diff --git a/docs/BUG_HISTORY.md b/docs/BUG_HISTORY.md
index 0af32f7a..f422491d 100644
--- a/docs/BUG_HISTORY.md
+++ b/docs/BUG_HISTORY.md
@@ -6187,6 +6187,7 @@
 - 相关记录：BUG-367、BUG-398、BUG-400、BUG-401、BUG-403、BUG-404
 - 复发自：BUG-403（动态四选项合同未贯穿 Python/排序）、BUG-404（Focus 与口语问题仍可分裂）
 - 修复版本：10.0.13
+- 后续复发：BUG-407
 
 ## BUG-406 | 点选题干只在无障碍 legend 里，卡片上看不到
 
@@ -6204,3 +6205,19 @@
 - 复发自：BUG-401（卡片隐藏题干）叠加 BUG-405（正文不再复述题干）
 - 修复版本：待发布
 
+## BUG-407 | 区分题目录被快照投影饿死，低分职业题压过已评分的高分 D24
+
+- 状态：resolved
+- 首次发现：2026-08-28
+- 最近更新：2026-08-28
+- 影响面：生时纠正 GET 点选卡、Mastra 工具读路径、`POST /api/rectification/agent`、contrast packet、method-followup
+- 用户现象：训练事件已经够、推理层已有高信息量 D24 探针，界面仍出低信息量事业存在题；题干修复后选题内容仍不对。
+- 触发条件：快照 `latest_result.candidates` 为空，或可信区间与推理活跃时刻不一致导致 `authoritativeCandidateProjection` fail-closed；Python `discriminating_event_probes` 仍给出低分事业题；已打开的事业区分卡把后续排序锁在原题上。
+- 根因：BUG-405 把排序公式改成按信息量取最高，但读路径组包不读 `inference_state.probes`，remaining D24 又依赖快照时刻。时刻被投影饿死后目录只剩职业题。评分落库用 `score.candidates` 能把 D24 写进推理层，GET/工具随后丢掉。已打开的低分 distinguish Focus 还在 method-followup 里优先于重新排序。
+- 修复：GET、Agent、工具共用一份 `rectificationFollowupCatalog`。合并未回答的推理探针与 Python 事件探针，按 `semantic_key` 去重留更高信息量。拼 remaining splits 用推理未淘汰时刻，不把 adopt 投影的空 `scores` 当成出题目录。`varga.d24`/`d5` 按质量题、`d9`/`d10` 按风格题补全。method-followup 对 packet 里全部 contrast 探针排序；已打开但 semantic_key 不是当前目录赢家的区分卡让位。评分落库仍用当次 `score.candidates`。Skill 版本保持 `10.0.13`。
+- 验证：`frontend/tests/rectification-decision-authority.test.ts` 空快照仍选出 D24；`frontend/tests/rectification-eight-method.test.ts` 低分事业题与已打开事业卡都不得压过可渲染 D24。
+- 防复发：出题目录必须来自推理探针加引擎探针，不得只吃 Python 事件探针或快照投影时刻。Adopt/展示投影 fail-closed 不得饿死出题。已打开的低分区分卡不得挡住更高分目录赢家。
+- 相关记录：BUG-405、BUG-406
+- 复发自：BUG-405（排序公式对，目录被投影饿死，已打开低分卡锁题）
+- 修复版本：待发布
+
diff --git a/frontend/src/app/api/rectification/agent/route.ts b/frontend/src/app/api/rectification/agent/route.ts
index ae826733..fe81a81e 100644
--- a/frontend/src/app/api/rectification/agent/route.ts
+++ b/frontend/src/app/api/rectification/agent/route.ts
@@ -21,16 +21,15 @@ import { resolveSessionLanguageModel } from "@/lib/model-catalog";
 import { createAdminSupabaseClient } from "@/lib/supabase/admin";
 import { createServerSupabaseClient } from "@/lib/supabase/server";
 import { defaultMessageOrigin, isRectificationMessageOrigin } from "@/lib/rectification-agentic/v9/message-origin";
-import { previousInferenceFromReceipt, askedDiscriminatorKeys } from "@/lib/rectification-agentic/v9/inference-adapter";
+import { previousInferenceFromReceipt } from "@/lib/rectification-agentic/v9/inference-adapter";
 import { parseAgentChoiceCopy } from "@/lib/rectification-agentic/v9/choice-card";
 import {
   classifyRectificationTurnIntent,
   optionIdForAnswerClass,
 } from "@/lib/rectification-agentic/v9/turn-intent-classifier";
-import { decideFromDossier, contrastPacketFromDossier } from "@/lib/rectification-agentic/v9/decision-from-dossier";
+import { decideFromDossier, rectificationFollowupCatalog } from "@/lib/rectification-agentic/v9/decision-from-dossier";
 import { persistServerOwnedFocus, openQuestionFromPersistedFocus } from "@/lib/rectification-agentic/v9/server-focus";
 import { buildMethodFollowupPlan } from "@/lib/rectification-agentic/v9/method-followup";
-import { refinementFromDecisionReceipt } from "@/lib/rectification-agentic/v9/refinement-packet";
 
 export const runtime = "nodejs";
 export const maxDuration = 240;
@@ -383,17 +382,15 @@ export async function POST(request: Request) {
       } else {
         const decision = decideFromDossier(dossier);
         if (decision.nextAction === "ask_candidate_discriminator") {
-          const refinement = refinementFromDecisionReceipt(dossier.latestResult?.decisionReceipt ?? null);
+          const catalog = rectificationFollowupCatalog(
+            dossier.latestResult,
+            dossier.evidence,
+          );
           const plan = buildMethodFollowupPlan({
             evidence: dossier.evidence,
             declinedTopics: dossier.conversationSummary.declinedSkippedTopics,
             sessionOutcome: "discriminate_candidates",
-            eventProbes: refinement.discriminating_event_probes,
-            askedProbeKeys: askedDiscriminatorKeys(
-              dossier.latestResult?.decisionReceipt,
-              dossier.evidence,
-            ),
-            contrastPacket: contrastPacketFromDossier(dossier),
+            ...catalog,
             candidatesSeparated: false,
           });
           const persisted = await persistServerOwnedFocus({
diff --git a/frontend/src/lib/rectification-agentic/core/candidate-contrast-packet.ts b/frontend/src/lib/rectification-agentic/core/candidate-contrast-packet.ts
index 7e42d427..9fd0ad0f 100644
--- a/frontend/src/lib/rectification-agentic/core/candidate-contrast-packet.ts
+++ b/frontend/src/lib/rectification-agentic/core/candidate-contrast-packet.ts
@@ -80,8 +80,11 @@ export type EngineContrastProbe = Readonly<{
   information_gain?: number;
   expected_outcomes?: readonly Readonly<{
     answer_class?: string;
+    outcomeId?: string;
     supports?: readonly string[];
+    supportsCandidateIds?: readonly string[];
     conflicts?: readonly string[];
+    conflictsCandidateIds?: readonly string[];
   }>[];
   left_time?: string;
   right_time?: string;
@@ -312,11 +315,12 @@ export function buildCandidateContrastPacket(input: {
     candidateTimes: input.candidateTimes ?? [],
     transitions: input.transitions ?? [],
   });
+  const presentKeys = new Set(fromEngine.map((item) => item.semanticKey));
   const fromVarga = vargaProbe(
     remainingSplits,
     input.candidateSetVersion,
     input.calculationResultId ?? null,
-    asked,
+    new Set([...asked, ...presentKeys]),
   );
   const probes = [...fromEngine, ...(fromVarga ? [fromVarga] : [])]
     .sort((left, right) => right.informationGain - left.informationGain);
@@ -515,15 +519,30 @@ export function conflictProbesFromContrast(
   });
 }
 
+function inferredContrastChoiceKind(
+  semanticKey: string,
+  explicit?: ContrastChoiceKind,
+): ContrastChoiceKind {
+  if (explicit === "varga_style" || explicit === "event_quality" || explicit === "existence") {
+    return explicit;
+  }
+  const layer = semanticKey.match(/^varga\.(d\d+)/)?.[1];
+  if (layer === "d9" || layer === "d10") return "varga_style";
+  if (layer === "d24" || layer === "d5") return "event_quality";
+  return "existence";
+}
+
 function probeFromEngine(
   probe: EngineContrastProbe,
   candidateSetVersion: string,
   calculationResultId: string | null,
 ): CandidateDiscriminatorProbe | null {
   const outcomes = (probe.expected_outcomes ?? []).flatMap((row) => {
-    const outcomeId = typeof row.answer_class === "string" ? row.answer_class : "";
-    const supports = row.supports ?? [];
-    const conflicts = row.conflicts ?? [];
+    const outcomeId = typeof row.answer_class === "string" && row.answer_class.trim()
+      ? row.answer_class
+      : typeof row.outcomeId === "string" ? row.outcomeId : "";
+    const supports = row.supports ?? row.supportsCandidateIds ?? [];
+    const conflicts = row.conflicts ?? row.conflictsCandidateIds ?? [];
     if (!outcomeId) return [];
     return [{ outcomeId, supportsCandidateIds: supports, conflictsCandidateIds: conflicts }];
   });
@@ -553,7 +572,7 @@ function probeFromEngine(
     domain: probe.domain ?? null,
     year: probe.year ?? null,
     semanticKey,
-    choiceKind: probe.choice_kind,
+    choiceKind: inferredContrastChoiceKind(semanticKey, probe.choice_kind),
     styleOptions: styleOptionsFromEngine(probe.style_options),
   };
 }
diff --git a/frontend/src/lib/rectification-agentic/v9/decision-from-dossier.ts b/frontend/src/lib/rectification-agentic/v9/decision-from-dossier.ts
index e3613a09..f430afd5 100644
--- a/frontend/src/lib/rectification-agentic/v9/decision-from-dossier.ts
+++ b/frontend/src/lib/rectification-agentic/v9/decision-from-dossier.ts
@@ -10,6 +10,7 @@ import {
   selectDiscriminatorProbe,
   volunteeredDomainsFromEvidence,
   type CandidateContrastPacket,
+  type EngineContrastProbe,
 } from "../core/candidate-contrast-packet.ts";
 import {
   decideRectification,
@@ -84,6 +85,22 @@ export function candidateScoresFromDossier(latest: DecisionDossier["latestResult
   return authoritativeCandidateProjection(latest).scores;
 }
 
+const CLOCK_TIME = /^(?:[01]\d|2[0-3]):[0-5]\d$/;
+
+export function discriminatorCandidateTimes(
+  latest: DecisionDossier["latestResult"],
+): string[] {
+  const inference = previousInferenceFromReceipt(latest?.decisionReceipt ?? null);
+  const fromInference = [...new Set(
+    (inference?.candidates ?? [])
+      .filter((item) => item.status !== "eliminated")
+      .map((item) => item.time)
+      .filter((time) => CLOCK_TIME.test(time)),
+  )];
+  if (fromInference.length >= 2) return fromInference;
+  return candidateScoresFromDossier(latest).map((item) => item.time);
+}
+
 function holdoutStatusFromInference(inference: ReturnType<typeof previousInferenceFromReceipt>) {
   if (!inference) return "unavailable" as const;
   const hasHoldout = inference.events.some((item) => item.usage === "holdout");
@@ -105,15 +122,47 @@ function holdoutStatusFromState(state: InferenceState) {
   return "unavailable" as const;
 }
 
-export function contrastPacketFromDossier(dossier: DecisionDossier): CandidateContrastPacket {
-  const windowScan = windowScanFromDecisionReceipt(dossier.latestResult?.decisionReceipt ?? null);
-  const refinement = refinementFromDecisionReceipt(dossier.latestResult?.decisionReceipt ?? null);
-  const inference = previousInferenceFromReceipt(dossier.latestResult?.decisionReceipt ?? null);
-  const candidateScores = candidateScoresFromDossier(dossier.latestResult);
+export function contrastPacketFromLatestResult(
+  latest: DecisionDossier["latestResult"],
+  evidence: DecisionDossier["evidence"] = [],
+): CandidateContrastPacket {
+  const windowScan = windowScanFromDecisionReceipt(latest?.decisionReceipt ?? null);
+  const refinement = refinementFromDecisionReceipt(latest?.decisionReceipt ?? null);
+  const inference = previousInferenceFromReceipt(latest?.decisionReceipt ?? null);
+  const answered = new Set((inference?.answered_probes ?? []).map((item) => item.id));
+  const fromInference: EngineContrastProbe[] = (inference?.probes ?? []).flatMap((probe) => {
+    if (answered.has(probe.id) || probe.information_gain <= 0) return [];
+    if (probe.source === "known_event_quality") return [];
+    return [{
+      semantic_key: probe.semantic_key,
+      candidate_split_hash: probe.candidate_split_hash,
+      domain: probe.domain,
+      year: probe.year > 0 ? probe.year : undefined,
+      user_meaning: probe.question,
+      information_gain: probe.information_gain,
+      expected_outcomes: probe.expected_outcomes,
+      candidate_ids: probe.candidate_ids,
+    }];
+  });
+  const merged = mergeEngineProbes(
+    fromInference,
+    refinement.discriminating_event_probes.map((probe) => ({
+      semantic_key: probe.semantic_key,
+      candidate_split_hash: probe.candidate_split_hash,
+      domain: probe.domain,
+      year: probe.year,
+      user_meaning: probe.user_meaning,
+      information_gain: probe.information_gain,
+      expected_outcomes: probe.expected_outcomes,
+      candidate_ids: probe.candidate_ids,
+      choice_kind: probe.choice_kind,
+      style_options: probe.style_options,
+    })),
+  );
   return buildCandidateContrastPacket({
-    candidateSetVersion: inference?.candidate_set_id ?? dossier.latestResult?.resultId ?? "none",
-    calculationResultId: dossier.latestResult?.resultId ?? null,
-    engineProbes: refinement.discriminating_event_probes,
+    candidateSetVersion: inference?.candidate_set_id ?? latest?.resultId ?? "none",
+    calculationResultId: latest?.resultId ?? null,
+    engineProbes: merged,
     vargaDifferences: [
       ...(windowScan?.d9_candidates_differ && windowScan.d9_sign_names.length >= 2
         ? [{ layer: "d9", signs: windowScan.d9_sign_names }]
@@ -122,16 +171,57 @@ export function contrastPacketFromDossier(dossier: DecisionDossier): CandidateCo
         ? [{ layer: "d10", signs: windowScan.d10_sign_names }]
         : []),
     ],
-    candidateTimes: candidateScores.map((item) => item.time),
+    candidateTimes: discriminatorCandidateTimes(latest),
     transitions: windowScan?.transitions ?? [],
-    askedKeys: askedDiscriminatorKeys(
-      dossier.latestResult?.decisionReceipt,
-      dossier.evidence,
-    ),
-    volunteeredDomains: volunteeredDomainsFromEvidence(dossier.evidence),
+    askedKeys: askedDiscriminatorKeys(latest?.decisionReceipt, evidence),
+    volunteeredDomains: volunteeredDomainsFromEvidence(evidence),
   });
 }
 
+function mergeEngineProbes(
+  ...groups: ReadonlyArray<readonly EngineContrastProbe[] | undefined>
+): EngineContrastProbe[] {
+  const byKey = new Map<string, EngineContrastProbe>();
+  for (const group of groups) {
+    for (const probe of group ?? []) {
+      const key = probe.semantic_key?.trim() ?? "";
+      if (!key) continue;
+      const current = byKey.get(key);
+      if (!current || (probe.information_gain ?? 0) > (current.information_gain ?? 0)) {
+        byKey.set(key, probe);
+      }
+    }
+  }
+  return [...byKey.values()];
+}
+
+export function contrastPacketFromDossier(dossier: DecisionDossier): CandidateContrastPacket {
+  return contrastPacketFromLatestResult(dossier.latestResult, dossier.evidence);
+}
+
+export function rectificationFollowupCatalog(
+  latest: DecisionDossier["latestResult"],
+  evidence: DecisionDossier["evidence"] = [],
+) {
+  const receipt = latest?.decisionReceipt ?? null;
+  const refinement = refinementFromDecisionReceipt(receipt);
+  const inference = previousInferenceFromReceipt(receipt);
+  return {
+    contrastPacket: contrastPacketFromLatestResult(latest, evidence),
+    topCandidateTimes: discriminatorCandidateTimes(latest),
+    askedProbeKeys: askedDiscriminatorKeys(receipt, evidence),
+    eventProbes: refinement.discriminating_event_probes,
+    eventClarificationProbes: refinement.event_clarification_probes,
+    evidenceCollectionProbes: refinement.evidence_collection_probes,
+    precisionStage: refinement.precision_stage?.current ?? null,
+    nakshatraBoundary: refinement.nakshatra_boundary,
+    oosBlindPrompts: refinement.oos_blind_prompts,
+    holdoutEvents: (inference?.events ?? [])
+      .filter((item) => item.usage === "holdout")
+      .map((item) => ({ domain: item.domain, year: item.year })),
+  };
+}
+
 function contrastPacketFromState(state: InferenceState): CandidateContrastPacket {
   const answered = new Set(state.answered_probes.map((item) => item.probe_id));
   return buildCandidateContrastPacket({
diff --git a/frontend/src/lib/rectification-agentic/v9/interview-state.ts b/frontend/src/lib/rectification-agentic/v9/interview-state.ts
index de64ae0a..5b2fafcb 100644
--- a/frontend/src/lib/rectification-agentic/v9/interview-state.ts
+++ b/frontend/src/lib/rectification-agentic/v9/interview-state.ts
@@ -6,15 +6,13 @@
  * Card identity is the persisted focus UUID plus the inference revision.
  */
 
-import { askedKeysFromLedgerEvidence } from "../core/candidate-contrast-packet.ts";
-import { askedProbeKeysFromReceipt, previousInferenceFromReceipt } from "./inference-adapter";
+import { previousInferenceFromReceipt } from "./inference-adapter";
 import {
-  contrastPacketFromDossier,
   decideFromDossier,
+  rectificationFollowupCatalog,
 } from "./decision-from-dossier";
 import { evidenceLedgerFingerprint } from "./tool-service";
 import { projectRectificationChoiceCard } from "./method-followup";
-import { refinementFromDecisionReceipt } from "./refinement-packet";
 import {
   internalObservationsFromWindowScan,
   windowScanFromDecisionReceipt,
@@ -62,11 +60,11 @@ export function choiceCardFromCaseDossier(dossier: {
   };
   turns?: readonly Readonly<{ role: string; text: string | null }>[];
 }): RectificationChoiceCard | null {
-  const windowScan = windowScanFromDecisionReceipt(dossier.latestResult?.decisionReceipt ?? null);
-  const observations = internalObservationsFromWindowScan(windowScan);
-  const refinement = refinementFromDecisionReceipt(dossier.latestResult?.decisionReceipt ?? null);
+  const catalog = rectificationFollowupCatalog(dossier.latestResult, dossier.evidence);
+  const observations = internalObservationsFromWindowScan(
+    windowScanFromDecisionReceipt(dossier.latestResult?.decisionReceipt ?? null),
+  );
   const inference = previousInferenceFromReceipt(dossier.latestResult?.decisionReceipt ?? null);
-  const contrastPacket = contrastPacketFromDossier(dossier);
   const decision = decideFromDossier(dossier, {
     currentEvidenceFingerprint: evidenceLedgerFingerprint(dossier.evidence as never),
   });
@@ -74,35 +72,23 @@ export function choiceCardFromCaseDossier(dossier: {
     .reverse()
     .find((turn) => turn.role === "assistant")
     ?.text ?? null;
-  const holdoutEvents = (inference?.events ?? [])
-    .filter((item) => item.usage === "holdout")
-    .map((item) => ({ domain: item.domain, year: item.year }));
   return projectRectificationChoiceCard({
     evidence: dossier.evidence,
     activeFocus: dossier.conversationSummary.activeFocus,
     declinedTopics: dossier.conversationSummary.declinedSkippedTopics,
     observations,
     sessionOutcome: decision.sessionOutcome,
+    ...catalog,
     precisionStage: decision.precisionStage === "collect_events"
       ? "collect_events"
       : decision.precisionStage === "ready_to_adopt"
         ? "ready_to_adopt"
-        : refinement.precision_stage?.current,
-    nakshatraBoundary: refinement.nakshatra_boundary,
-    oosBlindPrompts: refinement.oos_blind_prompts,
-    eventProbes: refinement.discriminating_event_probes,
-    eventClarificationProbes: refinement.event_clarification_probes,
-    evidenceCollectionProbes: refinement.evidence_collection_probes,
-    askedProbeKeys: [
-      ...askedProbeKeysFromReceipt(dossier.latestResult?.decisionReceipt),
-      ...askedKeysFromLedgerEvidence(dossier.evidence),
-    ],
+        : catalog.precisionStage,
     accepted: Boolean(dossier.case.acceptedTime),
     selectionAllowed: decision.selectionAllowed,
     proposeAllowed: decision.proposeAllowed,
     confirmationAllowed: decision.canConfirmExactMinute,
     caseRevision: inference?.revision ?? 0,
-    contrastPacket,
     candidateScores: decision.separation.ranked.map((item) => ({
       time: item.time,
       score: item.score,
@@ -111,7 +97,6 @@ export function choiceCardFromCaseDossier(dossier: {
     latestAssistantText,
     candidatesSeparated: decision.separation.sufficient,
     holdoutValidation: decision.holdoutValidation,
-    holdoutEvents,
   });
 }
 
diff --git a/frontend/src/lib/rectification-agentic/v9/method-followup.ts b/frontend/src/lib/rectification-agentic/v9/method-followup.ts
index 7fc80550..71addedc 100644
--- a/frontend/src/lib/rectification-agentic/v9/method-followup.ts
+++ b/frontend/src/lib/rectification-agentic/v9/method-followup.ts
@@ -36,6 +36,8 @@
  * Coverage complete never means adopt. Horary does not block cards.
  * A/B/C/D choice frames attach only when candidates already diverge
  * (event probes, precision stage, varga observation, nakshatra, or holdout).
+ * An already-open distinguish card yields if the live catalog winner is a
+ * different probe. Do not keep a low-gain Python event card over D24.
  */
 
 import {
@@ -499,9 +501,19 @@ function followupOwnedProbe(
   };
 }
 
+function persistedFocusProbeKey(focus: MethodFollowupFocus | null | undefined): string {
+  const key = focus?.expectedAnswerSchema?.semantic_key;
+  return typeof key === "string" && key.trim() ? key.trim() : "";
+}
+
+function rankedDiscriminatorKey(row: RankedDiscriminator | null): string {
+  if (!row) return "";
+  return row.eventProbe?.semantic_key ?? row.contrastProbe?.semanticKey ?? "";
+}
+
 function rankRenderableDiscriminators(input: {
   eventProbes: readonly DiscriminatingEventProbe[];
-  contrastProbe: CandidateDiscriminatorProbe | null;
+  contrastProbes: readonly CandidateDiscriminatorProbe[];
   askedKeys: ReadonlySet<string>;
   topCandidateTimes?: readonly string[];
 }): RankedDiscriminator[] {
@@ -520,7 +532,9 @@ function rankRenderableDiscriminators(input: {
   for (const probe of input.eventProbes) {
     push(renderableEventProbe(probe, input.askedKeys, top));
   }
-  push(input.contrastProbe ? renderableContrastProbe(input.contrastProbe, input.askedKeys, top) : null);
+  for (const probe of input.contrastProbes) {
+    push(renderableContrastProbe(probe, input.askedKeys, top));
+  }
   return rows.sort((left, right) => right.score - left.score || (right.eventProbe?.information_gain ?? right.contrastProbe?.informationGain ?? 0) - (left.eventProbe?.information_gain ?? left.contrastProbe?.informationGain ?? 0));
 }
 
@@ -811,6 +825,7 @@ export function buildMethodFollowupPlan(input: {
   accepted?: boolean;
   candidatesSeparated?: boolean;
   contrastPacket?: CandidateContrastPacket | null;
+  topCandidateTimes?: readonly string[];
   holdoutValidation?: HoldoutValidationStatus;
   holdoutEvents?: readonly Readonly<{ domain: string; year: number | null }>[];
 }): MethodFollowupPlan {
@@ -879,7 +894,7 @@ export function buildMethodFollowupPlan(input: {
 
   const sessionOutcome = input.sessionOutcome ?? "collect_evidence";
   const candidatesSeparated = input.candidatesSeparated === true;
-  const contrastProbe = selectDiscriminatorProbe(input.contrastPacket ?? null);
+  const contrastProbes = candidatesSeparated ? [] : [...(input.contrastPacket?.probes ?? [])];
   // Legacy known-event quality cards were never backed by an inference probe.
   // Ignore them so existing cases resume evidence collection instead of exposing a stale card.
   const focus = input.activeFocus?.intent === "clarify_event" ? null : input.activeFocus ?? null;
@@ -887,6 +902,20 @@ export function buildMethodFollowupPlan(input: {
     focus && (focus.intent === "reverse_verify" || focus.intent === "out_of_sample_check"),
   );
   const coverageComplete = blockingMethodsCovered(methods);
+  const askedKeys = new Set([
+    ...(input.askedProbeKeys ?? []),
+    ...askedKeysFromLedgerEvidence(input.evidence),
+  ]);
+  const rankedDiscriminators = dashaCovered && meetsAcceptanceEventQuality(input.evidence)
+    ? rankRenderableDiscriminators({
+      eventProbes: remainingConflictProbes(input.eventProbes, input.evidence, declined, askedKeys),
+      contrastProbes,
+      askedKeys,
+      topCandidateTimes: input.topCandidateTimes,
+    })
+    : [];
+  const bestDiscriminator = rankedDiscriminators[0] ?? null;
+  const catalogWinnerKey = rankedDiscriminatorKey(bestDiscriminator);
   const staleCollectFocus = Boolean(
     focus
     && focus.intent === "collect_method_evidence"
@@ -898,9 +927,17 @@ export function buildMethodFollowupPlan(input: {
       || (focus.targetDomain === "horary" && horaryStatus !== "uncovered")
     ),
   );
+  const staleDiscriminatorFocus = Boolean(
+    focus
+    && focus.intent === "distinguish_candidates"
+    && catalogWinnerKey
+    && persistedFocusProbeKey(focus)
+    && persistedFocusProbeKey(focus) !== catalogWinnerKey
+  );
   if (
     focus
     && !staleCollectFocus
+    && !staleDiscriminatorFocus
     && (sessionOutcome !== "adopt_representative"
       && sessionOutcome !== "validated_range"
       && sessionOutcome !== "exact_minute_confirmed"
@@ -1018,18 +1055,6 @@ export function buildMethodFollowupPlan(input: {
 
   let next: MethodFollowup | null = null;
   const stage = input.precisionStage ?? null;
-  const askedKeys = new Set([
-    ...(input.askedProbeKeys ?? []),
-    ...askedKeysFromLedgerEvidence(input.evidence),
-  ]);
-  const rankedDiscriminators = dashaCovered && meetsAcceptanceEventQuality(input.evidence)
-    ? rankRenderableDiscriminators({
-      eventProbes: remainingConflictProbes(input.eventProbes, input.evidence, declined, askedKeys),
-      contrastProbe: !candidatesSeparated ? contrastProbe : null,
-      askedKeys,
-    })
-    : [];
-  const bestDiscriminator = rankedDiscriminators[0] ?? null;
   const followupFromRanked = (ranked: RankedDiscriminator): MethodFollowup => {
     if (ranked.kind === "event" && ranked.eventProbe) {
       const conflictProbe = ranked.eventProbe;
diff --git a/frontend/src/mastra/rectification-v9-tools.ts b/frontend/src/mastra/rectification-v9-tools.ts
index 2c864fb6..ed4cded1 100644
--- a/frontend/src/mastra/rectification-v9-tools.ts
+++ b/frontend/src/mastra/rectification-v9-tools.ts
@@ -87,6 +87,10 @@ import {
   resolveEvidenceQuote,
 } from "@/lib/rectification-agentic/v9/evidence-quote";
 import { projectTurnDecision } from "@/lib/rectification-agentic/v9/turn-decision";
+import {
+  contrastPacketFromLatestResult,
+  rectificationFollowupCatalog,
+} from "@/lib/rectification-agentic/v9/decision-from-dossier";
 import { QUESTION_CONTRACT_VERSION } from "@/lib/rectification-agentic/v9/probe-question-contract";
 import {
   posteriorMap,
@@ -174,43 +178,17 @@ function holdoutStatusFromLatest(latest: NonNullable<DossierForTools["latestResu
   const hasHoldout = inference.events.some((item) => item.usage === "holdout");
   if (!hasHoldout) return "unavailable";
   if (inference.holdout_passed === true) return "passed";
-  if (inference.holdout_passed === false || inference.result_status === "validation_failed") return "failed";
+  if (inference.holdout_passed === false || inference.result_status === "validation_failed") {
+    return "failed";
+  }
   return "not_started";
 }
 
-function holdoutEventsFromLatest(latest: NonNullable<DossierForTools["latestResult"]> | null | undefined) {
-  const inference = previousInferenceFromReceipt(latest?.decisionReceipt ?? null);
-  return (inference?.events ?? [])
-    .filter((item) => item.usage === "holdout")
-    .map((item) => ({ domain: item.domain, year: item.year }));
-}
-
 function contrastPacketFromLatest(
   latest: NonNullable<DossierForTools["latestResult"]> | null | undefined,
   evidence: DossierForTools["evidence"] = [],
 ) {
-  const inference = previousInferenceFromReceipt(latest?.decisionReceipt ?? null);
-  const windowScan = windowScanFromDecisionReceipt(latest?.decisionReceipt ?? null);
-  const refinement = refinementFromDecisionReceipt(latest?.decisionReceipt ?? null);
-  const candidateTimes = candidateScoresFromLatest(latest).map((item) => item.time);
-  const vargaDifferences = [
-    ...(windowScan?.d9_candidates_differ && windowScan.d9_sign_names.length >= 2
-      ? [{ layer: "d9", signs: windowScan.d9_sign_names }]
-      : []),
-    ...(windowScan?.d10_candidates_differ && windowScan.d10_sign_names.length >= 2
-      ? [{ layer: "d10", signs: windowScan.d10_sign_names }]
-      : []),
-  ];
-  return buildCandidateContrastPacket({
-    candidateSetVersion: inference?.candidate_set_id ?? latest?.resultId ?? "none",
-    calculationResultId: latest?.resultId ?? null,
-    engineProbes: refinement.discriminating_event_probes,
-    vargaDifferences,
-    candidateTimes,
-    transitions: windowScan?.transitions ?? [],
-    askedKeys: askedDiscriminatorKeys(latest?.decisionReceipt, evidence),
-    volunteeredDomains: volunteeredDomainsFromEvidence(evidence),
-  });
+  return contrastPacketFromLatestResult(latest, evidence);
 }
 
 function snapshotSourceFromDossier(
@@ -251,9 +229,9 @@ function safeCaseProjection(
   const latest = dossier.latestResult;
   const windowScan = windowScanFromDecisionReceipt(latest?.decisionReceipt ?? null);
   const observations = internalObservationsFromWindowScan(windowScan);
-  const refinement = refinementFromDecisionReceipt(latest?.decisionReceipt ?? null);
+  const catalog = rectificationFollowupCatalog(latest, dossier.evidence);
+  const contrastPacket = catalog.contrastPacket;
   const accepted = Boolean(caseRow.acceptedTime);
-  const contrastPacket = contrastPacketFromLatest(latest, dossier.evidence);
   const candidateScores = candidateScoresFromLatest(latest);
   const holdoutValidation = holdoutStatusFromLatest(latest);
   const separation = evaluateCandidateSeparation(candidateScores);
@@ -268,19 +246,11 @@ function safeCaseProjection(
     declinedTopics: dossier.conversationSummary.declinedSkippedTopics,
     observations,
     sessionOutcome: "collect_evidence",
-    precisionStage: refinement.precision_stage?.current,
-    nakshatraBoundary: refinement.nakshatra_boundary,
-    oosBlindPrompts: refinement.oos_blind_prompts,
-    eventProbes: refinement.discriminating_event_probes,
-    eventClarificationProbes: refinement.event_clarification_probes,
-    evidenceCollectionProbes: refinement.evidence_collection_probes,
-    askedProbeKeys: askedDiscriminatorKeys(latest?.decisionReceipt, dossier.evidence),
+    ...catalog,
     birthDate: String(compute.baselineBirthSnapshot.birth_date ?? "") || null,
     accepted,
     candidatesSeparated: separation.sufficient,
-    contrastPacket,
     holdoutValidation,
-    holdoutEvents: holdoutEventsFromLatest(latest),
   });
   const userStopped = dossier.case.status === "paused";
   const confirmationGate = buildConfirmationGate({
@@ -324,17 +294,10 @@ function safeCaseProjection(
       declinedTopics: dossier.conversationSummary.declinedSkippedTopics,
       observations,
       sessionOutcome,
-      precisionStage: refinement.precision_stage?.current,
-      nakshatraBoundary: refinement.nakshatra_boundary,
-      oosBlindPrompts: refinement.oos_blind_prompts,
-      eventProbes: refinement.discriminating_event_probes,
-      eventClarificationProbes: refinement.event_clarification_probes,
-      evidenceCollectionProbes: refinement.evidence_collection_probes,
-      askedProbeKeys: askedDiscriminatorKeys(latest?.decisionReceipt, dossier.evidence),
+      ...catalog,
       birthDate: String(compute.baselineBirthSnapshot.birth_date ?? "") || null,
       accepted,
       candidatesSeparated: separation.sufficient,
-      contrastPacket,
       holdoutValidation,
     });
   const birthContext = safeBirthContext(compute);
@@ -630,8 +593,7 @@ function collectingFollowupForParsed(
 ) {
   const windowScan = windowScanFromDecisionReceipt(latest.decisionReceipt ?? null);
   const observations = internalObservationsFromWindowScan(windowScan);
-  const refinement = refinementFromDecisionReceipt(latest.decisionReceipt ?? null);
-  const contrastPacket = contrastPacketFromLatest(latest, parsed.evidence);
+  const catalog = rectificationFollowupCatalog(latest, parsed.evidence);
   const separation = evaluateCandidateSeparation(candidateScoresFromLatest(latest));
   return buildMethodFollowupPlan({
     evidence: parsed.evidence,
@@ -639,18 +601,10 @@ function collectingFollowupForParsed(
     declinedTopics: parsed.conversationSummary.declinedSkippedTopics,
     observations,
     sessionOutcome: "collect_evidence",
-    precisionStage: refinement.precision_stage?.current,
-    nakshatraBoundary: refinement.nakshatra_boundary,
-    oosBlindPrompts: refinement.oos_blind_prompts,
-    eventProbes: refinement.discriminating_event_probes,
-    eventClarificationProbes: refinement.event_clarification_probes,
-    evidenceCollectionProbes: refinement.evidence_collection_probes,
-    askedProbeKeys: askedDiscriminatorKeys(latest.decisionReceipt, parsed.evidence),
+    ...catalog,
     accepted: Boolean(parsed.case.acceptedTime),
     candidatesSeparated: separation.sufficient,
-    contrastPacket,
     holdoutValidation: holdoutStatusFromLatest(latest),
-    holdoutEvents: holdoutEventsFromLatest(latest),
   });
 }
 
@@ -660,7 +614,8 @@ function sessionAwareFollowupForParsed(
   options?: { birthDate?: string | null; snapshotCurrent?: boolean },
 ) {
   const collectingPlan = collectingFollowupForParsed(parsed, latest);
-  const contrastPacket = contrastPacketFromLatest(latest, parsed.evidence);
+  const catalog = rectificationFollowupCatalog(latest, parsed.evidence);
+  const contrastPacket = catalog.contrastPacket;
   const candidateScores = candidateScoresFromLatest(latest);
   const holdoutValidation = holdoutStatusFromLatest(latest);
   const sessionOutcome = conversationalSessionOutcome({
@@ -690,7 +645,6 @@ function sessionAwareFollowupForParsed(
   }
   const windowScan = windowScanFromDecisionReceipt(latest.decisionReceipt ?? null);
   const observations = internalObservationsFromWindowScan(windowScan);
-  const refinement = refinementFromDecisionReceipt(latest.decisionReceipt ?? null);
   const separation = evaluateCandidateSeparation(candidateScores);
   return {
     plan: buildMethodFollowupPlan({
@@ -699,19 +653,11 @@ function sessionAwareFollowupForParsed(
       declinedTopics: parsed.conversationSummary.declinedSkippedTopics,
       observations,
       sessionOutcome,
-      precisionStage: refinement.precision_stage?.current,
-      nakshatraBoundary: refinement.nakshatra_boundary,
-      oosBlindPrompts: refinement.oos_blind_prompts,
-      eventProbes: refinement.discriminating_event_probes,
-      eventClarificationProbes: refinement.event_clarification_probes,
-      evidenceCollectionProbes: refinement.evidence_collection_probes,
-      askedProbeKeys: askedDiscriminatorKeys(latest.decisionReceipt, parsed.evidence),
+      ...catalog,
       birthDate: options?.birthDate ?? null,
       accepted: Boolean(parsed.case.acceptedTime),
       candidatesSeparated: separation.sufficient,
-      contrastPacket,
       holdoutValidation,
-      holdoutEvents: holdoutEventsFromLatest(latest),
     }),
     contrastPacket,
     sessionOutcome,
diff --git a/frontend/tests/rectification-decision-authority.test.ts b/frontend/tests/rectification-decision-authority.test.ts
index fce15aa2..26d7152d 100644
--- a/frontend/tests/rectification-decision-authority.test.ts
+++ b/frontend/tests/rectification-decision-authority.test.ts
@@ -7,8 +7,9 @@ import {
   publicDecisionFields,
 } from "../src/lib/rectification-agentic/core/rectification-decision.ts";
 import { decideNextAction } from "../src/lib/rectification-agentic/core/decide-next-action.ts";
+import { selectDiscriminatorProbe } from "../src/lib/rectification-agentic/core/candidate-contrast-packet.ts";
 import { contrastPacketFromDossier, overlayPublicDecision } from "../src/lib/rectification-agentic/v9/decision-from-dossier.ts";
-import { conversationalSessionOutcome } from "../src/lib/rectification-agentic/v9/method-followup.ts";
+import { buildMethodFollowupPlan, conversationalSessionOutcome } from "../src/lib/rectification-agentic/v9/method-followup.ts";
 
 const SEPARATED = [
   { time: "04:48", score: 58 },
@@ -145,6 +146,158 @@ test("recorded education evidence does not suppress an unasked D24 discriminator
   assert.equal(packet.probes[0]?.expectedOutcomes.at(-1)?.outcomeId, "unsure");
 });
 
+test("scored inference catalog outranks a low-gain Python career probe when snapshot candidates are empty", () => {
+  const careerOutcomes = [
+    { answer_class: "yes", supports: ["04:45", "05:00", "05:14"], conflicts: ["05:15"] },
+    { answer_class: "weak_yes", supports: ["04:45", "05:00", "05:14"], conflicts: ["05:15"] },
+    { answer_class: "no", supports: ["05:15"], conflicts: ["04:45", "05:00", "05:14"] },
+    { answer_class: "unsure", supports: [], conflicts: [] },
+  ];
+  const d24Outcomes = [
+    { answer_class: "yes", supports: ["04:47"], conflicts: ["04:51", "04:53", "04:59", "05:00", "05:07", "05:12", "05:14", "05:15"] },
+    { answer_class: "weak_yes", supports: ["04:51", "04:53"], conflicts: ["04:47", "04:59", "05:00", "05:07", "05:12", "05:14", "05:15"] },
+    { answer_class: "no", supports: ["04:59"], conflicts: ["04:47", "04:51", "04:53", "05:00", "05:07", "05:12", "05:14", "05:15"] },
+    { answer_class: "unsure", supports: [], conflicts: [] },
+  ];
+  const inferenceCandidates = [
+    { id: "05:00", time: "05:00", cluster_range: ["05:00", "05:00"], prior_score: 22, posterior_score: 22, probability: 0.51, status: "active", rank: 1, strong_conflict_count: 0 },
+    { id: "05:07", time: "05:07", cluster_range: ["05:07", "05:07"], prior_score: 17, posterior_score: 17, probability: 0.15, status: "active", rank: 2, strong_conflict_count: 0 },
+    { id: "05:12", time: "05:12", cluster_range: ["05:12", "05:14"], prior_score: 17, posterior_score: 17, probability: 0.15, status: "equivalent", rank: 3, strong_conflict_count: 0 },
+    { id: "05:14", time: "05:14", cluster_range: ["05:12", "05:14"], prior_score: 17, posterior_score: 17, probability: 0.15, status: "equivalent", rank: 4, strong_conflict_count: 0 },
+    { id: "04:47", time: "04:47", cluster_range: ["04:47", "04:47"], prior_score: 6, posterior_score: 6, probability: 0.01, status: "active", rank: 5, strong_conflict_count: 0 },
+    { id: "04:51", time: "04:51", cluster_range: ["04:51", "04:51"], prior_score: 6, posterior_score: 6, probability: 0.01, status: "active", rank: 6, strong_conflict_count: 0 },
+    { id: "04:53", time: "04:53", cluster_range: ["04:53", "04:53"], prior_score: 6, posterior_score: 6, probability: 0.01, status: "active", rank: 7, strong_conflict_count: 0 },
+    { id: "04:59", time: "04:59", cluster_range: ["04:59", "04:59"], prior_score: 6, posterior_score: 6, probability: 0.01, status: "active", rank: 8, strong_conflict_count: 0 },
+    { id: "05:15", time: "05:15", cluster_range: ["05:15", "05:15"], prior_score: 3, posterior_score: 3, probability: 0.004, status: "active", rank: 9, strong_conflict_count: 0 },
+  ];
+  const evidence = [
+    { status: "confirmed", domain: "education", datePrecision: "month", occurredFrom: "2016-09-01", occurredTo: null, eventKind: "education_start" },
+    { status: "confirmed", domain: "relationship", datePrecision: "day", occurredFrom: "2024-08-08", occurredTo: null, eventKind: "relationship_end" },
+    { status: "confirmed", domain: "career", datePrecision: "month", occurredFrom: "2020-04-01", occurredTo: null, eventKind: "career_entry" },
+    { status: "confirmed", domain: "family", datePrecision: "year", occurredFrom: "2018-01-01", occurredTo: null, eventKind: "family_event" },
+  ];
+  const dossier = {
+    evidence,
+    conversationSummary: { activeFocus: null, declinedSkippedTopics: [] },
+    latestResult: {
+      resultId: "result-empty-snapshot",
+      candidates: [],
+      decisionReceipt: {
+        discriminating_event_probes: [{
+          role: "distinguish",
+          phase: "candidate_discriminator",
+          year: 2023,
+          year_label: "2023 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: false,
+          unique_minute_claim: false,
+          user_meaning: "时间范围锁定 2023 年前后；领域锁定 career。",
+          choice_kind: "existence",
+          information_gain: 0.56,
+          semantic_key: "career.2023.dasha_activation",
+          candidate_split_hash: "500ce694938305201fbab9ba",
+          candidate_ids: ["04:45", "05:00", "05:14", "05:15"],
+          expected_outcomes: careerOutcomes,
+          style_options: [
+            { label: "明确发生且时间吻合", answer_class: "yes" },
+            { label: "发生过但程度较弱", answer_class: "weak_yes" },
+            { label: "明确没有发生", answer_class: "no" },
+            { label: "这段记不清楚", answer_class: "unsure" },
+          ],
+        }],
+        inference_state: {
+          algorithm_version: "rectification-inference-v1",
+          candidate_set_id: "04:45-05:15:04:47,04:51,04:53,04:59,05:00,05:07,05:12,05:14,05:15",
+          revision: 1,
+          phase: "discrimination",
+          result_status: "discriminating",
+          range_start: "04:45",
+          range_end: "05:15",
+          candidates: inferenceCandidates,
+          events: [],
+          probes: [{
+            id: "probe:career.2023.dasha_activation:500ce694938305201fbab9ba",
+            year: 2023,
+            domain: "career",
+            source: "dasha_activation",
+            question: "时间范围锁定 2023 年前后；领域锁定 career。",
+            semantic_key: "career.2023.dasha_activation",
+            candidate_ids: ["04:45", "05:00", "05:14", "05:15"],
+            information_gain: 0.56,
+            expected_outcomes: careerOutcomes,
+            candidate_split_hash: "500ce694938305201fbab9ba",
+          }, {
+            id: "contrast:varga.d24.04:47/04:51|04:53/04:59/05:00/05:07|05:12/05:14|05:15",
+            year: 0,
+            domain: "education",
+            source: "varga_contrast",
+            question: "引擎给出的区分机会绑定 D24。",
+            semantic_key: "varga.d24.04:47/04:51|04:53/04:59/05:00/05:07|05:12/05:14|05:15",
+            candidate_ids: ["04:47", "04:51", "04:53", "04:59", "05:00", "05:07", "05:12", "05:14", "05:15"],
+            information_gain: 2.503258334775646,
+            expected_outcomes: d24Outcomes,
+            candidate_split_hash: "04:45-05:15:varga.d24",
+          }],
+          answered_probes: [],
+          rounds: [],
+          entropy: 2.0,
+          representative_time: "05:00",
+          credible_range: ["05:00", "05:14"],
+        },
+        window_scan: {
+          scanned: true,
+          d24_lagna_count: 6,
+          d24_candidates_differ: true,
+          transitions: [
+            { layer: "d24", at: "04:48", from_sign: "白羊座", to_sign: "金牛座" },
+            { layer: "d24", at: "04:54", from_sign: "金牛座", to_sign: "双子座" },
+            { layer: "d24", at: "05:00", from_sign: "双子座", to_sign: "巨蟹座" },
+            { layer: "d24", at: "05:06", from_sign: "巨蟹座", to_sign: "狮子座" },
+            { layer: "d24", at: "05:13", from_sign: "狮子座", to_sign: "处女座" },
+          ],
+        },
+      },
+    },
+    case: { acceptedTime: null },
+  };
+  const packet = contrastPacketFromDossier(dossier);
+  const selected = selectDiscriminatorProbe(packet);
+  assert.match(selected?.semanticKey ?? "", /^varga\.d24\./);
+  assert.ok((selected?.informationGain ?? 0) > 2);
+  assert.doesNotMatch(selected?.semanticKey ?? "", /career\.2023/);
+
+  const plan = buildMethodFollowupPlan({
+    evidence,
+    eventProbes: packet.probes.flatMap((probe) => probe.semanticKey.startsWith("career.")
+      ? [{
+        year: 2023,
+        year_label: "2023 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: false,
+        unique_minute_claim: false,
+        user_meaning: "时间范围锁定 2023 年前后。",
+        role: "distinguish",
+        phase: "candidate_discriminator",
+        information_gain: 0.56,
+        semantic_key: "career.2023.dasha_activation",
+        candidate_split_hash: "500ce694938305201fbab9ba",
+        candidate_ids: ["04:45", "05:00", "05:14", "05:15"],
+        expected_outcomes: careerOutcomes,
+      }]
+      : []),
+    contrastPacket: packet,
+    candidatesSeparated: false,
+  });
+  assert.match(plan.next_followup?.semantic_key ?? "", /^varga\.d24\./);
+  assert.doesNotMatch(plan.next_followup?.semantic_key ?? "", /career\.2023/);
+});
+
 test("public candidate cards follow the inference ranking and hide an inconsistent state", () => {
   const decision = decideRectification({
     methodCoverageAll: true,
diff --git a/frontend/tests/rectification-eight-method.test.ts b/frontend/tests/rectification-eight-method.test.ts
index 6805ed1e..1e9ccf25 100644
--- a/frontend/tests/rectification-eight-method.test.ts
+++ b/frontend/tests/rectification-eight-method.test.ts
@@ -1825,6 +1825,52 @@ test("low-gain career event probe does not outrank a renderable high-gain D24 co
   assert.ok((plan.next_followup?.selection_score ?? 0) > 0.56);
 });
 
+test("already-open low-gain career card yields to the high-gain D24 catalog winner", () => {
+  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, "varga.d24.05:00/05:07|05:10|05:14");
+  assert.doesNotMatch(plan.next_followup?.semantic_key ?? "", /career\.2023/);
+});
+
 const DUMP_COVERAGE = [
   {
     status: "confirmed" as const,