fix(rectification): ask from the scored probe catalog, not snapshot leftovers

Empty snapshot candidates were starving remaining D24 splits, so the TypeScript follow-up chain asked the low-gain Python career probe. Read paths now share one inference+engine catalog and yield a stale low-gain distinguish card to the current winner. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-28 09:48:57 +08:00
parent 4bf074b2bf
commit ca6252ecb6
9 changed files with 417 additions and 139 deletions
@@ -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,
@@ -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,