestResult?.candidateRangeFingerprint, rangeFingerprint);
    assert.equal(readVedastroMinuteSensitiveStatus(parsedCached?.latestResult?.decisionReceipt), "failed");
    assert.equal(authoritativeCandidateProjection(parsedCached!.latestResult!).candidates.length, 2);
    const accounting = fakeAccounting({
      ...receiptHandlers,
      get_agentic_rectification_case_dossier: () => dossierFixture({
        evidence,
        latestResult: cached,
      }),
      get_agentic_rectification_case_compute: () => compute,
      refresh_agentic_rectification_vedastro_validation: () => ({
        result_id: RESULT_ID,
        decision_receipt: refreshedReceipt,
      }),
      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: "2026-08-27T00:00:00.000Z",
          resolved_at: null,
        },
        idempotent: false,
      }),
    });
    const tools = createRectificationV9Tools({
      userId: USER_ID,
      caseId: CASE_ID,
      turnId: TURN_ID,
      userMessage: "继续比较",
      accounting: accounting.client as never,
    });
    const result = await (tools["rectification-compare-candidates"] as unknown as {
      execute(input: unknown): Promise<{
        cached: boolean;
        candidates: Array<{ time: string; relativeSupport: number }>;
        confirmation_gate: { blockers: Array<{ id: string; status: string }> };
      }>;
    }).execute({ caseId: CASE_ID });

    const refreshCall = accounting.calls.find((call) =>
      call.fn === "refresh_agentic_rectification_vedastro_validation"
    );
    assert.ok(refreshCall, JSON.stringify(accounting.calls.map((call) => call.fn)));
    assert.deepEqual(refreshCall.args.p_validation, {
      status: "passed",
      search_events_primary_supports_local_winner: true,
      can_confirm_exact_minute: true,
      failure: null,
    });
    assert.equal(refreshCall.args.p_minute_sensitive_status, "passed");
    assert.equal(
      accounting.calls.some((call) => call.fn === "persist_agentic_rectification_candidate_v2"),
      false,
    );
    assert.equal(result.cached, true);
    assert.deepEqual(result.candidates.map((item) => [item.time, item.relativeSupport]), [
      ["05:02", 58],
      ["04:55", 42],
    ]);
    assert.ok(result.confirmation_gate, JSON.stringify(result));
    assert.equal(
      result.confirmation_gate.blockers.find((gate) => gate.id === "vedastro_minute_sensitive")?.status,
      "passed",
    );
  } finally {
    restore();
  }
});

test("evidence batch returns the persisted choice prompt as open_question", async () => {
  const restore = stubEngine({
    ...ENGINE_SCORE,
    decision_receipt: {
      ...ENGINE_SCORE.decision_receipt,
      discriminating_event_probes: [{
        year: 2023,
        year_label: "2023 年前后",
        domain: "relocation",
        event_family: "搬家、离乡或长期异地",
        source: "dasha_activation",
        tracks: ["vimshottari", "narayana"],
        tracks_agree: false,
        unique_minute_claim: false,
        user_meaning: "年份锁定 2023 年前后。事件家族：搬家、离乡或长期异地。",
        role: "distinguish",
        phase: "candidate_discriminator",
        information_gain: 1.09,
        semantic_key: "relocation.2023.dasha_activation",
        candidate_set_version: "set-test",
        candidate_split_hash: "set-test:relocation:2023",
        candidate_ids: ["04:50", "04:51"],
        expected_outcomes: [
          { answer_class: "yes", supports: ["04:50"], conflicts: ["04:51"] },
          { answer_class: "no", supports: ["04:51"], conflicts: ["04:50"] },
        ],
        style_options: DYNAMIC_STYLE_OPTIONS,
        choice_kind: "existence",
      }],
    },
  });
  try {
    const accounting = fakeAccounting({
      ...receiptHandlers,
      get_agentic_rectification_case_dossier: () => dossierFixture({
        evidence: [educationEvidence, relationshipEvidence, familyEvidence, careerEvidence],
        evidenceCount: 4,
        latestResult: null,
      }),
      get_agentic_rectification_case_compute: () => computeFixture(),
      record_agentic_rectification_evidence_batch: () => ({
        items: [{
          index: 0,
          outcome: "accepted",
          evidence_id: EDUCATION_ID,
          status: "confirmed",
          idempotent: false,
          clarification_fields: [],
          error_code: null,
        }],
        accepted_count: 1,
        needs_clarification_count: 0,
        rejected_count: 0,
        focus_id: null,
      }),
      persist_agentic_rectification_candidate_v2: (_fn, args) => ({
        ...candidateSnapshotFixture({
          decisionReceipt: args.p_decision_receipt as Record<string, unknown>,
        }),
        cached: false,
      }),
      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: "2026-08-25T14:47:09.000Z",
          resolved_at: null,
        },
        idempotent: false,
      }),
    });
    const tools = createRectificationV9Tools({
      userId: USER_ID,
      caseId: CASE_ID,
      turnId: TURN_ID,
      userMessage: "2016年6月高考结束",
      accounting: accounting.client as never,
    });
    const result = await (tools["rectification-record-evidence-batch"] as unknown as {
      execute(input: unknown): Promise<{
        accepted_count: number;
        rescore: { status: string };
        open_question: { prompt?: string } | null;
      }>;
    }).execute({
      caseId: CASE_ID,
      items: [{
        quote: "2016年6月高考结束",
        proposedKind: "education_milestone",
        subject: "self",
        domain: "education",
        datePrecision: "month",
        occurredFrom: "2016-06",
        summary: "2016年6月一次学业节点",
      }],
    });
    assert.equal(result.accepted_count, 1);
    assert.equal(result.rescore.status, "completed");
    const setFocus = accounting.calls.find((call) => call.fn === "set_agentic_rectification_conversation_focus");
    const schema = setFocus?.args.p_expected_answer_schema as { choice?: { prompt?: string } } | undefined;
    assert.match(schema?.choice?.prompt ?? "", /2023 年前后/);
    assert.match(result.open_question?.prompt ?? "", /2023 年前后/);
    assert.match(result.open_question?.prompt ?? "", /搬家、离乡或长期异地/);
    assert.doesNotMatch(result.open_question?.prompt ?? "", /高考/);
  } finally {
    restore();
  }
});

test("rescore failure does not fail the evidence write", async () => {
  const accounting = fakeAccounting({
    ...receiptHandlers,
    get_agentic_rectification_case_dossier: () => dossierFixture({
      evidence: [educationEvidence],
    }),
    get_agentic_rectification_case_compute: () => computeFixture(),
    record_agentic_rectification_evidence_batch: () => ({
      items: [{
        index: 0,
        outcome: "accepted",
        evidence_id: EDUCATION_ID,
        status: "confirmed",
        idempotent: false,
        clarification_fields: [],
        error_code: null,
      }],
      accepted_count: 1,
      needs_clarification_count: 0,
      rejected_count: 0,
      focus_id: null,
    }),
  });
  const tools = createRectificationV9Tools({
    userId: USER_ID,
    caseId: CASE_ID,
    turnId: TURN_ID,
    userMessage: "2016年6月高考结束",
    accounting: accounting.client as never,
  });
  const result = await (tools["rectification-record-evidence-batch"] as unknown as {
    execute(input: unknown): Promise<{
      accepted_count: number;
      rescore: { status: string; error_code: string | null };
    }>;
  }).execute({
    caseId: CASE_ID,
    items: [{
      quote: "2016年6月高考结束",
      proposedKind: "education_milestone",
      subject: "self",
      domain: "education",
      datePrecision: "month",
      occurredFrom: "2016-06",
      summary: "2016年6月一次学业节点",
    }],
  });
  assert.equal(result.accepted_count, 1);
  assert.equal(result.rescore.status, "failed");
  assert.ok(result.rescore.error_code);
});

test("public tool surface stays at 14 and new cases bind 10.0.14", () => {
  assert.equal(PUBLIC_RECTIFICATION_TOOLS.length, 14);
  assert.equal(RECTIFICATION_SKILL_VERSION, "10.0.14");
  const deprecated = resolveExactSkillPackage(
    "jyotish-birth-time-rectification",
    "10.0.2",
    "8d7aa2d4bea0414e9a89ef908ccbc8c708c98f79f5b78ae4f7dc229b5f7dbb30",
  );
  assert.equal(deprecated.status, "deprecated");
  const plateauCandidates = [
      { candidateId: CANDIDATE_ID, time: "04:45", rank: 1, relativeSupport: 40, tiedMinuteCount: 25 },
      { candidateId: SECOND_CANDIDATE_ID, time: "04:46", rank: 2, relativeSupport: 35, tiedMinuteCount: 25 },
      { candidateId: THIRD_CANDIDATE_ID, time: "04:47", rank: 3, relativeSupport: 25, tiedMinuteCount: 25 },
    ];
  const plateau = latestResultToolProjection({
    resultId: RESULT_ID,
    candidates: plateauCandidates,
    selectionAllowed: true,
    confirmationAllowed: true,
    representativeTime: "04:45",
    selectedTime: null,
    selectionKind: null,
    algorithmVersion: "rectification-v5",
    decisionReceipt: {
      acceptance_allowed: true,
      selection_allowed: true,
      propose_allowed: true,
      confirmation_allowed: false,
      inference_state: producedInferenceState(plateauCandidates.map((item) => ({
        id: item.candidateId,
        time: item.time,
        relative_support: item.relativeSupport,
      }))),
      execution_ledger: [
        { method: "vimshottari-dasha", status: "executed" },
        { method: "narayana-dasha", status: "executed" },
      ],
    },
  }, collectDecision(plateauCandidates));
  assert.equal(plateau.confirmation_allowed, false);
  assert.equal(plateau.unique_minute_claim, false);
  assert.match(String(plateau.skill_verification_report), /Dasha \+ Gochara/);
  assert.match(String(plateau.skill_verification_report), /candidate_range_not_birth_time_truth/);
  assert.doesNotMatch(String(plateau.skill_verification_report), UNIQUE_MINUTE_COPY);
  assert.equal(plateau.session_outcome, "collect_evidence");
  // Previously this asserted multi_adapter_consensus from execution names alone.
  // The execution ledger does not prove same-range support or raw-result agreement.
  assert.equal(plateau.rectification_label, "manual_pattern_consensus");
  assert.deepEqual(plateau.executed_methods, ["vimshottari-dasha", "narayana-dasha"]);
  const collecting = latestResultToolProjection({
    resultId: RESULT_ID,
    candidates: plateauCandidates,
    selectionAllowed: true,
    confirmationAllowed: false,
    representativeTime: "04:45",
    selectedTime: null,
    selectionKind: null,
    algorithmVersion: "rectification-v5",
    decisionReceipt: {
      execution_ledger: [
        { method: "vimshottari-dasha", status: "executed" },
        { method: "narayana-dasha", status: "executed" },
      ],
    },
  }, decideRectification({
    engineCeiling: OPEN_ENGINE_CAPABILITY_CEILING,
    methodCoverageAll: false,
    trainingGateOpen: false,
    datedEventCount: 2,
    datedDomainCount: 2,
    candidateScores: plateauCandidates.map((item) => ({
      time: item.time,
      score: item.relativeSupport,
    })),
  }));
  assert.equal(collecting.rectification_label, "user_history_verification_required");
  assert.equal(collecting.evidence_stop_reason, "insufficient_dated_events");
  assert.deepEqual(collecting.executed_methods, ["vimshottari-dasha", "narayana-dasha"]);

  const exhaustedCandidates = plateauCandidates.map((item, index) => ({
    ...item,
    relativeSupport: index < 2 ? 40 : 20,
  }));
  const completed = latestResultToolProjection({
    resultId: RESULT_ID,
    candidates: exhaustedCandidates,
    selectionAllowed: true,
    confirmationAllowed: false,
    representativeTime: "04:45",
    selectedTime: null,
    selectionKind: null,
    algorithmVersion: "rectification-v5",
    decisionReceipt: {
      acceptance_allowed: true,
      selection_allowed: true,
      propose_allowed: true,
      confirmation_allowed: false,
    },
  }, decideRectification({
    engineCeiling: OPEN_ENGINE_CAPABILITY_CEILING,
    methodCoverageAll: true,
    trainingGateOpen: true,
    datedEventCount: 3,
    datedDomainCount: 2,
    candidateScores: exhaustedCandidates.map((item) => ({
      time: item.time,
      score: item.relativeSupport,
    })),
  }));
  assert.equal(completed.termination_copy, "当前最优结果是候选时间段，而不是已经确认的唯一出生分钟。临时代表时间仅用于下一轮验证与比较。");
  const skill = readFileSync(new URL("../../skills/jyotish-birth-time-rectification/SKILL.md", import.meta.url), "utf8");
  assert.match(skill, /method_followup_plan/);
  assert.match(skill, /感情 → 事业 → 家人 → 职业 → 占问/);
  assert.match(skill, /外貌、体质、胎记或疤痕不得追问/);
  assert.match(skill, /KP 观察不计分、不挡提出门/);
  assert.match(skill, /唯一领先和宽度≤5只挡确认门/);
  assert.match(skill, /D9\/D10 类型表是校时方法/);
  assert.doesNotMatch(skill, /±5 分钟确定性/);
  assert.doesNotMatch(skill, /KP 政策跳过不挡提出门/);
  const tools = readFileSync(new URL("../src/mastra/rectification-v9-tools.ts", import.meta.url), "utf8");
  assert.match(tools, /function agentVisibleLatestProjection/);
  assert.match(tools, /candidate_contrast_packet: _packet/);
  assert.match(tools, /current_probe: null/);
});

test("Mastra hides active candidates when the receipt range is corrupted", () => {
  const candidates = [
    { candidateId: CANDIDATE_ID, time: "05:00", rank: 1, relativeSupport: 58, tiedMinuteCount: 1 },
    { candidateId: SECOND_CANDIDATE_ID, time: "05:07", rank: 2, relativeSupport: 42, tiedMinuteCount: 1 },
  ];
  const inferenceState = producedInferenceState([
    { id: CANDIDATE_ID, time: "05:00", relative_support: 58 },
    { id: SECOND_CANDIDATE_ID, time: "05:07", relative_support: 42 },
  ]);
  const latest = {
    resultId: RESULT_ID,
    candidates,
    selectionAllowed: true,
    confirmationAllowed: false,
    representativeTime: "05:00",
    selectedTime: null,
    selectionKind: null,
    algorithmVersion: "rectification-v5",
    decisionReceipt: {
      acceptance_allowed: true,
      selection_allowed: true,
      propose_allowed: true,
      confirmation_allowed: false,
      inference_state: inferenceState,
    },
  };
  const session = decideRectification({
    engineCeiling: OPEN_ENGINE_CAPABILITY_CEILING,
    methodCoverageAll: true,
    userStopped: true,
    candidateScores: candidates.map((item) => ({ time: item.time, score: item.relativeSupport })),
    holdoutValidation: "passed",
    snapshotCurrent: true,
    trainingGateOpen: true,
  });
  const valid = latestResultToolProjection(latest, session);
  assert.deepEqual((valid.candidates as typeof candidates).map((item) => item.time), ["05:00", "05:07"]);
  assert.equal(valid.selection_allowed, true);

  const invalid = latestResultToolProjection({
    ...latest,
    decisionReceipt: {
      ...latest.decisionReceipt,
      inference_state: { ...inferenceState, credible_range: ["04:00", "04:10"] },
    },
  }, session);
  assert.deepEqual(invalid.candidates, []);
  assert.equal(invalid.representative_time, null);
  assert.equal(invalid.selection_allowed, false);
});

test("family then occupation then horary follow the method plan without appearance or marks", () => {
  const afterFamily = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "year", occurredFrom: "2016-01-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "year", occurredFrom: "2018-01-01", occurredTo: null },
      { status: "confirmed", domain: "career", datePrecision: "year", occurredFrom: "2019-01-01", occurredTo: null },
      { status: "confirmed", domain: "family", datePrecision: "year", occurredFrom: "2020-01-01", occurredTo: null },
    ],
  });
  assert.equal(afterFamily.next_followup?.method_id, "occupation");
  assert.equal(afterFamily.next_followup?.domain, "occupation");
  assert.equal(afterFamily.methods.find((item) => item.method_id === "appearance")?.status, "skipped_by_policy");
  assert.equal(afterFamily.methods.find((item) => item.method_id === "marks")?.status, "skipped_by_policy");
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: afterFamily.next_followup,
    methods: afterFamily.methods,
  }), "collect_evidence");
  assert.equal(afterFamily.next_followup?.choice_frame, null);
  assert.match(afterFamily.next_followup?.user_prompt_hint ?? "", /自然语言/);
  assert.doesNotMatch(afterFamily.next_followup?.user_prompt_hint ?? "", /A\/B\/C\/D/);
  const afterOccupation = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "year", occurredFrom: "2016-01-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "year", occurredFrom: "2018-01-01", occurredTo: null },
      { status: "confirmed", domain: "career", datePrecision: "year", occurredFrom: "2019-01-01", occurredTo: null },
      { status: "confirmed", domain: "family", datePrecision: "year", occurredFrom: "2020-01-01", occurredTo: null },
      { status: "confirmed", domain: "occupation", datePrecision: "unknown", occurredFrom: null, occurredTo: null },
    ],
  });
  assert.equal(afterOccupation.next_followup?.method_id, "horary");
  assert.match(afterOccupation.next_followup?.user_prompt_hint ?? "", /第 10 宫|D10|占问/);
  assert.match(afterOccupation.next_followup?.user_prompt_hint ?? "", /自然语言/);
  assert.doesNotMatch(afterOccupation.next_followup?.user_prompt_hint ?? "", /A\/B\/C\/D/);
  assert.doesNotMatch(JSON.stringify(afterFamily), /外貌|疤痕|胎记/);
  const afterHorary = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "year", occurredFrom: "2016-01-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "year", occurredFrom: "2018-01-01", occurredTo: null },
      { status: "confirmed", domain: "career", datePrecision: "year", occurredFrom: "2019-01-01", occurredTo: null },
      { status: "confirmed", domain: "family", datePrecision: "year", occurredFrom: "2020-01-01", occurredTo: null },
      { status: "confirmed", domain: "occupation", datePrecision: "unknown", occurredFrom: null, occurredTo: null },
      { status: "confirmed", domain: "horary", datePrecision: "day", occurredFrom: "2024-01-01", occurredTo: null },
    ],
  });
  assert.equal(afterHorary.next_followup, null);
});

test("precision stage lagna_frame waits for uncovered career before asking another dated event", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "year", occurredFrom: "2016-01-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "year", occurredFrom: "2018-01-01", occurredTo: null },
    ],
    precisionStage: "lagna_frame",
  });
  // 原值: relatives
  // 新值: d10_career
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.method_id, "d10_career");
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(isOfferBlockingFollowup(plan.next_followup, plan.methods), true);
  assert.doesNotMatch(JSON.stringify(plan), UNIQUE_MINUTE_COPY);
});

test("lagna_frame after classic coverage does not keep a tie in discrimination without a remaining split", () => {
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE,
    precisionStage: "lagna_frame",
  });
  assert.equal(plan.next_followup?.source, "precision_stage");
  assert.equal(plan.next_followup?.ask_theme, "dated_event");
  assert.equal(isOfferBlockingFollowup(plan.next_followup, plan.methods), true);
  assert.equal(isOfferBlockingFollowup(plan.next_followup, plan.methods, { separated: true }), false);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    discriminatorProbe: null,
    candidateScores: [
      { time: "05:00", score: 34 },
      { time: "05:01", score: 33 },
      { time: "05:02", score: 33 },
    ],
    // 原断言 provisional_range（并列不采用）→ 新断言 adopt_representative。
  }), "adopt_representative");
});

test("precision stage d4 asks home change not family, and d5 asks education", () => {
  const covered = CLASSIC_COVERAGE;
  const d4 = buildMethodFollowupPlan({
    evidence: covered,
    precisionStage: "d4_refine",
  });
  assert.equal(d4.next_followup?.source, "precision_stage");
  assert.equal(d4.next_followup?.method_id, "d4_home");
  assert.equal(d4.next_followup?.domain, "relocation");
  assert.equal(d4.next_followup?.ask_theme, "home_change");
  assert.doesNotMatch(d4.next_followup?.user_prompt_hint ?? "", /家人/);
  const legacyTheme = buildMethodFollowupPlan({
    evidence: [...covered],
    precisionStage: "theme_refine",
  });
  assert.equal(legacyTheme.next_followup?.ask_theme, "home_change");
  const d5 = buildMethodFollowupPlan({
    evidence: covered.filter((item) => item.domain !== "education"),
    precisionStage: "d5_refine",
  });
  assert.equal(d5.next_followup?.method_id, "d5_education");
  assert.equal(d5.next_followup?.domain, "education");
  assert.doesNotMatch(JSON.stringify(d4), UNIQUE_MINUTE_COPY);
});

test("accepted representative time reverse-verifies predicted events then hands off", () => {
  const careerProbe = {
    year: 2018,
    year_label: "2018 年前后",
    domain: "career" as const,
    event_family: "入职、升职或职责明显加重",
    source: "dasha_activation" as const,
    tracks: ["vimshottari", "narayana"] as const,
    tracks_agree: true,
    unique_minute_claim: false as const,
    user_meaning: "年份锁定 2018 年前后。请写成一句自然语言，问是否入职或职责加重。",
    role: "reverse_verify" as const,
    style_options: DYNAMIC_STYLE_OPTIONS,
  };
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      datePrecision: "month",
      occurredFrom: "2016-06-01",
      occurredTo: null,
    }],
    accepted: true,
    eventProbes: [
      {
        ...careerProbe,
        domain: "education",
        source: "known_event_quality",
        role: "distinguish",
        event_family: "高考或重要考试发挥明显失常、压力很大",
      },
      careerProbe,
    ],
    oosBlindPrompts: [{
      domain: "family",
      user_meaning: "校时还没用过家人这条线。有没有一件没提过、但记得大概时间的家人变化？",
      used_for_scoring: false,
    }],
    activeFocus: {
      intent: "collect_method_evidence",
      targetDomain: "relationship",
      targetKind: null,
    },
  });
  assert.equal(plan.next_followup?.method_id, "reverse_verify");
  assert.equal(plan.next_followup?.domain, "career");
  assert.equal(plan.next_followup?.choice_frame?.scoring, true);
  assert.match(plan.next_followup?.choice_frame?.why ?? "", /2018 年前后/);
  assert.equal(plan.deferred_followup, null);
  assert.equal(plan.next_followup?.source, "reverse_verify");
  const action = buildNextUserAction({
    scorableCount: 3,
    evidenceCount: 3,
    hasLatestResult: true,
    selectionAllowed: true,
    sessionOutcome: "adopt_representative",
    nextFollowup: plan.next_followup,
    workingTime: "05:07",
    accepted: true,
  });
  assert.equal(action.id, "verify_adopted_time");
  assert.equal(action.on_user_stop.id, "start_consultation");
  assert.doesNotMatch(JSON.stringify({ plan, action }), UNIQUE_MINUTE_COPY);
});

test("accepted representative time without remaining probes hands off to consultation", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      datePrecision: "month",
      occurredFrom: "2016-06-01",
      occurredTo: null,
    }],
    accepted: true,
    eventProbes: [{
      year: 2016,
      year_label: "2016 年前后",
      domain: "education",
      event_family: "高考或重要考试发挥明显失常、压力很大",
      source: "known_event_quality",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: true,
      unique_minute_claim: false,
      user_meaning: "年份锁定 2016 年前后。已有高考经历。",
      role: "distinguish",
    }],
  });
  assert.equal(plan.next_followup, null);
  assert.equal(plan.deferred_followup, null);
  const action = buildNextUserAction({
    scorableCount: 3,
    evidenceCount: 3,
    hasLatestResult: true,
    selectionAllowed: true,
    sessionOutcome: "adopt_representative",
    nextFollowup: plan.next_followup,
    workingTime: "05:07",
    accepted: true,
  });
  assert.equal(action.id, "start_consultation");
  assert.match(action.user_meaning, /看盘/);
  assert.match(action.user_meaning, /改选/);
  assert.doesNotMatch(JSON.stringify({ plan, action }), UNIQUE_MINUTE_COPY);
});

test("confirmed relationship evidence skips generic D9 followups unless a real probe is bound", () => {
  const evidence = CLASSIC_COVERAGE;
  const precision = buildMethodFollowupPlan({ evidence, precisionStage: "d9_refine" });
  const varga = buildMethodFollowupPlan({
    evidence,
    observations: [{ layer: "d9", candidates_differ: true, ask_theme: "relationship_style" }],
  });
  assert.notEqual(precision.next_followup?.domain, "relationship");
  assert.notEqual(varga.next_followup?.domain, "relationship");

  const probed = buildMethodFollowupPlan({
    evidence,
    precisionStage: "d9_refine",
    contrastPacket: {
      candidateSetVersion: "05:00-05:14",
      vargaDifferences: [],
      probes: [{
        probeId: "contrast:varga.d9.05:00|05:14",
        candidateSetVersion: "05:00-05:14",
        question: "当前几个候选在关系盘上还分得开。",
        expectedOutcomes: [
          { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:14"] },
          { outcomeId: "weak_yes", supportsCandidateIds: ["05:14"], conflictsCandidateIds: ["05:00"] },
        ],
        candidateSplitHash: "varga.d9.05:00|05:14",
        informationGain: 1.4,
        sourceFeatures: [{ technique: "D9", calculationResultId: RESULT_ID }],
        domain: "relationship",
        year: null,
        semanticKey: "varga.d9.05:00|05:14",
        choiceKind: "varga_style",
        styleOptions: [
          { label: "相处更主动热情", answerClass: "yes", sign: "白羊座" },
          { label: "相处更深刻占有", answerClass: "weak_yes", sign: "天蝎座" },
        ],
      }],
    },
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      year: 2021,
      year_label: "2021 年前后",
      domain: "relationship",
      event_family: "关系状态或相处方式明显变化",
      semantic_key: "relationship.2021.dasha_activation",
      candidate_split_hash: "set-test:relationship:2021",
    }],
  });
  assert.equal(probed.next_followup?.domain, "relationship");
  assert.equal(probed.next_followup?.source, "event_probe");
  assert.equal(probed.next_followup?.semantic_key, "varga.d9.05:00|05:14");
  assert.doesNotMatch(probed.next_followup?.semantic_key ?? "", /relationship\.2021/);
});

test("d9_refine after relationship still asks uncovered career first", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "month", occurredFrom: "2016-09-01", occurredTo: null },
      { status: "confirmed", domain: "education", datePrecision: "month", occurredFrom: "2020-06-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "day", occurredFrom: "2024-05-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "day", occurredFrom: "2024-08-08", occurredTo: null },
    ],
    precisionStage: "d9_refine",
  });
  assert.equal(plan.next_followup?.method_id, "d10_career");
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: false,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
  }), "collect_evidence");
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
  }), "collect_evidence");
});

test("career evidence does not cover occupation method; occupation no longer blocks adopt", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "year", occurredFrom: "2016-01-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "day", occurredFrom: "2024-05-01", occurredTo: null },
      { status: "confirmed", domain: "career", datePrecision: "day", occurredFrom: "2019-09-01", occurredTo: null },
      { status: "confirmed", domain: "family", datePrecision: "year", occurredFrom: "2020-01-01", occurredTo: null },
    ],
  });
  assert.equal(plan.next_followup?.method_id, "occupation");
  assert.equal(plan.methods.find((item) => item.method_id === "occupation")?.status, "uncovered");
  assert.equal(blockingMethodsCovered(plan.methods), true);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    candidateScores: [
      { time: "05:00", score: 34 },
      { time: "05:01", score: 33 },
      { time: "05:02", score: 33 },
    ],
  }), "adopt_representative");
});

test("draft occupation_note without a date covers occupation and does not adopt a tie", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "year", occurredFrom: "2016-01-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "day", occurredFrom: "2024-05-01", occurredTo: null },
      { status: "confirmed", domain: "career", datePrecision: "day", occurredFrom: "2019-09-01", occurredTo: null },
      { status: "confirmed", domain: "family", datePrecision: "year", occurredFrom: "2020-01-01", occurredTo: null },
      {
        status: "draft",
        domain: "occupation",
        datePrecision: "unknown",
        occurredFrom: null,
        occurredTo: null,
        eventKind: "occupation_note",
        summary: "职业类型轨迹为技术开发",
      },
    ],
  });
  assert.equal(plan.methods.find((item) => item.method_id === "occupation")?.status, "covered");
  assert.notEqual(plan.next_followup?.method_id, "occupation");
  // 原断言 sessionOutcome≠adopt_representative（并列不采用）→ 新断言 adopt_representative。
  // 为什么：occupation 覆盖后并列候选仍应交付代表性采用；确认门保持关闭。
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    candidateScores: [
      { time: "05:00", score: 34 },
      { time: "05:01", score: 33 },
      { time: "05:02", score: 33 },
    ],
  }), "adopt_representative");
});

test("stale occupation collect focus does not keep interviewing after occupation is covered", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "year", occurredFrom: "2016-01-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "day", occurredFrom: "2024-05-01", occurredTo: null },
      { status: "confirmed", domain: "career", datePrecision: "day", occurredFrom: "2019-09-01", occurredTo: null },
      { status: "confirmed", domain: "family", datePrecision: "year", occurredFrom: "2020-01-01", occurredTo: null },
      {
        status: "draft",
        domain: "occupation",
        datePrecision: "unknown",
        occurredFrom: null,
        occurredTo: null,
        eventKind: "occupation_note",
        summary: "前端工程师",
      },
    ],
    activeFocus: {
      intent: "collect_method_evidence",
      targetDomain: "occupation",
      targetKind: "occupation_note",
    },
  });
  // 原断言 sessionOutcome≠adopt_representative → 新断言 adopt_representative。
  // 为什么：职业已覆盖后不得继续追问；并列候选走代表性采用，而不是停在无出口访谈。
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    candidateScores: [
      { time: "05:00", score: 34 },
      { time: "05:01", score: 33 },
      { time: "05:02", score: 33 },
    ],
  }), "adopt_representative");
});

test("D9/D10 contrast after occupation coverage asks a discriminator, not adopt", () => {
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
    contrastPacket: {
      candidateSetVersion: "05:00-05:02",
      vargaDifferences: [
        { layer: "d9", signs: ["天秤", "天蝎", "射手"] },
        { layer: "d10", signs: ["巨蟹", "狮子", "处女"] },
      ],
      probes: [{
        probeId: "contrast:varga.d10.05:00|05:01|05:02",
        candidateSetVersion: "05:00-05:02",
        question: "当前几个候选在事业盘上还分得开。请核对一段还没用进评分的职业前事。",
        expectedOutcomes: [
          { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:01", "05:02"] },
          { outcomeId: "weak_yes", supportsCandidateIds: ["05:01"], conflictsCandidateIds: ["05:00", "05:02"] },
          { outcomeId: "no", supportsCandidateIds: ["05:02"], conflictsCandidateIds: ["05:00", "05:01"] },
        ],
        candidateSplitHash: "varga.d10.05:00|05:01|05:02",
        informationGain: 0.12,
        sourceFeatures: [{ technique: "D10", calculationResultId: RESULT_ID }],
        domain: "career",
        year: null,
        semanticKey: "varga.d10.05:00|05:01|05:02",
        choiceKind: "varga_style",
        styleOptions: [
          { label: "做事偏领导推进", answerClass: "yes", sign: "白羊座" },
          { label: "做事偏研究转化", answerClass: "weak_yes", sign: "天蝎座" },
        ],
      }],
    },
  });
  assert.equal(plan.next_followup?.intent, "distinguish_candidates");
  assert.match(plan.next_followup?.user_prompt_hint ?? "", /职业前事|事业盘/);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    discriminatorProbe: {
      probeId: "contrast:varga.d10",
      candidateSetVersion: "05:00-05:02",
      question: "核对一段还没用进评分的职业前事",
      expectedOutcomes: [
        { outcomeId: "a", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:01"] },
        { outcomeId: "b", supportsCandidateIds: ["05:01"], conflictsCandidateIds: ["05:00"] },
      ],
      candidateSplitHash: "varga.d10",
      informationGain: 0.12,
      sourceFeatures: [{ technique: "D10", calculationResultId: RESULT_ID }],
      domain: "career",
      year: null,
      semanticKey: "varga.d10",
    },
    candidateScores: [
      { time: "05:00", score: 34 },
      { time: "05:01", score: 33 },
      { time: "05:02", score: 33 },
    ],
  }), "discriminate_candidates");
});

test("answered duty language skips window D10 and collects a dated education event instead of a yearless D24 card", () => {
  const packet = {
    candidateSetVersion: "05:00-05:07",
    vargaDifferences: [
      { layer: "d10", signs: ["巨蟹座", "狮子座", "处女座"] },
      { layer: "d24", signs: ["05:00", "05:06|05:07"] },
    ],
    probes: [{
      probeId: "contrast:varga.d24.05:00/05:06|05:07",
      candidateSetVersion: "05:00-05:07",
      question: "当前几个候选在学业盘上还分得开。请核对一段还没用进评分的学业前事。",
      expectedOutcomes: [
        { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:06", "05:07"] },
        { outcomeId: "no", supportsCandidateIds: ["05:06", "05:07"], conflictsCandidateIds: ["05:00"] },
      ],
      candidateSplitHash: "varga.d24.05:00/05:06|05:07",
      informationGain: 0.16,
      sourceFeatures: [{ technique: "D24", calculationResultId: RESULT_ID }],
      domain: "education",
      year: null,
      semanticKey: "varga.d24.05:00/05:06|05:07",
    }],
  };
  const evidence = [
    ...CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
    {
      status: "draft" as const,
      domain: "occupation",
      datePrecision: "unknown" as const,
      occurredFrom: null,
      occurredTo: null,
      eventKind: "occupation_note",
      summary: "医疗器械算法，第三个（技术执行）",
    },
  ];
  const plan = buildMethodFollowupPlan({
    evidence,
    contrastPacket: packet,
  });
  assert.equal(plan.next_followup?.domain, "education");
  assert.equal(plan.next_followup?.intent, "collect_method_evidence");
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.match(plan.next_followup?.user_prompt_hint ?? "", /记得住年份/);
  assert.doesNotMatch(plan.next_followup?.semantic_key ?? "", /varga\.d10/);
});

test("yearless D24 yields to a dated career dasha instead of borrowing the recorded education year", () => {
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      year: 2023,
      year_label: "2023 年前后",
      semantic_key: "career.2023.dasha_activation",
      information_gain: 0.56,
      candidate_split_hash: "set-test:career:2023",
    }],
    contrastPacket: {
      candidateSetVersion: "05:00-05:14",
      vargaDifferences: [],
      probes: [{
        probeId: "contrast:varga.d24.05:00/05:07|05:10|05:14",
        candidateSetVersion: "05:00-05:14",
        question: "当前几个候选在学业盘上还分得开。",
        expectedOutcomes: [
          { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:07", "05:10", "05:14"] },
          { outcomeId: "no", supportsCandidateIds: ["05:07", "05:10", "05:14"], conflictsCandidateIds: ["05:00"] },
        ],
        candidateSplitHash: "varga.d24.05:00/05:07|05:10|05:14",
        informationGain: 2.503258,
        sourceFeatures: [{ technique: "D24", calculationResultId: RESULT_ID }],
        domain: "education",
        year: null,
        semanticKey: "varga.d24.05:00/05:07|05:10|05:14",
        choiceKind: "event_quality",
      }],
    },
    candidatesSeparated: false,
  });
  assert.equal(plan.next_followup?.semantic_key, "career.2023.dasha_activation");
  assert.equal(plan.next_followup?.choice_frame?.period, "2023 年前后");
  assert.match(plan.next_followup?.choice_frame?.prompt ?? "", /2023 年前后，有没有/);
  assert.doesNotMatch(plan.next_followup?.choice_frame?.prompt ?? "", /2016 年前后/);
  assert.doesNotMatch(plan.next_followup?.semantic_key ?? "", /varga\.d24/);
});

const YEARLESS_D12 = {
  probeId: "contrast:varga.d12.05:00/05:07",
  candidateSetVersion: "05:00-05:07",
  question: "当前几个候选在六亲盘上还分得开。请核对一段还没用进评分的家人前事。",
  expectedOutcomes: [
    { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:07"] },
    { outcomeId: "no", supportsCandidateIds: ["05:07"], conflictsCandidateIds: ["05:00"] },
  ],
  candidateSplitHash: "varga.d12.05:00/05:07",
  informationGain: 3.5,
  sourceFeatures: [{ technique: "D12", calculationResultId: RESULT_ID }],
  domain: "family",
  year: null,
  semanticKey: "varga.d12.05:00/05:07",
  choiceKind: "existence" as const,
};

test("yearless family contrast yields to a dated career discriminator", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      datedEvidence("education", "2016"),
      datedEvidence("relationship", "2018"),
      datedEvidence("relocation", "2014"),
      datedEvidence("health_pressure", "2012"),
      {
        status: "confirmed" as const,
        domain: "occupation",
        datePrecision: "unknown" as const,
        occurredFrom: null,
        occurredTo: null,
        eventKind: "occupation_note",
      },
    ],
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      year: 2020,
      year_label: "2020 年 4 月前后",
      information_gain: 0.56,
      semantic_key: "career.2020.dasha_activation",
    }],
    contrastPacket: {
      candidateSetVersion: "05:00-05:07",
      vargaDifferences: [],
      probes: [YEARLESS_D12],
    },
    candidatesSeparated: false,
  });
  assert.notEqual(plan.next_followup?.domain, "family");
  assert.equal(plan.next_followup?.choice_frame != null, true);
  assert.match(plan.next_followup?.choice_frame?.period ?? "", /2020 年 4 月前后/);
  assert.match(plan.next_followup?.choice_frame?.prompt ?? "", /2020 年 4 月前后，有没有/);
  assert.doesNotMatch(plan.next_followup?.choice_frame?.prompt ?? "", /那段时间/);
});

test("yearless family contrast without a dated discriminator asks dated family collect", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      ...CLASSIC_COVERAGE.filter((item) => item.domain !== "horary" && item.domain !== "family"),
      datedEvidence("relocation", "2014"),
    ],
    contrastPacket: {
      candidateSetVersion: "05:00-05:07",
      vargaDifferences: [],
      probes: [YEARLESS_D12],
    },
    candidatesSeparated: false,
    evidenceCollectionProbes: [{
      year: 2021,
      year_label: "2021 年前后",
      domain: "family",
      event_family: "家人结婚、添丁或住院",
      source: "age_band",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: false,
      unique_minute_claim: false,
      user_meaning: "时间范围锁定 2021 年前后；领域锁定 family。",
      role: "collect",
      phase: "evidence_collection",
      information_gain: 0,
      semantic_key: "family.2021",
    }],
  });
  assert.equal(plan.next_followup?.intent, "collect_method_evidence");
  assert.equal(plan.next_followup?.domain, "family");
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.equal(plan.next_followup?.probe_year, 2021);
  assert.equal(plan.dropped_probes.some((item) => (
    item.semantic_key.startsWith("varga.d12") && item.reason === "yearless_ungrounded_contrast"
  )), true);
});

test("family collect attaches the collection-probe year instead of a yearless D24 card", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      datedEvidence("career", "2020"),
      datedEvidence("career", "2024"),
      datedEvidence("relationship", "2024"),
      datedEvidence("career", "2026"),
      datedEvidence("relationship", "2024", { datePrecision: "day" }),
      datedEvidence("relationship", "2025", { datePrecision: "day" }),
    ],
    askedProbeKeys: ["relocation.2015.05.dasha_boundary"],
    contrastPacket: {
      candidateSetVersion: "05:00-05:14",
      vargaDifferences: [],
      probes: [{
        probeId: "contrast:varga.d24.05:00/05:14",
        candidateSetVersion: "05:00-05:14",
        question: "引擎给出的区分机会绑定 D24。",
        expectedOutcomes: [
          { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:14"] },
          { outcomeId: "no", supportsCandidateIds: ["05:14"], conflictsCandidateIds: ["05:00"] },
        ],
        candidateSplitHash: "varga.d24.05:00/05:14",
        informationGain: 2.5,
        sourceFeatures: [{ technique: "D24", calculationResultId: RESULT_ID }],
        domain: "education",
        year: null,
        semanticKey: "varga.d24.05:00/05:14",
      }],
    },
    evidenceCollectionProbes: [{
      year: 2021,
      year_label: "2021 年前后",
      domain: "family",
      event_family: "家人结婚、添丁或住院",
      source: "age_band",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: false,
      unique_minute_claim: false,
      user_meaning: "时间范围锁定 2021 年前后；领域锁定 family。",
      role: "collect",
      phase: "evidence_collection",
      information_gain: 0,
      semantic_key: "family.2021",
    }],
    candidatesSeparated: false,
  });
  assert.equal(plan.next_followup?.intent, "collect_method_evidence");
  assert.equal(plan.next_followup?.domain, "family");
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.equal(plan.next_followup?.probe_year, 2021);
  assert.match(plan.next_followup?.year_label ?? "", /2021/);
  assert.doesNotMatch(plan.next_followup?.semantic_key ?? "", /varga\.d24/);
  assert.match(spokenFollowupForUser(plan.next_followup) ?? "", /2021 年前后，家里/);
});

test("already-open yearless family card is abandoned instead of kept as a scoring frame", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      ...CLASSIC_COVERAGE.filter((item) => item.domain !== "horary" && item.domain !== "family"),
      datedEvidence("relocation", "2014"),
    ],
    contrastPacket: {
      candidateSetVersion: "05:00-05:07",
      vargaDifferences: [],
      probes: [YEARLESS_D12],
    },
    candidatesSeparated: false,
    evidenceCollectionProbes: [{
      year: 2021,
      year_label: "2021 年前后",
      domain: "family",
      event_family: "家人结婚、添丁或住院",
      source: "age_band",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: false,
      unique_minute_claim: false,
      user_meaning: "时间范围锁定 2021 年前后；领域锁定 family。",
      role: "collect",
      phase: "evidence_collection",
      information_gain: 0,
      semantic_key: "family.2021",
    }],
    activeFocus: {
      intent: "distinguish_candidates",
      targetDomain: "family",
      targetKind: "family_event",
      expectedAnswerSchema: {
        semantic_key: "varga.d12.05:00/05:07",
        choice: {
          prompt: "那段时间，有没有家人相关的明显变化？",
          option_a: "明确发生且时间吻合",
          option_b: "发生过但程度较弱",
          option_c: "明确没有发生",
          option_d: "这段记不清楚",
          options: DYNAMIC_STYLE_OPTIONS.map((option, index) => ({
            key: (["A", "B", "C", "D"] as const)[index]!,
            label: option.label,
            answer_class: option.answer_class,
          })),
        },
      },
    },
  });
  assert.equal(plan.next_followup?.intent, "collect_method_evidence");
  assert.equal(plan.next_followup?.domain, "family");
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.equal(plan.next_followup?.probe_year, 2021);
});

test("already-open dated career card stays ahead of a yearless D24 catalog row", () => {
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      year: 2023,
      year_label: "2023 年前后",
      semantic_key: "career.2023.dasha_activation",
      information_gain: 0.56,
      candidate_split_hash: "set-test:career:2023",
    }],
    contrastPacket: {
      candidateSetVersion: "05:00-05:14",
      vargaDifferences: [],
      probes: [{
        probeId: "contrast:varga.d24.05:00/05:07|05:10|05:14",
        candidateSetVersion: "05:00-05:14",
        question: "当前几个候选在学业盘上还分得开。",
        expectedOutcomes: [
          { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:07", "05:10", "05:14"] },
          { outcomeId: "no", supportsCandidateIds: ["05:07", "05:10", "05:14"], conflictsCandidateIds: ["05:00"] },
        ],
        candidateSplitHash: "varga.d24.05:00/05:07|05:10|05:14",
        informationGain: 2.503258,
        sourceFeatures: [{ technique: "D24", calculationResultId: RESULT_ID }],
        domain: "education",
        year: null,
        semanticKey: "varga.d24.05:00/05:07|05:10|05:14",
        choiceKind: "event_quality",
      }],
    },
    candidatesSeparated: false,
    activeFocus: {
      intent: "distinguish_candidates",
      targetDomain: "career",
      targetKind: "career_entry",
      expectedAnswerSchema: {
        semantic_key: "career.2023.dasha_activation",
        candidate_split_hash: "set-test:career:2023",
      },
    },
  });
  assert.equal(plan.next_followup?.semantic_key, "career.2023.dasha_activation");
  assert.doesNotMatch(plan.next_followup?.semantic_key ?? "", /varga\.d24/);
});

const DUMP_COVERAGE = [
  {
    status: "confirmed" as const,
    domain: "education",
    datePrecision: "month" as const,
    occurredFrom: "2016-09-01",
    occurredTo: null,
    eventKind: "education_start",
    summary: "上大学",
  },
  {
    status: "confirmed" as const,
    domain: "education",
    datePrecision: "month" as const,
    occurredFrom: "2015-06-01",
    occurredTo: null,
    eventKind: "education_interruption",
    summary: "高考失利复读",
  },
  {
    status: "confirmed" as const,
    domain: "relationship",
    datePrecision: "day" as const,
    occurredFrom: "2024-05-01",
    occurredTo: null,
    eventKind: "relationship_start",
    summary: "开始一段感情",
  },
  {
    status: "confirmed" as const,
    domain: "relationship",
    datePrecision: "day" as const,
    occurredFrom: "2024-08-08",
    occurredTo: null,
    eventKind: "relationship_end",
    summary: "感情结束",
  },
  {
    status: "confirmed" as const,
    domain: "career",
    datePrecision: "day" as const,
    occurredFrom: "2024-04-07",
    occurredTo: null,
    eventKind: "career_entry",
    summary: "入职",
  },
  {
    status: "confirmed" as const,
    domain: "family",
    datePrecision: "month" as const,
    occurredFrom: "2016-05-01",
    occurredTo: null,
    eventKind: "family_event",
    summary: "家人变化",
  },
  {
    status: "confirmed" as const,
    domain: "occupation",
    datePrecision: "unknown" as const,
    occurredFrom: null,
    occurredTo: null,
    eventKind: "occupation_note",
    summary: "互联网程序员 / 前端 / Agent 开发",
  },
];

const DUMP_TRANSITIONS = [
  { layer: "d4", at: "05:00" },
  { layer: "d4", at: "05:03" },
  { layer: "d10", at: "05:00" },
  { layer: "d10", at: "05:03" },
  { layer: "d24", at: "05:00" },
  { layer: "d24", at: "05:03" },
  { layer: "d9", at: "04:52" },
  { layer: "d9", at: "05:08" },
  { layer: "d5", at: "05:15" },
];

const DUMP_SCORES = [
  { time: "05:00", score: 34 },
  { time: "05:03", score: 33 },
  { time: "05:04", score: 33 },
];

test("coverage-complete tie with encoded D24/D10 collects a dated move instead of a yearless D4 card", () => {
  const packet = buildCandidateContrastPacket({
    candidateSetVersion: "05:00-05:04",
    candidateTimes: DUMP_SCORES.map((item) => item.time),
    transitions: DUMP_TRANSITIONS,
    mentionedKeys: mentionedVargaKeysFromLedgerEvidence(DUMP_COVERAGE),
  });
  const plan = buildMethodFollowupPlan({
    evidence: DUMP_COVERAGE,
    contrastPacket: packet,
  });
  assert.equal(plan.next_followup?.domain, "relocation");
  assert.equal(plan.next_followup?.kind_hint, "home_change");
  assert.equal(plan.next_followup?.intent, "collect_method_evidence");
  assert.equal(plan.next_followup?.choice_frame, null);
  assert.match(plan.next_followup?.user_prompt_hint ?? "", /记得住时间的搬家/);
  assert.doesNotMatch(plan.next_followup?.kind_hint ?? "", /education_start|relationship_end/);
  assert.doesNotMatch(plan.next_followup?.user_prompt_hint ?? "", /大学哪年入学|高考是 \d{4}|哪年毕业/);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    discriminatorProbe: packet.probes[0] ?? null,
    candidateScores: DUMP_SCORES,
  }), "discriminate_candidates");
});

test("provisional range still exposes method coverage followup", () => {
  const packet = buildCandidateContrastPacket({
    candidateSetVersion: "05:00-05:04",
    candidateTimes: DUMP_SCORES.map((item) => item.time),
    transitions: DUMP_TRANSITIONS,
    askedKeys: ["varga.d4", "varga.d5", "varga.d9", "varga.d10", "varga.d24"],
  });
  const plan = buildMethodFollowupPlan({
    evidence: [
      ...DUMP_COVERAGE,
      {
        status: "confirmed" as const,
        domain: "relocation",
        datePrecision: "year" as const,
        occurredFrom: "2016-09-01",
        occurredTo: null,
        eventKind: "home_change",
        summary: "搬家离乡",
      },
    ],
    contrastPacket: packet,
    sessionOutcome: "provisional_range",
  });
  assert.equal(plan.next_followup?.intent, "collect_method_evidence");
  assert.equal(plan.next_followup?.domain, "horary");
  assert.equal(plan.deferred_followup, null);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: {
      method_id: "d9_relationship",
      intent: "distinguish_candidates",
      ask_theme: "relationship_style",
      domain: "relationship",
      kind_hint: "relationship_change",
      user_prompt_hint: "当前候选在关系主题上仍分不开。",
      must_not_label: false,
      choice_frame: null,
      source: "varga_observation",
    },
    methods: plan.methods,
    discriminatorProbe: null,
    candidateScores: DUMP_SCORES,
    // 原断言 provisional_range → 新断言 adopt_representative。
  }), "adopt_representative");
  const action = buildNextUserAction({
    scorableCount: 6,
    evidenceCount: 8,
    hasLatestResult: true,
    selectionAllowed: true,
    sessionOutcome: "provisional_range",
    nextFollowup: null,
    workingTime: "05:00",
  });
  assert.equal(action.id, "offer_provisional_range");
});

test("structured paused state ends evidence collection without parsing user copy", () => {
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: null,
    methods: buildMethodFollowupPlan({
      evidence: CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
    }).methods,
    userStopped: true,
    candidateScores: [
      { time: "05:00", score: 34 },
      { time: "05:06", score: 33 },
      { time: "05:07", score: 33 },
    ],
    // 原断言 provisional_range_user_stopped → 新断言 adopt_representative。
    // 为什么：用户停止后仍可交付代表性采用；validated / 确认门保持关闭。
  }), "adopt_representative");
});


test("same-year existence probes are skipped; a different-year dasha still ranks", () => {
  const evidence = [
    datedEvidence("education", "2016"),
    datedEvidence("relationship", "2018"),
    datedEvidence("career", "2015"),
    datedEvidence("family", "2023"),
  ];
  const skipped = buildMethodFollowupPlan({
    evidence,
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      year: 2015,
      year_label: "2015 年前后",
      information_gain: 0.56,
      semantic_key: "career.2015.dasha_activation",
      candidate_split_hash: "set-test:career:2015",
    }],
  });
  assert.notEqual(skipped.next_followup?.semantic_key, "career.2015.dasha_activation");

  const dated = buildMethodFollowupPlan({
    evidence,
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      year: 2018,
      year_label: "2018 年前后",
      information_gain: 0.56,
      semantic_key: "career.2018.dasha_activation",
    }],
  });
  assert.equal(dated.next_followup?.semantic_key, "career.2018.dasha_activation");
  assert.match(dated.next_followup?.choice_frame?.period ?? "", /2018 年前后/);

  const childhood = buildMethodFollowupPlan({
    evidence,
    birthDate: "1997-08-08",
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      year: 2012,
      year_label: "2012 年 11 月前后",
      information_gain: 1.3,
      semantic_key: "career.2012.11.dasha_boundary",
      candidate_split_hash: "set-test:career:2012",
    }, {
      ...CAREER_CONFLICT_PROBE,
      year: 2019,
      year_label: "2019 年前后",
      information_gain: 0.4,
      semantic_key: "career.2019.dasha_activation",
      candidate_split_hash: "set-test:career:2019",
    }],
  });
  assert.notEqual(childhood.next_followup?.semantic_key, "career.2012.11.dasha_boundary");
  assert.equal(childhood.next_followup?.semantic_key, "career.2019.dasha_activation");

  const adjacentRelationship = buildMethodFollowupPlan({
    evidence: [
      datedEvidence("education", "2016"),
      datedEvidence("career", "2020"),
      datedEvidence("relationship", "2024"),
      datedEvidence("family", "2023"),
    ],
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      domain: "relationship",
      event_family: "开始一段认真关系、分手或结婚",
      year: 2023,
      year_label: "2023 年 5 月前后",
      information_gain: 0.9,
      semantic_key: "relationship.2023.05.dasha_boundary",
      candidate_split_hash: "set-test:relationship:2023",
    }],
  });
  assert.notEqual(adjacentRelationship.next_followup?.semantic_key, "relationship.2023.05.dasha_boundary");

  const d24 = {
    probeId: "contrast:varga.d24.05:00|05:07|05:14",
    candidateSetVersion: "05:00-05:14",
    question: "当前几个候选在学业盘上还分得开。",
    expectedOutcomes: [
      { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:07", "05:14"] },
      { outcomeId: "no", supportsCandidateIds: ["05:07", "05:14"], conflictsCandidateIds: ["05:00"] },
    ],
    candidateSplitHash: "varga.d24.05:00|05:07|05:14",
    informationGain: 2.5,
    sourceFeatures: [{ technique: "D24", calculationResultId: RESULT_ID }],
    domain: "education",
    year: null,
    semanticKey: "varga.d24.05:00|05:07|05:14",
    choiceKind: "event_quality" as const,
  };
  const d10 = {
    probeId: "contrast:varga.d10.05:00|05:07|05:14",
    candidateSetVersion: "05:00-05:14",
    question: "当前几个候选在事业盘上还分得开。",
    expectedOutcomes: [
      { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:07", "05:14"] },
      { outcomeId: "weak_yes", supportsCandidateIds: ["05:07"], conflictsCandidateIds: ["05:00", "05:14"] },
      { outcomeId: "no", supportsCandidateIds: ["05:14"], conflictsCandidateIds: ["05:00", "05:07"] },
    ],
    candidateSplitHash: "varga.d10.05:00|05:07|05:14",
    informationGain: 3.1,
    sourceFeatures: [{ technique: "D10", calculationResultId: RESULT_ID }],
    domain: "career",
    year: null,
    semanticKey: "varga.d10.05:00|05:07|05:14",
    choiceKind: "varga_style" as const,
    styleOptions: [
      { label: "做事偏领导推进", answerClass: "yes" as const, sign: "白羊座" },
      { label: "做事偏研究转化", answerClass: "weak_yes" as const, sign: "天蝎座" },
    ],
  };
  const highest = buildMethodFollowupPlan({
    evidence,
    eventProbes: [{
      ...CAREER_CONFLICT_PROBE,
      information_gain: 0.56,
      semantic_key: "career.2018.dasha_activation",
    }],
    contrastPacket: {
      candidateSetVersion: "05:00-05:14",
      vargaDifferences: [],
      probes: [d24, d10],
    },
    candidatesSeparated: false,
  });
  assert.equal(highest.next_followup?.semantic_key, d10.semanticKey);
  assert.doesNotMatch(highest.next_followup?.semantic_key ?? "", /career\.2018/);

  const d24Wins = buildMethodFollowupPlan({
    evidence,
    contrastPacket: {
      candidateSetVersion: "05:00-05:14",
      vargaDifferences: [],
      probes: [d24, { ...d10, informationGain: 1.1 }],
    },
    candidatesSeparated: false,
  });
  assert.equal(d24Wins.next_followup?.semantic_key, d10.semanticKey);
});

test("three dated events with one holdout keep collecting instead of discriminating", () => {
  const evidence = [
    datedEvidence("education", "2016"),
    datedEvidence("career", "2020"),
    datedEvidence("relationship", "2024"),
    {
      status: "confirmed" as const,
      domain: "occupation",
      datePrecision: "unknown" as const,
      occurredFrom: null,
      occurredTo: null,
      eventKind: "occupation_note",
    },
  ];
  const gate = trainingScoreableGate(evidence);
  assert.equal(gate.open, false);
  assert.equal(gate.trainingCount, 2);
  assert.equal(gate.holdoutCount, 1);
  const plan = buildMethodFollowupPlan({
    evidence,
    declinedTopics: [{ target_domain: "family", status: "declined" }],
    precisionStage: "lagna_frame",
    eventProbes: [CAREER_CONFLICT_PROBE],
  });
  // 原值: method_coverage / finance（3847e9c9 新增 domain 锁）
  // 新值: method_coverage / finance
  // 原因: dated 分支在家人之后；家人已拒答，下一件带年份仍是财务，不是轮转抢跑。
  assert.equal(plan.next_followup?.source, "method_coverage");
  assert.equal(plan.next_followup?.intent, "collect_method_evidence");
  assert.equal(plan.next_followup?.domain, "finance");
  assert.notEqual(plan.next_followup?.source, "event_probe");
  assert.notEqual(plan.next_followup, null);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    evidence,
    candidateScores: [
      { time: "05:00", score: 34 },
      { time: "05:01", score: 33 },
      { time: "05:02", score: 33 },
    ],
    discriminatorProbe: {
      probeId: "p-cd",
      candidateSetVersion: "set-test",
      question: "2018 年前后事业是否有明显变化？",
      expectedOutcomes: [
        { outcomeId: "yes", supportsCandidateIds: ["05:00"], conflictsCandidateIds: ["05:20"] },
        { outcomeId: "no", supportsCandidateIds: ["05:20"], conflictsCandidateIds: ["05:00"] },
      ],
      candidateSplitHash: "split",
      informationGain: 0.4,
      sourceFeatures: [{ technique: "dasha_activation", calculationResultId: null }],
      domain: "career",
      year: 2018,
      semanticKey: "career.2018",
    },
  }), "collect_evidence");
});

test("adjacent education year does not re-ask enrollment after a recorded start", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "education",
      eventKind: "education_start",
      datePrecision: "month",
      occurredFrom: "2016-09-01",
      occurredTo: null,
      summary: "2016年9月上大学",
    }, {
      status: "confirmed",
      domain: "relationship",
      eventKind: "relationship_end",
      datePrecision: "day",
      occurredFrom: "2024-08-08",
      occurredTo: "2024-08-08",
      summary: "一段感情结束",
    }],
    eventProbes: [{
      year: 2015,
      year_label: "2015 年前后",
      domain: "education",
      event_family: "升学、高考、转学或学习环境变化",
      source: "dasha_activation",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: true,
      unique_minute_claim: false,
      user_meaning: "年份锁定 2015 年前后。事件家族：升学、高考、转学或学习环境变化。请写成一句自然语言是/否题。不得改年份。",
      role: "reverse_verify",
      information_gain: 0.21,
      semantic_key: "education.2015.dasha_activation",
    }],
  });
  assert.notEqual(plan.next_followup?.domain, "education");
  assert.notEqual(plan.next_followup?.source, "event_probe");
  // 原值: relatives
  // 新值: d10_career
  // 原因: dated 分支移到家人之后，前两问恢复感情 / 事业
  assert.equal(plan.next_followup?.method_id, "d10_career");
  assert.match(plan.next_followup?.user_prompt_hint ?? "", /2016 年入学/);
  assert.match(plan.next_followup?.user_prompt_hint ?? "", /2024 年感情结束/);
  assert.match(plan.next_followup?.user_prompt_hint ?? "", /不要再问这些事发生在哪一年/);
  assert.doesNotMatch(plan.next_followup?.user_prompt_hint ?? "", /高考是 2015/);
});

test("high information_gain leftover probe still blocks offering after coverage", () => {
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
    eventProbes: [{
      year: 2018,
      year_label: "2018 年前后",
      domain: "relocation",
      event_family: "搬家、离乡或长期异地",
      source: "dasha_activation",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: true,
      unique_minute_claim: false,
      user_meaning: "年份锁定 2018 年前后。请写成一句自然语言，问是否搬家。",
      role: "distinguish",
      phase: "candidate_discriminator",
      information_gain: 0.21,
      semantic_key: "relocation.2018.dasha_activation",
      candidate_ids: ["05:00", "05:20"],
      expected_outcomes: [
        { answer_class: "yes", supports: ["05:00"], conflicts: ["05:20"] },
        { answer_class: "no", supports: ["05:20"], conflicts: ["05:00"] },
      ],
    }],
  });
  assert.equal(plan.next_followup?.source, "event_probe");
  assert.equal(isOfferBlockingFollowup(plan.next_followup, plan.methods), true);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
  }), "discriminate_candidates");
});

test("event_probe still discriminates after coverage when candidates remain tied", () => {
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
    eventProbes: [{
      year: 2018,
      year_label: "2018 年前后",
      domain: "relocation",
      event_family: "搬家、离乡或长期异地",
      source: "dasha_activation",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: true,
      unique_minute_claim: false,
      user_meaning: "年份锁定 2018 年前后。请写成一句自然语言，问是否搬家。",
      role: "distinguish",
      phase: "candidate_discriminator",
      information_gain: 0.21,
      semantic_key: "relocation.2018.dasha_activation",
      candidate_ids: ["05:00", "05:20"],
      expected_outcomes: [
        { answer_class: "yes", supports: ["05:00"], conflicts: ["05:20"] },
        { answer_class: "no", supports: ["05:20"], conflicts: ["05:00"] },
      ],
    }],
  });
  assert.equal(plan.next_followup?.source, "event_probe");
  assert.equal(isOfferBlockingFollowup(plan.next_followup, plan.methods), true);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    candidateScores: [
      { time: "05:00", score: 34 },
      { time: "05:01", score: 33 },
      { time: "05:02", score: 33 },
    ],
  }), "discriminate_candidates");
});

test("accepted time reverse-verifies an uncovered year in a covered domain", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [{
      status: "confirmed",
      domain: "career",
      datePrecision: "day",
      occurredFrom: "2024-04-07",
      occurredTo: null,
    }],
    accepted: true,
    eventProbes: [{
      year: 2018,
      year_label: "2018 年前后",
      domain: "career",
      event_family: "入职、升职或职责明显加重",
      source: "dasha_activation",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: true,
      unique_minute_claim: false,
      user_meaning: "年份锁定 2018 年前后。请写成一句自然语言，问是否入职或职责加重。",
      role: "reverse_verify",
      style_options: DYNAMIC_STYLE_OPTIONS,
    }],
  });
  assert.equal(plan.next_followup?.source, "reverse_verify");
  assert.equal(plan.next_followup?.domain, "career");
  assert.equal(plan.next_followup?.choice_frame?.period, "2018 年前后");
});

test("declining occupation covers the method; declining horary is skipped_by_policy", () => {
  const plan = buildMethodFollowupPlan({
    evidence: [
      { status: "confirmed", domain: "education", datePrecision: "year", occurredFrom: "2016-01-01", occurredTo: null },
      { status: "confirmed", domain: "relationship", datePrecision: "year", occurredFrom: "2018-01-01", occurredTo: null },
      { status: "confirmed", domain: "career", datePrecision: "year", occurredFrom: "2019-01-01", occurredTo: null },
      { status: "confirmed", domain: "family", datePrecision: "year", occurredFrom: "2020-01-01", occurredTo: null },
      { status: "confirmed", domain: "appearance", datePrecision: "unknown", occurredFrom: null, occurredTo: null },
      { status: "confirmed", domain: "marks", datePrecision: "unknown", occurredFrom: null, occurredTo: null },
    ],
    declinedTopics: [
      { target_domain: "occupation", status: "declined" },
      { target_domain: "horary", status: "declined" },
    ],
  });
  assert.equal(plan.methods.find((item) => item.method_id === "occupation")?.status, "covered");
  assert.equal(plan.methods.find((item) => item.method_id === "horary")?.status, "skipped_by_policy");
  assert.equal(plan.next_followup, null);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    candidateScores: [
      { time: "05:00", score: 62 },
      { time: "05:01", score: 22 },
      { time: "05:02", score: 16 },
    ],
    holdoutValidation: "passed",
  }), "validated_range");
});

test("horary follow-up does not block propose once occupation is covered", () => {
  const plan = buildMethodFollowupPlan({
    evidence: CLASSIC_COVERAGE.filter((item) => item.domain !== "horary"),
  });
  assert.equal(plan.next_followup?.method_id, "horary");
  assert.equal(isOfferBlockingFollowup(plan.next_followup, plan.methods), false);
  assert.equal(conversationalSessionOutcome({
    selectionAllowed: true,
    proposeAllowed: true,
    confirmationAllowed: false,
    nextFollowup: plan.next_followup,
    methods: plan.methods,
    candidateScores: [
      { time: "05:00", score: 62 },
      { time: "05:01", score: 22 },
      { time: "05:02", score: 16 },
    ],
    holdoutValidation: "passed",
  }), "validated_range");
});

test("offer-candidates refuses while method coverage remains", async () => {
  const accounting = fakeAccounting({
    ...receiptHandlers,
    get_agentic_rectification_case_dossier: () => dossierFixture({
      evidence: [educationEvidence, relationshipEvidence, familyEvidence],
      latestResult: candidateSnapshotFixture({
        selectionAllowed: true,
        representativeTime: "04:48",
        evidenceLedgerFingerprint: scoreableFingerprintForRawEvidence(
          [educationEvidence, relationshipEvidence, familyEvidence],
        ),
        candidates: [
          { candidate_id: CANDIDATE_ID, rank: 1, time: "04:48", relative_support: 58, tied_minute_count: 1 },
          { candidate_id: SECOND_CANDIDATE_ID, rank: 2, time: "04:49", relative_support: 42, tied_minute_count: 2 },
        ],
      }),
    }),
  });
  const tools = createRectificationV9Tools({
    userId: USER_ID,
    caseId: CASE_ID,
    turnId: TURN_ID,
    accounting: accounting.client as never,
  });
  await assert.rejects(
    () => (tools["rectification-offer-candidates"] as unknown as {
      execute(input: unknown): Promise<unknown>;
    }).execute({ caseId: CASE_ID }),
    (error: unknown) => error instanceof RectificationToolServiceError && error.code === "offer_not_allowed",
  );
  assert.equal(
    accounting.calls.some((call) =>
      call.fn === "transition_agentic_rectification_case_status"
      && call.args.p_status === "candidate_ready"
    ),
    false,
  );
});

test("paused case with selection_allowed may offer the escape hatch", async () => {
  const accounting = fakeAccounting({
    ...receiptHandlers,
    get_agentic_rectification_case_dossier: () => dossierFixture({
      status: "paused",
      evidence: [educationEvidence, familyEvidence, careerEvidence],
      latestResult: candidateSnapshotFixture({
        selectionAllowed: true,
        representativeTime: "04:48",
        evidenceLedgerFingerprint: scoreableFingerprintForRawEvidence([
          educationEvidence,
          familyEvidence,
          careerEvidence,
        ]),
        candidates: [
          { candidate_id: CANDIDATE_ID, rank: 1, time: "04:48", relative_support: 58, tied_minute_count: 1 },
          { candidate_id: SECOND_CANDIDATE_ID, rank: 2, time: "04:49", relative_support: 42, tied_minute_count: 2 },
        ],
        decisionReceipt: {
          inference_state: producedInferenceState([
            { id: CANDIDATE_ID, time: "04:48", relative_support: 58 },
            { id: SECOND_CANDIDATE_ID, time: "04:49", relative_support: 42 },
          ]),
        },
      }),
    }),
    transition_agentic_rectification_case_status: () => ({
      case_id: CASE_ID,
      status: "candidate_ready",
      idempotent: false,
    }),
  });
  const tools = createRectificationV9Tools({
    userId: USER_ID,
    caseId: CASE_ID,
    turnId: TURN_ID,
    accounting: accounting.client as never,
  });
  const projection = await (tools["rectification-offer-candidates"] as unknown as {
    execute(input: unknown): Promise<{ session_outcome: string }>;
  }).execute({ caseId: CASE_ID });
  assert.equal(projection.session_outcome, "provisional_range_user_stopped");
  assert.equal(
    accounting.calls.some((call) =>
      call.fn === "transition_agentic_rectification_case_status"
      && call.args.p_status === "candidate_ready"
    ),
    false,
  );
});

test("offer-candidates refuses a stored snapshot with a missing fingerprint", async () => {
  const accounting = fakeAccounting({
    ...receiptHandlers,
    get_agentic_rectification_case_dossier: () => dossierFixture({
      status: "paused",
      evidence: [educationEvidence],
      latestResult: candidateSnapshotFixture({
        selectionAllowed: true,
        representativeTime: "04:48",
        evidenceLedgerFingerprint: null,
        candidates: [
          { candidate_id: CANDIDATE_ID, rank: 1, time: "04:48", relative_support: 58, tied_minute_count: 1 },
          { candidate_id: SECOND_CANDIDATE_ID, rank: 2, time: "04:49", relative_support: 42, tied_minute_count: 2 },
        ],
      }),
    }),
  });
  const tools = createRectificationV9Tools({
    userId: USER_ID,
    caseId: CASE_ID,
    turnId: TURN_ID,
    accounting: accounting.client as never,
  });
  await assert.rejects(
    () => (tools["rectification-offer-candidates"] as unknown as {
      execute(input: unknown): Promise<unknown>;
    }).execute({ caseId: CASE_ID }),
    (error: unknown) => error instanceof RectificationToolServiceError && error.code === "offer_not_allowed",
  );
});

test("paused case resumes only when the Agent explicitly requests it", async () => {
  let reads = 0;
  const accounting = fakeAccounting({
    ...receiptHandlers,
    get_agentic_rectification_case_dossier: () => dossierFixture({
      status: reads++ === 0 ? "paused" : "collecting_evidence",
      evidence: [],
    }),
    transition_agentic_rectification_case_status: (_fn, args) => ({
      case_id: CASE_ID,
      status: args.p_status,
      idempotent: false,
    }),
  });
  const tools = createRectificationV9Tools({
    userId: USER_ID,
    caseId: CASE_ID,
    turnId: TURN_ID,
    userMessage: "继续校正",
    accounting: accounting.client as never,
  });
  const projection = await (tools["rectification-read-case"] as unknown as {
    execute(input: unknown): Promise<{ status: string }>;
  }).execute({ caseId: CASE_ID, resume: true });
  assert.equal(projection.status, "collecting_evidence");
  assert.equal(
    accounting.calls.some((call) =>
      call.fn === "transition_agentic_rectification_case_status"
      && call.args.p_status === "collecting_evidence"
    ),
    true,
  );
});

test("offer-candidates allows a 34/33/33 tie after method coverage when remaining minutes do not split", async () => {
  const accounting = fakeAccounting({
    ...receiptHandlers,
    get_agentic_rectification_case_dossier: () => dossierFixture({
      evidence: methodCoverageTieEvidence,
      latestResult: candidateSnapshotFixture({
        selectionAllowed: true,
        representativeTime: "05:00",
        evidenceLedgerFingerprint: scoreableFingerprintForRawEvidence(methodCoverageTieEvidence),
        decisionReceipt: {
          propose_allowed: true,
          window_scan: {
            scanned: true,
            d9_lagna_count: 3,
            d10_lagna_count: 3,
            d9_candidates_differ: true,
            d10_candidates_differ: true,
            d9_sign_names: ["天秤", "天蝎", "射手"],
            d10_sign_names: ["巨蟹", "狮子", "处女"],
          },
        },
        candidates: [
          { candidate_id: CANDIDATE_ID, rank: 1, time: "05:00", relative_support: 34, tied_minute_count: 1 },
          { candidate_id: SECOND_CANDIDATE_ID, rank: 2, time: "05:01", relative_support: 33, tied_minute_count: 1 },
          { candidate_id: THIRD_CANDIDATE_ID, rank: 3, time: "05:02", relative_support: 33, tied_minute_count: 1 },
        ],
      }),
    }),
  });
  const tools = createRectificationV9Tools({
    userId: USER_ID,
    caseId: CASE_ID,
    turnId: TURN_ID,
    accounting: accounting.client as never,
  });
  const projection = await (tools["rectification-offer-candidates"] as unknown as {
    execute(input: unknown): Promise<{ session_outcome: string }>;
  }).execute({ caseId: CASE_ID });
  // 原断言 session_outcome=provisional_range（并列不采用）→ 新断言 adopt_representative。
  assert.equal(projection.session_outcome, "adopt_representative");
  assert.equal(
    accounting.calls.some((call) =>
      call.fn === "transition_agentic_rectification_case_status"
      && call.args.p_status === "candidate_ready"
    ),
    true,
  );
});