Jesse_Chen
85b14b13ae
When coverage still blocks adopt and no renderable discriminator remains, narrate the numeric range and persist a spoken collect instead of promising a time card that cannot be clicked. Recompute probe information gain on active candidates and drop zero-split probes explicitly. Co-authored-by: Cursor <cursoragent@cursor.com>
571 lines
21 KiB
TypeScript
571 lines
21 KiB
TypeScript
import assert from "node:assert/strict";
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import { readFileSync } from "node:fs";
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import test from "node:test";
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import { buildInferenceState } from "../src/lib/rectification-agentic/core/build-state.ts";
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import {
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inspectDiscriminatorProbes,
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withCompletedContrastOptions,
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type CandidateDiscriminatorProbe,
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} from "../src/lib/rectification-agentic/core/candidate-contrast-packet.ts";
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import type { ConflictProbe } from "../src/lib/rectification-agentic/core/types.ts";
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import {
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contrastPacketFromDossier,
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decideFromDossier,
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followupAsksRenderableDiscriminator,
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rectificationFollowupCatalog,
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type DecisionDossier,
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} from "../src/lib/rectification-agentic/v9/decision-from-dossier.ts";
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import { buildMethodFollowupPlan } from "../src/lib/rectification-agentic/v9/method-followup.ts";
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import { evidenceLedgerFingerprint } from "../src/lib/rectification-agentic/v9/tool-service.ts";
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const CAREER_2020_04: ConflictProbe = {
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id: "probe:career.2020.04.dasha_boundary",
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semantic_key: "career.2020.04.dasha_boundary",
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candidate_split_hash: "career.2020.04",
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domain: "career",
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year: 2020,
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question: "2020 年 4 月前后有没有入职或换工作？",
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candidate_ids: ["05:00", "05:10"],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:10"] },
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{ answer_class: "no", supports: ["05:10"], conflicts: ["05:00"] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 0.9,
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source: "dasha_boundary",
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choice_kind: "existence",
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};
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const REL_2024_05: ConflictProbe = {
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id: "probe:relationship.2024.05.dasha_boundary",
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semantic_key: "relationship.2024.05.dasha_boundary",
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candidate_split_hash: "relationship.2024.05",
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domain: "relationship",
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year: 2024,
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question: "2024 年 5 月前后有没有开始一段认真关系？",
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candidate_ids: ["05:00", "05:10"],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:10"] },
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{ answer_class: "no", supports: ["05:10"], conflicts: ["05:00"] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 0.85,
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source: "dasha_boundary",
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choice_kind: "existence",
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};
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const D24: ConflictProbe = {
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id: "contrast:varga.d24.05:00/05:10",
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semantic_key: "varga.d24.05:00/05:10",
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candidate_split_hash: "varga.d24.05:00/05:10",
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domain: "education",
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year: 0,
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question: "有没有学业或考试发挥明显失常、压力特别大的时候？",
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candidate_ids: ["05:00", "05:10"],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:10"] },
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{ answer_class: "no", supports: ["05:10"], conflicts: ["05:00"] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 2.5,
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source: "varga_contrast",
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choice_kind: "event_quality",
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};
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const D12: ConflictProbe = {
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id: "contrast:varga.d12.05:00/05:10",
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semantic_key: "varga.d12.05:00/05:10",
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candidate_split_hash: "varga.d12.05:00/05:10",
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domain: "family",
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year: 0,
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question: "家里有没有结婚、添丁或住院这类事？",
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candidate_ids: ["05:00", "05:10"],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:10"] },
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{ answer_class: "no", supports: ["05:10"], conflicts: ["05:00"] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 0.8,
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source: "varga_contrast",
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choice_kind: "existence",
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};
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const D9_STYLE_NO_OPTIONS: ConflictProbe = {
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id: "contrast:varga.d9.巨蟹座/狮子座",
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semantic_key: "varga.d9.巨蟹座/狮子座",
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candidate_split_hash: "varga.d9.巨蟹座/狮子座",
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domain: "relationship",
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year: 0,
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question: "亲密关系里更接近下面哪一种相处方式？",
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candidate_ids: ["05:00", "05:10"],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:10"] },
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{ answer_class: "weak_yes", supports: ["05:10"], conflicts: ["05:00"] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 1.4,
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source: "varga_contrast",
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choice_kind: "varga_style",
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};
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const D10_STYLE_NO_OPTIONS: ConflictProbe = {
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id: "contrast:varga.d10.天秤座/天蝎座",
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semantic_key: "varga.d10.天秤座/天蝎座",
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candidate_split_hash: "varga.d10.天秤座/天蝎座",
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domain: "career",
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year: 0,
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question: "平时做事更接近下面哪一种职责风格？",
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candidate_ids: ["05:00", "05:10"],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:10"] },
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{ answer_class: "weak_yes", supports: ["05:10"], conflicts: ["05:00"] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 1.2,
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source: "varga_contrast",
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choice_kind: "varga_style",
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};
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const FAMILY_2021_COLLECT = {
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year: 2021,
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year_label: "2021 年前后",
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domain: "family" as const,
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event_family: "家人结婚、添丁或住院",
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source: "age_band" as const,
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tracks: ["vimshottari", "narayana"] as const,
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tracks_agree: false,
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unique_minute_claim: false as const,
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user_meaning: "时间范围锁定 2021 年前后；领域锁定 family。",
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role: "collect" as const,
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phase: "evidence_collection" as const,
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information_gain: 0,
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semantic_key: "family.2021",
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candidate_split_hash: "family:2021",
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candidate_ids: [] as const,
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expected_outcomes: [] as const,
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choice_kind: "existence" as const,
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};
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const EVIDENCE = [
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{
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id: "e-career-entry",
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status: "confirmed",
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domain: "career",
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datePrecision: "month",
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occurredFrom: "2020-04-01",
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occurredTo: null,
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eventKind: "career_entry",
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},
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{
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id: "e-career-exit",
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status: "confirmed",
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domain: "career",
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datePrecision: "month",
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occurredFrom: "2020-10-01",
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occurredTo: null,
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eventKind: "career_exit",
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},
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{
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id: "e-rel-start",
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status: "confirmed",
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domain: "relationship",
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datePrecision: "month",
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occurredFrom: "2024-05-01",
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occurredTo: null,
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eventKind: "relationship_start",
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},
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{
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id: "e-rel-end",
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status: "confirmed",
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domain: "relationship",
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datePrecision: "day",
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occurredFrom: "2024-08-08",
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occurredTo: null,
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eventKind: "relationship_end",
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},
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] as const;
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function contrastFromConflict(probe: ConflictProbe): CandidateDiscriminatorProbe {
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return {
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probeId: probe.id,
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candidateSetVersion: "05:00-05:10",
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question: probe.question,
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expectedOutcomes: probe.expected_outcomes.map((row) => ({
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outcomeId: row.answer_class,
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supportsCandidateIds: row.supports,
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conflictsCandidateIds: row.conflicts,
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})),
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candidateSplitHash: probe.candidate_split_hash,
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informationGain: probe.information_gain,
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sourceFeatures: [{ technique: probe.domain, calculationResultId: null }],
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domain: probe.domain,
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year: probe.year > 0 ? probe.year : null,
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semanticKey: probe.semantic_key,
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choiceKind: probe.choice_kind,
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};
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}
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function deadlockDossier(): { dossier: DecisionDossier; state: ReturnType<typeof buildInferenceState> } {
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const state = buildInferenceState({
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range_start: "05:00",
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range_end: "05:10",
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candidates: [
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{ id: "05:00", time: "05:00", relative_support: 18 },
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{ id: "05:10", time: "05:10", relative_support: 16 },
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],
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events: [
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{ id: "e-career-entry", domain: "career", year: 2020, precision: "month" },
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{ id: "e-career-exit", domain: "career", year: 2020, precision: "month" },
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{ id: "e-rel-start", domain: "relationship", year: 2024, precision: "month" },
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{ id: "e-rel-end", domain: "relationship", year: 2024, precision: "day" },
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],
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probes: [CAREER_2020_04, REL_2024_05, D24, D12, D9_STYLE_NO_OPTIONS, D10_STYLE_NO_OPTIONS],
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answered_probes: [
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{
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probe_id: CAREER_2020_04.id,
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semantic_key: CAREER_2020_04.semantic_key,
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candidate_split_hash: CAREER_2020_04.candidate_split_hash,
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answer_class: "yes",
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classified_from: "choice",
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},
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{
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probe_id: REL_2024_05.id,
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semantic_key: REL_2024_05.semantic_key,
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candidate_split_hash: REL_2024_05.candidate_split_hash,
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answer_class: "yes",
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classified_from: "choice",
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},
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],
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});
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const dossier: DecisionDossier = {
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evidence: EVIDENCE,
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conversationSummary: { activeFocus: null, declinedSkippedTopics: [] },
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latestResult: {
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resultId: "55555555-5555-4555-8555-555555555555",
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candidates: state.candidates.map((item) => ({
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candidateId: item.id,
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time: item.time,
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rank: item.rank,
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relativeSupport: Math.round(item.posterior_score),
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})),
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representativeTime: state.representative_time,
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evidenceLedgerFingerprint: evidenceLedgerFingerprint(EVIDENCE as never),
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decisionReceipt: {
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inference_state: state,
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evidence_collection_probes: [FAMILY_2021_COLLECT],
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},
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},
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case: { acceptedTime: null },
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};
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return { dossier, state };
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}
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test("deadlock case: decision and followup agree and still render a D9/D10 style card", () => {
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const { dossier } = deadlockDossier();
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const inspected = inspectDiscriminatorProbes(contrastPacketFromDossier(dossier));
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assert.equal(inspected.selected?.semanticKey, D9_STYLE_NO_OPTIONS.semantic_key);
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const d9 = withCompletedContrastOptions(contrastFromConflict(D9_STYLE_NO_OPTIONS));
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const d10 = withCompletedContrastOptions(contrastFromConflict(D10_STYLE_NO_OPTIONS));
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assert.equal(d9.ok, true);
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assert.equal(d10.ok, true);
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if (d9.ok) assert.equal(d9.probe.choiceKind, "varga_style");
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if (d10.ok) assert.equal(d10.probe.choiceKind, "varga_style");
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const catalog = rectificationFollowupCatalog(dossier.latestResult, dossier.evidence);
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const plan = buildMethodFollowupPlan({
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evidence: dossier.evidence,
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declinedTopics: dossier.conversationSummary.declinedSkippedTopics,
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sessionOutcome: "discriminate_candidates",
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...catalog,
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candidatesSeparated: false,
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});
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assert.equal(followupAsksRenderableDiscriminator(plan.next_followup), true);
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assert.equal(plan.next_followup?.intent, "distinguish_candidates");
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assert.ok(plan.next_followup?.choice_frame);
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assert.equal(plan.next_followup?.choice_kind, "varga_style");
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assert.match(plan.next_followup?.semantic_key ?? "", /^varga\.d(9|10)\./);
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assert.equal(
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plan.dropped_probes.some((item) => item.semantic_key.startsWith("varga.d9.") || item.semantic_key.startsWith("varga.d10.")),
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false,
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JSON.stringify(plan.dropped_probes),
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);
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const decision = decideFromDossier(dossier);
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assert.equal(decision.nextAction, "ask_candidate_discriminator");
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assert.equal(decision.sessionOutcome, "discriminate_candidates");
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assert.equal(decision.canConfirmExactMinute, false);
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});
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test("varga_style probes missing style_options complete the same way in inspect and followup ranking", () => {
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const probe = contrastFromConflict(D9_STYLE_NO_OPTIONS);
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const inspected = inspectDiscriminatorProbes({
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candidateSetVersion: "05:00-05:10",
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vargaDifferences: [],
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probes: [probe],
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});
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assert.ok(inspected.selected);
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assert.equal(inspected.selected?.choiceKind, "varga_style");
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assert.equal(inspected.dropped.length, 0);
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const plan = buildMethodFollowupPlan({
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evidence: EVIDENCE,
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sessionOutcome: "discriminate_candidates",
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candidatesSeparated: false,
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contrastPacket: {
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candidateSetVersion: "05:00-05:10",
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vargaDifferences: [],
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probes: [probe],
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},
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});
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assert.equal(plan.next_followup?.semantic_key, probe.semanticKey);
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assert.equal(plan.next_followup?.choice_kind, "varga_style");
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assert.ok(plan.next_followup?.choice_frame);
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assert.equal(plan.dropped_probes.some((item) => item.semantic_key === probe.semanticKey), false);
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});
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test("method coverage incomplete still asks a renderable D9 style card", () => {
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const { dossier } = deadlockDossier();
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const decision = decideFromDossier(dossier);
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const catalog = rectificationFollowupCatalog(dossier.latestResult, dossier.evidence);
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const plan = buildMethodFollowupPlan({
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evidence: dossier.evidence,
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declinedTopics: [],
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sessionOutcome: "discriminate_candidates",
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...catalog,
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candidatesSeparated: false,
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});
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assert.equal(plan.methods.find((item) => item.method_id === "relatives")?.status, "uncovered");
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assert.equal(plan.methods.find((item) => item.method_id === "occupation")?.status, "uncovered");
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assert.equal(decision.nextAction, "ask_candidate_discriminator");
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assert.equal(plan.next_followup?.intent, "distinguish_candidates");
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assert.ok(plan.next_followup?.choice_frame);
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assert.notEqual(plan.next_followup?.source, "method_coverage");
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assert.equal(decision.probe?.semanticKey, plan.next_followup?.semantic_key);
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});
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test("clock-key varga_style without signs fail-closes to dated family collect instead of a dead-end discriminator", () => {
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const d9Clock: ConflictProbe = {
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...D9_STYLE_NO_OPTIONS,
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id: "contrast:varga.d9.05:00/05:10",
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semantic_key: "varga.d9.05:00/05:10",
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candidate_split_hash: "varga.d9.05:00/05:10",
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};
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const d10Clock: ConflictProbe = {
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...D10_STYLE_NO_OPTIONS,
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id: "contrast:varga.d10.05:00/05:10",
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semantic_key: "varga.d10.05:00/05:10",
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candidate_split_hash: "varga.d10.05:00/05:10",
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};
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const { dossier } = deadlockDossier();
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const state = buildInferenceState({
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range_start: "05:00",
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range_end: "05:10",
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candidates: [
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{ id: "05:00", time: "05:00", relative_support: 18 },
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{ id: "05:10", time: "05:10", relative_support: 16 },
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],
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events: [
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{ id: "e-career-entry", domain: "career", year: 2020, precision: "month" },
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{ id: "e-career-exit", domain: "career", year: 2020, precision: "month" },
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{ id: "e-rel-start", domain: "relationship", year: 2024, precision: "month" },
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{ id: "e-rel-end", domain: "relationship", year: 2024, precision: "day" },
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],
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probes: [CAREER_2020_04, REL_2024_05, D24, D12, d9Clock, d10Clock],
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answered_probes: [
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{
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probe_id: CAREER_2020_04.id,
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semantic_key: CAREER_2020_04.semantic_key,
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candidate_split_hash: CAREER_2020_04.candidate_split_hash,
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answer_class: "yes",
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classified_from: "choice",
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},
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{
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probe_id: REL_2024_05.id,
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semantic_key: REL_2024_05.semantic_key,
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candidate_split_hash: REL_2024_05.candidate_split_hash,
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answer_class: "yes",
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classified_from: "choice",
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},
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],
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});
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const clockDossier: DecisionDossier = {
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...dossier,
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latestResult: {
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...dossier.latestResult!,
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decisionReceipt: {
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inference_state: state,
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evidence_collection_probes: [FAMILY_2021_COLLECT],
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},
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},
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};
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const inspected = inspectDiscriminatorProbes(contrastPacketFromDossier(clockDossier));
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assert.equal(inspected.selected, null);
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assert.equal(inspected.dropped.some((item) => (
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item.semantic_key === d9Clock.semantic_key && item.reason === "yearless_ungrounded_contrast"
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)), true);
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const d9 = withCompletedContrastOptions(contrastFromConflict(d9Clock));
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assert.equal(d9.ok, true);
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if (d9.ok) assert.equal(d9.probe.choiceKind, "existence");
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const catalog = rectificationFollowupCatalog(clockDossier.latestResult, clockDossier.evidence);
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const plan = buildMethodFollowupPlan({
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evidence: clockDossier.evidence,
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declinedTopics: [],
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sessionOutcome: "discriminate_candidates",
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...catalog,
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candidatesSeparated: false,
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});
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assert.equal(followupAsksRenderableDiscriminator(plan.next_followup), 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.notEqual(plan.next_followup?.semantic_key, d9Clock.semantic_key);
|
|
assert.notEqual(plan.next_followup?.semantic_key, d10Clock.semantic_key);
|
|
|
|
const decision = decideFromDossier(clockDossier);
|
|
assert.notEqual(decision.nextAction, "ask_candidate_discriminator");
|
|
assert.equal(decision.probe, null);
|
|
assert.equal(decision.canConfirmExactMinute, false);
|
|
});
|
|
|
|
test("unrenderable contrast probes are recorded in dropped_probes instead of being dropped silently", () => {
|
|
const unrenderable = contrastFromConflict({
|
|
...D9_STYLE_NO_OPTIONS,
|
|
information_gain: 0,
|
|
semantic_key: "varga.d9.dropped",
|
|
candidate_split_hash: "varga.d9.dropped",
|
|
id: "contrast:varga.d9.dropped",
|
|
});
|
|
const inspected = inspectDiscriminatorProbes({
|
|
candidateSetVersion: "05:00-05:10",
|
|
vargaDifferences: [],
|
|
probes: [unrenderable],
|
|
});
|
|
assert.equal(inspected.selected, null);
|
|
assert.equal(inspected.dropped.some((item) => item.semantic_key === unrenderable.semanticKey), true);
|
|
|
|
const plan = buildMethodFollowupPlan({
|
|
evidence: EVIDENCE,
|
|
sessionOutcome: "discriminate_candidates",
|
|
candidatesSeparated: false,
|
|
contrastPacket: {
|
|
candidateSetVersion: "05:00-05:10",
|
|
vargaDifferences: [],
|
|
probes: [unrenderable],
|
|
},
|
|
});
|
|
assert.equal(
|
|
plan.dropped_probes.some((item) => item.semantic_key === unrenderable.semanticKey),
|
|
true,
|
|
JSON.stringify(plan.dropped_probes),
|
|
);
|
|
});
|
|
|
|
test("agent message path no longer returns the discriminator dead-end copy", () => {
|
|
const route = readFileSync(new URL("../src/app/api/rectification/agent/route.ts", import.meta.url), "utf8");
|
|
const fastPath = route.slice(
|
|
route.indexOf('if (action === "message")'),
|
|
route.indexOf("const requestTime"),
|
|
);
|
|
assert.doesNotMatch(fastPath, /目前没有可继续区分/);
|
|
assert.match(route, /nonConvergingRangeNarration/);
|
|
assert.match(fastPath, /persistNextInterviewIfIdle/);
|
|
assert.match(fastPath, /!plan\.next_followup/);
|
|
assert.ok(fastPath.includes("ask_candidate_discriminator"));
|
|
assert.match(fastPath, /isRenderableChoiceOpenQuestion\(open\)/);
|
|
assert.ok(route.indexOf("classifyRectificationTurnIntent") < route.indexOf("runV9AgentTurn({"));
|
|
});
|
|
|
|
test("decision.probe matches the followup the plan will actually ask", () => {
|
|
const { dossier } = deadlockDossier();
|
|
const catalog = rectificationFollowupCatalog(dossier.latestResult, dossier.evidence);
|
|
const plan = buildMethodFollowupPlan({
|
|
evidence: dossier.evidence,
|
|
declinedTopics: dossier.conversationSummary.declinedSkippedTopics,
|
|
sessionOutcome: "discriminate_candidates",
|
|
...catalog,
|
|
candidatesSeparated: false,
|
|
});
|
|
const decision = decideFromDossier(dossier);
|
|
assert.equal(decision.nextAction, "ask_candidate_discriminator");
|
|
assert.equal(decision.probe?.semanticKey, plan.next_followup?.semantic_key);
|
|
assert.match(plan.next_followup?.semantic_key ?? "", /^varga\.d(9|10)\./);
|
|
assert.notEqual(decision.probe?.semanticKey, D24.semantic_key);
|
|
});
|
|
|
|
test("birthDate gating matches the real followup plan on an under-age event probe", () => {
|
|
const { dossier: base } = deadlockDossier();
|
|
const childhood = {
|
|
year: 2015,
|
|
year_label: "2015 年前后",
|
|
domain: "career" as const,
|
|
event_family: "入职或职责变化",
|
|
source: "dasha_activation" as const,
|
|
tracks: ["vimshottari", "narayana"] as const,
|
|
tracks_agree: true,
|
|
unique_minute_claim: false as const,
|
|
user_meaning: "2015 年前后有没有入职或职责变化？",
|
|
role: "distinguish" as const,
|
|
phase: "candidate_discriminator" as const,
|
|
information_gain: 3.2,
|
|
semantic_key: "career.2015.dasha_activation",
|
|
candidate_split_hash: "career.2015",
|
|
candidate_ids: ["05:00", "05:10"] as const,
|
|
expected_outcomes: [
|
|
{ answer_class: "yes", supports: ["05:00"], conflicts: ["05:10"] },
|
|
{ answer_class: "no", supports: ["05:10"], conflicts: ["05:00"] },
|
|
],
|
|
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" },
|
|
],
|
|
choice_kind: "existence" as const,
|
|
};
|
|
const dossier: DecisionDossier = {
|
|
...base,
|
|
latestResult: base.latestResult
|
|
? {
|
|
...base.latestResult,
|
|
decisionReceipt: {
|
|
...(base.latestResult.decisionReceipt ?? {}),
|
|
discriminating_event_probes: [childhood],
|
|
},
|
|
}
|
|
: null,
|
|
};
|
|
const birthDate = "2010-01-15";
|
|
const catalog = rectificationFollowupCatalog(dossier.latestResult, dossier.evidence);
|
|
const planOpen = buildMethodFollowupPlan({
|
|
evidence: dossier.evidence,
|
|
declinedTopics: [],
|
|
sessionOutcome: "discriminate_candidates",
|
|
...catalog,
|
|
candidatesSeparated: false,
|
|
});
|
|
const planGated = buildMethodFollowupPlan({
|
|
evidence: dossier.evidence,
|
|
declinedTopics: [],
|
|
sessionOutcome: "discriminate_candidates",
|
|
...catalog,
|
|
birthDate,
|
|
candidatesSeparated: false,
|
|
});
|
|
assert.equal(planOpen.next_followup?.semantic_key, childhood.semantic_key);
|
|
assert.notEqual(planGated.next_followup?.semantic_key, childhood.semantic_key);
|
|
const decision = decideFromDossier(dossier, { birthDate });
|
|
assert.equal(
|
|
decision.nextAction === "ask_candidate_discriminator",
|
|
followupAsksRenderableDiscriminator(planGated.next_followup),
|
|
);
|
|
if (followupAsksRenderableDiscriminator(planGated.next_followup)) {
|
|
assert.equal(decision.probe?.semanticKey, planGated.next_followup?.semantic_key);
|
|
} else {
|
|
assert.equal(decision.nextAction, "ask_fact_collection");
|
|
}
|
|
});
|