#376

Reviewed-on: #375
Co-authored-by: miteruzo <miteruzo@naver.com>
Co-committed-by: miteruzo <miteruzo@naver.com>

AGENTS.md

@@ -125,6 +125,64 @@ npm run preview
 - TypeScript and TSX use 4-space logical indentation.
 - In TypeScript and TSX only, replace every leading run of 8 spaces with a tab.
 - Tabs are only for leading indentation, never for spaces after non-space text.
+- TypeScript and TSX imports may stay on one line if they remain within the
+  line limit; do not expand short type-only imports mechanically.
+- In TypeScript and TSX, when a function takes one destructured object
+  argument plus an inline type, prefer this shape when it fits locally:
+
+```ts
+const helper = (
+    { value, flag }: { value: string
+		       flag:  boolean },
+): Result => {
+  // ...
+}
+```
+
+- In TypeScript and TSX, put `switch` case block braces on their own lines
+  when a case needs a lexical block:
+
+```ts
+case 'yes':
+case 'no':
+  {
+    const expected = valueFor (item)
+    return expected == null || expected === answer
+  }
+```
+
+- In TypeScript and TSX, use `value == null` and `value != null` as the
+  default nullish checks. Do not use `=== null`, `=== undefined`,
+  `!== null`, or `!== undefined`.
+- If code appears to need a distinction between `null` and `undefined`, treat
+  that as a design smell and revise the logic to avoid the distinction.
+  External library APIs that explicitly require distinguishing the two are the
+  only exception.
+- In TypeScript and TSX, keep short arrays on one line when they fit under the
+  line limit; break arrays only when readability or line length requires it.
+- In TypeScript and TSX, when a ternary expression is split across multiple
+  lines, align `?` and `:` with the condition expression. Do not indent `?` and
+  `:` one extra level under the condition.
+
+```ts
+const value =
+    condition
+    ? consequent
+    : alternate
+```
+
+- In TypeScript and TSX, keep short ternary expressions on one line when they
+  fit cleanly under the line limit.
+- In TypeScript and TSX, prefer ternary expressions for simple conditional
+  value selection. Do not replace a clear ternary with `if` statements, and do
+  not introduce immediately invoked functions just to avoid or reformat a
+  ternary expression.
+- In TypeScript and TSX, do not write `let` followed by later `if` assignments
+  when the value can be expressed as a single `const` initializer. Prefer
+  `const` because it prevents accidental later reassignment.
+- When fixing formatting, change formatting only. Do not change expression
+  structure, control flow, or variable mutability unless the requested style
+  explicitly requires it.
 - Do not add production dependencies without explicit approval.
 - Do not create, modify, or run tests unless the user explicitly asks for
   test work. When the user asks for tests, keep working and rerun them until

backend/app/controllers/gekanator_games_controller.rb

@@ -50,7 +50,7 @@ class GekanatorGamesController < ApplicationController
             questions,
             post_id: game.correct_post_id,
             user: current_user,
-            limit: 2)
+            limit: 6)
 
     render json: {
       questions: selected.map { |question| extra_question_json(question) }

backend/spec/models/tag_name_sanitisation_rule_spec.rb

@@ -1,6 +1,10 @@
 require 'rails_helper'
 
 RSpec.describe TagNameSanitisationRule, type: :model do
+  before do
+    described_class.unscoped.delete_all
+  end
+
   describe '.sanitise' do
     before do
       described_class.create!(priority: 10, source_pattern: '_', replacement: '')

backend/spec/requests/gekanator_learning_spec.rb

@@ -206,6 +206,40 @@ RSpec.describe 'Gekanator learning API', type: :request do
       expect(example.gekanator_game_id).to eq(json['id'])
     end
 
+    it 'learns accepted post_similarity answers from main game logs' do
+      sign_in_as admin
+
+      question = create_post_similarity_question!(text: '泣いてる?')
+
+      expect {
+        post '/gekanator/games', params: {
+          guessed_post_id: guessed_post.id,
+          correct_post_id: correct_post.id,
+          answers: [
+            {
+              question_id: "post-similarity:#{question.id}",
+              question_text: '泣いてる?',
+              answer: 'partial',
+              original_answer: 'partial'
+            }
+          ]
+        }
+      }.to change { GekanatorQuestionExample.count }.by(1)
+
+      expect(response).to have_http_status(:created)
+      expect(json['learned_example_count']).to eq(1)
+
+      example = GekanatorQuestionExample.last
+      expect(example).to have_attributes(
+        gekanator_question_id: question.id,
+        post_id: correct_post.id,
+        user_id: admin.id,
+        answer: 'partial',
+        source: 'post_game_answer'
+      )
+      expect(example.gekanator_game_id).to eq(json['id'])
+    end
+
     it 'does not learn fact questions or nico tag questions from main game logs' do
       sign_in_as admin
 
@@ -475,28 +509,59 @@ RSpec.describe 'Gekanator learning API', type: :request do
   end
 
   describe 'GET /gekanator/games/:id/extra_questions' do
-    it 'returns at most two accepted user_suggested post_similarity questions without duplicates' do
+    it 'returns at most six accepted user_suggested post_similarity questions without duplicates' do
       sign_in_as admin
 
+      lowest = create_post_similarity_question!(
+        text: 'lowest?',
+        priority_weight: 0.5
+      )
       low = create_post_similarity_question!(
         text: 'low?',
         priority_weight: 1.0
       )
-      high = create_post_similarity_question!(
-        text: 'high?',
-        priority_weight: 3.0
-      )
       middle = create_post_similarity_question!(
         text: 'middle?',
+        priority_weight: 1.5
+      )
+      medium_high = create_post_similarity_question!(
+        text: 'medium high?',
         priority_weight: 2.0
       )
+      high = create_post_similarity_question!(
+        text: 'high?',
+        priority_weight: 2.5
+      )
+      higher = create_post_similarity_question!(
+        text: 'higher?',
+        priority_weight: 2.8
+      )
+      highest = create_post_similarity_question!(
+        text: 'highest?',
+        priority_weight: 3.0
+      )
+      overflow = create_post_similarity_question!(
+        text: 'overflow?',
+        priority_weight: 2.2
+      )
 
       get "/gekanator/games/#{game.id}/extra_questions"
 
       expect(response).to have_http_status(:ok)
-      expect(json['questions'].length).to eq(2)
+      expect(json['questions'].length).to eq(6)
-      expect(json['questions'].map { _1['id'] }.uniq.length).to eq(2)
+      expect(json['questions'].map { _1['id'] }.uniq.length).to eq(6)
-      expect(json['questions'].map { _1['id'] }).to all(be_in([low.id, high.id, middle.id]))
+      expect(json['questions'].map { _1['id'] }).to all(
+        be_in([
+          lowest.id,
+          low.id,
+          middle.id,
+          medium_high.id,
+          high.id,
+          higher.id,
+          highest.id,
+          overflow.id,
+        ])
+      )
     end
 
     it 'can return questions that already have an example for the correct post' do
@@ -519,6 +584,37 @@ RSpec.describe 'Gekanator learning API', type: :request do
       expect(json['questions'].map { _1['id'] }).to include(existing.id)
     end
 
+    it 'prioritizes questions the current user has not answered' do
+      sign_in_as admin
+
+      answered = create_post_similarity_question!(
+        text: 'already answered?',
+        priority_weight: 3.0
+      )
+      GekanatorQuestionExample.create!(
+        gekanator_question: answered,
+        post: other_post,
+        user: admin,
+        answer: 'yes',
+        source: 'post_game_extra'
+      )
+
+      unanswered =
+          6.times.map { |index|
+            create_post_similarity_question!(
+              text: "unanswered #{index}?",
+              priority_weight: 0.5
+            )
+          }
+
+      get "/gekanator/games/#{game.id}/extra_questions"
+
+      expect(response).to have_http_status(:ok)
+      expect(json['questions'].map { _1['id'] }).to match_array(
+        unanswered.map(&:id)
+      )
+    end
+
     it 'can return questions already asked in the game using snake_case question_id' do
       sign_in_as admin
 

frontend/src/lib/gekanator.test.ts

@@ -4,6 +4,7 @@ import { apiPost } from '@/lib/api'
 import {
   buildGekanatorQuestions,
   expectedAnswerForQuestion,
+  learnedSemanticSideForPost,
   questionIdForCondition,
   restoreGekanatorQuestion,
   saveGekanatorExtraQuestionAnswers,
@@ -188,6 +189,33 @@ describe('expectedAnswerForQuestion', () => {
   })
 })
 
+describe('learnedSemanticSideForPost', () => {
+  it('classifies post_similarity examples as positive, negative, or unknown', () => {
+    const question: StoredGekanatorQuestion = {
+      id: 'post-similarity:10',
+      text: '喜多ちゃんが泣いてる?',
+      kind: 'post_similarity',
+      source: 'user_suggested',
+      priorityWeight: 1.2,
+      condition: {
+        type: 'post-similarity',
+        postId: 123,
+        answer: 'partial',
+        threshold: 0.65,
+      },
+      exampleAnswers: {
+        1: 'yes',
+        2: 'probably_no',
+      },
+    }
+
+    expect(learnedSemanticSideForPost(question, post({ id: 1 }))).toBe('positive')
+    expect(learnedSemanticSideForPost(question, post({ id: 2 }))).toBe('negative')
+    expect(learnedSemanticSideForPost(question, post({ id: 3 }))).toBe('unknown')
+    expect(learnedSemanticSideForPost(question, post({ id: 123 }))).toBe('positive')
+  })
+})
+
 describe('restoreGekanatorQuestion', () => {
   it('uses default source and priority weight when omitted', () => {
     const question = restoreGekanatorQuestion({
@@ -248,7 +276,7 @@ describe('restoreGekanatorQuestion', () => {
     })
 
     expect(question.test(post({ id: 1 }))).toBe(true)
-    expect(question.test(post({ id: 2 }))).toBe(false)
+    expect(question.test(post({ id: 2 }))).toBe(true)
   })
 
   it('normalizes legacy title-length-greater-than questions', () => {
@@ -372,6 +400,10 @@ describe('Gekanator API writers', () => {
           type: 'tag',
           key: 'character:喜多郁代',
         },
+        questionMode: 'normal',
+        questionPurpose: 'effective_user_suggested',
+        effectiveQuestion: true,
+        learningQuestion: false,
         answer: 'yes',
         originalAnswer: 'partial',
       },
@@ -396,6 +428,10 @@ describe('Gekanator API writers', () => {
             type: 'tag',
             key: 'character:喜多郁代',
           },
+          question_mode: 'normal',
+          question_purpose: 'effective_user_suggested',
+          effective_question: true,
+          learning_question: false,
           answer: 'yes',
           original_answer: 'partial',
         },

frontend/src/lib/gekanator.ts

@@ -9,11 +9,24 @@ export type GekanatorAnswerValue =
     | 'probably_no'
     | 'unknown'
 
+export type LearnedSemanticSide =
+    | 'positive'
+    | 'negative'
+    | 'unknown'
+
+export type GekanatorQuestionPurpose =
+    | 'effective_user_suggested'
+    | 'learning_user_suggested'
+    | 'normal'
+
 export type GekanatorAnswerLog = {
     questionId:       string
     questionText:     string
     questionCondition?:  GekanatorQuestionCondition
     questionMode?:    'normal' | 'winning_run'
+    questionPurpose?: GekanatorQuestionPurpose
+    effectiveQuestion?: boolean
+    learningQuestion?: boolean
     answer:           GekanatorAnswerValue
     originalAnswer:   GekanatorAnswerValue }
 
@@ -163,6 +176,26 @@ const directExampleAnswerFor = (
   return null
 }
 
+
+export const isLearnedSemanticQuestion = (
+    question:  StoredGekanatorQuestion | GekanatorQuestion,
+): boolean =>
+    question.kind === 'post_similarity'
+    && question.source === 'user_suggested'
+
+
+export const learnedSemanticSideForAnswer = (
+    answer:  GekanatorAnswerValue | null,
+): LearnedSemanticSide => {
+  if (answer === 'yes' || answer === 'partial')
+    return 'positive'
+
+  if (answer === 'no' || answer === 'probably_no')
+    return 'negative'
+
+  return 'unknown'
+}
+
 const countBy = <T extends string | number> (values: T[]): Map<T, number> => {
   const counts = new Map<T, number> ()
   values.forEach (value => counts.set (value, (counts.get (value) ?? 0) + 1))
@@ -285,8 +318,8 @@ const questionMatches = (
 ): boolean => {
   const directAnswer = directExampleAnswerFor (question, post)
   if (directAnswer)
-    return question.condition.type === 'post-similarity'
+    return question.kind === 'post_similarity'
-	? directAnswer === question.condition.answer
+	? learnedSemanticSideForAnswer (directAnswer) === 'positive'
 	: directAnswer === 'yes'
 
   switch (question.condition.type)
@@ -328,6 +361,11 @@ export const expectedAnswerForQuestion = (
 
   switch (question.condition.type)
   {
+  case 'post-similarity':
+    if (question.condition.postId === post.id)
+      return question.condition.answer
+
+    return null
   case 'tag':
   case 'source':
   case 'original-year':
@@ -338,12 +376,17 @@ export const expectedAnswerForQuestion = (
   case 'title-has-ascii':
   case 'title-contains':
     return questionMatches (post, question) ? 'yes' : 'no'
-  case 'post-similarity':
-    return null
   }
 }
 
 
+export const learnedSemanticSideForPost = (
+    question:  StoredGekanatorQuestion | GekanatorQuestion | undefined,
+    post:      Post | null,
+): LearnedSemanticSide =>
+    learnedSemanticSideForAnswer (expectedAnswerForQuestion (question, post))
+
+
 export const restoreGekanatorQuestion = (
     question:  StoredGekanatorQuestion,
 ): GekanatorQuestion => {
@@ -423,15 +466,15 @@ export const buildGekanatorQuestions = (
   const originalYears = countBy (
       posts
       .map (originalYearOf)
-      .filter ((year): year is number => year !== null))
+      .filter ((year): year is number => year != null))
   const originalMonths = countBy (
       posts
       .map (originalMonthOf)
-      .filter ((month): month is number => month !== null))
+      .filter ((month): month is number => month != null))
   const originalMonthDays = countBy (
       posts
       .map (originalMonthDayOf)
-      .filter ((monthDay): monthDay is string => monthDay !== null))
+      .filter ((monthDay): monthDay is string => monthDay != null))
   const titleLengthMedian = median (posts.map (post => post.title?.length ?? 0))
   const titleWordCounts =
       includeTitleContains
@@ -593,6 +636,10 @@ export const saveGekanatorGame = async ({
 	    question_id: answer.questionId,
 	    question_text: answer.questionText,
 	    question_condition: answer.questionCondition ?? null,
+	    question_mode: answer.questionMode,
+	    question_purpose: answer.questionPurpose,
+	    effective_question: answer.effectiveQuestion,
+	    learning_question: answer.learningQuestion,
 	    answer: answer.answer,
 	    original_answer: answer.originalAnswer })) })
 

frontend/src/lib/gekanatorCandidateRecovery.test.ts

@@ -11,6 +11,7 @@ import type {
     GekanatorAnswerValue,
     GekanatorQuestion,
 } from '@/lib/gekanator'
+import type { RecoveredCandidateState } from '@/lib/gekanatorCandidateRecovery'
 import type { Post } from '@/types'
 
 
@@ -78,6 +79,15 @@ const answer = (
 })
 
 
+const recoveredState = (
+    answerCountAtRecovery:  number,
+    scoreAtRecovery = 0,
+): RecoveredCandidateState => ({
+  answerCountAtRecovery,
+  scoreAtRecovery,
+})
+
+
 describe('candidatePostsFor', () => {
   it('does not hard-filter semantic post_similarity answers', () => {
     const posts = [post (1), post (2), post (3)]
@@ -99,8 +109,8 @@ describe('candidatePostsFor', () => {
 	softenedQuestionIds: new Set (),
 	rejectedPostIds: new Set (),
 	recoveredCandidatePosts: new Map ([
-	    [1, 1],
+	    [1, recoveredState (1)],
-	    [3, 1],
+	    [3, recoveredState (1)],
 	]) })
 
     expect(candidates.map (candidate => candidate.id)).toEqual ([1, 2, 3])
@@ -122,8 +132,8 @@ describe('candidatePostsFor', () => {
 	softenedQuestionIds: new Set (),
 	rejectedPostIds: new Set (),
 	recoveredCandidatePosts: new Map ([
-	    [1, 1],
+	    [1, recoveredState (1)],
-	    [3, 1],
+	    [3, recoveredState (1)],
 	]) })
 
     expect(candidates.map (candidate => candidate.id)).toEqual ([3])
@@ -142,7 +152,7 @@ describe('candidatePostsFor', () => {
 	answers: [answer (question, 'yes')],
 	softenedQuestionIds: new Set (),
 	rejectedPostIds: new Set ([1]),
-	recoveredCandidatePosts: new Map ([[1, 1]]) })
+	recoveredCandidatePosts: new Map ([[1, recoveredState (1)]]) })
 
     expect(candidates.map (candidate => candidate.id)).toEqual ([2])
   })
@@ -209,7 +219,7 @@ describe('recoverCandidatePosts', () => {
 	posts,
 	scores,
 	rejectedPostIds: new Set ([10]),
-	recoveredCandidatePosts: new Map ([[8, 1]]),
+	recoveredCandidatePosts: new Map ([[8, recoveredState (1, 8)]]),
 	eligiblePostIds: new Set ([9]),
 	answerCountAtRecovery: 2,
 	recoveryStepCount: 0,
@@ -218,7 +228,10 @@ describe('recoverCandidatePosts', () => {
     expect(recovered?.recoveryStepCount).toBe (1)
     expect([...(recovered?.recoveredCandidatePosts.keys () ?? [])])
       .toEqual ([8, 7, 6, 5, 4])
-    expect(recovered?.recoveredCandidatePosts.get (7)).toBe (2)
+    expect(recovered?.recoveredCandidatePosts.get (7)).toEqual ({
+	answerCountAtRecovery: 2,
+	scoreAtRecovery: 7,
+    })
   })
 
   it('does not add posts when recovered and eligible candidates already hit the target', () => {
@@ -230,9 +243,9 @@ describe('recoverCandidatePosts', () => {
 	scores,
 	rejectedPostIds: new Set (),
 	recoveredCandidatePosts: new Map ([
-	    [1, 1],
+	    [1, recoveredState (1, 1)],
-	    [2, 1],
+	    [2, recoveredState (1, 2)],
-	    [3, 1],
+	    [3, recoveredState (1, 3)],
 	]),
 	eligiblePostIds: new Set ([4, 5, 6]),
 	answerCountAtRecovery: 2,

frontend/src/lib/gekanatorCandidateRecovery.ts

@@ -1,53 +1,49 @@
-import { expectedAnswerForQuestion } from '@/lib/gekanator'
+import { isLearnedSemanticQuestion,
+	 learnedSemanticSideForPost } from '@/lib/gekanator'
 
-import type {
+import type { GekanatorAnswerLog, GekanatorAnswerValue, GekanatorQuestion } from '@/lib/gekanator'
-    GekanatorAnswerLog,
-    GekanatorAnswerValue,
-    GekanatorQuestion,
-} from '@/lib/gekanator'
 import type { Post } from '@/types'
 
-
 export type RecoveredCandidatePost = {
     postId:                number
-    answerCountAtRecovery: number }
+    answerCountAtRecovery: number
+    scoreAtRecovery:       number }
+
+export type RecoveredCandidateState = {
+    answerCountAtRecovery: number
+    scoreAtRecovery:       number }
 
 
-const questionIsFactLikeForHardFiltering = (
+const questionSupportsAnswerBasedHardFiltering = (question:  GekanatorQuestion): boolean =>
-    question:  GekanatorQuestion,
+    !(isLearnedSemanticQuestion (question)
-): boolean =>
+      || (question.kind === 'tag'
-    !(question.kind === 'post_similarity'
-      || (
-	  question.kind === 'tag'
 	  && question.condition.type === 'tag'
 	  && !(question.condition.key.startsWith ('nico:'))))
 
 
-export const candidatePostsFor = ({
+export const candidatePostsFor = (
-    posts,
+    { posts,
-    questions,
+      questions,
-    answers,
+      answers,
-    softenedQuestionIds,
+      softenedQuestionIds,
-    rejectedPostIds,
+      rejectedPostIds,
-    recoveredCandidatePosts,
+      recoveredCandidatePosts }: { posts:                   Post[]
-}: {
+				   questions:               GekanatorQuestion[]
-    posts:                   Post[]
+				   answers:                 GekanatorAnswerLog[]
-    questions:               GekanatorQuestion[]
+				   softenedQuestionIds:     Set<string>
-    answers:                 GekanatorAnswerLog[]
+				   rejectedPostIds:         Set<number>
-    softenedQuestionIds:     Set<string>
+				   recoveredCandidatePosts: Map<number, RecoveredCandidateState> },
-    rejectedPostIds:         Set<number>
+): Post[] => {
-    recoveredCandidatePosts: Map<number, number>
-}): Post[] => {
   const questionById = new Map (questions.map (question => [question.id, question]))
 
   return posts.filter (post => {
     if (rejectedPostIds.has (post.id))
       return false
 
-    const answerCountAtRecovery = recoveredCandidatePosts.get (post.id)
+    const recoveredCandidate = recoveredCandidatePosts.get (post.id)
 
     return answers.every ((answer, index) => {
-      if (answerCountAtRecovery !== undefined && index < answerCountAtRecovery)
+      if (recoveredCandidate != null && index < recoveredCandidate.answerCountAtRecovery)
 	return true
 
       if (softenedQuestionIds.has (answer.questionId))
@@ -56,16 +52,19 @@ export const candidatePostsFor = ({
       const question = questionById.get (answer.questionId)
       if (!(question))
 	return true
-      if (!(questionIsFactLikeForHardFiltering (question)))
+      if (!(questionSupportsAnswerBasedHardFiltering (question)))
 	return true
 
       switch (answer.answer)
       {
       case 'yes':
-      case 'no': {
+      case 'no':
-	const expected = expectedAnswerForQuestion (question, post)
+	{
-	return expected === null || expected === 'unknown' || expected === answer.answer
+	  const expected = learnedSemanticSideForPost (question, post)
-      }
+	  return expected === 'unknown'
+	      || (answer.answer === 'yes' && expected === 'positive')
+	      || (answer.answer === 'no' && expected === 'negative')
+	}
       default:
 	return true
       }
@@ -74,33 +73,27 @@ export const candidatePostsFor = ({
 }
 
 
-export const hardFilteredPostsForAnswer = ({
+export const hardFilteredPostsForAnswer = (
-    posts,
+    { posts, question, answer }: { posts:     Post[]
-    question,
+				   question:  GekanatorQuestion
-    answer,
+				   answer:    GekanatorAnswerValue },
-}: {
+): Post[] => {
-    posts:     Post[]
+  if (!(questionSupportsAnswerBasedHardFiltering (question)))
-    question:  GekanatorQuestion
-    answer:    GekanatorAnswerValue
-}): Post[] => {
-  if (!(questionIsFactLikeForHardFiltering (question)))
     return posts
 
   if (!(answer === 'yes' || answer === 'no'))
     return posts
 
   return posts.filter (post => {
-    const expected = expectedAnswerForQuestion (question, post)
+    const side = learnedSemanticSideForPost (question, post)
-    return expected === null || expected === 'unknown' || expected === answer
+    return side === 'unknown'
+	|| (answer === 'yes' && side === 'positive')
+	|| (answer === 'no' && side === 'negative')
   })
 }
 
 
-const concreteAnswerOptions: GekanatorAnswerValue[] = [
+const concreteAnswerOptions: GekanatorAnswerValue[] = ['yes', 'no', 'partial', 'probably_no']
-    'yes',
-    'no',
-    'partial',
-    'probably_no']
 
 
 export const allConcreteAnswerOptionsExhausted = (
@@ -119,53 +112,48 @@ const nextRecoveryTargetSize = (recoveryStepCount: number): number =>
     6 * (2 ** recoveryStepCount)
 
 
-export const recoverCandidatePosts = ({
+export const recoverCandidatePosts = (
-    posts,
+    { posts,
-    scores,
+      scores,
-    rejectedPostIds,
+      rejectedPostIds,
-    recoveredCandidatePosts,
+      recoveredCandidatePosts,
-    eligiblePostIds,
+      eligiblePostIds,
-    answerCountAtRecovery,
+      answerCountAtRecovery,
-    recoveryStepCount,
+      recoveryStepCount }: { posts:                   Post[]
-}: {
+			     scores:                  Map<number, number>
-    posts:                   Post[]
+			     rejectedPostIds:         Set<number>
-    scores:                  Map<number, number>
+			     recoveredCandidatePosts: Map<number, RecoveredCandidateState>
-    rejectedPostIds:         Set<number>
+			     eligiblePostIds:         Set<number>
-    recoveredCandidatePosts: Map<number, number>
+			     answerCountAtRecovery:   number
-    eligiblePostIds:         Set<number>
+			     recoveryStepCount:       number },
-    answerCountAtRecovery:   number
+): { recoveredCandidatePosts: Map<number, RecoveredCandidateState>
-    recoveryStepCount:       number
+     recoveryStepCount:       number } | null => {
-}): {
-    recoveredCandidatePosts: Map<number, number>
-    recoveryStepCount:       number
-} | null => {
   const recovered = new Map (recoveredCandidatePosts)
   const targetSize = nextRecoveryTargetSize (recoveryStepCount)
-  const countedPostIds = new Set ([
+  const countedPostIds = new Set ([...eligiblePostIds, ...recovered.keys ()])
-      ...eligiblePostIds,
-      ...recovered.keys ()])
   const addCount = targetSize - countedPostIds.size
   if (addCount <= 0)
-    return {
+    {
-	recoveredCandidatePosts: recovered,
+      return { recoveredCandidatePosts: recovered,
-	recoveryStepCount: recoveryStepCount + 1 }
+	       recoveryStepCount: recoveryStepCount + 1 }
+    }
 
-  const candidates = posts
+  const candidates =
-      .filter (post =>
+      posts
-	  !(rejectedPostIds.has (post.id))
+      .filter (post => (!(rejectedPostIds.has (post.id))
-	  && !(eligiblePostIds.has (post.id))
+			&& !(eligiblePostIds.has (post.id))
-	  && !(recovered.has (post.id)))
+			&& !(recovered.has (post.id))))
-      .sort ((a, b) =>
+      .sort ((a, b) => ((scores.get (b.id) ?? Number.NEGATIVE_INFINITY)
-	  (scores.get (b.id) ?? Number.NEGATIVE_INFINITY)
+			- (scores.get (a.id) ?? Number.NEGATIVE_INFINITY)))
-	      - (scores.get (a.id) ?? Number.NEGATIVE_INFINITY))
       .slice (0, addCount)
 
   if (candidates.length === 0)
     return null
 
-  candidates.forEach (post => recovered.set (post.id, answerCountAtRecovery))
+  candidates.forEach (post => recovered.set (post.id, {
+      answerCountAtRecovery,
+      scoreAtRecovery: scores.get (post.id) ?? 0 }))
 
-  return {
+  return { recoveredCandidatePosts: recovered,
-      recoveredCandidatePosts: recovered,
+	   recoveryStepCount: recoveryStepCount + 1 }
-      recoveryStepCount: recoveryStepCount + 1 }
 }

frontend/src/pages/GekanatorPage.test.tsx

@@ -60,6 +60,14 @@ const gekanatorBackdropSource = gekanatorPageSource.slice (
     gekanatorPageSource.indexOf ('const GekanatorBackdrop'),
     gekanatorPageSource.indexOf ('const expectedAnswerFor'))
 
+const gekanatorChooseQuestionSource = gekanatorPageSource.slice (
+    gekanatorPageSource.indexOf ('const chooseQuestion'),
+    gekanatorPageSource.indexOf ('const winningRunPriorityFor'))
+
+const gekanatorFallbackQuestionSource = gekanatorPageSource.slice (
+    gekanatorPageSource.indexOf ('const chooseFallbackQuestion'),
+    gekanatorPageSource.indexOf ('const shouldEnterGuessPhase'))
+
 
 describe('GekanatorBackdrop regression structure', () => {
   it('keeps displayedBackdropMode as the render-time source of truth', () => {
@@ -103,6 +111,30 @@ describe('GekanatorBackdrop regression structure', () => {
 })
 
 
+describe('Gekanator question selection regression structure', () => {
+  it('prefers normal questions after user_suggested quota has been met', () => {
+    const normalFallbackIndex = gekanatorChooseQuestionSource.indexOf (
+      'else if (normalPool.length > 0)')
+    const effectiveFallbackIndex = gekanatorChooseQuestionSource.indexOf (
+      'else if (effectiveUserSuggestedPool.length > 0)')
+
+    expect(normalFallbackIndex).toBeGreaterThan(0)
+    expect(effectiveFallbackIndex).toBeGreaterThan(0)
+    expect(normalFallbackIndex).toBeLessThan(effectiveFallbackIndex)
+  })
+
+  it('does not let fallback questions bypass user_suggested purpose tracking', () => {
+    expect(gekanatorFallbackQuestionSource).toContain (
+      "question.source !== 'user_suggested'")
+  })
+
+  it('does not show a fixed extra-question count in the extra learning UI', () => {
+    expect(gekanatorPageSource).not.toContain ('追加で 2 問まで答えてください。')
+    expect(gekanatorPageSource).toContain ('追加で質問に答えてください。')
+  })
+})
+
+
 describe('isQuestionHardFilteredAfterAnswers', () => {
   it('blocks only contradictory or redundant month questions after a yes answer', () => {
     const previous: GekanatorQuestionCondition = { type: 'original-month', month: 12 }