From 044880f7bda686f2ae91be019f5fc7dd5a484f46 Mon Sep 17 00:00:00 2001
From: Ethan <ethanj@gmail.com>
Date: Mon, 27 Apr 2026 11:13:47 -0700
Subject: [PATCH 1/4] Fix current-state ranking for temporal comparisons

---
 .../__tests__/current-state-ranking.test.ts   |   9 +
 src/services/current-state-ranking.ts         |   9 +-
 src/services/query-keyword-matches.ts         |  60 +++-
 src/services/temporal-endpoint-evidence.ts    | 262 +++++++++++++++++-
 4 files changed, 325 insertions(+), 15 deletions(-)

diff --git a/src/services/__tests__/current-state-ranking.test.ts b/src/services/__tests__/current-state-ranking.test.ts
index 6fcbd1d..9444511 100644
--- a/src/services/__tests__/current-state-ranking.test.ts
+++ b/src/services/__tests__/current-state-ranking.test.ts
@@ -71,6 +71,15 @@ describe('isCurrentStateQuery', () => {
     expect(isCurrentStateQuery('How much weight have I lost so far?')).toBe(true);
   });
 
+  it('rejects temporal comparison quantity queries', () => {
+    expect(isCurrentStateQuery(
+      "How many months lapsed between Sam's first and second doctor's appointment?",
+    )).toBe(false);
+    expect(isCurrentStateQuery(
+      'How long did James and Samantha date before deciding to move in together?',
+    )).toBe(false);
+  });
+
   it('blocks quantity starters that contain historical markers', () => {
     expect(isCurrentStateQuery('How many things did I previously own?')).toBe(false);
     expect(isCurrentStateQuery('How often did I used to run?')).toBe(false);
diff --git a/src/services/current-state-ranking.ts b/src/services/current-state-ranking.ts
index 1e9302a..64aa6cf 100644
--- a/src/services/current-state-ranking.ts
+++ b/src/services/current-state-ranking.ts
@@ -30,6 +30,10 @@ const CURRENT_DOMAIN_MARKERS = [
  * itself implies current-state intent.
  */
 const QUANTITY_STARTERS = ['how many ', 'how often ', 'how long ', 'how much '];
+const TEMPORAL_COMPARISON_MARKERS = [
+  ' between ', ' first ', ' second ', ' before ', ' after ',
+  ' elapsed ', ' lapsed ', ' passed ',
+];
 
 /**
  * Markers in result content indicating the fact describes current state.
@@ -127,7 +131,10 @@ export function isCurrentStateQuery(query: string): boolean {
   const padded = ` ${query.toLowerCase()} `;
   if (HISTORICAL_QUERY_MARKERS.some((marker) => padded.includes(marker))) return false;
   if (CURRENT_QUERY_MARKERS.some((marker) => padded.includes(marker))) return true;
-  if (QUANTITY_STARTERS.some((starter) => padded.trimStart().startsWith(starter))) return true;
+  if (QUANTITY_STARTERS.some((starter) => padded.trimStart().startsWith(starter))) {
+    const isTemporalComparison = TEMPORAL_COMPARISON_MARKERS.some((marker) => padded.includes(marker));
+    return !isTemporalComparison;
+  }
   const startsWithQuestionWord = CURRENT_QUERY_STARTERS.some((starter) => padded.startsWith(starter));
   return startsWithQuestionWord && CURRENT_DOMAIN_MARKERS.some((marker) => padded.includes(marker));
 }
diff --git a/src/services/query-keyword-matches.ts b/src/services/query-keyword-matches.ts
index 3c56ee3..c63545a 100644
--- a/src/services/query-keyword-matches.ts
+++ b/src/services/query-keyword-matches.ts
@@ -2,7 +2,63 @@
  * Shared query keyword matching utilities for retrieval-time reranking.
  */
 
+const IRREGULAR_KEYWORD_NORMALIZATION: Record<string, string> = {
+  won: 'win',
+  winning: 'win',
+  met: 'meet',
+  meeting: 'meet',
+  began: 'begin',
+  begun: 'begin',
+  started: 'start',
+  starting: 'start',
+  moved: 'move',
+  moving: 'move',
+  dated: 'date',
+  dating: 'date',
+  adopted: 'adopt',
+  adopting: 'adopt',
+  adoption: 'adopt',
+  expanded: 'expand',
+  expanding: 'expand',
+};
+const KEYWORD_STEM_SUFFIXES = ['ing', 'ed', 'es', 's'];
+
+/** Collapse light verb-form differences so event matching is less brittle. */
+export function normalizeKeywordToken(token: string): string {
+  const irregular = IRREGULAR_KEYWORD_NORMALIZATION[token];
+  if (irregular) return irregular;
+  for (const suffix of KEYWORD_STEM_SUFFIXES) {
+    if (token.length > suffix.length + 2 && token.endsWith(suffix)) {
+      return token.slice(0, -suffix.length);
+    }
+  }
+  return token;
+}
+
+/** Normalize free text into a whitespace-joined token string. */
+function normalizeKeywordText(text: string): string {
+  return text
+    .toLowerCase()
+    .replace(/\b([a-z]+)'s\b/g, '$1')
+    .replace(/[^a-z0-9\s-]/g, ' ')
+    .split(/\s+/)
+    .filter(Boolean)
+    .map(normalizeKeywordToken)
+    .join(' ');
+}
+
 export function countKeywordMatches(content: string, keywords: string[]): number {
-  const lower = content.toLowerCase();
-  return keywords.filter((keyword) => lower.includes(keyword)).length;
+  const normalizedContent = normalizeKeywordText(content);
+  const contentTokens = new Set(normalizedContent.split(/\s+/).filter(Boolean));
+  const normalizedKeywords = [...new Set(
+    keywords
+      .map(normalizeKeywordText)
+      .filter(Boolean),
+  )];
+
+  return normalizedKeywords.filter((keyword) => (
+    keyword.includes(' ')
+      ? normalizedContent.includes(keyword)
+      : contentTokens.has(keyword)
+  )).length;
 }
diff --git a/src/services/temporal-endpoint-evidence.ts b/src/services/temporal-endpoint-evidence.ts
index 8b53e67..047babf 100644
--- a/src/services/temporal-endpoint-evidence.ts
+++ b/src/services/temporal-endpoint-evidence.ts
@@ -1,23 +1,48 @@
 /**
- * Query-aware temporal endpoint evidence formatting.
+ * Query-aware temporal evidence formatting.
  *
- * Produces a compact first/second endpoint block for repeated-event temporal
- * questions, such as "How many months lapsed between the first and second
- * doctor's appointment?". The formatter only emits when retrieved memories
- * contain two distinct dates that match the event terms in the query.
+ * Produces compact date-bearing evidence blocks for temporal questions. For
+ * repeated-event comparisons it emits explicit first/second endpoints; for
+ * broader temporal questions it emits a small set of high-overlap candidate
+ * memories with their dates.
  */
 
 import type { SearchResult } from '../db/memory-repository.js';
 import { formatDateLabel, formatDuration } from './temporal-format.js';
 
 const REPEATED_EVENT_QUERY = /\bbetween\b[\s\S]*\bfirst\b[\s\S]*\bsecond\b|\bfirst\b[\s\S]*\bsecond\b/i;
+const TEMPORAL_QUERY = /\b(when|how long|how many months|how many years|how many weeks|how many days|between|before|after|as of|recently)\b/i;
+const DURATION_QUERY = /\b(how long|how many months|how many years|how many weeks|how many days|between|before|after)\b/i;
 const EVIDENCE_MAX_CHARS = 160;
 const QUERY_TERM_MIN_LENGTH = 4;
+const GENERAL_TEMPORAL_LIMIT = 3;
+const STEM_SUFFIXES = ['ing', 'ed', 'es', 's'];
+const SUBJECT_MATCH_BONUS = 2;
+const EVENT_GROUP_MATCH_BONUS = 2;
+const PLANNING_PENALTY = 3;
+const DURATION_ENDPOINT_LIMIT = 2;
+const PLANNING_MARKERS = [
+  'plan to', 'planned to', 'planning to', 'going to', 'will ', 'wants to',
+  'want to', 'thinking of', 'thinking about', 'considering', 'decided to make',
+  'make a new appointment', 'book a new appointment',
+];
+const QUERY_SUBJECT_STOP_WORDS = new Set([
+  'When', 'What', 'Where', 'Why', 'How', 'Who', 'Which',
+  'Did', 'Does', 'Do', 'Has', 'Have', 'Had',
+  'The', 'A', 'An', 'As', 'Of', 'And', 'Or',
+  'First', 'Second',
+]);
+const MONTH_NAMES = new Set([
+  'January', 'February', 'March', 'April', 'May', 'June',
+  'July', 'August', 'September', 'October', 'November', 'December',
+]);
 
 const QUERY_EVENT_STOP_WORDS = new Set([
   'between', 'first', 'second', 'months', 'month', 'weeks', 'week',
   'days', 'many', 'much', 'long', 'lapsed', 'elapsed', 'passed',
   'what', 'when', 'where', 'which', 'with', 'from', 'that', 'this',
+  'before', 'after', 'recently', 'start', 'started', 'plan', 'planned',
+  'recent', 'current', 'present', 'did', 'does', 'have', 'been',
 ]);
 
 const EVENT_SYNONYMS: Record<string, string[]> = {
@@ -25,6 +50,27 @@ const EVENT_SYNONYMS: Record<string, string[]> = {
   doctor: ['doctor', "doctor's", 'doctors', 'doc', 'medical', 'health'],
 };
 
+const IRREGULAR_NORMALIZATION: Record<string, string> = {
+  won: 'win',
+  winning: 'win',
+  met: 'meet',
+  meeting: 'meet',
+  began: 'begin',
+  begun: 'begin',
+  started: 'start',
+  starting: 'start',
+  moved: 'move',
+  moving: 'move',
+  dated: 'date',
+  dating: 'date',
+  adopted: 'adopt',
+  adopting: 'adopt',
+  adoption: 'adopt',
+  expanded: 'expand',
+  expanding: 'expand',
+  presence: 'present',
+};
+
 /** Reverse index: each synonym → its canonical key. Built once at module load. */
 const SYNONYM_TO_CANONICAL: Map<string, string> = (() => {
   const index = new Map<string, string>();
@@ -49,6 +95,24 @@ interface EndpointCandidate {
  */
 type ConceptGroup = string[];
 
+interface TemporalCandidate {
+  dateKey: string;
+  memory: SearchResult;
+  score: number;
+  subjectMatches: number;
+  eventGroupMatches: number;
+  isPlanningLike: boolean;
+}
+
+export function buildTemporalEvidenceBlock(
+  memories: SearchResult[],
+  query: string,
+): string {
+  const repeatedEventBlock = buildRepeatedEventEndpointBlock(memories, query);
+  if (repeatedEventBlock) return repeatedEventBlock;
+  return buildGeneralTemporalEvidenceBlock(memories, query);
+}
+
 /** Build endpoint lines for repeated-event temporal comparisons. */
 export function buildRepeatedEventEndpointBlock(
   memories: SearchResult[],
@@ -76,6 +140,27 @@ function isRepeatedEventQuery(query: string): boolean {
   return REPEATED_EVENT_QUERY.test(query.toLowerCase());
 }
 
+function buildGeneralTemporalEvidenceBlock(
+  memories: SearchResult[],
+  query: string,
+): string {
+  if (!TEMPORAL_QUERY.test(query.toLowerCase())) return '';
+  const queryTerms = extractGeneralTemporalTerms(query);
+  const subjectTerms = extractQuerySubjects(query);
+  const conceptGroups = extractEventConceptGroups(query);
+  if (queryTerms.length === 0) return '';
+  const candidates = selectGeneralTemporalCandidates(memories, queryTerms, subjectTerms, conceptGroups);
+  if (candidates.length === 0) return '';
+  const endpointLines = buildGeneralDurationEndpointLines(candidates, query);
+  if (endpointLines.length > 0) {
+    return ['Temporal evidence candidates:', ...endpointLines].join('\n');
+  }
+  return [
+    'Temporal evidence candidates:',
+    ...candidates.map((candidate) => formatEndpointLine('matching event', candidate)),
+  ].join('\n');
+}
+
 /**
  * Extract one ConceptGroup per distinct canonical event term in the query.
  * Plural and synonym forms collapse to the same group via SYNONYM_TO_CANONICAL;
@@ -83,13 +168,7 @@ function isRepeatedEventQuery(query: string): boolean {
  */
 function extractEventConceptGroups(query: string): ConceptGroup[] {
   const source = extractOrdinalClauses(query);
-  const rawTerms = source
-    .toLowerCase()
-    .replace(/\b([a-z]+)'s\b/g, '$1')
-    .replace(/[^a-z0-9'\s-]/g, ' ')
-    .split(/\s+/)
-    .filter((term) => term.length >= QUERY_TERM_MIN_LENGTH)
-    .filter((term) => !QUERY_EVENT_STOP_WORDS.has(term));
+  const rawTerms = extractTemporalTerms(source);
 
   const seenCanonicals = new Set<string>();
   const groups: ConceptGroup[] = [];
@@ -112,6 +191,29 @@ function extractOrdinalClauses(query: string): string {
   return pieces.join(' ') || query;
 }
 
+function extractGeneralTemporalTerms(query: string): string[] {
+  return extractTemporalTerms(query);
+}
+
+function extractTemporalTerms(query: string): string[] {
+  return query
+    .toLowerCase()
+    .replace(/\b([a-z]+)'s\b/g, '$1')
+    .replace(/[^a-z0-9'\s-]/g, ' ')
+    .split(/\s+/)
+    .filter((term) => term.length >= QUERY_TERM_MIN_LENGTH)
+    .filter((term) => !QUERY_EVENT_STOP_WORDS.has(term));
+}
+
+function extractQuerySubjects(query: string): string[] {
+  const subjectMatches = query.match(/\b[A-Z][a-z]+(?:'s)?\b/g) ?? [];
+  const normalized = subjectMatches
+    .map((subject) => subject.replace(/'s$/i, ''))
+    .filter((subject) => !QUERY_SUBJECT_STOP_WORDS.has(subject))
+    .filter((subject) => !MONTH_NAMES.has(subject));
+  return [...new Set(normalized.map((subject) => subject.toLowerCase()))];
+}
+
 function findEndpointCandidates(
   memories: SearchResult[],
   conceptGroups: ConceptGroup[],
@@ -122,6 +224,19 @@ function findEndpointCandidates(
     .sort((left, right) => left.memory.created_at.getTime() - right.memory.created_at.getTime());
 }
 
+function selectGeneralTemporalCandidates(
+  memories: SearchResult[],
+  queryTerms: string[],
+  subjectTerms: string[],
+  conceptGroups: ConceptGroup[],
+): TemporalCandidate[] {
+  return memories
+    .map((memory) => scoreGeneralTemporalCandidate(memory, queryTerms, subjectTerms, conceptGroups))
+    .filter((candidate): candidate is TemporalCandidate => candidate !== null)
+    .sort((left, right) => compareGeneralTemporalCandidates(left, right))
+    .slice(0, GENERAL_TEMPORAL_LIMIT);
+}
+
 /**
  * A candidate qualifies only if every concept group in the query has at
  * least one synonym present in the memory's content. Score is the number
@@ -137,6 +252,129 @@ function scoreEndpointCandidate(
   return { dateKey: formatDateLabel(memory.created_at), memory, score: matched };
 }
 
+function scoreGeneralTemporalCandidate(
+  memory: SearchResult,
+  queryTerms: string[],
+  subjectTerms: string[],
+  conceptGroups: ConceptGroup[],
+): TemporalCandidate | null {
+  const lowerContent = memory.content.toLowerCase();
+  const tokenSet = buildNormalizedTokenSet(memory.content);
+  const termMatches = queryTerms.reduce(
+    (total, term) => total + (tokenSet.has(normalizeTemporalTerm(term)) ? 1 : 0),
+    0,
+  );
+  const subjectMatches = countSubjectMatches(lowerContent, subjectTerms);
+  const eventGroupMatches = countMatchedEventGroups(lowerContent, conceptGroups);
+  const isPlanningLike = containsPlanningMarker(lowerContent);
+  const score = termMatches
+    + (subjectMatches * SUBJECT_MATCH_BONUS)
+    + (eventGroupMatches * EVENT_GROUP_MATCH_BONUS)
+    - (isPlanningLike ? PLANNING_PENALTY : 0);
+  if (score <= 0) return null;
+  return {
+    dateKey: formatDateLabel(memory.created_at),
+    memory,
+    score,
+    subjectMatches,
+    eventGroupMatches,
+    isPlanningLike,
+  };
+}
+
+function compareGeneralTemporalCandidates(
+  left: TemporalCandidate,
+  right: TemporalCandidate,
+): number {
+  if (left.score !== right.score) return right.score - left.score;
+  if (left.eventGroupMatches !== right.eventGroupMatches) {
+    return right.eventGroupMatches - left.eventGroupMatches;
+  }
+  if (left.subjectMatches !== right.subjectMatches) {
+    return right.subjectMatches - left.subjectMatches;
+  }
+  if (left.isPlanningLike !== right.isPlanningLike) {
+    return left.isPlanningLike ? 1 : -1;
+  }
+  return left.memory.created_at.getTime() - right.memory.created_at.getTime();
+}
+
+function buildNormalizedTokenSet(content: string): Set<string> {
+  return new Set(
+    content
+      .toLowerCase()
+      .replace(/\b([a-z]+)'s\b/g, '$1')
+      .replace(/[^a-z0-9'\s-]/g, ' ')
+      .split(/\s+/)
+      .filter((token) => token.length >= QUERY_TERM_MIN_LENGTH)
+      .map(normalizeTemporalTerm),
+  );
+}
+
+function normalizeTemporalTerm(term: string): string {
+  const irregular = IRREGULAR_NORMALIZATION[term];
+  if (irregular) return irregular;
+  for (const suffix of STEM_SUFFIXES) {
+    if (term.length > suffix.length + 2 && term.endsWith(suffix)) {
+      return term.slice(0, -suffix.length);
+    }
+  }
+  return term;
+}
+
+function buildGeneralDurationEndpointLines(
+  candidates: TemporalCandidate[],
+  query: string,
+): string[] {
+  if (!DURATION_QUERY.test(query.toLowerCase())) return [];
+  const selected = selectDurationEndpoints(candidates);
+  if (selected.length < DURATION_ENDPOINT_LIMIT) return [];
+  return [
+    formatEndpointLine('earliest matching event', selected[0]),
+    formatEndpointLine('latest matching event', selected[1]),
+    `- elapsed between endpoints: ${formatDuration(diffDays(
+      selected[0].memory.created_at,
+      selected[1].memory.created_at,
+    ))}`,
+  ];
+}
+
+function selectDurationEndpoints(candidates: TemporalCandidate[]): TemporalCandidate[] {
+  const stableCandidates = preferCompletedCandidates(candidates);
+  const byDate = new Map<string, TemporalCandidate>();
+  for (const candidate of stableCandidates) {
+    const existing = byDate.get(candidate.dateKey);
+    if (!existing || compareGeneralTemporalCandidates(candidate, existing) < 0) {
+      byDate.set(candidate.dateKey, candidate);
+    }
+  }
+  const distinct = [...byDate.values()].sort((left, right) =>
+    left.memory.created_at.getTime() - right.memory.created_at.getTime(),
+  );
+  if (distinct.length < DURATION_ENDPOINT_LIMIT) return [];
+  return [distinct[0], distinct[distinct.length - 1]];
+}
+
+function preferCompletedCandidates(candidates: TemporalCandidate[]): TemporalCandidate[] {
+  const completed = candidates.filter((candidate) => !candidate.isPlanningLike);
+  return completed.length >= DURATION_ENDPOINT_LIMIT ? completed : candidates;
+}
+
+function countSubjectMatches(content: string, subjectTerms: string[]): number {
+  return subjectTerms.reduce(
+    (total, subject) => total + (content.includes(subject) ? 1 : 0),
+    0,
+  );
+}
+
+function countMatchedEventGroups(content: string, conceptGroups: ConceptGroup[]): number {
+  return conceptGroups.filter((group) => group.some((synonym) => content.includes(synonym))).length;
+}
+
+function containsPlanningMarker(content: string): boolean {
+  return PLANNING_MARKERS.some((marker) => content.includes(marker));
+}
+
 function selectDistinctDateEndpoints(candidates: EndpointCandidate[]): EndpointCandidate[] {
   const byDate = new Map<string, EndpointCandidate>();
   for (const candidate of candidates) {

From f0b39bdfb8088b84ac5f7d6efb16711cd1c63425 Mon Sep 17 00:00:00 2001
From: Ethan <ethanj@gmail.com>
Date: Mon, 27 Apr 2026 11:13:57 -0700
Subject: [PATCH 2/4] Add tournament-win supplemental extraction coverage

---
 src/services/__tests__/quick-extraction.test.ts      | 11 +++++++++++
 .../__tests__/supplemental-extraction.test.ts        | 12 ++++++++++++
 src/services/quick-extraction.ts                     |  4 ++--
 3 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/src/services/__tests__/quick-extraction.test.ts b/src/services/__tests__/quick-extraction.test.ts
index c1cc62f..7fe185d 100644
--- a/src/services/__tests__/quick-extraction.test.ts
+++ b/src/services/__tests__/quick-extraction.test.ts
@@ -113,4 +113,15 @@ describe('quickExtractFacts', () => {
     expect(facts.some((fact) => fact.fact.includes('Nate has had the turtles for 3 years now'))).toBe(true);
     expect(facts.some((fact) => fact.fact.includes('Sam had a check-up with Sam\'s doctor a few days ago'))).toBe(true);
   });
+
+  it('captures tournament wins from conversational first-person event sentences', () => {
+    const facts = quickExtractFacts(
+      [
+        '[Session date: 2022-08-22]',
+        'Nate: Woah Joanna, I won an international tournament yesterday! It was wild.',
+      ].join('\n'),
+    );
+
+    expect(facts.some((fact) => fact.fact.includes('won an international tournament yesterday (on August 21, 2022)'))).toBe(true);
+  });
 });
diff --git a/src/services/__tests__/supplemental-extraction.test.ts b/src/services/__tests__/supplemental-extraction.test.ts
index f105a62..0c9a5fc 100644
--- a/src/services/__tests__/supplemental-extraction.test.ts
+++ b/src/services/__tests__/supplemental-extraction.test.ts
@@ -133,4 +133,16 @@ describe('mergeSupplementalFacts', () => {
     expect(merged.some((fact) => fact.fact.includes('Nate has had the turtles for 3 years now'))).toBe(true);
     expect(merged.some((fact) => fact.fact.includes('Sam had a check-up with Sam\'s doctor a few days ago'))).toBe(true);
   });
+
+  it('backfills tournament-win facts when the primary extractor misses them', () => {
+    const merged = mergeSupplementalFacts(
+      [baseFact({ fact: 'As of August 22 2022, Nate makes a living as a professional gamer and is passionate about his career.' })],
+      [
+        '[Session date: 2022-08-22]',
+        'Nate: Woah Joanna, I won an international tournament yesterday! It was wild.',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact.includes('won an international tournament yesterday (on August 21, 2022)'))).toBe(true);
+  });
 });
diff --git a/src/services/quick-extraction.ts b/src/services/quick-extraction.ts
index 1517c3d..9f7ce00 100644
--- a/src/services/quick-extraction.ts
+++ b/src/services/quick-extraction.ts
@@ -47,11 +47,11 @@ const MONTH_NAMES = [
 ];
 const SPEAKER_PREFIX_PATTERN = /^[A-Z][A-Za-z0-9' -]{1,40}:\s*/;
 const IMPLICIT_FIRST_PERSON_EVENT_PATTERN =
-  /^(?:started|starting|built|building|developed|developing|created|creating|launched|launching|opened|opening|accepted|receiv(?:ed|ing)|got|had|went|attended|visited|reading|posted|hosting|working|looking|planning|taking|took)\b/i;
+  /^(?:started|starting|built|building|developed|developing|created|creating|launched|launching|opened|opening|accepted|receiv(?:ed|ing)|got|had|went|attended|visited|reading|posted|hosting|working|looking|planning|taking|took|won|winning)\b/i;
 
 /** Patterns that indicate a user is stating a fact about themselves. */
 const FIRST_PERSON_PATTERNS = [
-  /\bI\s+(?:am|was|have|had|use|used|like|liked|prefer|preferred|love|loved|hate|hated|need|needed|want|wanted|work|worked|live|lived|study|studied|started|finished|completed|built|created|made|bought|got|moved|joined|left|quit|switched|tried|learned|know|knew|think|thought|believe|believed|feel|felt|plan|planned|decided|chose|picked|signed|enrolled|attended|visited|went|add|added|implement|implemented|submit|submitted|receive|received|take|took|score|scored|launch|launched|apply|applied|consider|considered|advise|advised|recommend|recommended|call|called|focus|focused|support|supported|find|found|design|designed)\b/i,
+  /\bI\s+(?:am|was|have|had|use|used|like|liked|prefer|preferred|love|loved|hate|hated|need|needed|want|wanted|work|worked|live|lived|study|studied|started|finished|completed|built|created|made|bought|got|moved|joined|left|quit|switched|tried|learned|know|knew|think|thought|believe|believed|feel|felt|plan|planned|decided|chose|picked|signed|enrolled|attended|visited|went|add|added|implement|implemented|submit|submitted|receive|received|take|took|score|scored|launch|launched|apply|applied|consider|considered|advise|advised|recommend|recommended|call|called|focus|focused|support|supported|find|found|design|designed|win|won)\b/i,
   /\bmy\s+(?:name|job|role|team|company|project|favorite|preference|goal|plan|background|experience|hobby|family|wife|husband|partner|son|daughter|kid|dog|cat|address|email|phone|stack|setup|workflow|necklace|book|books|song|songs|painting|photo|poster|library|store|pet|pets|bowl)\b/i,
   /\bwe\s+(?:use|used|have|had|built|created|switched|moved|started|decided|chose|plan|are|were)\b/i,
   /\bI['']m\s+(?:a|an|the|from|based|working|building|using|looking|trying|planning|learning|studying|interested|responsible|currently)\b/i,

From 83c7c901e4b19a89629e409a98252b686bb25d0a Mon Sep 17 00:00:00 2001
From: Ethan <ethanj@gmail.com>
Date: Mon, 27 Apr 2026 11:14:06 -0700
Subject: [PATCH 3/4] Improve temporal ranking and packaging evidence

---
 .../__tests__/retrieval-format.test.ts        | 27 ++++++++-
 .../__tests__/subject-aware-ranking.test.ts   | 55 +++++++++++++++++-
 .../temporal-endpoint-evidence.test.ts        | 53 ++++++++++++++++-
 src/services/retrieval-format.ts              | 16 ++---
 src/services/subject-aware-ranking.ts         | 58 +++++++++++++++++--
 5 files changed, 189 insertions(+), 20 deletions(-)

diff --git a/src/services/__tests__/retrieval-format.test.ts b/src/services/__tests__/retrieval-format.test.ts
index 7ed0166..ad83c00 100644
--- a/src/services/__tests__/retrieval-format.test.ts
+++ b/src/services/__tests__/retrieval-format.test.ts
@@ -244,6 +244,29 @@ describe('formatTieredInjection', () => {
     expect(result).toContain('Repeated event endpoints:');
     expect(result).toContain('elapsed between endpoints: ~3 months (83 days)');
   });
+
+  it('suppresses the generic timeline summary when query-aware temporal evidence is present', () => {
+    const memories = [
+      makeResult({ id: 'first', content: "Sam had a doctor's appointment as a wake-up call.", created_at: new Date('2023-05-24T00:00:00Z') }),
+      makeResult({ id: 'second', content: 'Sam had another doctor appointment after changing diet.', created_at: new Date('2023-08-15T00:00:00Z') }),
+      makeResult({ id: 'plan', content: 'Sam decided to make a new appointment in January.', created_at: new Date('2024-01-10T00:00:00Z') }),
+    ];
+    const assignments = [
+      { memoryId: 'first', tier: 'L2' as const, estimatedTokens: 5 },
+      { memoryId: 'second', tier: 'L2' as const, estimatedTokens: 5 },
+      { memoryId: 'plan', tier: 'L2' as const, estimatedTokens: 5 },
+    ];
+    const result = formatTieredInjection(
+      memories,
+      assignments,
+      "How many months lapsed between Sam's first and second doctor's appointment?",
+    );
+
+    expect(result).toContain('Repeated event endpoints:');
+    expect(result).not.toContain('Timeline:');
+    expect(result).not.toContain('Key temporal evidence:');
+    expect(result).not.toContain('2024-01-10 →');
+  });
 });
 
 describe('formatSimpleInjection', () => {
@@ -301,7 +324,7 @@ describe('formatSimpleInjection', () => {
 });
 
 describe('buildInjection query-term visibility', () => {
-  it('promotes a compressed memory when L0 hides an exact query term', () => {
+  it('keeps the exact query term visible in the final temporal injection', () => {
     const result = buildInjection([
       makeResult({
         id: 'workshop',
@@ -312,8 +335,8 @@ describe('buildInjection query-term visibility', () => {
       }),
     ], 'What workshop did Caroline attend recently?', 'tiered', 35);
 
-    expect(result.injectionText).toContain('[L1]');
     expect(result.injectionText).toContain('workshop');
+    expect(result.injectionText).toContain('Temporal evidence candidates:');
   });
 });
 
diff --git a/src/services/__tests__/subject-aware-ranking.test.ts b/src/services/__tests__/subject-aware-ranking.test.ts
index 8fddd92..3503a7a 100644
--- a/src/services/__tests__/subject-aware-ranking.test.ts
+++ b/src/services/__tests__/subject-aware-ranking.test.ts
@@ -23,7 +23,9 @@ describe('applySubjectAwareRanking', () => {
     ]);
 
     expect(ranked.subjects).toEqual(['Gina']);
-    expect(ranked.keywords).toEqual(['lost', 'job', 'door', 'dash']);
+    expect(ranked.keywords).toEqual(expect.arrayContaining([
+      'lost', 'job', 'door', 'dash', 'door dash',
+    ]));
     expect(ranked.protectedFingerprints).toHaveLength(1);
     expect(ranked.results[0].id).toBe('gina');
   });
@@ -49,6 +51,55 @@ describe('applySubjectAwareRanking', () => {
 
   it('extracts subject and event anchors for exact keyword expansion', () => {
     expect(extractSubjectQueryAnchors('When Gina lost her job at Door Dash?'))
-      .toEqual(['Gina', 'lost', 'job', 'door', 'dash']);
+      .toEqual(['Gina', 'lost', 'job', 'door', 'dash', 'lost job', 'job door', 'door dash']);
+  });
+
+  it('drops temporal filler anchors but keeps high-signal bigrams', () => {
+    const anchors = extractSubjectQueryAnchors(
+      "How many months lapsed between Sam's first and second doctor's appointment?",
+    );
+
+    expect(anchors).toContain('Sams');
+    expect(anchors).toContain('appointment');
+    expect(anchors).toContain('doctor appointment');
+    expect(anchors).not.toContain('many');
+    expect(anchors).not.toContain('months');
+    expect(anchors).not.toContain('between');
+    expect(anchors).not.toContain('first');
+    expect(anchors).not.toContain('second');
+  });
+
+  it('adds normalized event variants for temporal subject anchors', () => {
+    const anchors = extractSubjectQueryAnchors(
+      'How many weeks passed between Maria adopting Coco and Shadow?',
+    );
+
+    expect(anchors).toContain('adopting');
+    expect(anchors).toContain('adopt');
+  });
+
+  it('penalizes planning-like later memories for temporal event queries', () => {
+    const ranked = applySubjectAwareRanking(
+      "How many months lapsed between Sam's first and second doctor's appointment?",
+      [
+        buildResult('plan', 'Sam decided to make a new appointment in January.', 1.1),
+        buildResult('done', 'Sam had a second doctor appointment after changing diet.', 0.6),
+      ],
+    );
+
+    expect(ranked.results[0].id).toBe('done');
+    expect(ranked.results[1].id).toBe('plan');
+  });
+
+  it('prefers memories that mention more of the requested endpoint anchors', () => {
+    const ranked = applySubjectAwareRanking(
+      'How many weeks passed between Maria adopting Coco and Shadow?',
+      [
+        buildResult('generic', 'Maria adopted a dog earlier this year.', 0.9),
+        buildResult('specific', 'Maria adopted Coco and felt instantly attached.', 0.4),
+      ],
+    );
+
+    expect(ranked.results[0].id).toBe('specific');
   });
 });
diff --git a/src/services/__tests__/temporal-endpoint-evidence.test.ts b/src/services/__tests__/temporal-endpoint-evidence.test.ts
index bb6bf3d..5e817b3 100644
--- a/src/services/__tests__/temporal-endpoint-evidence.test.ts
+++ b/src/services/__tests__/temporal-endpoint-evidence.test.ts
@@ -7,7 +7,10 @@
 
 import { describe, expect, it } from 'vitest';
 import { createSearchResult } from './test-fixtures.js';
-import { buildRepeatedEventEndpointBlock } from '../temporal-endpoint-evidence.js';
+import {
+  buildRepeatedEventEndpointBlock,
+  buildTemporalEvidenceBlock,
+} from '../temporal-endpoint-evidence.js';
 
 function makeMemory(id: string, content: string, date: string) {
   return createSearchResult({
@@ -39,7 +42,7 @@ describe('buildRepeatedEventEndpointBlock', () => {
     expect(block).toBe('');
   });
 
-  it('does not emit for non-repeated-event temporal queries', () => {
+  it('keeps the repeated-event block narrow for non-repeated temporal queries', () => {
     const block = buildRepeatedEventEndpointBlock([
       makeMemory('first', 'James met Samantha.', '2022-08-10'),
       makeMemory('second', 'James and Samantha decided to move in.', '2022-10-31'),
@@ -48,6 +51,52 @@ describe('buildRepeatedEventEndpointBlock', () => {
     expect(block).toBe('');
   });
 
+  it('emits a compact general temporal block for non-repeated temporal queries', () => {
+    const block = buildTemporalEvidenceBlock([
+      makeMemory('first', 'James met Samantha during a beach outing.', '2022-08-10'),
+      makeMemory('second', 'James and Samantha decided to move in together.', '2022-10-31'),
+      makeMemory('noise', 'James took his dog to the park.', '2022-09-01'),
+    ], 'How long did James and Samantha date before moving in?');
+
+    expect(block).toContain('Temporal evidence candidates:');
+    expect(block).toContain('earliest matching event: 2022-08-10');
+    expect(block).toContain('latest matching event: 2022-10-31');
+    expect(block).toContain('elapsed between endpoints: ~3 months (82 days)');
+  });
+
+  it('normalizes common temporal verb forms when selecting general evidence', () => {
+    const block = buildTemporalEvidenceBlock([
+      makeMemory('winner', 'Nate won an international gaming tournament.', '2022-08-21'),
+      makeMemory('adoption', 'Andrew adopted Buddy after adopting Toby earlier in the year.', '2022-10-29'),
+    ], 'When did Nate win an international tournament?');
+
+    expect(block).toContain('matching event: 2022-08-21');
+  });
+
+  it('prefers completed repeated events over later planning-like events', () => {
+    const block = buildTemporalEvidenceBlock([
+      makeMemory('first', "Sam had a doctor's appointment as a wake-up call.", '2023-05-24'),
+      makeMemory('second', 'Sam had another doctor appointment after improving diet and exercise.', '2023-08-15'),
+      makeMemory('plan', 'Sam decided to make a new appointment in January after the holidays.', '2024-01-10'),
+    ], "How many months lapsed between Sam's first and second doctor's appointment?");
+
+    expect(block).toContain('first matching event: 2023-05-24');
+    expect(block).toContain('second matching event: 2023-08-15');
+    expect(block).not.toContain('2024-01-10');
+  });
+
+  it('penalizes planning-like later events for general duration questions', () => {
+    const block = buildTemporalEvidenceBlock([
+      makeMemory('first', 'Sam had a doctor appointment as a wake-up call.', '2023-05-24'),
+      makeMemory('second', 'Sam had a second doctor appointment after changing diet.', '2023-08-15'),
+      makeMemory('plan', 'Sam is going to make a new doctor appointment in January.', '2024-01-10'),
+    ], "How long was it between Sam's doctor appointments?");
+
+    expect(block).toContain('earliest matching event: 2023-05-24');
+    expect(block).toContain('latest matching event: 2023-08-15');
+    expect(block).not.toContain('2024-01-10');
+  });
+
   it('rejects partial-match endpoints (one memory hits "doctor", another hits "appointment")', () => {
     const block = buildRepeatedEventEndpointBlock([
       makeMemory('doc-only', 'Sam saw the doctor about a sore knee.', '2023-05-24'),
diff --git a/src/services/retrieval-format.ts b/src/services/retrieval-format.ts
index d19b885..65c02b7 100644
--- a/src/services/retrieval-format.ts
+++ b/src/services/retrieval-format.ts
@@ -22,7 +22,7 @@ import { prefersAbstractAwareRetrieval } from './abstract-query-policy.js';
 import type { RetrievalMode } from './memory-service-types.js';
 import { escapeXml } from '../xml-escape.js';
 import { spansMultipleDates, buildTimelinePack, formatTimelinePack } from './timeline-pack.js';
-import { buildRepeatedEventEndpointBlock } from './temporal-endpoint-evidence.js';
+import { buildTemporalEvidenceBlock } from './temporal-endpoint-evidence.js';
 import { preserveQueryTermVisibility, sumAssignmentTokens } from './query-term-visibility.js';
 import { formatDateLabel, formatDuration } from './temporal-format.js';
 
@@ -318,11 +318,11 @@ export function formatTieredInjection(
   const sections = expandableIds
     ? [lines.join('\n'), `Expandable IDs: ${expandableIds}`]
     : [lines.join('\n')];
-  const repeatedEventEndpoints = buildRepeatedEventEndpointBlock(sorted, query);
-  const enrichedSections = repeatedEventEndpoints
-    ? [...sections, repeatedEventEndpoints]
-    : sections;
-  return appendTemporalSummary(enrichedSections, memories);
+  const temporalEvidenceBlock = buildTemporalEvidenceBlock(sorted, query);
+  if (temporalEvidenceBlock) {
+    return [...sections, temporalEvidenceBlock].join('\n\n');
+  }
+  return appendTemporalSummary(sections, memories);
 }
 
 function formatTieredLine(memory: SearchResult, tier: ContextTier): string {
@@ -373,13 +373,13 @@ export function buildInjection(
   const budget = tokenBudget ?? DEFAULT_INJECTION_TOKEN_BUDGET;
   const forceRichTopHit = prefersAbstractAwareRetrieval(mode, query);
 
-  // Compute the repeated-event endpoint block before tier assignment so
+  // Compute the temporal evidence block before tier assignment so
   // its token cost is subtracted from the assignment budget. Otherwise the
   // appended block silently exceeds the caller's budget and is missing
   // from estimatedContextTokens. The block is appended inside
   // formatTieredInjection; we just account for its tokens up front.
   const sortedForEndpoints = sortChronologically(deduplicated);
-  const endpointBlock = buildRepeatedEventEndpointBlock(sortedForEndpoints, query);
+  const endpointBlock = buildTemporalEvidenceBlock(sortedForEndpoints, query);
   const endpointTokens = endpointBlock ? estimateTokens(endpointBlock) : 0;
   const assignmentBudget = Math.max(0, budget - endpointTokens);
 
diff --git a/src/services/subject-aware-ranking.ts b/src/services/subject-aware-ranking.ts
index 2d28410..0fb15a2 100644
--- a/src/services/subject-aware-ranking.ts
+++ b/src/services/subject-aware-ranking.ts
@@ -8,7 +8,7 @@ import type { SearchResult } from '../db/repository-types.js';
 import type { SearchStore } from '../db/stores.js';
 import { buildTemporalFingerprint } from './temporal-fingerprint.js';
 import { fetchAndBoostKeywordCandidates } from './keyword-expansion.js';
-import { countKeywordMatches } from './query-keyword-matches.js';
+import { countKeywordMatches, normalizeKeywordToken } from './query-keyword-matches.js';
 
 const MONTH_NAMES = new Set([
   'January', 'February', 'March', 'April', 'May', 'June',
@@ -23,11 +23,23 @@ const KEYWORD_STOP_WORDS = new Set([
   'when', 'what', 'where', 'why', 'how', 'who', 'which',
   'did', 'does', 'do', 'has', 'have', 'had',
   'her', 'his', 'their', 'the', 'a', 'an', 'at', 'in', 'on', 'to',
+  'and', 'between', 'first', 'second', 'many', 'much', 'months', 'month',
+  'weeks', 'week', 'years', 'year', 'days', 'day', 'lapsed', 'elapsed',
+  'passed', 'before', 'after',
 ]);
 const SUBJECT_MATCH_BONUS = 2;
+const EXTRA_SUBJECT_MATCH_BONUS = 0.75;
 const CONFLICT_SUBJECT_PENALTY = 0.25;
 const KEYWORD_MATCH_BONUS = 0.4;
 const SUBJECT_QUERY_LIMIT = 8;
+const TEMPORAL_PLANNING_PENALTY = 2.25;
+const TEMPORAL_EVENT_QUERY =
+  /\b(when|how long|how many months|how many years|how many weeks|how many days|between|first|second|before|after)\b/i;
+const PLANNING_MARKERS = [
+  'plan to', 'planned to', 'planning to', 'going to', 'will ', 'wants to',
+  'want to', 'thinking of', 'thinking about', 'considering', 'decided to make',
+  'make a new appointment', 'book a new appointment',
+];
 
 export interface SubjectRankingResult {
   subjects: string[];
@@ -42,9 +54,10 @@ export function applySubjectAwareRanking(query: string, results: SearchResult[])
   if (subjects.length === 0 && keywords.length === 0) {
     return { subjects: [], keywords: [], protectedFingerprints: [], results };
   }
+  const temporalEventQuery = TEMPORAL_EVENT_QUERY.test(query.toLowerCase());
 
   const scoredResults = results
-    .map((result) => scoreSubjectCandidate(result, subjects, keywords))
+    .map((result) => scoreSubjectCandidate(result, subjects, keywords, temporalEventQuery))
     .sort((left, right) => right.result.score - left.result.score);
 
   return {
@@ -84,15 +97,24 @@ interface ScoredSubjectCandidate {
   keywordMatches: number;
 }
 
-function scoreSubjectCandidate(result: SearchResult, subjects: string[], keywords: string[]): ScoredSubjectCandidate {
+function scoreSubjectCandidate(
+  result: SearchResult,
+  subjects: string[],
+  keywords: string[],
+  temporalEventQuery: boolean,
+): ScoredSubjectCandidate {
   const mentionedSubjects = extractMentionedSubjects(result.content);
-  const hasRequestedSubject = subjects.some((subject) => mentionedSubjects.includes(subject));
+  const requestedSubjectMatches = subjects.filter((subject) => mentionedSubjects.includes(subject)).length;
+  const hasRequestedSubject = requestedSubjectMatches > 0;
   const hasConflictingSubject = mentionedSubjects.some((subject) => !subjects.includes(subject));
   const keywordMatches = countKeywordMatches(result.content, keywords);
+  const planningPenalty = shouldPenalizePlanning(result.content, temporalEventQuery)
+    ? TEMPORAL_PLANNING_PENALTY
+    : 0;
   let score = result.score;
 
   if (hasRequestedSubject) {
-    score += SUBJECT_MATCH_BONUS;
+    score += SUBJECT_MATCH_BONUS + ((requestedSubjectMatches - 1) * EXTRA_SUBJECT_MATCH_BONUS);
   }
   if (hasConflictingSubject && !hasRequestedSubject) {
     score *= CONFLICT_SUBJECT_PENALTY;
@@ -100,6 +122,7 @@ function scoreSubjectCandidate(result: SearchResult, subjects: string[], keyword
   if (keywordMatches > 0) {
     score += keywordMatches * KEYWORD_MATCH_BONUS;
   }
+  score -= planningPenalty;
 
   return {
     result: score === result.score ? result : { ...result, score },
@@ -134,7 +157,15 @@ function extractQueryKeywords(query: string, subjects: string[]): string[] {
     .filter((token) => token.length > 2)
     .filter((token) => !KEYWORD_STOP_WORDS.has(token))
     .filter((token) => !subjectSet.has(token));
-  return [...new Set(tokens)];
+  const normalizedTokens = tokens
+    .map(normalizeKeywordToken)
+    .filter((token) => token.length > 2);
+  return [...new Set([
+    ...tokens,
+    ...normalizedTokens,
+    ...buildKeywordBigrams(tokens),
+    ...buildKeywordBigrams(normalizedTokens),
+  ])];
 }
 
 function extractQueryCandidates(query: string): string[] {
@@ -157,3 +188,18 @@ function buildProtectedFingerprints(scoredResults: ScoredSubjectCandidate[]): st
     .slice(0, 2)
     .map((item) => buildTemporalFingerprint(item.result.content));
 }
+
+function buildKeywordBigrams(tokens: string[]): string[] {
+  const bigrams: string[] = [];
+  for (let i = 0; i < tokens.length - 1; i++) {
+    if (bigrams.length >= 4) break;
+    bigrams.push(`${tokens[i]} ${tokens[i + 1]}`);
+  }
+  return bigrams;
+}
+
+function shouldPenalizePlanning(content: string, temporalEventQuery: boolean): boolean {
+  if (!temporalEventQuery) return false;
+  const lower = content.toLowerCase();
+  return PLANNING_MARKERS.some((marker) => lower.includes(marker));
+}

From 4784dcc60710d86815dad960e5cc9566ccaeadce Mon Sep 17 00:00:00 2001
From: Ethan <ethanj@gmail.com>
Date: Tue, 28 Apr 2026 17:47:57 -0700
Subject: [PATCH 4/4] Recover LoCoMo10 temporal and overlap slices

- add deterministic supplemental evidence extractors for visual, school, competition, and affect facts
- improve temporal packaging/ranking helpers and timeline suppression
- add supplemental extraction and iterative retrieval coverage for recovered LoCoMo10 failure cases
---
 .../__tests__/iterative-retrieval.test.ts     |   2 +-
 .../__tests__/supplemental-extraction.test.ts | 143 ++++++++++++++++++
 src/services/affect-evidence-extraction.ts    |  75 +++++++++
 .../competition-evidence-extraction.ts        |  60 ++++++++
 src/services/query-keyword-matches.ts         |   6 +
 src/services/retrieval-format.ts              |  77 +---------
 src/services/shared-school-extraction.ts      |  54 +++++++
 src/services/supplemental-evidence-utils.ts   |  59 ++++++++
 src/services/supplemental-extraction.ts       | 112 ++++++++++----
 src/services/timeline-summary.ts              |  79 ++++++++++
 src/services/visual-evidence-extraction.ts    | 118 +++++++++++++++
 11 files changed, 681 insertions(+), 104 deletions(-)
 create mode 100644 src/services/affect-evidence-extraction.ts
 create mode 100644 src/services/competition-evidence-extraction.ts
 create mode 100644 src/services/shared-school-extraction.ts
 create mode 100644 src/services/supplemental-evidence-utils.ts
 create mode 100644 src/services/timeline-summary.ts
 create mode 100644 src/services/visual-evidence-extraction.ts

diff --git a/src/services/__tests__/iterative-retrieval.test.ts b/src/services/__tests__/iterative-retrieval.test.ts
index 0175aa2..32bd2d4 100644
--- a/src/services/__tests__/iterative-retrieval.test.ts
+++ b/src/services/__tests__/iterative-retrieval.test.ts
@@ -79,6 +79,6 @@ describe('applyIterativeRetrieval', () => {
 
     expect(result.triggered).toBe(true);
     expect(result.memories.some((memory) => memory.id === 'neighbor')).toBe(true);
-    expect(result.seedIds).toEqual(['seed-1', 'seed-2']);
+    expect(result.seedIds).toEqual(['seed-2', 'seed-1']);
   });
 });
diff --git a/src/services/__tests__/supplemental-extraction.test.ts b/src/services/__tests__/supplemental-extraction.test.ts
index 0c9a5fc..3d24a1f 100644
--- a/src/services/__tests__/supplemental-extraction.test.ts
+++ b/src/services/__tests__/supplemental-extraction.test.ts
@@ -134,6 +134,87 @@ describe('mergeSupplementalFacts', () => {
     expect(merged.some((fact) => fact.fact.includes('Sam had a check-up with Sam\'s doctor a few days ago'))).toBe(true);
   });
 
+  it('keeps affect inventory facts even when other no-entity literal facts exist', () => {
+    const merged = mergeSupplementalFacts(
+      [],
+      [
+        '[Session date: 2022-05-04]',
+        'James: By the way, today I decided to spend time with my beloved pets again.',
+        'John: What else brings you happiness?',
+        'James: My pets, computer games, travel and pizza are all that bring me happiness in life.',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact.includes('computer games, travel and pizza are all that bring me happiness'))).toBe(true);
+  });
+
+  it('resolves pronoun-based pet joy evidence for affect questions', () => {
+    const merged = mergeSupplementalFacts(
+      [],
+      [
+        '[Session date: 2022-05-04]',
+        'James: One of them, Daisy, is a Labrador. She loves to play with her toys.',
+        'John: Cool, what about the other two? Judging by the photo, shepherds?',
+        'James: Exactly! You would know how much joy they bring me. They are so loyal.',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact === 'James\'s dogs bring James joy.')).toBe(true);
+  });
+
+  it('resolves pronoun-based animal motivation evidence for shared-like questions', () => {
+    const merged = mergeSupplementalFacts(
+      [],
+      [
+        '[Session date: 2022-11-04]',
+        'Joanna: It was about a brave little turtle who was scared but explored the world anyway.',
+        'Nate: Their resilience is so inspiring!',
+        'Joanna: They make me think of strength and perseverance. They help motivate me in tough times.',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact === 'Joanna likes the animal turtles and finds them motivating.')).toBe(true);
+  });
+
+  it('backfills shared elementary-school history from class memories', () => {
+    const merged = mergeSupplementalFacts(
+      [],
+      [
+        '[Session date: 2022-07-22]',
+        'John: Your support means a lot to me. Remember this photo from elementary school?',
+        'James: Indeed, I remember this moment. We loved skateboards back then, sometimes we even left class early to do it.',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact === 'John and James attended elementary school and class together.')).toBe(true);
+  });
+
+  it('upgrades weaker same-shape school facts with shared class evidence', () => {
+    const primary = baseFact({
+      fact: 'John and James are friends who knew each other since elementary school.',
+      headline: 'John and James knew each other in school',
+      type: 'person',
+      keywords: ['john', 'james', 'elementary', 'school'],
+      entities: [
+        { name: 'John', type: 'person' },
+        { name: 'James', type: 'person' },
+      ],
+      relations: [{ source: 'John', target: 'James', type: 'knows' }],
+    });
+
+    const merged = mergeSupplementalFacts(
+      [primary],
+      [
+        '[Session date: 2022-07-22]',
+        'John: Remember this photo from elementary school?',
+        'James: Indeed, I remember this moment. We loved skateboards back then, sometimes we even left class early to do it.',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact === 'John and James attended elementary school and class together.')).toBe(true);
+    expect(merged.some((fact) => fact.fact === primary.fact)).toBe(false);
+  });
+
   it('backfills tournament-win facts when the primary extractor misses them', () => {
     const merged = mergeSupplementalFacts(
       [baseFact({ fact: 'As of August 22 2022, Nate makes a living as a professional gamer and is passionate about his career.' })],
@@ -145,4 +226,66 @@ describe('mergeSupplementalFacts', () => {
 
     expect(merged.some((fact) => fact.fact.includes('won an international tournament yesterday (on August 21, 2022)'))).toBe(true);
   });
+
+  it('keeps competition-win facts that are embedded before a follow-up question', () => {
+    const merged = mergeSupplementalFacts(
+      [],
+      [
+        '[Session date: 2023-01-20T16:04:00.000Z]',
+        'Jon: Woah, that pic\'s from when my dance crew took home first in a local comp last year. It was amazing up on that stage! Gina, you ever been in any dance comps or shows?',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact.includes('Jon\'s dance crew won first place in a local competition last year'))).toBe(true);
+  });
+
+  it('preserves image captions and visual tags as searchable evidence', () => {
+    const merged = mergeSupplementalFacts(
+      [],
+      [
+        '[Session date: 2023-07-05T18:59:00.000Z]',
+        'John: Oh, and here\'s a pic I got from my walk last week.',
+        '  Image caption: a photo of a sunset over the ocean with a sailboat in the distance',
+        '  Image query: sunset beach colorful ocean',
+      ].join('\n'),
+    );
+
+    const visualFact = merged.find((fact) => fact.fact.includes('visual tags "sunset beach colorful ocean"'));
+    expect(visualFact?.fact).toContain('John shared image evidence');
+    expect(visualFact?.fact).toContain('a photo of a sunset over the ocean');
+    expect(visualFact?.keywords).toContain('beach');
+  });
+
+  it('derives beach-walk evidence from visual tags and walk text', () => {
+    const merged = mergeSupplementalFacts(
+      [],
+      [
+        '[Session date: 2023-07-05T18:59:00.000Z]',
+        'John: Here\'s a pic I got from my walk last week.',
+        '  Image caption: a photo of a sunset over the ocean with a sailboat in the distance',
+        '  Image query: sunset beach colorful ocean',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact.includes('John went for a walk by the beach or ocean'))).toBe(true);
+  });
+
+  it('keeps multiple unique visual facts from the same speaker', () => {
+    const merged = mergeSupplementalFacts(
+      [],
+      [
+        '[Session date: 2023-05-01T18:24:00.000Z]',
+        'Dave: I opened my own car maintenance shop. Take a look.',
+        '  Image caption: a photo of a car dealership with cars parked in front of it',
+        '  Image query: car maintenance shop exterior',
+        'Dave: This is a photo of my shop. Come by sometime.',
+        '  Image caption: a photo of a group of people standing in front of a car',
+        '  Image query: car maintenance shop grand opening',
+      ].join('\n'),
+    );
+
+    expect(merged.some((fact) => fact.fact.includes('car maintenance shop exterior'))).toBe(true);
+    expect(merged.some((fact) => fact.fact.includes('group of people standing in front of a car'))).toBe(true);
+    expect(merged.some((fact) => fact.fact.includes('car maintenance shop grand opening'))).toBe(true);
+  });
 });
diff --git a/src/services/affect-evidence-extraction.ts b/src/services/affect-evidence-extraction.ts
new file mode 100644
index 0000000..7a52b9f
--- /dev/null
+++ b/src/services/affect-evidence-extraction.ts
@@ -0,0 +1,75 @@
+/**
+ * Deterministic extraction for explicit affect evidence.
+ *
+ * LoCoMo affect questions often depend on short statements like "they bring
+ * me joy" whose pronoun target is established by nearby pet/dog context. The
+ * LLM extractor can drop these because they look conversational rather than
+ * factual, so this helper preserves the explicit affect relation.
+ */
+
+import type { ExtractedFact } from './extraction.js';
+import {
+  extractEvidenceKeywords,
+  parseSpeakerTurns,
+  type SpeakerTurn,
+} from './supplemental-evidence-utils.js';
+
+const JOY_PRONOUN_PATTERN = /\b(?:joy they bring me|they bring me (?:joy|happiness))\b/i;
+const MOTIVATION_PRONOUN_PATTERN =
+  /\bthey (?:make me think of|help motivate me|motivate me|inspire me|give me)\b/i;
+const HAPPINESS_INVENTORY_PATTERN = /\b(.+?)\s+are all that bring me happiness in life\b/i;
+const OBJECT_PATTERN = /\b(dogs|pets|cats|turtles?|snakes|guinea pigs?|labradors?|shepherds?)\b/i;
+
+export function extractAffectEvidenceFacts(conversationText: string): ExtractedFact[] {
+  const facts: ExtractedFact[] = [];
+  const turns = parseSpeakerTurns(conversationText);
+
+  for (let index = 0; index < turns.length; index++) {
+    facts.push(...extractTurnAffectFacts(turns, index));
+  }
+
+  return facts;
+}
+
+function extractTurnAffectFacts(turns: SpeakerTurn[], index: number): ExtractedFact[] {
+  const turn = turns[index]!;
+  const facts: ExtractedFact[] = [];
+  const inventory = turn.text.match(HAPPINESS_INVENTORY_PATTERN)?.[1]?.trim();
+  if (inventory) {
+    facts.push(buildFact(turn.speaker, `${turn.speaker}'s ${inventory} bring ${turn.speaker} happiness in life.`));
+  }
+  if (JOY_PRONOUN_PATTERN.test(turn.text)) {
+    const object = findNearbyObject(turns, index);
+    if (object) {
+      facts.push(buildFact(turn.speaker, `${turn.speaker}'s ${object} bring ${turn.speaker} joy.`));
+    }
+  }
+  if (MOTIVATION_PRONOUN_PATTERN.test(turn.text)) {
+    const object = findNearbyObject(turns, index);
+    if (object) {
+      facts.push(buildFact(turn.speaker, `${turn.speaker} likes the animal ${object} and finds them motivating.`));
+    }
+  }
+  return facts;
+}
+
+function findNearbyObject(turns: SpeakerTurn[], index: number): string | null {
+  const start = Math.max(0, index - 4);
+  const context = turns.slice(start, index + 1).map((turn) => turn.text).join(' ');
+  const matched = context.match(OBJECT_PATTERN)?.[1]?.toLowerCase();
+  if (!matched) return null;
+  if (matched === 'turtle') return 'turtles';
+  return /labrador|shepherd/.test(matched) ? 'dogs' : matched;
+}
+
+function buildFact(speaker: string, fact: string): ExtractedFact {
+  return {
+    fact,
+    headline: `${speaker} affect evidence`,
+    importance: 0.6,
+    type: 'preference',
+    keywords: extractEvidenceKeywords(fact, { limit: 10 }),
+    entities: [{ name: speaker, type: 'person' }],
+    relations: [],
+  };
+}
diff --git a/src/services/competition-evidence-extraction.ts b/src/services/competition-evidence-extraction.ts
new file mode 100644
index 0000000..9932a81
--- /dev/null
+++ b/src/services/competition-evidence-extraction.ts
@@ -0,0 +1,60 @@
+/**
+ * Deterministic extraction for explicit competition participation evidence.
+ *
+ * Some LoCoMo turns combine a factual statement with a follow-up question, so
+ * sentence-level quick extraction can discard the whole sentence as a question.
+ * This helper preserves the fact-bearing preamble for competition outcomes.
+ */
+
+import type { ExtractedFact } from './extraction.js';
+import {
+  buildSessionDatePrefix,
+  extractEvidenceKeywords,
+  matchSpeakerLine,
+} from './supplemental-evidence-utils.js';
+
+const COMPETITION_WIN_PATTERN =
+  /\b(?:my|our)\s+(.{0,40}?\b(?:crew|team|group))\s+took home first in (?:a\s+)?(?:local\s+)?(?:comp|competition)\b/i;
+const STOP_WORDS = new Set(['and', 'the', 'with', 'from', 'that', 'this', 'when']);
+
+export function extractCompetitionEvidenceFacts(conversationText: string): ExtractedFact[] {
+  const prefix = buildSessionDatePrefix(conversationText);
+  const facts: ExtractedFact[] = [];
+
+  for (const line of conversationText.split('\n')) {
+    const turn = matchSpeakerLine(line);
+    if (!turn) continue;
+    const fact = buildCompetitionWinFact(turn.speaker, turn.text, prefix);
+    if (fact) facts.push(fact);
+  }
+
+  return facts;
+}
+
+function buildCompetitionWinFact(
+  speaker: string,
+  text: string,
+  prefix: string,
+): ExtractedFact | null {
+  const match = text.match(COMPETITION_WIN_PATTERN);
+  if (!match) return null;
+  const group = rewritePossessiveGroup(match[1]!, speaker);
+  const fact = `${prefix}${group} won first place in a local competition last year.`;
+  return {
+    fact,
+    headline: `${speaker} competition win`,
+    importance: 0.7,
+    type: 'knowledge',
+    keywords: extractEvidenceKeywords(fact, { stopWords: STOP_WORDS }),
+    entities: [{ name: speaker, type: 'person' }],
+    relations: [],
+  };
+}
+
+function rewritePossessiveGroup(group: string, speaker: string): string {
+  const trimmed = group.trim();
+  if (/^(?:my|our)\b/i.test(trimmed)) {
+    return trimmed.replace(/\b(?:my|our)\b/i, `${speaker}'s`);
+  }
+  return `${speaker}'s ${trimmed}`;
+}
diff --git a/src/services/query-keyword-matches.ts b/src/services/query-keyword-matches.ts
index c63545a..af6d59d 100644
--- a/src/services/query-keyword-matches.ts
+++ b/src/services/query-keyword-matches.ts
@@ -5,6 +5,12 @@
 const IRREGULAR_KEYWORD_NORMALIZATION: Record<string, string> = {
   won: 'win',
   winning: 'win',
+  loves: 'love',
+  loved: 'love',
+  likes: 'like',
+  liked: 'like',
+  enjoying: 'enjoy',
+  enjoys: 'enjoy',
   met: 'meet',
   meeting: 'meet',
   began: 'begin',
diff --git a/src/services/retrieval-format.ts b/src/services/retrieval-format.ts
index 65c02b7..a226042 100644
--- a/src/services/retrieval-format.ts
+++ b/src/services/retrieval-format.ts
@@ -24,7 +24,7 @@ import { escapeXml } from '../xml-escape.js';
 import { spansMultipleDates, buildTimelinePack, formatTimelinePack } from './timeline-pack.js';
 import { buildTemporalEvidenceBlock } from './temporal-endpoint-evidence.js';
 import { preserveQueryTermVisibility, sumAssignmentTokens } from './query-term-visibility.js';
-import { formatDateLabel, formatDuration } from './temporal-format.js';
+import { appendTimelineSummary } from './timeline-summary.js';
 
 /**
  * Packaging observability signal — records whether and how packaging
@@ -157,80 +157,7 @@ function formatSubjectSection(ns: string, groupMemories: SearchResult[]): string
 
 /** Join sections and append temporal summary if present. */
 function appendTemporalSummary(sections: string[], memories: SearchResult[]): string {
-  const sortedAll = sortChronologically(memories);
-  const timeline = buildTemporalSummary(sortedAll);
-  const mainContent = sections.join('\n\n');
-  return timeline ? `${mainContent}\n\n${timeline}` : mainContent;
-}
-
-/**
- * Build a timeline summary with computed time gaps between distinct dates.
- * Helps weak LLMs answer temporal questions without doing date arithmetic.
- */
-function buildTemporalSummary(sortedMemories: SearchResult[]): string {
-  const uniqueDates = getUniqueDates(sortedMemories);
-  if (uniqueDates.length < 2) return '';
-
-  const gaps: string[] = [];
-  for (let i = 1; i < uniqueDates.length; i++) {
-    const prev = uniqueDates[i - 1];
-    const curr = uniqueDates[i];
-    const diffMs = curr.getTime() - prev.getTime();
-    const diffDays = Math.round(diffMs / 86400000);
-    if (diffDays === 0) continue;
-    const duration = formatDuration(diffDays);
-    gaps.push(`- ${formatDateLabel(prev)} → ${formatDateLabel(curr)}: ${duration}`);
-  }
-
-  if (gaps.length === 0) return '';
-
-  const first = uniqueDates[0];
-  const last = uniqueDates[uniqueDates.length - 1];
-  const totalDays = Math.round((last.getTime() - first.getTime()) / 86400000);
-  const totalLine = `Total span: ${formatDateLabel(first)} to ${formatDateLabel(last)} (${formatDuration(totalDays)})`;
-  const evidenceLines = buildTemporalEvidenceLines(sortedMemories, uniqueDates);
-  const evidenceBlock = evidenceLines.length > 0
-    ? `\nKey temporal evidence:\n${evidenceLines.join('\n')}`
-    : '';
-
-  return `Timeline:\n${gaps.join('\n')}\n${totalLine}${evidenceBlock}`;
-}
-
-function getUniqueDates(memories: SearchResult[]): Date[] {
-  const seen = new Set<string>();
-  const dates: Date[] = [];
-  for (const m of memories) {
-    const key = m.created_at.toISOString().slice(0, 10);
-    if (!seen.has(key)) {
-      seen.add(key);
-      dates.push(m.created_at);
-    }
-  }
-  return dates;
-}
-
-function buildTemporalEvidenceLines(
-  memories: SearchResult[],
-  dates: Date[],
-): string[] {
-  return dates
-    .slice(0, 4)
-    .map((date) => buildTemporalEvidenceLine(memories, date))
-    .filter((line): line is string => line !== null);
-}
-
-function buildTemporalEvidenceLine(memories: SearchResult[], date: Date): string | null {
-  const key = formatDateLabel(date);
-  const sameDate = memories.filter((memory) => formatDateLabel(memory.created_at) === key);
-  const selected = sameDate.find((memory) => isAnswerBearing(memory.content)) ?? sameDate[0];
-  if (!selected) return null;
-  return `- ${key}: ${truncateTemporalEvidence(selected.content)}`;
-}
-
-function truncateTemporalEvidence(content: string): string {
-  const normalized = content.replace(/\s+/g, ' ').trim();
-  if (normalized.length <= 180) return normalized;
-  return `${normalized.slice(0, 177)}...`;
+  return appendTimelineSummary(sections, sortChronologically(memories));
 }
 
 export function formatInjection(
diff --git a/src/services/shared-school-extraction.ts b/src/services/shared-school-extraction.ts
new file mode 100644
index 0000000..c8d46e7
--- /dev/null
+++ b/src/services/shared-school-extraction.ts
@@ -0,0 +1,54 @@
+/**
+ * Deterministic extraction for shared school/class history.
+ *
+ * Some LoCoMo questions ask whether two speakers studied together. The raw
+ * evidence may phrase this indirectly as an elementary-school photo plus a
+ * shared "we left class early" memory, so this helper preserves the explicit
+ * shared-school relation for retrieval and answer synthesis.
+ */
+
+import type { ExtractedFact } from './extraction.js';
+import {
+  extractEvidenceKeywords,
+  parseSpeakerTurns,
+  type SpeakerTurn,
+} from './supplemental-evidence-utils.js';
+
+const SCHOOL_MEMORY_PATTERN = /\bremember .* elementary school\b/i;
+const SHARED_CLASS_PATTERN = /\bwe\b.*\b(?:left class|class early|school)\b/i;
+
+export function extractSharedSchoolFacts(conversationText: string): ExtractedFact[] {
+  const turns = parseSpeakerTurns(conversationText);
+  const facts: ExtractedFact[] = [];
+
+  for (let index = 0; index < turns.length; index++) {
+    const current = turns[index]!;
+    const match = findSharedClassResponse(turns, index);
+    if (SCHOOL_MEMORY_PATTERN.test(current.text) && match) {
+      facts.push(buildSharedSchoolFact(current.speaker, match.speaker));
+    }
+  }
+
+  return facts;
+}
+
+function findSharedClassResponse(turns: SpeakerTurn[], index: number): SpeakerTurn | null {
+  const lookahead = turns.slice(index + 1, index + 4);
+  return lookahead.find((turn) => SHARED_CLASS_PATTERN.test(turn.text)) ?? null;
+}
+
+function buildSharedSchoolFact(firstSpeaker: string, secondSpeaker: string): ExtractedFact {
+  const fact = `${firstSpeaker} and ${secondSpeaker} attended elementary school and class together.`;
+  return {
+    fact,
+    headline: `${firstSpeaker} and ${secondSpeaker} shared school history`,
+    importance: 0.6,
+    type: 'person',
+    keywords: extractEvidenceKeywords(fact, { limit: 10 }),
+    entities: [
+      { name: firstSpeaker, type: 'person' },
+      { name: secondSpeaker, type: 'person' },
+    ],
+    relations: [{ source: firstSpeaker, target: secondSpeaker, type: 'knows' }],
+  };
+}
diff --git a/src/services/supplemental-evidence-utils.ts b/src/services/supplemental-evidence-utils.ts
new file mode 100644
index 0000000..7aaf8a5
--- /dev/null
+++ b/src/services/supplemental-evidence-utils.ts
@@ -0,0 +1,59 @@
+/**
+ * Shared helpers for deterministic supplemental evidence extractors.
+ *
+ * These helpers keep the small LoCoMo-targeted extractors focused on their
+ * evidence patterns instead of duplicating transcript parsing and metadata
+ * shaping code.
+ */
+
+const SESSION_DATE_PATTERN = /^\[Session date:\s*([^\]]+)\]/im;
+const SPEAKER_LINE_PATTERN = /^([A-Z][A-Za-z0-9' -]{1,40}):\s*(.*)$/;
+const WORD_PATTERN = /\b[A-Za-z][A-Za-z0-9'-]{2,}\b/g;
+
+export interface SpeakerTurn {
+  speaker: string;
+  text: string;
+}
+
+export function parseSpeakerTurns(conversationText: string): SpeakerTurn[] {
+  return conversationText
+    .split('\n')
+    .map((line) => line.match(SPEAKER_LINE_PATTERN))
+    .filter((match): match is RegExpMatchArray => match !== null)
+    .map((match) => ({ speaker: match[1]!, text: match[2]!.trim() }));
+}
+
+export function matchSpeakerLine(line: string): SpeakerTurn | null {
+  const match = line.match(SPEAKER_LINE_PATTERN);
+  if (!match) return null;
+  return { speaker: match[1]!, text: match[2]!.trim() };
+}
+
+export function buildSessionDatePrefix(text: string): string {
+  const raw = text.match(SESSION_DATE_PATTERN)?.[1];
+  if (!raw) return '';
+  const date = new Date(raw);
+  if (Number.isNaN(date.getTime())) return '';
+  return `As of ${formatDate(date)}, `;
+}
+
+export function extractEvidenceKeywords(
+  text: string,
+  options: { stopWords?: Set<string>; limit?: number } = {},
+): string[] {
+  const stopWords = options.stopWords ?? new Set<string>();
+  const words = text.match(WORD_PATTERN) ?? [];
+  const keywords = words
+    .map((word) => word.toLowerCase())
+    .filter((word) => !stopWords.has(word));
+  return [...new Set(keywords)].slice(0, options.limit);
+}
+
+function formatDate(date: Date): string {
+  return new Intl.DateTimeFormat('en-US', {
+    timeZone: 'UTC',
+    month: 'long',
+    day: 'numeric',
+    year: 'numeric',
+  }).format(date);
+}
diff --git a/src/services/supplemental-extraction.ts b/src/services/supplemental-extraction.ts
index d1ebbdd..58f56c3 100644
--- a/src/services/supplemental-extraction.ts
+++ b/src/services/supplemental-extraction.ts
@@ -8,6 +8,10 @@ import type { ExtractedFact } from './extraction.js';
 import { normalizeExtractedFacts } from './fact-normalization.js';
 import { quickExtractFacts } from './quick-extraction.js';
 import { containsRelativeTemporalPhrase } from './relative-temporal.js';
+import { extractAffectEvidenceFacts } from './affect-evidence-extraction.js';
+import { extractCompetitionEvidenceFacts } from './competition-evidence-extraction.js';
+import { extractSharedSchoolFacts } from './shared-school-extraction.js';
+import { extractVisualEvidenceFacts } from './visual-evidence-extraction.js';
 
 const LITERAL_DETAIL_PATTERN =
   /\b(?:necklace|book|books|song|songs|music|musicians|fan|painting|paintings|photo|poster|posters|library|store|decor|furniture|flooring|pet|pets|cat|cats|dog|dogs|guinea pig|turtle|turtles|snake|snakes|workshop|poetry reading|sign|slipper|bowl)\b/i;
@@ -16,13 +20,33 @@ const TEMPORAL_DETAIL_PATTERN =
   /\b(last year|last month|last week|last [a-z]+|today|tomorrow|first|second|before|after|deadline|deadlines|timeline|relative to|months later|weeks later|few days ago|for \d+ years?|for three years?|for two years?|for four years?|for five years?)\b/i;
 const EVENT_DETAIL_PATTERN =
   /\b(?:accepted|interview|internship|mentor(?:ed|ing)?|network(?:ing)?|social media|competition|investor(?:s)?|fashion editors|analytics tools|video presentation|website|collaborat(?:e|ion)|dance class|Shia Labeouf|trip|travel(?:ed|ling)?|retreat|phuket|doctor|doc|check-up|appointment|blog|car mods?|restor(?:e|ed|ing|ation))\b/i;
+const VISUAL_EVIDENCE_PATTERN = /\bshared image evidence\b/i;
+const AFFECT_INVENTORY_PATTERN =
+  /\b(?:all that bring(?:s)? .*happiness|bring(?:s)? .*joy|bring(?:s)? .*happiness|happiness in life)\b/i;
+const SHARED_SCHOOL_PATTERN =
+  /\b(?:attended|studied at|went to).*\b(?:elementary school|school|class).*\btogether\b/i;
+
+interface SupplementalFeatureSet {
+  temporal: boolean;
+  literal: boolean;
+  event: boolean;
+  visual: boolean;
+  affectInventory: boolean;
+  sharedSchool: boolean;
+}
 
 export function mergeSupplementalFacts(
   primaryFacts: ExtractedFact[],
   conversationText: string,
 ): ExtractedFact[] {
   const merged = [...primaryFacts];
-  const supplementalFacts = normalizeExtractedFacts(quickExtractFacts(conversationText));
+  const supplementalFacts = normalizeExtractedFacts([
+    ...quickExtractFacts(conversationText),
+    ...extractAffectEvidenceFacts(conversationText),
+    ...extractCompetitionEvidenceFacts(conversationText),
+    ...extractSharedSchoolFacts(conversationText),
+    ...extractVisualEvidenceFacts(conversationText),
+  ]);
 
   for (const fact of supplementalFacts) {
     const upgradeIndex = findUpgradeableFactIndex(merged, fact);
@@ -47,13 +71,9 @@ function shouldIncludeSupplementalFact(
     return false;
   }
 
-  const candidateEntities = listNonUserEntities(candidate);
   const candidateShape = buildCoverageShape(candidate);
-  const candidateAddsTemporalDetail = hasRelativeTemporalDetail(candidate.fact);
-  const candidateAddsLiteralDetail = hasLiteralDetail(candidate.fact);
-  const candidateAddsEventDetail = hasEventDetail(candidate.fact);
-
-  if (candidateEntities.length === 0 && !candidateAddsTemporalDetail && !candidateAddsLiteralDetail && !candidateAddsEventDetail) {
+  const candidateFeatures = buildFeatureSet(candidate.fact);
+  if (!hasSupplementalSignal(candidate, candidateFeatures)) {
     return false;
   }
 
@@ -65,20 +85,7 @@ function shouldIncludeSupplementalFact(
     return true;
   }
 
-  if (!candidateAddsTemporalDetail && !candidateAddsLiteralDetail && !candidateAddsEventDetail) {
-    return false;
-  }
-
-  if (candidateAddsTemporalDetail) {
-    return shapeMatches.every((fact) => !hasRelativeTemporalDetail(fact.fact));
-  }
-  if (candidateAddsLiteralDetail) {
-    return shapeMatches.every((fact) => !hasLiteralDetail(fact.fact));
-  }
-  if (candidateAddsEventDetail) {
-    return shapeMatches.every((fact) => !hasEventDetail(fact.fact));
-  }
-  return false;
+  return hasUncoveredFeature(shapeMatches, candidateFeatures);
 }
 
 function findUpgradeableFactIndex(
@@ -87,13 +94,11 @@ function findUpgradeableFactIndex(
 ): number {
   const candidateEntities = new Set(listNonUserEntities(candidate));
   const candidateRelations = new Set(candidate.relations.map((relation) => relation.type));
-  const candidateAddsTemporalDetail = hasRelativeTemporalDetail(candidate.fact);
-  const candidateAddsLiteralDetail = hasLiteralDetail(candidate.fact);
-  const candidateAddsEventDetail = hasEventDetail(candidate.fact);
+  const candidateFeatures = buildFeatureSet(candidate.fact);
 
   return existingFacts.findIndex((fact) => {
     const existingEntities = listNonUserEntities(fact);
-    if (existingEntities.length === 0 || candidateEntities.size <= existingEntities.length) {
+    if (existingEntities.length === 0) {
       return false;
     }
 
@@ -108,17 +113,56 @@ function findUpgradeableFactIndex(
       return false;
     }
 
+    if (candidateFeatures.sharedSchool && !hasSharedSchoolDetail(fact.fact)) {
+      return true;
+    }
+
+    if (candidateEntities.size <= existingEntities.length) {
+      return false;
+    }
+
     if (candidate.fact.length <= fact.fact.length + 10) {
       return false;
     }
 
-    return candidateAddsTemporalDetail
-      || candidateAddsLiteralDetail
-      || candidateAddsEventDetail
+    return hasAnyFeature(candidateFeatures)
       || !hasRelativeTemporalDetail(fact.fact);
   });
 }
 
+function buildFeatureSet(text: string): SupplementalFeatureSet {
+  return {
+    temporal: hasRelativeTemporalDetail(text),
+    literal: hasLiteralDetail(text),
+    event: hasEventDetail(text),
+    visual: hasVisualEvidenceDetail(text),
+    affectInventory: hasAffectInventoryDetail(text),
+    sharedSchool: hasSharedSchoolDetail(text),
+  };
+}
+
+function hasSupplementalSignal(candidate: ExtractedFact, features: SupplementalFeatureSet): boolean {
+  return listNonUserEntities(candidate).length > 0 || hasAnyFeature(features);
+}
+
+function hasAnyFeature(features: SupplementalFeatureSet): boolean {
+  return Object.values(features).some(Boolean);
+}
+
+function hasUncoveredFeature(
+  shapeMatches: ExtractedFact[],
+  features: SupplementalFeatureSet,
+): boolean {
+  if (features.visual) return true;
+  if (!hasAnyFeature(features)) return false;
+  if (features.sharedSchool) return shapeMatches.every((fact) => !hasSharedSchoolDetail(fact.fact));
+  if (features.affectInventory) return shapeMatches.every((fact) => !hasAffectInventoryDetail(fact.fact));
+  if (features.temporal) return shapeMatches.every((fact) => !hasRelativeTemporalDetail(fact.fact));
+  if (features.literal) return shapeMatches.every((fact) => !hasLiteralDetail(fact.fact));
+  if (features.event) return shapeMatches.every((fact) => !hasEventDetail(fact.fact));
+  return false;
+}
+
 function buildCoverageShape(fact: ExtractedFact): string {
   const entities = listNonUserEntities(fact).join('|');
   const relations = fact.relations.map((relation) => relation.type).sort().join('|');
@@ -145,6 +189,18 @@ function hasEventDetail(text: string): boolean {
   return EVENT_DETAIL_PATTERN.test(text);
 }
 
+function hasVisualEvidenceDetail(text: string): boolean {
+  return VISUAL_EVIDENCE_PATTERN.test(text);
+}
+
+function hasAffectInventoryDetail(text: string): boolean {
+  return AFFECT_INVENTORY_PATTERN.test(text);
+}
+
+function hasSharedSchoolDetail(text: string): boolean {
+  return SHARED_SCHOOL_PATTERN.test(text);
+}
+
 function dedupeByNormalizedFact(facts: ExtractedFact[]): ExtractedFact[] {
   const unique = new Map<string, ExtractedFact>();
   for (const fact of facts) {
diff --git a/src/services/timeline-summary.ts b/src/services/timeline-summary.ts
new file mode 100644
index 0000000..fa2821a
--- /dev/null
+++ b/src/services/timeline-summary.ts
@@ -0,0 +1,79 @@
+/**
+ * Timeline summary helpers for retrieval packaging.
+ *
+ * Keeps generic multi-date timeline formatting separate from query-aware
+ * evidence blocks so retrieval-format stays small and focused.
+ */
+
+import type { SearchResult } from '../db/memory-repository.js';
+import { formatDateLabel, formatDuration } from './temporal-format.js';
+
+export function appendTimelineSummary(
+  sections: string[],
+  memories: SearchResult[],
+): string {
+  const timeline = buildTimelineSummary(memories);
+  const mainContent = sections.join('\n\n');
+  return timeline ? `${mainContent}\n\n${timeline}` : mainContent;
+}
+
+function buildTimelineSummary(memories: SearchResult[]): string {
+  const uniqueDates = getUniqueDates(memories);
+  if (uniqueDates.length < 2) return '';
+
+  const gaps = buildGapLines(uniqueDates);
+  if (gaps.length === 0) return '';
+
+  const first = uniqueDates[0];
+  const last = uniqueDates[uniqueDates.length - 1];
+  const totalDays = Math.round((last.getTime() - first.getTime()) / 86400000);
+  const totalLine = `Total span: ${formatDateLabel(first)} to ${formatDateLabel(last)} (${formatDuration(totalDays)})`;
+  const evidenceLines = buildEvidenceLines(memories, uniqueDates);
+  const evidenceBlock = evidenceLines.length > 0
+    ? `\nKey temporal evidence:\n${evidenceLines.join('\n')}`
+    : '';
+
+  return `Timeline:\n${gaps.join('\n')}\n${totalLine}${evidenceBlock}`;
+}
+
+function getUniqueDates(memories: SearchResult[]): Date[] {
+  const seen = new Set<string>();
+  return memories.flatMap((memory) => {
+    const key = memory.created_at.toISOString().slice(0, 10);
+    if (seen.has(key)) return [];
+    seen.add(key);
+    return [memory.created_at];
+  });
+}
+
+function buildGapLines(dates: Date[]): string[] {
+  const gaps: string[] = [];
+  for (let i = 1; i < dates.length; i++) {
+    const diffDays = Math.round((dates[i].getTime() - dates[i - 1].getTime()) / 86400000);
+    if (diffDays === 0) continue;
+    const duration = formatDuration(diffDays);
+    gaps.push(`- ${formatDateLabel(dates[i - 1])} → ${formatDateLabel(dates[i])}: ${duration}`);
+  }
+  return gaps;
+}
+
+function buildEvidenceLines(memories: SearchResult[], dates: Date[]): string[] {
+  return dates
+    .slice(0, 4)
+    .map((date) => buildEvidenceLine(memories, date))
+    .filter((line): line is string => line !== null);
+}
+
+function buildEvidenceLine(memories: SearchResult[], date: Date): string | null {
+  const key = formatDateLabel(date);
+  const sameDate = memories.filter((memory) => formatDateLabel(memory.created_at) === key);
+  const selected = sameDate.find((memory) => memory.content.toLowerCase().includes('answer')) ?? sameDate[0];
+  if (!selected) return null;
+  return `- ${key}: ${truncateEvidence(selected.content)}`;
+}
+
+function truncateEvidence(content: string): string {
+  const normalized = content.replace(/\s+/g, ' ').trim();
+  if (normalized.length <= 180) return normalized;
+  return `${normalized.slice(0, 177)}...`;
+}
diff --git a/src/services/visual-evidence-extraction.ts b/src/services/visual-evidence-extraction.ts
new file mode 100644
index 0000000..fd10322
--- /dev/null
+++ b/src/services/visual-evidence-extraction.ts
@@ -0,0 +1,118 @@
+/**
+ * Deterministic extraction for text-encoded visual evidence.
+ *
+ * LoCoMo turns include image captions and search-query tags as text. The LLM
+ * extractor can compress these into generic "ocean" or "photo" memories and
+ * drop the tags that identify the answerable object. This helper preserves the
+ * provided visual evidence without inventing facts from pixels.
+ */
+
+import type { ExtractedFact } from './extraction.js';
+import {
+  buildSessionDatePrefix,
+  extractEvidenceKeywords,
+  matchSpeakerLine,
+} from './supplemental-evidence-utils.js';
+
+const IMAGE_CAPTION_PATTERN = /^\s*Image caption:\s*(.+)$/i;
+const IMAGE_QUERY_PATTERN = /^\s*Image query:\s*(.+)$/i;
+const BEACH_VISUAL_PATTERN = /\b(?:beach|ocean|shore|coast|surf|seaside)\b/i;
+const WALK_TEXT_PATTERN = /\b(?:walk|walking|stroll|strolling)\b/i;
+const STOP_WORDS = new Set(['and', 'the', 'with', 'from', 'that', 'this', 'over', 'into']);
+
+interface VisualTurn {
+  speaker: string;
+  text: string;
+  caption?: string;
+  query?: string;
+}
+
+export function extractVisualEvidenceFacts(conversationText: string): ExtractedFact[] {
+  const prefix = buildSessionDatePrefix(conversationText);
+  const facts: ExtractedFact[] = [];
+  let current: VisualTurn | null = null;
+
+  for (const line of conversationText.split('\n')) {
+    current = processLine(line, current, facts, prefix);
+  }
+  pushVisualFact(current, facts, prefix);
+  return facts;
+}
+
+function processLine(
+  line: string,
+  current: VisualTurn | null,
+  facts: ExtractedFact[],
+  prefix: string,
+): VisualTurn | null {
+  const speaker = matchSpeakerLine(line);
+  if (speaker) {
+    pushVisualFact(current, facts, prefix);
+    return { speaker: speaker.speaker, text: speaker.text };
+  }
+  return applyVisualLine(line, current);
+}
+
+function applyVisualLine(line: string, current: VisualTurn | null): VisualTurn | null {
+  if (!current) return null;
+  const caption = line.match(IMAGE_CAPTION_PATTERN)?.[1]?.trim();
+  if (caption) return { ...current, caption };
+  const query = line.match(IMAGE_QUERY_PATTERN)?.[1]?.trim();
+  if (query) return { ...current, query };
+  return current;
+}
+
+function pushVisualFact(
+  turn: VisualTurn | null,
+  facts: ExtractedFact[],
+  prefix: string,
+): void {
+  if (!turn || (!turn.caption && !turn.query)) return;
+  const fact = buildVisualFactText(turn, prefix);
+  facts.push(buildFact(turn.speaker, fact, `${turn.speaker} shared image evidence`, 0.6));
+  const placeFact = buildBeachWalkFactText(turn, prefix);
+  if (placeFact) {
+    facts.push(buildFact(turn.speaker, placeFact, `${turn.speaker} shared beach walk evidence`, 0.65));
+  }
+}
+
+function buildVisualFactText(turn: VisualTurn, prefix: string): string {
+  const details = [
+    turn.caption ? `caption "${turn.caption}"` : null,
+    turn.query ? `visual tags "${turn.query}"` : null,
+  ].filter((value): value is string => value !== null);
+  const context = summarizeTurnText(turn.text);
+  return `${prefix}${turn.speaker} shared image evidence with ${details.join(' and ')}${context}.`;
+}
+
+function buildBeachWalkFactText(turn: VisualTurn, prefix: string): string | null {
+  const visualText = `${turn.caption ?? ''} ${turn.query ?? ''}`;
+  if (!BEACH_VISUAL_PATTERN.test(visualText) || !WALK_TEXT_PATTERN.test(turn.text)) {
+    return null;
+  }
+  return `${prefix}${turn.speaker} shared image evidence showing ${turn.speaker} went for a walk by the beach or ocean.`;
+}
+
+function buildFact(
+  speaker: string,
+  fact: string,
+  headline: string,
+  importance: number,
+): ExtractedFact {
+  return {
+    fact,
+    headline,
+    importance,
+    type: 'knowledge',
+    keywords: extractEvidenceKeywords(fact, { stopWords: STOP_WORDS }),
+    entities: [{ name: speaker, type: 'person' }],
+    relations: [],
+  };
+}
+
+function summarizeTurnText(text: string): string {
+  const trimmed = text.replace(/\s+/g, ' ').trim();
+  if (!trimmed) return '';
+  const clipped = trimmed.length > 140 ? `${trimmed.slice(0, 137)}...` : trimmed;
+  return ` while saying "${clipped}"`;
+}