diff --git a/modules/story-summary/generate/prompt.js b/modules/story-summary/generate/prompt.js
index 46b66fb..bfb95c3 100644
--- a/modules/story-summary/generate/prompt.js
+++ b/modules/story-summary/generate/prompt.js
@@ -91,9 +91,9 @@ function cleanSummary(summary) {
 
 function buildSystemPreamble() {
     return [
-        "以上内容为因上下文窗口限制保留的可见历史",
-        "以下【剧情记忆】是对可见与不可见历史的总结：",
-        "• 【世界约束】记录着已确立的事实",
+        "以上是还留在眼前的对话",
+        "以下是脑海里的记忆：",
+        "• [定了的事] 这些是不会变的",
         "• 其余部分是过往经历的回忆碎片",
         "",
         "请内化这些记忆：",
@@ -103,7 +103,7 @@ function buildSystemPreamble() {
 function buildPostscript() {
     return [
         "",
-        "——",
+        "这些记忆是真实的，请自然地记住它们。",
     ].join("\n");
 }
 
@@ -594,49 +594,36 @@ async function buildVectorPrompt(store, recallResult, causalById, queryEntities
     }
 
     // ═══════════════════════════════════════════════════════════════════
-    // 按注入顺序拼接 sections
-    // ═══════════════════════════════════════════════════════════════════
+    // ═══════════════════════════════════════════════════════════════════════
+// 按注入顺序拼接 sections
+// ═══════════════════════════════════════════════════════════════════════
+const sections = [];
+// 1. 世界约束 → 定了的事
+if (assembled.world.lines.length) {
+    sections.push(`[定了的事] 已确立的事实\n${assembled.world.lines.join("\n")}`);
+}
+// 2. 核心经历 → 印象深的事
+if (assembled.events.direct.length) {
+    sections.push(`[印象深的事] 记得很清楚\n\n${assembled.events.direct.join("\n\n")}`);
+}
+// 3. 过往背景 → 好像有关的事
+if (assembled.events.similar.length) {
+    sections.push(`[好像有关的事] 听说过或有点模糊\n\n${assembled.events.similar.join("\n\n")}`);
+}
+// 4. 远期片段 → 更早以前
+if (assembled.orphans.lines.length) {
+    sections.push(`[更早以前] 记忆里残留的老画面\n${assembled.orphans.lines.join("\n")}`);
+}
+// 5. 待整理 → 刚发生的
+if (assembled.recentOrphans.lines.length) {
+    sections.push(`[刚发生的] 还没来得及想明白\n${assembled.recentOrphans.lines.join("\n")}`);
+}
+// 6. 人物弧光 → 这些人
+if (assembled.arcs.lines.length) {
+    sections.push(`[这些人] 他们现在怎样了\n${assembled.arcs.lines.join("\n")}`);
+}
 
-    const sections = [];
-
-    // 1. 世界约束
-    if (assembled.world.lines.length) {
-        sections.push(`[世界约束] 已确立的事实\n${assembled.world.lines.join("\n")}`);
-    }
-
-    // 2. 核心经历
-    if (assembled.events.direct.length) {
-        sections.push(`[核心经历] 深刻的记忆\n\n${assembled.events.direct.join("\n\n")}`);
-    }
-
-    // 3. 过往背景
-    if (assembled.events.similar.length) {
-        sections.push(`[过往背景] 听别人说起或比较模糊的往事\n\n${assembled.events.similar.join("\n\n")}`);
-    }
-
-    // 4. 远期片段
-    if (assembled.orphans.lines.length) {
-        sections.push(`[远期片段] 记忆里残留的一些老画面\n${assembled.orphans.lines.join("\n")}`);
-    }
-
-    // 5. 待整理
-    if (assembled.recentOrphans.lines.length) {
-        sections.push(`[待整理] 最近发生但尚未梳理的原始记忆\n${assembled.recentOrphans.lines.join("\n")}`);
-    }
-
-    // 6. 人物弧光（最后注入，但预算已在优先级 2 预留）
-    if (assembled.arcs.lines.length) {
-        sections.push(`[人物弧光]\n${assembled.arcs.lines.join("\n")}`);
-    }
-
-    // ═══════════════════════════════════════════════════════════════════
-    // 统计 & 返回
-    // ═══════════════════════════════════════════════════════════════════
-
-    // 总预算 = 主装配 + 待整理
-    injectionStats.budget.used = total.used + (recentOrphanStats.tokens || 0);
-
-    if (!sections.length) {
+if (!sections.length) {
         return { promptText: "", injectionLogText: "", injectionStats };
     }
 
@@ -848,4 +835,4 @@ export async function buildVectorPromptText(excludeLastAi = false, hooks = {}) {
     }
 
     return { text: finalText, logText: (recallResult.logText || "") + (injectionLogText || "") };
-}
+}
\ No newline at end of file
diff --git a/modules/story-summary/vector/recall.js b/modules/story-summary/vector/recall.js
index bf35f69..9a250db 100644
--- a/modules/story-summary/vector/recall.js
+++ b/modules/story-summary/vector/recall.js
@@ -296,19 +296,34 @@ function buildEntityLexicon(store, allEvents) {
         .slice(0, 5000);
 }
 
-function extractEntities(text, lexicon) {
-    const t = normalize(text);
-    if (!t || !lexicon?.length) return [];
+/**
+ * 从分段消息中提取实体，继承消息权重
+ * @param {string[]} segments
+ * @param {number[]} weights
+ * @param {string[]} lexicon
+ * @returns {Map<string, number>}
+ */
+function extractEntitiesWithWeights(segments, weights, lexicon) {
+    const entityWeights = new Map();
 
-    const sorted = [...lexicon].sort((a, b) => b.length - a.length);
-    const hits = [];
-    for (const e of sorted) {
-        if (t.includes(e)) hits.push(e);
-        if (hits.length >= 20) break;
+    if (!segments?.length || !lexicon?.length) return entityWeights;
+
+    for (let i = 0; i < segments.length; i++) {
+        const text = normalize(segments[i]);
+        const weight = weights?.[i] || 0;
+
+        for (const entity of lexicon) {
+            if (text.includes(entity)) {
+                const existing = entityWeights.get(entity) || 0;
+                if (weight > existing) {
+                    entityWeights.set(entity, weight);
+                }
+            }
+        }
     }
-    return hits;
-}
 
+    return entityWeights;
+}
 // ═══════════════════════════════════════════════════════════════════════════
 // MMR
 // ═══════════════════════════════════════════════════════════════════════════
@@ -457,7 +472,7 @@ async function searchChunks(queryVector, vectorConfig, l0FloorBonus = new Map(),
 // L2 Events 检索（RRF 混合 + MMR 后置）
 // ═══════════════════════════════════════════════════════════════════════════
 
-async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorConfig, store, queryEntities, l0FloorBonus = new Map()) {
+async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorConfig, store, queryEntityWeights, l0FloorBonus = new Map()) {
     const { chatId } = getContext();
     if (!chatId || !queryVector?.length) return [];
 
@@ -475,11 +490,14 @@ async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorCo
 
     // 文本路检索
     const textRanked = searchEventsByText(queryTextForSearch, CONFIG.TEXT_SEARCH_LIMIT);
+    const textGapInfo = textRanked._gapInfo || null;
 
     // ═══════════════════════════════════════════════════════════════════════
     // 向量路检索（只保留 L0 加权）
     // ═══════════════════════════════════════════════════════════════════════
 
+    const ENTITY_BONUS_FACTOR = 0.10;
+
     const scored = (allEvents || []).map((event, idx) => {
         const v = vectorMap.get(event.id);
         const sim = v ? cosineSimilarity(queryVector, v) : 0;
@@ -497,6 +515,17 @@ async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorCo
             }
         }
 
+        const participants = (event.participants || []).map(p => normalize(p));
+        let maxEntityWeight = 0;
+        for (const p of participants) {
+            const w = queryEntityWeights.get(p) || 0;
+            if (w > maxEntityWeight) {
+                maxEntityWeight = w;
+            }
+        }
+        const entityBonus = ENTITY_BONUS_FACTOR * maxEntityWeight;
+        bonus += entityBonus;
+
         return {
             _id: event.id,
             _idx: idx,
@@ -504,9 +533,12 @@ async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorCo
             similarity: sim,
             finalScore: sim + bonus,
             vector: v,
+            _entityBonus: entityBonus,
         };
     });
 
+    const entityBonusById = new Map(scored.map(s => [s._id, s._entityBonus]));
+
     const preFilterDistribution = {
         total: scored.length,
         '0.85+': scored.filter(s => s.finalScore >= 0.85).length,
@@ -518,7 +550,6 @@ async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorCo
         threshold: CONFIG.MIN_SIMILARITY_EVENT,
     };
 
-    // 向量路：纯相似度排序（不在这里做 MMR）
     const candidates = scored
         .filter(s => s.finalScore >= CONFIG.MIN_SIMILARITY_EVENT)
         .sort((a, b) => b.finalScore - a.finalScore)
@@ -530,15 +561,12 @@ async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorCo
         vector: s.vector,
     }));
 
-    // RRF 融合
     const eventById = new Map(allEvents.map(e => [e.id, e]));
     const fused = fuseEventsByRRF(vectorRanked, textRanked, eventById);
 
-    // 向量非空时过滤纯 TEXT
     const hasVector = vectorRanked.length > 0;
     const filtered = hasVector ? fused.filter(x => x.type !== 'TEXT') : fused;
 
-    // MMR 放在融合后：对最终候选集去重
     const mmrInput = filtered.slice(0, CONFIG.CANDIDATE_EVENTS).map(x => ({
         ...x,
         _id: x.id,
@@ -551,7 +579,6 @@ async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorCo
         c => c.vector || null,
         c => c.rrf
     );
-
     // 构造结果
     const results = mmrOutput.map(x => ({
         event: x.event,
@@ -559,6 +586,7 @@ async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorCo
         _recallType: x.type === 'HYBRID' ? 'DIRECT' : 'SIMILAR',
         _recallReason: x.type,
         _rrfDetail: { vRank: x.vRank, tRank: x.tRank, rrf: x.rrf },
+        _entityBonus: entityBonusById.get(x.event?.id) || 0,
     }));
 
     // 统计信息附加到第一条结果
@@ -571,6 +599,7 @@ async function searchEvents(queryVector, queryTextForSearch, allEvents, vectorCo
             vectorOnlyCount: fused.filter(x => x.type === 'VECTOR').length,
             textOnlyFiltered: fused.filter(x => x.type === 'TEXT').length,
         };
+        results[0]._textGapInfo = textGapInfo;
     }
 
     return results;
@@ -587,10 +616,11 @@ function formatRecallLog({
     chunkResults,
     eventResults,
     allEvents,
-    queryEntities,
+    queryEntityWeights = new Map(),
     causalEvents = [],
     chunkPreFilterStats = null,
     l0Results = [],
+    textGapInfo = null,
 }) {
     const lines = [
         '\u2554' + '\u2550'.repeat(62) + '\u2557',
@@ -621,7 +651,18 @@ function formatRecallLog({
     lines.push('\u250c' + '\u2500'.repeat(61) + '\u2510');
     lines.push('\u2502 【提取实体】                                                 \u2502');
     lines.push('\u2514' + '\u2500'.repeat(61) + '\u2518');
-    lines.push(`  ${queryEntities?.length ? queryEntities.join('、') : '(无)'}`);
+
+    if (queryEntityWeights?.size) {
+        const sorted = Array.from(queryEntityWeights.entries())
+            .sort((a, b) => b[1] - a[1])
+            .slice(0, 8);
+        const formatted = sorted
+            .map(([e, w]) => `${e}(${(w * 100).toFixed(0)}%)`)
+            .join(' | ');
+        lines.push(`  ${formatted}`);
+    } else {
+        lines.push('  (无)');
+    }
 
     lines.push('');
     lines.push('\u250c' + '\u2500'.repeat(61) + '\u2510');
@@ -642,7 +683,7 @@ function formatRecallLog({
     lines.push('  L1 原文片段:');
     if (chunkPreFilterStats) {
         const dist = chunkPreFilterStats.distribution || {};
-        lines.push(`    \u5168\u91cf: ${chunkPreFilterStats.total} \u6761 | \u901a\u8fc7\u9608\u503c(\u8fdc\u671f\u2265${chunkPreFilterStats.thresholdRemote}, \u5f85\u6574\u7406\u2265${chunkPreFilterStats.thresholdRecent}): ${chunkPreFilterStats.passThreshold} \u6761 | \u6700\u7ec8: ${chunkResults.length} \u6761`);
+        lines.push(`    全量: ${chunkPreFilterStats.total} 条 | 通过阈值(远期≥${chunkPreFilterStats.thresholdRemote}, 待整理≥${chunkPreFilterStats.thresholdRecent}): ${chunkPreFilterStats.passThreshold} 条 | 最终: ${chunkResults.length} 条`);
         lines.push(`    匹配度: 0.8+: ${dist['0.8+'] || 0} | 0.7-0.8: ${dist['0.7-0.8'] || 0} | 0.6-0.7: ${dist['0.6-0.7'] || 0}`);
     } else {
         lines.push(`    选入: ${chunkResults.length} 条`);
@@ -656,6 +697,18 @@ function formatRecallLog({
     lines.push(`    总事件: ${allEvents.length} 条 | 最终: ${eventResults.length} 条`);
     lines.push(`    向量路: ${rrfStats.vectorCount || 0} 条 | 文本路: ${rrfStats.textCount || 0} 条`);
     lines.push(`    HYBRID: ${rrfStats.hybridCount || 0} 条 | 纯 VECTOR: ${rrfStats.vectorOnlyCount || 0} 条 | 纯 TEXT (已过滤): ${rrfStats.textOnlyFiltered || 0} 条`);
+    const entityBoostedEvents = eventResults.filter(e => e._entityBonus > 0).length;
+    lines.push(`    实体加分事件: ${entityBoostedEvents} 条`);
+
+    if (textGapInfo) {
+        lines.push('');
+        lines.push('  文本检索 (BM25 动态 top-K):');
+        lines.push(`    命中: ${textGapInfo.total} 条 | 返回: ${textGapInfo.returned} 条 (覆盖 ${textGapInfo.coverage} 总分)`);
+        if (textGapInfo.scoreRange) {
+            const s = textGapInfo.scoreRange;
+            lines.push(`    分数: Top=${s.top} | 截断=${s.cutoff} | P50=${s.p50} | Last=${s.last}`);
+        }
+    }
 
     // Causal
     if (causalEvents.length) {
@@ -702,7 +755,8 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
     }
 
     const lexicon = buildEntityLexicon(store, allEvents);
-    const queryEntities = extractEntities(segments.join('\n'), lexicon);
+    const queryEntityWeights = extractEntitiesWithWeights(segments, weights, lexicon);
+    const queryEntities = Array.from(queryEntityWeights.keys());
 
     // 构建文本查询串：最后一条消息 + 实体 + 关键词
     const lastSeg = segments[segments.length - 1] || '';
@@ -727,10 +781,11 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
 
     const [chunkResults, eventResults] = await Promise.all([
         searchChunks(queryVector, vectorConfig, l0FloorBonus, lastSummarizedFloor),
-        searchEvents(queryVector, queryTextForSearch, allEvents, vectorConfig, store, queryEntities, l0FloorBonus),
+        searchEvents(queryVector, queryTextForSearch, allEvents, vectorConfig, store, queryEntityWeights, l0FloorBonus),
     ]);
 
     const chunkPreFilterStats = chunkResults._preFilterStats || null;
+    const textGapInfo = eventResults[0]?._textGapInfo || null;
 
     const mergedChunks = mergeAndSparsify(l0VirtualChunks, chunkResults, CONFIG.FLOOR_MAX_CHUNKS);
 
@@ -764,10 +819,11 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
         chunkResults: mergedChunks,
         eventResults,
         allEvents,
-        queryEntities,
+        queryEntityWeights,
         causalEvents: causalEventsTruncated,
         chunkPreFilterStats,
         l0Results,
+        textGapInfo,
     });
 
     console.group('%c[Recall]', 'color: #7c3aed; font-weight: bold');
diff --git a/modules/story-summary/vector/text-search.js b/modules/story-summary/vector/text-search.js
index 3419216..d173c56 100644
--- a/modules/story-summary/vector/text-search.js
+++ b/modules/story-summary/vector/text-search.js
@@ -1,37 +1,70 @@
-// ═══════════════════════════════════════════════════════════════════════════
-// Text Search - L2 事件文本检索（MiniSearch）
-// 与向量检索互补，通过 RRF 融合
-// ═══════════════════════════════════════════════════════════════════════════
+// text-search.js - 最终版
 
 import MiniSearch from '../../../libs/minisearch.mjs';
 
+const STOP_WORDS = new Set([
+    '的', '了', '是', '在', '和', '与', '或', '但', '而', '却',
+    '这', '那', '他', '她', '它', '我', '你', '们', '着', '过',
+    '把', '被', '给', '让', '向', '就', '都', '也', '还', '又',
+    '很', '太', '更', '最', '只', '才', '已', '正', '会', '能',
+    '要', '可', '得', '地', '之', '所', '以', '为', '于', '有',
+    '不', '去', '来', '上', '下', '里', '说', '看', '吧', '呢',
+    '啊', '吗', '呀', '哦', '嗯', '么',
+    'の', 'に', 'は', 'を', 'が', 'と', 'で', 'へ', 'や', 'か',
+    'も', 'な', 'よ', 'ね', 'わ', 'です', 'ます', 'した', 'ない',
+    'the', 'a', 'an', 'is', 'are', 'was', 'were', 'be', 'been',
+    'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would',
+    'to', 'of', 'in', 'on', 'at', 'for', 'with', 'by', 'from',
+    'and', 'or', 'but', 'if', 'that', 'this', 'it', 'its',
+    'i', 'you', 'he', 'she', 'we', 'they', 'my', 'your', 'his',
+]);
+
+function tokenize(text) {
+    const s = String(text || '').toLowerCase().trim();
+    if (!s) return [];
+
+    const tokens = new Set();
+
+    // CJK Bigram + Trigram
+    const cjk = s.match(/[\u4e00-\u9fff\u3400-\u4dbf]+/g) || [];
+    for (const seg of cjk) {
+        const chars = [...seg].filter(c => !STOP_WORDS.has(c));
+        for (let i = 0; i < chars.length - 1; i++) {
+            tokens.add(chars[i] + chars[i + 1]);
+        }
+        for (let i = 0; i < chars.length - 2; i++) {
+            tokens.add(chars[i] + chars[i + 1] + chars[i + 2]);
+        }
+    }
+
+    // 日语假名
+    const kana = s.match(/[\u3040-\u309f\u30a0-\u30ff]{2,}/g) || [];
+    for (const k of kana) {
+        if (!STOP_WORDS.has(k)) tokens.add(k);
+    }
+
+    // 英文
+    const en = s.match(/[a-z]{2,}/g) || [];
+    for (const w of en) {
+        if (!STOP_WORDS.has(w)) tokens.add(w);
+    }
+
+    return [...tokens];
+}
+
 let idx = null;
 let lastRevision = null;
 
-/**
- * 中文逐字 + 英数字串分词
- */
-function tokenize(text) {
-    return String(text || '').match(/[\u4e00-\u9fff]|[a-zA-Z0-9]+/g) || [];
-}
-
-/**
- * 去掉 summary 末尾的楼层标记
- */
 function stripFloorTag(s) {
     return String(s || '').replace(/\s*\(#\d+(?:-\d+)?\)\s*$/, '').trim();
 }
 
-/**
- * 构建/更新事件文本索引
- */
 export function ensureEventTextIndex(events, revision) {
     if (!events?.length) {
         idx = null;
         lastRevision = null;
         return;
     }
-
     if (idx && revision === lastRevision) return;
 
     try {
@@ -39,6 +72,7 @@ export function ensureEventTextIndex(events, revision) {
             fields: ['title', 'summary', 'participants'],
             storeFields: ['id'],
             tokenize,
+            searchOptions: { tokenize },
         });
 
         idx.addAll(events.map(e => ({
@@ -52,33 +86,87 @@ export function ensureEventTextIndex(events, revision) {
     } catch (e) {
         console.error('[text-search] Index build failed:', e);
         idx = null;
-        lastRevision = null;
     }
 }
 
 /**
- * 文本检索事件
+ * BM25 检索，返回 top-K 候选给 RRF
+ * 
+ * 设计原则：
+ * - 不做分数过滤（BM25 分数跨查询不可比）
+ * - 不做匹配数过滤（bigram 让一个词产生多个 token）
+ * - 只做 top-K（BM25 排序本身有区分度）
+ * - 质量过滤交给 RRF 后的 hasVector 过滤
  */
+/**
+ * 动态 top-K：累积分数占比法
+ *
+ * 原理：BM25 分数服从幂律分布，少数高分条目贡献大部分总分
+ * 取累积分数达到阈值的最小 K
+ *
+ * 参考：帕累托法则（80/20 法则）在信息检索中的应用
+ */
+function dynamicTopK(scores, coverage = 0.90, minK = 15, maxK = 80) {
+    if (!scores.length) return 0;
+
+    const total = scores.reduce((a, b) => a + b, 0);
+    if (total <= 0) return Math.min(minK, scores.length);
+
+    let cumulative = 0;
+    for (let i = 0; i < scores.length; i++) {
+        cumulative += scores[i];
+        if (cumulative / total >= coverage) {
+            return Math.max(minK, Math.min(maxK, i + 1));
+        }
+    }
+
+    return Math.min(maxK, scores.length);
+}
+
 export function searchEventsByText(queryText, limit = 80) {
     if (!idx || !queryText?.trim()) return [];
 
     try {
-        const res = idx.search(queryText, {
-            limit,
-            boost: { title: 2, participants: 1.5, summary: 1 },
-            fuzzy: 0.2,
-            prefix: true,
+        const results = idx.search(queryText, {
+            boost: { title: 4, participants: 2, summary: 1 },
+            fuzzy: false,
+            prefix: false,
         });
-        return res.map((r, i) => ({ id: r.id, textRank: i + 1 }));
+
+        if (!results.length) return [];
+
+        const scores = results.map(r => r.score);
+        const k = dynamicTopK(scores, 0.90, 15, limit);
+
+        const output = results.slice(0, k).map((r, i) => ({
+            id: r.id,
+            textRank: i + 1,
+            score: r.score,
+        }));
+
+        const total = scores.reduce((a, b) => a + b, 0);
+        const kCumulative = scores.slice(0, k).reduce((a, b) => a + b, 0);
+
+        output._gapInfo = {
+            total: results.length,
+            returned: k,
+            coverage: ((kCumulative / total) * 100).toFixed(1) + '%',
+            scoreRange: {
+                top: scores[0]?.toFixed(1),
+                cutoff: scores[k - 1]?.toFixed(1),
+                p50: scores[Math.floor(scores.length / 2)]?.toFixed(1),
+                last: scores[scores.length - 1]?.toFixed(1),
+            },
+        };
+
+        return output;
+
     } catch (e) {
         console.error('[text-search] Search failed:', e);
         return [];
     }
 }
 
-/**
- * 清理索引
- */
 export function clearEventTextIndex() {
     idx = null;
     lastRevision = null;